WO2012092649A1 - Process control - Google Patents

Abstract

A system (110) for controlling a physical process, the system (110) comprising a processor means (122) and a storage means (124), the storage means (124) having a software application stored thereon, whereby the processor means (122) is operable, under control of the software application, to: receive data associated with an expected performance of the process; receive data associated with a desired performance of the process; process the received data to transform the received data from a first state to a second state providing an indication of performance of the process; and perform an action on the basis of the indication of performance, the action resulting in a physical effect.

Description

PROCESS CONTROL

TECHNICAL FIELD

[0001] The present invention relates generally to process control and, in particular, to a method and system for controlling a process.

[0002] Although the present invention will be described with particular reference to controlling a production process, it will be appreciated that the present invention may be used in any process.

[0003] Throughout the specification, unless the context requires otherwise, the word "comprise" or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated integer or group of integers but not the exclusion of any other integer or group of integers.

[0004] Furthermore, throughout the specification, unless the context requires otherwise, the word "include" or variations such as "includes" or "including", will be understood to imply the inclusion of a stated integer or group of integers but not the exclusion of any other integer or group of integers.

[0005] Additionally, throughout the specification, unless the context requires otherwise, the words "substantially" or "about" will be understood to not be limited to the value for the range qualified by the terms.

BACKGROUND ART

[0006] Each document, reference, patent application or patent cited in this text is expressly incorporated herein in their entirety by reference, which means that it should be read and considered by the reader as part of this text. That the document, reference, patent application, or patent cited in this text is not repeated in this text is merely for reasons of conciseness.

[0007] The following discussion of the background to the invention is intended to facilitate an understanding of the present invention only. It should be appreciated that the discussion is not an acknowledgement or admission that any of the material referred to was published, known or part of the common general knowledge of the person skilled in the art in any jurisdiction as at the priority date of the invention.

[0008] Bulk material handling stockyard systems comprising automated stacking and reclaiming equipment and machinery and other automated or semi- automated material movement or production systems are often complex, including a plurality of subsystems, with each subsystem being interdependent on other subsystems and system inputs. For simplicity, such processing systems can be represented as a single functional block which, for a given or prescribed quantifiable input or set of quantifiable inputs into the process, produces a correlated or corresponding output or set of outputs. These inputs represent variables affecting the dynamic response and therefore the output of the process. Examples of inputs within a production process or system include: the mass and geometry of physical input items or articles; the unique properties of the product(s) being handled; production control system signals and settings including set-points and performance targets; the performance and capability of any automation control logic; mechanical components; and electrical and electronic systems. The system performance output is typically a production throughput rate and a desired cumulative production quantity.

[0009] Theoretically, the output of a production system over a given or particular period of time can be ensured by providing a human operator or "supervisory controller" of the process, or equipment, with sufficient sensory measured system data to diagnose any observed issues and make any corresponding system corrections required. The operator/controller is responsible for assuring outputted production rates and levels remain within acceptable limits, for example. To be able to effectively perform this function, the operator/controller requires extensive experience and/or training in operating the particular type of system, and the corresponding ability to determine the required responses to events, such as the appropriate corrective responses to undesired production variations. This may either be through the development of expertise, and/or the ability to locate the appropriate information within sufficient time to effectively respond . [0010] In practice, in many instances the experience and/or procedure based knowledge of the operator/controller is only sufficient for diagnosing and responding to the more simple and discernible events, such as responding to common alarms. For all other issues (i.e. less common or more complex events and corresponding actions) either a visual inspection of the equipment or process is conducted to try and diagnose the cause, the issue is ignored due to a lack of causal information or diagnosis ability or experience or training, or if the issue is considered to be of enough significance or systemic in nature, then additional resources maybe utilized to investigate the issue further.

[0011 ] Figure 1 of the drawings depicts a block diagram of a typical, prior art system 10 and method for optimising the throughput of a production system. A focus of production process optimisation is achieving current, or future, production targets. In the instances where long term production targets are set, these targets are typically divided into a sequence of shorter term targets or goals (for example, quarterly, monthly, etc) which are further cascaded down to the smallest logical milestone period (such as, for example, a single personnel shift).

[0012] To achieve the allocated production targets the production system 10 depicted in Figure 1 operates as follows. Production process 12, which typically would comprise many operably coupled or interconnected subsystems of a mechanical, structural, electrical, electronic and/or control system nature.

Process inputs 14 represent all of the system's variables which either directly or indirectly affect the output performance of the system 10. These inputs 14 are diagrammatically directed into the "Process" functional block 12 which is a representation of the system's 10 function that given the required inputs, produces the production or process output(s) 16. Production output 16 is typically measured in terms of a rate, although quality, reliability and cumulative quantities are often equally relevant. In summary, process 12 represents the production process that is the subject of an optimisation process.

[0013] To establish a production system, which is able to achieve both short and long term targets, a first requirement is an automatic quantitative recording of the inputs 14, output(s) and system state 18, of the overall process and its individual sub-systems. This is principally achieved from an arrangement of operably coupled sensors distributed throughout the system 10. The sensory measured values and calculations derived from measurements of the sensors are stored in a process database 20 or data historian. For this information to be useful in optimising the system 10, its availability and accessibility must be timely relative to the desired response time, of an adequate resolution to identify the smallestsignificant events, and accurate enough to make informed judgments or decisions.

[0014] While the production process is operating, it is the task of a human operator or controller to visually monitor and control, the production process and in particular the production output. This is typically achieved through a form of Supervisory Control and Data Acquisition (SCADA) and Human Machine

Interface (HMI) system which presents process data to the human

operator/controller and, via which, the human operator/controller monitors and controls the process. Particularly, the ongoing task of the operator/controller is to monitor the actual output being achieved versus either a threshold level or a bounded target range. For processes that do not exhibit a single sustained output rate, this task is significantly more complex. For example, in the case of a bucket-wheel reclaimer machine, there are different achievable levels of production rate output when digging each geometrically different section of a stockpile of material. In this case a target production rate is required per section to enable a meaningful interpretation of performance to be evaluated. A breakdown of target production rates to process sequence steps, like this, does not always occur and subsequently the ability of the operator/controller to judge output performance levels is impeded.

[0015] When an event requiring action, such as a negative variance in the output production level versus a threshold level, is able to be identified, the

operator/controller will ideally be able to determine the cause of the event and implement the required action, such as rectifying it, before it has a significant impact on the production process 12. The first step in this situation is for the operator/controller to obtain process information from the process database 20 via the SCADA or HMI system as described previously. This data then needs to be interpreted by the operator/controller. The ability of the operator/controller to diagnose the cause(s) of the event, and the action required, depends on factors including the form or format of the information available, the skill level of the operator/controller and constraints such as the time that the operator/controller has available, amongst their other responsibilities, to analyse the information. In some instances a specialist may be utilised to either assist or take responsibility of this process. If the causes(s) are able to be identified, then the next step is to prioritise and quantify the impact that each cause has on the overall production output(s) 16 of the system 10. The causal events may also be classified as either a "special cause", indicating a fundamental or unusual or unexpected change in the process 12, or a "common cause", indicating a normal or expected change in the process 12 with respect to historical variation. The final classification applied is an indication of the difficulty or effort or resources required to resolve the identified cause. A typical scale would extend from simple or "immediately correctable", to "correctable in the short term", to "difficult to correct" and requiring significant change of the system 10, through to "uncorrectable".

[0016] The cause(s) with the most significant contribution are termed "primary cause(s)". Assuming the operator/controller is able to identify the primary cause(s), they can use the effect/event class and the rectification difficulty ranking to, theoretically, decide on a course of an action. Typically, the operator/controller is able to correct "common" or "special cause" issues that are "immediately correctable". All other primary or root causes have to be reported up the chain of command such that the appropriate party can take corrective action(s), or at least make a decision about whether to pursue corrective action(s).

[0017] The process described above is one that is achievable through one of the existing defect elimination and continuous improvement methodologies.

However, it should be noted that these methodologies are typically utilised reactively with dedicated technical resources.

[0018] Limitations of or problems associated with existing automated material movement and other processing systems and methods of production process optimisation, particular when being utilised at or near capacity, include: 1) The primary. indicator of performance used is production process output, which is a lagging indicator and which provides little information to facilitate diagnosis of the cause(s) of production variation(s).

2) Assuming the inputs into the system are measured, are of an adequate resolution and accuracy, and logically stored, they are usually in a raw and/or complicated form which makes it difficult to perform timely diagnosis as required to facilitate timely responsive actions.

3) Even over the longer term, the method of diagnosing areas of

performance deficiency and identifying opportunities for improvement is to assign dedicated and capable technical resources, where the effectiveness of the outcome is largely dependent on their abilities. If the resources are capable, the outcome is still delayed significantly from the incidence of these events and is therefore considered reactive. The delay also reduces the sense of immediacy, and therefore reduces the likelihood that corrective or preventative actions will occur, be of quality and/or be sustainable.

4) There is typically little or a very slow pace of business or organisational learning, with respect to production processes, as the frontline monitoring of the system is far removed in time and in responsibility from the process of diagnosis and response. This slowness translates into propagation of issues or undesirable characteristics, such as system design flaws, in the case where organisational expansion is faster than system optimisation or expansion activities.

5) The method is very manually intensive and reliant on the skill of personnel in a role which does not normally expect such skills or dedicated additional technical resources.

6) Analysis tools and abilities in using such tools are required of human operators/controllers.

7) There is typically limited ability to capture the performance deviation events, contributing causes and best practice corrective action because of the difficulty in obtaining this information and the lack of a contextual framework to both embed and automatically retrieve this information from when the event reoccurs.

8) Even if all, or most, of the limiting factors are overcome and an

improvement realised, it would be at a slow rate compared to the rate at which issues are identified. The outcome is a slow rate of business or organisational learning, which can result in the propagation of issues or undesirable

characteristics, such as system design flaws during production expansion activities, or assembly into completed products, for example.

[0019] It is against this background that the present invention has been developed.

SUMMARY OF INVENTION

[0020] It is an object of the present invention to overcome, or at least ameliorate, one or more of the deficiencies of the prior art mentioned above, or to provide the consumer with a useful or commercial choice.

[0021] Other objects and advantages of the present invention will become apparent from the following description, taken in connection with the

accompanying drawings, wherein, by way of illustration and example, a preferred embodiment of the present invention is disclosed.

[0022] According to a first broad aspect of the present invention, there is provided a system for controlling a physical process, the system comprising a processor means and a storage means, the storage means having a software application stored thereon, whereby the processor means is operable, under control of the software application, to: receive data associated with an expected performance of the process; receive data associated with a desired performance of the process; process the received data to transform the received data from a first state to a second state providing an indication of performance of the process; and perform an action on the basis of the indication of performance, the action resulting in a physical effect on the process.

[0023] According to a second broad aspect of the present invention, there is provided a system for controlling a process, the system comprising processor means and a storage means, the storage means having a software application and a database stored thereon, whereby the processor means is operable, under control of the software application, to: determine an expected performance of the process; determine a desired performance of the process; process the expected performance of the process and the desired performance of the process to generate an indication of performance; and perform an action on the basis of the indication of performance.

[0024] Preferably, the expected performance and/or the desired performance is or is related to an actual performance of the process, and may comprise an actual output of the process.

[0025] Preferably, to determine an expected performance of the process, the processor means is operable, under control of the software application, to: generate a first set of variables, wherein each variable in the first set of variables is able to influence the performance of the process; allocate a respective value to each of the variables in the first set of variables; generate a performance variable associated with the performance of the process; and allocate a performance value to the performance variable.

[0026] Preferably, the allocation comprises assigning or generating the value(s), and may comprise a sensory measurement or calculation. [0027] Preferably, each variable in the first set of variables is associated with, relates or corresponds to an input to the process.

[0028] Preferably, the value allocated to each of the variables in the first set of variables is associated with, relates or corresponds to a benchmark of the respective variabje. In embodiments of the invention, this may mean that there is a corresponding benchmark for each measured input to the process. As will be described in further detail, in embodiments of the invention the benchmark is used as a comparison with the measured input to determine (in conjunction with the relevant input-output relationship) a performance impact that the input has.

[0029] Preferably, the performance of the process is associated with, relates or corresponds to an output of the process.

[0030] Preferably, the performance value allocated to the performance variable is associated with, relates or corresponds to a benchmark of the performance variable.

[0031] Preferably, the processing comprises determining a relationship between the value of each variable in the first set of variables and the performance value of the performance variable.

[0032] Preferably, the system comprises at least one sensor operable to sense data associated with the desired performance of the process and is operable to determine an actual performance of the process via processing of said sensed data.

[0033] In an embodiment of the invention, at least one sensor belongs to a first set of sensors. In one implementation, data from sensors in the first set of sensors is associated with the desired performance of the process and the first set of variables (which may be inputs to the process) and the system is operable to determine the expected (which may be or correspond to actual) performance of the process via processing of the sensed data. In such an embodiment, the sensory data is used to determine a value for each input (historic, current, predicted, or a combination of these) and each benchmark (desired performance), and then by processing this data an expected performance can be calculated (which should be close to or equal to the actual performance).

[0034] Preferably, to perform the processing, the processor means is operable, under control of the software application, to: determine a second set of variables, wherein each variable in the second set of variables is associated with a variation in the determined actual performance of the process from the performance value attributed to the influence of a respective variable in the first set of variables; allocate a respective value to each of the variables in the second set of variables on the basis of the determined relationship between the value of the respective variable in the first set of variables and the performance value of the performance variable; and generate the indication of performance on the basis of the allocated values of each of the variables in the second set of variables.

[0035] Preferably, the performed action comprises controlling the process to result in a physical effect.

[0036] According to a third broad aspect of the present invention, there is provided a method for controlling a process, the method comprising: determining an expected performance of the process; determining a desired performance of the process; processing the expected performance of the process and the desired performance of the process to generate an indication of performance; and performing an action on the basis of the indication of performance.

[0037] As will be described in further detail, the indication of performance may comprise a "performance index" or expected performance of the process based on measured and/or estimated value(s) of inputs to the process. In embodiments of the invention, actual performance of the process may be compared to expected performance during calibration activities only. During primary operation of such an embodiment, expected performance may be determined by aggregating the impact of the performance variation of the inputs from their benchmark values.

[0038] Preferably, determining an expected performance of the process comprises: generating a first set of variables, wherein each variable in the first set of variables is able to influence the performance of the process; allocating a respective value to each of the variables in the first set of variables; generating a performance variable associated with the performance of the process; and allocating a performance value to the performance variable.

[0039] Preferably, the allocation comprises assigning or generating the value(s), and may comprise a sensory measurement or calculation.

[0040] In embodiments of the invention, the performance variable may be determined from the first set of variables, using the relationship between process input(s) and/or output(s) and the corresponding berichmark(s).

[0041] Preferably, each variable in the first set of variables is associated with, relates or corresponds to an input to the process.

[0042] Preferably, the value allocated to each of the variables in the first set of variables is associated with, relates or corresponds to a benchmark of the respective variable. In embodiments of the invention, this may mean that there is a corresponding benchmark for each measured input to the process. As will be described in further detail, in embodiments of the invention the benchmark is used as a comparison with the measured input to determine (in conjunction with the relevant input-output relationship) a performance impact that the input has. [0043] Preferably, the performance of the process is associated with an output of the process.

[0044] Preferably, the performance value allocated to the performance variable is associated with a benchmark of the performance variable.

[0045] Preferably, the processing comprises determining a relationship between the value of each variable in the first set of variables and the performance value of the performance variable. The relationship may be determined prior to operating or undertaking the method, and, in embodiments of the invention, may or may not be updated during operation.

[0046] Preferably, determining a desired performance of the process comprises measuring the actual performance of the process.

[0047] Preferably, the processing comprises: determining a second set of variables, wherein each variable in the second set of variables is associated with a variation in the determined actual performance of the process from the performance value attributed to the influence of a respective variable in the first set of variables; allocating a respective value to each of the variables in the second set of variables on the basis of the determined relationship between the value of the respective variable in the first set of variables and the performance value of the performance variable; and generating the indication of performance on the basis of the allocated values of each of the variables in the second set of variables.

[0048] Preferably, the performed action comprises controlling the process to result in a physical effect.

[0049] According to a fourth broad aspect of the present invention, there is provided a computer-readable storage medium on which is stored instructions that, when executed by a computing means, causes the computing means to perform the method for controlling a process according to the third broad aspect of the present invention as hereinbefore described.

[0050] According to a fifth broad aspect of the present invention, there is provided a computing means programmed to carry out the method for controlling a process according to the third broad aspect of the present invention as hereinbefore described.

[0051] According to a sixth broad aspect of the present invention, there is provided a computer program, including at least one instruction capable of being executed by a computer system, which implements the method for controlling a process according to the third broad aspect of the present invention as

hereinbefore described.

[0052] According to a seventh broad aspect of the present invention, there is provided a data signal including at least one instruction being capable of being received and interpreted by a computing system, wherein the instruction implements the method for controlling a process according to the third broad aspect of the present invention as hereinbefore described.

BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order that the invention may be more fully understood and put into practice, a preferred embodiment thereof will now be described with reference to the accompanying drawings, in which:

Figure 1 depicts a block system diagram of a conventional arrangement of a prior art process control system implementing a method of optimising an automated or semi-automated production system;

Figure 2 depicts a system diagram of a first embodiment of a system for controlling a process in accordance with an aspect of the present invention;

Figure 3 depicts a controller computer of the system depicted in Figure 2;

Figure 4 depicts a processor and a storage device of the controller computer depicted in Figure 3; Figure 5 depicts a bucket-wheel reclaimer with luffing/tilt motion and bucket-wheel rotation marked/indicated controlled by the system depicted in Figure 2;

Figure 6 depicts the bucket-wheel reclaimer depicted in Figure 5 with slewing/yaw motion and long travel (linear translation) motion marked/indicated;

Figure 7 depicts a cross-section of an example stockpile of material being processed by the bucket-wheel reclaimer depicted in Figure 5, the cross- section being divided into 3 Segments described as benches;

Figure 8 depicts the stockpile cross-section depicted in Figure 7 divided into 3 Segments described as benches, and further divided into 3 more

Segments per bench to provide 9 total Segments; and

Figure 9 depicts a graphical representation of an example stockpile of material being processed by the bucket-wheel reclaimer depicted in Figure 5, the stockpile being divided into nine cross-sectional Segments that are reclaimed in 2 blocks.

DESCRIPTION OF EMBODIMENTS

[0054] In the drawings, like features have been referenced with like reference numbers. ^"

[0055] In Figure 2, there is depicted an embodiment of a system 110 for controlling a process in accordance with an aspect of the present invention. The system 110 may be referred to as a "Production Process Optimisation System" · (PPOS).

[0056] In the embodiment described, the process is a physical process comprising a bulk material handling operation performed by a reclaiming machine ("reclaimer") 112 in relation to a longitudinally stacked stockpile of material 13 such as ore, cereal, coal, minerals and limestone at an inland site to load a train from stockpiled product. [0057] In alternative embodiments of the invention, processes other than bulk material handling operations may be controlled. In this regard, it should be appreciated that the invention is not limited in regard to the process or processes to which it can be applied, and, for example, may be used to control any automated or semi-automated production processes or systems, as well as processes other than that of production and manufacture, including, for example, one or a combination of:

Retail (e.g. sales volumes/revenue/profit);

Marketing (e.g. effectiveness);

Accounting (e.g. account management);

Economics (e.g. monitoring Economic activity);

Machine or equipment performance (e.g.

Motor/drive/engine/equipment); and

Processes relating to individual assets, pieces of equipment, components of equipment.

[0058] The reclaimer 112 is of bucket wheel type and is fully automated, rail supported, and boom mounted, and comprises all components found in a typical reclaimer of such type. The construction, use and operation of reclaimers are well known to persons skilled in the art and need not be described in any further detail herein.

[0059] The system 110 comprises a set of system and application software (software set) stored and run on a controller computer 14 as depicted in Figure 3. Software in the software set,*or any set of instructions or programs for the computer 114, can be written in any suitable language, as are well known to persons skilled in the art. Software in the software set can be provided as standalone applications or via a network, depending on the system requirements.

[0060] In alternative embodiments of the invention, the software may comprise one or more modules and may be implemented in hardware. In such a case, for example, the modules may be implemented with any one or a combination of the following technologies, which are each well known in the art: a discrete logic circuit(s) having logic gates for implementing logic functions upon data signals, an application specific integrated circuit (ASIC) having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA) and the like.

[0061] The computer 114 can be of any suitable type, including: a programmable logic controller (PLC); digital signal processor (DSP); microcontroller; personal, notebook or tablet computer such as that marketed under the trade mark IPAD® by Apple Inc; or a smartphone, such as that marketed under the trade mark IPHONE® by Apple Inc.; or a dedicated server or networked servers.

[0062] In the embodiment described, the computer 114 includes display means In the form of a monitor or visual display 116, a container such as a box 118 for housing various, operably connected components of the computer 114 such as a motherboard, processing means, disk drives and power supply of the computer 114, and control means such as a keyboard 120 and other suitable peripheral devices such as a mouse (not depicted). Together, the display 116, keyboard 120 and other peripheral devices provide a user interface or Human or Man Machine Interface (HMI) to enable a human user or operator to interact with the software set via a Graphical User Interface (GUI).

[0063] Referring to Figure 4, processing means of the computer 114 includes a central processor 122. The computer 114 also includes a storage means, device or medium such as a memory device 124 for the storage and running of software, including the software of the software set. The processor 122 is operable to perform actions under control of the software of the software set, as will be described in further detail below, including processing/executing instructions and managing the flow of data and information through the computer 114. For example, the processor 122 can be any custom made or commercially available processor, a central processing unit (CPU), a data signal processor (DSP) or an auxiliary processor among several processors associated with the computer 114. In embodiments of the invention, the processing means may be a semiconductor based microprocessor (in the form of a microchip) or a

macroprocessor, for example.

[0064] In embodiments of the invention, the storage means, device or medium can include any one or combination of volatile memory elements (e.g., random access memory (RAM) such as dynamic random access memory (DRAM), static random access memory (SRAM)) and non-volatile memory elements (e.g., read only memory (ROM), erasable programmable read only memory (EPROM), electronically erasable programmable read only memory (EEPROM),

programmable read only memory (PROM), tape, compact disc read only memory (CD-ROM), etc.). The storage medium may incorporate electronic, magnetic, optical and/or other types of storage media. Furthermore, the storage medium can have a distributed architecture, where various components are situated remote from one another, but can be accessed by the processing means. For example, the ROM may store various instructions, programs, software, or applications to be executed by the processing means to control the operation of the reader and the RAM may temporarily store variables or results of the operations.

[0065] Where the word "store" is used in the context of the present invention, it is to be understood as including reference to the retaining or holding of data or information both permanently and/or temporarily in the storage means, device or medium for later retrieval, and momentarily or instantaneously, for example as part of a processing operation being performed by the system 110.

[0066] Additionally, where the terms "system" and "device" are used in the context of the present invention, they are to be understood as including reference to any group of functionally related or interacting, interrelated, interdependent or associated components or elements that may be located in proximity to, or separate from, each other.

[0067] The use and operation of computers using software applications, HMIs and GUIs, is well-known to persons skilled in the art and need not be described in any further detail herein except as is relevant to the present invention.

[0068] In the embodiment described, the software set comprises: an operating system (not shown) and a control application relating to the process to be controlled.

[0069] Any suitable communication protocol can be used to facilitate the communication of information or data between components of the system 10, and between the system 110 and other devices, including wired and wireless, as are well known to persons skilled in the art and need not be described in any further detail herein except as is relevant to the present invention.

[0070] The system 110 also comprises a set of sensors, individual sensors within the set of sensors being in selectable data communication with the computer 114. Sensors within the set of sensors are relevant to the process and are operable to gather data thereon and communicate it to the computer 114.

•

[0071] Data or information received by the computer 14, for example inputted by a user or operator via the HMI, or communicated by a sensor of the set of sensors or other system or device such as a networked device, is stored in a process database 128.

[0072] The control application comprises control logic such that the system 110 is operable, under control of the control application, to: determine an expected performance of the process; determining a desired performance of the process; process the expected performance of the process and the desired performance of the process to generate an indication of performance of the process; and to perform an action on the basis of the indication of performance.

[0073] To determine the expected performance of the process, the system 10 is operable, under control of the control application, to: generate a first set of variables, wherein each variable in the first set of variables is able to influence the performance of the process; allocate a respective value to each of the variables in the first set of variables; generate a performance variable associated with the performance of the process; and to allocate a performance value to the performance variable. Preferably, the desired performance is or is related to an actual performance of the process, and may comprise an actual output of the process.

[0074] The processing comprises determining a relationship between the value of each variable in the first set of variables and the performance value of the performance variable. In the described embodiment, the relationship is determined prior to the task of determining the performance value. The relationship may be predetermined or calculated. [0075] Via sensors of the set of sensors, the system 110 is operable to measure the actual performance of the process. A measure of or other information or data associated with the performance of the process can be received by the system 110 via sources other than, or additional to, sensors, including from manual input via the HMI, or collected or calculated by another data source, system or device and communicated to the system 110.

[0076] The processing also comprises determining a second set of variables, wherein each variable in the second set of variables is associated with a variation in the determined actual performance of the process (as measured via sensors in the set of sensors or received via another source) from the

performance value attributed to the influence of a respective variable in the first set of variables; allocating a respective value to each of the variables in the second set of variables on the basis of the determined relationship between the value of the respective variable in the first set of variables and the performance value of the performance variable; and generating the indication of performance on the basis of the allocated values of each of the variables in the second set of variables. Action undertaken by the system 110 on the basis of the indication of performance includes controlling the process, in addition to other actions, as will be described in further detail below.

[0077] In embodiments of the invention, determining the expected performance of the process comprises receiving data associated with the expected

performance of the process. Determining the desired performance of the process may comprise receiving data associated with the desired performance of the process. In such embodiments, the received data may be processed to transform the received data from a first state to a second state providing the indication of performance of the process, and an action performed on the basis of the indication of performance. The action results in a physical effect which may be on or associated with the process. In embodiments of the invention, the action leads to a substantial physical effect resulting in an artificially created state of affairs.

[0078] In embodiments of the invention, the word "determining" is understood to include receiving or accessing the relevant data or information. [0079] The data may be received by accessing the storage means, device or medium or via other communication or transmission.

[0080] As will be described in further detail, expected performance may refer to expected input and output performances of the process. Desired performance may refer to a target or benchmark performance of the process inputs and outputs. Indication of performance may refer to an indication of the output performance in terms of the input performance factors and their corresponding impact on output performance.

[0081] The database 128 is coupled to the computer 114 and is in data communication therewith in order to enable data to be read to and from the database 28, as is well known to persons skilled in the art. Any suitable database structure can be used. The database can be provided locally as a component of the computer 1 4 (such as in the memory device 124) or remotely such as on a remote server, as can the set of software. In an embodiment, several computers can be set up in this way to have a network client-server application. There is at least one database for the system 110 in the embodiment described - the database 128 - and it is stored in the memory device 24 of the computer 114. In alternative embodiments of the invention, there may be more than one database.

[0082] The use and operation of computer databases are well known to persons skilled in the art and need not be described in any further detail herein except as is relevant to the present invention.

[0083] The system 110, under control of relevant applications of the software set, is operable to execute actions as described herein according to the data and information received by the computer 114.

[0084] The above and other features and advantages of the embodiment of the invention will now be further described with reference to the system 110 in use.

[0085] In the embodiment described, the system 110 is intended to control the process so that an output of a set of outputs from the process is preferably optimised, thereby resulting in having a substantial and tangible physical effect. [0086] The performance of the process is associated with the output. The output comprises a performance parameter of interest or concern of the reclaimer 112 being an average rate of material moved per operating shift or time period of the reclaimer 112 - that is, the production rate. To facilitate the determination of the production rate, the set of sensors comprises a weighing sensor or sensory element or device operably positioned under a discharge section or portion of a material discharge boom of the reclaimer 112. The production rate is calculated from the time weighted summation of individual readings from the weighing sensor. From this information the average production rate can be calculated for a prescribed or selected period or duration of time. In this regard, relevant time periods for reclaimers can include, for example, the time taken to reclaim a section of a stockpile of material, reclaim an entire stockpile of material, load a train, or load a ship.

[0087] Particularly, the weighing sensor is operable to sense or measure the weight of material discharged over time and to communicate the readings via a signal over a communication link to a central programmable logic controller (PLC) of the reclaimer 112 for use in the automated control thereof. The PLC is operable to process the received signal to scale and convert it into instantaneous mass and production rate variable values, which are then further communicated to a supervisory control and data acquisition (SCADA) system of the reclaimer 112, data historian or other data storage device or element, and the database 128 of the system 110.

[0088] In alternative embodiments of the invention, alternative or additional outputs or other parameters of or associated with the performance of the process may be controlled, as required or desired. For example, in the case of reclaimer machines, other possible production outputs, other than the one used in this example, include: total cumulative production; machine performance levels relative to that required to achieve a desired level of reliability; the likelihood of a machine or its components to continue to operate without failing; holistic asset performance metrics; and production output quality and quality consistency.

[0089] The output or performance of the reclaimer 112 may be affected or influenced, directly or indirectly, by one or a combination of more than one variable or factors. These variables and factors may comprise numerous complex and operably coupled subsystems or components of the reclaimer 112 which include, but are not limited to, those used to produce the following machine movements: bucket-wheel rotation, boom slewing (yaw motion), boom luffing (tilt motion), and whole machine long travelling (linear translation). These subsystems each have structural, mechanical, electrical, electronic, and control aspects that may directly or indirectly affect or influence the reclaimer 12 machines output performance. These inter-related subsystems can be simplified into a single process model consisting of process inputs 130, directed into a process block representing the entire system of the reclaimer 112, and the corresponding production output(s) 132.

[0090] For this embodiment, a simplified set of process inputs will be described to represent a case of an input variable that has a continuous measured value that can vary over a range of values, and a case of discrete event based and frequency measured events, to demonstrate the difference in their treatments according to aspects of the invention. A discrete event may refer to both binary (e.g. on or off) or ordinal (e.g. type , 2, 3 or 4) sets of outcomes. In the embodiment, the two described inputs or factors are stockpile width and machine stoppage, bucket-wheel drive overload alarms. The width of a stockpile of material can be measured by appropriate sensors of the set of sensors utilising the range of slewing angles used to reclaim the stockpile, or preferably from sensory feedback from a stacking system or machine used to build the stockpile of material. Bucket-wheel drive overload alarms occur when the digging forces experienced by a motor of the reclaimer 112 indicate that it is being overloaded due to an incident or event which may include, for example: development and/or presentation of a product in a manner beyond or approaching the machines mechanical capability; or slumping, collapsing, or sluffing of material onto the bucket-wheel of the reclaimer 112 due to the digging method or inconsistent stockpile geometry. The bucket-wheel drive overload alarms are generated from analysis of signals associated with performance or operational parameters or indicators generated by sensors or measurement elements or devices and based on the outcome of predetermined algorithms executed by the SCADA of the reclaimer 112. Common examples of such parameters or indicators for an electric motor are current usage or other more direct measures of thermal capacity usage. Being a discrete event, bucket-wheel drive overload alarms are measured based on the number, frequency and/or duration of the occurrence(s) during a time period or duration of interest.

[0091] When considering a complete, real world, system implementation, preferably all of the process inputs and other variables or factors that may have a measurable effect or influence on the process output(s) would be included in conjunction with the two already mentioned. Examples of such process inputs and variables or factors for the given scenario may include, but are not limited to: reclaiming sequence used; bench height; machine stoppage alarms; causes of any machine stoppage alarms; the type and properties of the product(s) or article/item produced and/or handled; weather effects; reclaim rate; mechanical component efficiency; stockpile length and geometry; controller performance; and the controller set-points and settings used.

[0092] Preferably, the process is segmented to create more meaningful outcomes from the analysis of process input and output parameter or

characteristic variables. Particularly, segmentation has benefits in and is relevant to performance analysis of production systems and processes.

[0093] Segmentation may involve dividing a process into process segments or batches that represent a characteristic and preferably a substantially or fairly constant and consistent performance level. For example, when bucket-wheel reclaimers operate on a triangular or flat-topped stockpile of material, they commonly reclaim a single horizontal bench or terrace of material at a time. Each of these benches is of a varying width and often of varying heights resulting in different performance expectations for each of these batch segments. It is therefore advantageous to analyse the performance for each of these segments separately based on the relative performance expectations for each of them. In an exemplary example, each of these benches may be further sub-divided based on the variation in characteristic reclaiming performance as a bucket-wheel reclaimer slews from one side of the stockpile to the other. Typically, the reclaiming performance when slewing through the central full height section of the stockpile is much greater and more consistent than that experienced through the low digging sloping sides of the stockpile. Therefore the bench could be divided into 2 edge segments and a central segment which would be initially analysed separately. For automated production line manufacturing applications similar divisions into batch or other suitable continuous process segments would apply.

[0094] In the embodiment described, the system 110 is operable to provide leading indicators of performance, and in particular performance degradation, of the process by execution of the control application implementing an algorithm that uses sensory determined values of system input variables to calculate or determine, for each smallest relevant continuous segment, or batch component, of the process:

• a performance level of each system input variable taken into account or consideration;

• an impact that each system input variable is expected or predicted to have on the system output; and

• an overall expected or predicted output performance based on such inputs.

It is further operable to provide an operator or user, via the HMI, with:

• an indication of when output performance is expected to be below a predetermined capability level(s);

• an indication or identification of which input variable is expected to cause the performance degradation;

• an indication or identification of a location within the stockpile of the performance degradation;

• a quantified and meaningful measure of the impact that the input variable will or may have on overall performance output of the process; and

• direct access to the relevant procedures, drawings, diagrams and other relevant documentation or information to address the expected performance variance.

[0095] As will be described in further detail below, the system 110 is operable to evaluate a likely "difficulty to correct" ranking of the defect based on the nature of the cause or event, its location and the magnitude of its impact. The system 1 10 is also operable to provide to an operator or user, via the HMI, procedures for addressing issues based on the same criteria. Having the convenience of predictive or real time diagnostics that provide this type of leading indicators and proactive response information allows the operator and subsequently the business to make timely and substantiated decisions. Informed decision making of this type may either reduce the impact or entirely prevent the performance degradation, both in the short, medium, and long term.

[0096] The system 10 has a plurality of modes or states of operation in the embodiment described, including a first or "set up" mode or state in which a first operator or user, possessing appropriate technical skills, is able to initialise the system 1 10 for controlling the process, and a second or "ongoing control" mode or state in which a second operator or user, having limited technical skills, is able to supervise the ongoing control of the process by the system 110.

[0097] Upon first execution of the control application the system 110 enters the first or set up mode, in which the processor 122, under control of the control application, is operable to generate a record for the process, the record comprising a set of process information, and to record, store or hold details of the same in a corresponding process record in the database 128. In the embodiment described, the process record includes data or information entered or inputted by the first operator via the HMI in response to prompts from the system 110, communicated via sensors of the set of sensors, and arising from processing undertaken by the system 110 itself, as will be described in further detail below. Process Sets

[0098] Preferably, the process is divided up into a plurality of logical Process Sets, which may be based on relevant processing time interval or process duration, for example. Time intervals include but are not limited to hour(s), shift(s) or day(s). Process durations may be defined by either a characteristic of the process or an actual time duration required to process or complete a specified batch size and/or number of batches, for example. In the following formulae, the subscript "i" is used as a sequentially applied unique identification number for each of the number of unique "Process Sets" (Ni) that occur in a given time range (for example, per year). A person skilled in the art would be capable of choosing the most appropriate method of defining a Process Set for a given process.

[0099] In the embodiment described, the system 1 10 is operable to enable the first user or operator to choose from a selection displayed via the HMI to define the Process Set as either a personnel shift period, for a time interval based set, or the train loading duration, for the process duration. Details of the selection made by the first operator are stored as a value for a Process Set variable of the process record.

[0100] For the described example, the train-loading duration will be referred to. Process Segments

[0101] Preferably, the process is divided up into logical Segments or batches of similar input and/or output performance characteristics or variables and preferably of a substantially or fairly constant and consistent value. The purpose of segmentation is to group elements having similar performance levels to allow more meaningful, accurate, consistent, and robust analysis outcomes as well as the ability to more accurately determine the location, within the process, of performance degradation.

[0102] The subscript "/' is used to represent the identification number for each of the Nj unique "Segments" of a typical Process Set. A Segment may occur more than once during the Process Set; however, each Segment has only a single identifier such as an identification number in the embodiment described. A person skilled in the art would be capable of choosing the most appropriate segmentation method.

[0 03] In the embodiment described , the system 110 is operable to enable the user to enter details regarding the segmentation via the HMI. The segmentation details inputted are stored as a value for a Segment variable of the process record. The segmentation method may be entered as a configuration and the actual identification of segments then automated or automatically performed by the system 110. [0104] For the described example, the Process Set is divided into nine Segments as related to the stockpile cross-section, as shown in Figure 8 of the drawings and previously described.

[0105] For an implementation where reclaiming (also known as "block

reclaiming") is utilised, the Segments may be defined as the nine cross-sectional regions that occur once per block. For example, if the train loading duration comprised of two blocks then there would be nine Segments that occurred twice per Process Set as depicted in Figure 9. Process Output(s)

[0106] The performance of the process (output in the embodiment described) of interest is defined. This may be, for example, a production rate, a production quality, system reliability, or another type of performance summary metric. The chosen process output of interest for each Segment may be represented by the Y_j as shown in Equation 1 below.

[0107] The system 110 is operable to enable the first operator to enter details regarding the process output of interest via the HMI. The process output details inputted are stored as an output (performance) variable of the process record.

[0108] For the described example, process output of interest is defined as the average reclaiming rate, calculated from the total tonnes reclaimed in a given operational time, as the output of concern Y for each Segment of each Process Set (as shown in Equation 1). Process Input(s)

[0109] All of the direct and indirect input factors which affect or influence the output system performance for the process as a whole, individual Process Sets, and/or Segments, and which are to be taken into account or considered, are identified and defined. These can be of any form including, for example, continuous variables, state variables, specific events, or alarms. [01 10] The subscript "k" is used to represent an identification number for each of the Nk unique input factors for each Segment. Each input factor is therefore represented in Equation 1 by X up to X_JNt for the N_k input factors which affect the system output of the each Segment, represented by Y_r For simplicity.in the description, each of the input factors X_k is assumed to be defined universally for all Segments from j to N, . i.e. the input factor represented by the identification number one for segment one represents the same input for all segments from one to N_j . A person skilled in the art would be capable of choosing an

appropriate method of assigning identification numbers to each input factor.

[0111] Each input factor can be determined through methods that include, but are not limited to, a single measured value or a time weighted average of sufficient terms to be indicative of typical performance for the given Segment.

Equation 1

[0 12] The system 1 10 is operable to enable the first operator to enter details regarding the input factors via the HMI. The input factor details inputted are stored as a first set of variables, each able to influence the process output, of the process record.

[0113] As previously described, the two example process inputs, affecting the process output, may be defined as Segment width and bucket-wheel overload alarm occurrences ( ^ and X_j2 in Equation 1). In the embodiment described, the Segment width is measured in metres and is relevant for the central three

Segments (as depicted in Figure 7). The bucket-wheel overload alarm

occurrences may be measured as the frequency that they occur for each

Segment per Process Set, or the frequency of occurrence per a standard time interval (for example number of occurrences per hour). In the described example, reference will be made to the number of occurrences per hour for each

occurrence of each segment. [0114] For the implementation to be of adequate accuracy and robustness for reliable usage, at least the key inputs that affect output would preferably be defined. For the most effective exemplary implementation, all of the inputs that affect output would be defined.

5. Benchmark(s) of Input(s)

[0115] Equipment performance benchmarks for each of the input factors, as represented by X_B for each value of j and k , may be determined. These can be determined by analysing the historical frequency of occurrence of relative values of the input factor for the given process Segment. Those skilled in the art would understand that this can be achieved by utilising statistical methods such as analysis using histograms, to show the frequency of occurrence at each performance level. If the distribution can be statistically shown to approximate normality then a performance benchmark based on the following relationship may be used,

^XB_jk - ^X jk + ^ajk ⁽ _jk

Equation 2

[0116] In Equation 2 the benchmark for the kth process input on the jth segment, X_B , consists of X _Jk and a_jk which represents the mean value and standard deviation respectively of a distribution of historical values X_jk for a given

Segment j and input k. The value a_jk represents the multiplication factor utilised to set an appropriate equipment performance benchmark for the same Segment and input. The value for a_jk is typically between one and two; however, any positive value greater than zero is suggested. In some scenarios it is also necessary for a_Jk to be negative. A person skilled in the art would have the ability to choose a suitable value. The benchmark chosen is intended to represent a value that has been shown historically as being realistic and sustainable over time, or may be a known best practice for a comparable process arrangement, but greater than current mean performance, X_Jk . A

unique benchmark for each input variable and Segment may be required.

[0117] In the example described, to calculate a benchmark for the number of bucket-wheel overload alarm occurrences (X_j2) for each of the Segments, a process suited to discrete events with an inverse relationship (output

performance increases as the number of alarm occurrences decreases) with output performance is required. An example implementation involves the production of a histogram showing the frequency that each number of alarm occurrences for defined ranges of alarm occurrences. Typical defined ranges, which may be referred to as bin sizes, would divide the frequencies into one or a number of ranges such that the resultant distribution is sufficiently descriptive and the level of normality can be ascertained.

[0118] For the described example, it is assumed that each alarm results in a complete production stoppage and that the average stoppage time is sufficiently consistent to be treated as constant. It is also assumed that the frequency distribution closely approximates normality. In this case it is then straightforward to determine the distribution mean X and standard deviation σ that describe the distribution as per Equation 2. Based on these values the value of a , the number of standard deviations to the left of the mean (i.e. a will be negative in this instance), can be determined. The resultant benchmark represents the realistically attainable number of alarms that the process can be consistently restricted to that produces increased production process output from recent historic performance. In some instances for alarms the benchmark may be zero if this is considered a realistic proposition.

[0119] To calculate a benchmark for the Segment width (X_ji) a process suited to continuous inputs with a positive relationship (output performance increases as the Segment width increases) with output performance is required. An example implementation involves the production of a histogram showing the frequency that each width occurs within defined ranges. Typical defined ranges, which may be referred to as bin sizes, would divide the frequencies into one or a number of ranges such that the resultant distribution is sufficiently descriptive and the level of normality can be ascertained. [0120] It is also assumed that the distribution closely approximates normality. In this case it is then straightforward to determine the distribution mean X and standard deviation σ that describe the distribution as per Equation 2. Based on these values the value of a , the number of standard deviations to the right of the mean (ie a will be positive in this instance), can be determined. The resultant benchmark represents the realistically attainable Segment width that the process can be consistently produced ("stacked" in practical terms) to increased production process output from recent historic performance.

[0121] For scenarios or embodiments of the invention where a more complex or complicated relationship exists, a person skilled in the art would be able to determine a robust and consistent method for choosing an appropriate benchmark. These methods may include, although are not limited to, genetic algorithms, neural networks or decision trees.

[0122] The system 110 is operable to enable the first operator to enter details regarding the determined performance benchmarks for each of the input factors via the HMI. The performance benchmark details inputted are stored as a respective value allocated to the corresponding or respective input factor of the first set of variables, of the process record.

[0123] In embodiments of the invention, the benchmarking method may either be completely manual or semi-automated such that the configuration parameters for determining appropriate benchmarks are entered manually and the actual calculation or determination of the benchmarks are then automated.

6. Benchmark(s) of Outputs

[0124] Equipment performance benchmarks for the process output of interest, as represented by Y_B for each Segment j , may be determined. These can be determined by analysing the historical frequency of occurrence of relative values of the input factor for the given process Segment, as per step 4 above and Equation 3.

Y_Bj = Y_{j +} a_jn_i Equation 3

[0125] In the embodiment described, to calculate a benchmark for the average reclaim rate output (Y_j) for each Segment a process suited to continuous output is required. An example implementation involves the production of a histogram showing the frequency that each defined range of average reclaim rates occurs. A typical defined range, which may be referred to as bin size, would divide the frequencies into one or a number of ranges such that the resultant distribution is sufficiently descriptive and the level of normality can be ascertained.

[0126] It is also assumed that the distribution closely approximates normality. In this case it is then straightforward to determine the distribution mean X and standard deviation σ that describe the distribution as per Equation 3. Based on these values the value of a , the number of standard deviations to the right of the mean (ie a will be positive), can be determined. The resultant benchmark represents the realistically attainable average reclaim rate output for each Segment that can be consistently produced by the process that is greater than exhibited in recent historic performance.

[0127] For scenarios or embodiments where a more complex or complicated relationship exists, a person skilled in the art would be able to determine a robust and consistent method for choosing an appropriate benchmark. In an exemplary implementation, the benchmark output would be determined using the process input benchmarks so that both the input and output benchmarks are consistent. These methods may include, although are not limited to, genetic algorithms, neural networks or decision trees.

[0128] The system 110 is operable to enable the first operator to enter details regarding the determined output performance benchmark via the HMI. The output performance benchmark details inputted are stored as a respective value allocated to the corresponding or respective output variable, of the process record.

[0129] In embodiments of the invention, the benchmarking method may either be completely manual or semi-automated such that the configuration parameters for determining appropriate benchmarks are entered manually and the actual calculation or determination of the benchmarks are then automated.

7. Relationships

[0130] The relationship between the inputs or factors and the system

performance output for each of the _V_y logical Segment groupings in step 1 may be determined. In other words, determine an accurate mathematical function which allows the output f_y to be determined from the process inputs X to X_JNk , for a given Segment j, as represented by Equation 1.

[0131] There are a number of statistical methods that the person skilled in the art could use to determine, or predict the outcome of, the relationship. These possible methods include, but are not limited to, using known and verified relationships; historical analysis utilising multiple regression, or individual regression statistical methods for each input variable; decision trees; genetic algorithms; or neural networks. Regression is a common method that would be used for continuous variable process inputs where a first principles mathematical model is not available, prohibitively complex, inadequate for highly variable systems, or has not yet been reliably established. A simple example of the input- output relationship that would be produced by individual regression analysis, assuming a linear relationship, is shown in Equation 4, below. In this equation, b_Jk represents the gradient of the relationship, and c_jk represents the value of

FyWhen X_Jk is zero.

Y_j = f{X_jk ) = b_jkX_jk + c_jk

Equation 4

[0132] For a discrete event the process input X_jk needs to be defined as the frequency of occurrence of the event, or similar. If the event causes a complete process stoppage for a time period of T_s the relationship between the process input and the process output can be described using Equation 5 and Equation 6, where, • X_Jk represents the measured frequency of occurrence of the discrete event, as represented by the frth process input for Segment/

• f_s represents the calculated, measured or estimated equivalent stoppage time period caused each occurrence of the discrete event, as represented by the Wh process input for Segment /

• Y_Jk represents the estimated equivalent system output that can be determined as appropriate from the benchmark, a calculation, or a measurement, as represented by the kth process input for

Segment

• x_B represents the benchmark frequency of occurrence of the discrete event, as represented by the /rth process input for

Segment /

• T_Bg represents the benchmark equivalent stoppage time period caused each occurrence of the discrete event, as represented by the frth process input for Segment /.

Υ = Υ_{Βί +} ΑΥ_β

Equation 5

^{Δ Y}jk - ^YB_j ^TBS_jk ^XB_jk + ^Yjk ^TS_jk ^X jk Equation 6

[0133] Those skilled in the art would be able to derive an appropriate relationship for a given discrete event variable based on the characteristics of the process and the effect of the event on the process. An exemplary implementation would regularly update the constituent variables within the relationship. [0134] In the embodiment described, an accurate relationship between the inputs and the output average reclaiming rate for a train loading Process Set needs to be determined. There are a number of statistical methods that the person skilled in the art could use to determine the relationship. These possible methods include but are not limited to using known and verified dynamic relationships; historical analysis utilizing multiple regression analysis, or individual regression analysis for each input variable; decision trees; genetic algorithms; or neural networks.

[0135] For continuous variable inputs such as stockpile width, regression analysis is an effective method that can be utilized. If individual regression, is utilized to determine the relationship for stockpile width, where a linear relationship exists, an equation for the relationship such as that shown in

Equation 4 is generated.

[0136] For discrete variable inputs such as bucket-wheel alarm occurrences where the alarm event causes a complete process stoppage, an equation such as that shown in Equation 5 and Equation 6 can be derived by those skilled in the art.

[0137] The system 110 is operable to enable the first operator to enter details regarding the determined relationship(s) via the HMI. The relationship details inputted are stored in the process record. In embodiments of the invention, the method of determining the input-output relationship may either be completely manual or semi-automated such that the configuration parameters for

determining appropriate relationships are entered manually and the actual calculation or determination of the relationships are then automated.

8. Performance Impact

[0138] An "impact" (or effect or influence) on the process output given a value for each process input may be determined, via an algorithm for example that may be implemented by the control application. If calculated prior to the end of the Process Set, this would comprise an expected or forecast performance impact rather than an actual performance impact. Process or performance impact may be defined as the variation in process output from a predetermined benchmark caused by a given process input or set of inputs. There are a number of algebraic and statistical methods that the person skilled in the art could use to determine the impact on the process output given a value for each process input, and the relationship developed in step 5 hereinbefore described. These methods depend on the nature of the process input or factor and the characteristic effect or influence that it has on process output. The impact of most process inputs may be described using either method (a) for continuous variables, or method (b) for discrete event based variables (for example, equipment stoppage, pause, or rate change type events), as set out below: a. For continuous variables the impact can be described by the following general Equation 7 and the derivation shown in Equation 8, where M_Jk

represents the impact of the "current" input value of the kth input variable (X_Jk ) for the jth Segment. M_Jk j_s calculated from the change in output Υ# represented by AY_Jk caused by the change in X_Jk represented by AX_jk . The relationship between AY_Jk and AX_jk is based on the multiplication value b_jk from Equation 4 where AX_Jk represents the difference between the value of the process input X_jk and the benchmark x_B for the process input k for the Segment

M_jk = AY_Jk

Equation 7

A Y_Jk = b_jk AX_Jk = b_jk (X_Jk - X_Bjk )

Equation 8 b. For discrete event based variables the impact can be calculated by those skilled in the art using calculations such as that shown in Equation 6 for the relationship defined in Equation 5.

[0139] The calculated impact M_jk provides a measure of the quantified change in process output for the given Segment. This value can be converted to a percentage impact M_jk% by dividing the change in process output AY_Jk by the benchmark process output, Y_B as shown in Equation 9.

Equation 9

[0140] As described above, the process or performance "impact" can be defined as the variation in process output from the calculated benchmark, caused by a given process input or set of input(s) for a process Segment.

[0141] For Segment width, the impact may be the change in the expected process output which can be calculated using the relationship in the previous step, when both the current value and the benchmark value are tested. For this example, the value of b , or gradient of the linear relationship, multiplied by the difference between the current measured value and the benchmark (see

Equation 7 and Equation 8) provides a measure of impact in the units of the process output, tonnes per hour.

[0142] For bucket-wheel overload alarms per Segment, the impact is determined in the same way as for width, utilising the relationship established in the previous step. The equation for the impact, as determined using the relationship shown in Equation 7, is described in Equation 6. The impact is also provided in the units of the process output, tonnes per hour.

[0143] For both the process inputs defined in this example, the impact can be converted into a percentage impact by dividing the change in process output by the benchmark process output as described in Equation 9.

[0144] The system 110 is operable to enable the first operator to enter details regarding the impact or effect on the output for each of the input(s) via the HMI.

[0145] This facilitates setting up the system 110 to calculate the performance impact. The inputted details are stored in the process record as a second set of variables, wherein each variable in the second set of variables is associated with a variation in the measured actual process output from the output value attributed to the influence of a respective variable of the first set of variables. Each impact variable represents, approximates or equals the variation in the actual output performance (historic, current or future), and is calculated from the first set of variables (input values), in the embodiment described.

[0146] In embodiments of the invention, the method of determining the impact of the input on the process output may either be completely manual or semi- automated such that the configuration parameters for determining the impact are entered manually and the actual calculation or determination of the impact are then automated.

9. Performance Indication - Aggregation

[0147] To determine the overall impact on output performance from all, or a select combination of segments and process sets, the relevant performance impacts need to be aggregated to generate an indication or measure of overall performance. This may be done, for example, via an algorithm that may be implemented by the control application. Generating the performance indication may comprise aggregating the performance impacts and calculating a higher level Aggregate Performance Index (API). An aggregated performance indicator for each Segment j can be calculated using Equation 10. ;% = (^ - 100 % )+ 100 %

Equation 10

[0148] To aggregate the aggregated performance indicators for each Segment the relative contribution of each classified segment on each Process Set's output performance needs to be determined in the embodiment described. This impact weighting can be determined on either a time or a production basis in the embodiment. For a time based weighting, the typical production operation period for the given Segment is divided by the production operation period for the entire Process Set (see Equation 11). Similarly for a production based, or otherwise, weighting the production value for the given Segment is divided by the

production value for the entire Process Set. Both methods can be described using Equation 1 such that the addition of weightings for each Segment within a Process Set adds to 100%. The weighting calculation can either be calculated using current data, derived from historical data, or a combination of both. ω .

— ^- x l00 %

Equation 11

[0149] Similar to the aggregated performance indicators for each Segment, the aggregated performance indicator for each Process Set, referred to as the API, can be calculated using Equation 12.

Equation 12

[0150] The performance impact of both stockpile width and bucket-wheel overload alarms on each Segment can be aggregated by adding the individual percentage impacts (relative to 100%) as calculated in the previous step and represented algebraically in Equation 10.

[0151] To enable the aggregation of these individual Segments into a single overall aggregated performance index (API) for the Process Set(s), the relative impact weighting of each Segment needs to be calculated. For the given example, the weighting could be calculated based on reclaimer operating time in each Segment relative to the total operating time for the Process Set, or the reclaimed tonnes in each Segment relative to the total reclaimed tonnes for the Process Set. For the remainder of this example the tonnes based weightings will be used and calculated using Equation 11. A person skilled in the art would be able to choose an effective weighting method.

[0152] The API can then be calculated for the overall Process Set or Sets using a weighted summation of the performance impact of the two input factors on each Segment as shown in Equation 2. This value is expressed as a percentage where 100% represents output performance at the benchmark average tonnes per hour rate. The magnitude of any deviation above that of 100% represents an expected performance output of that same magnitude above the benchmark level. Similarly, the magnitude of any deviation below 100% represents and expected performance output of the same magnitude below the benchmark level. Based on this relationship it is therefore a process of multiplying the output benchmark performance level by the API to determine the expected output performance level in tonnes per hour.

[0153] In the embodiment described, the processor 122, under control of the control application, is operable to generate an overall API for the process, via processing as described above of the relevant data and information held in the process record, and to store details of the generated overall API in the process record as an API variable.

[0154] In an alternative embodiment of the invention, the system 110 is operable to enable the first operator to enter details regarding the overall API via the HMI. In such a case, the inputted details of the overall API are stored in the process record as an API variable.

10. Predicted Performance Impact

[0155] In circumstances where not all of the process inputs or factors are measurable and/or determinable at a given or particular point or moment in time, the process impact (of those indeterminable inputs or factors) may be predicted. This may be done, for example, via an algorithm that may be implemented by the control application. In this situation the estimated process impact M_% can be determined as described in step 7 using estimates of any process inputs not known with a high level of confidence. [0156] If no inputs are known at all then a long term historic average of a sufficient number, / , of relevant previous Process Sets, , to be indicative of the expected process input X_jk for Segment j may be used as shown in Equation

13.

^■\ )jk + X (i-2)jk + + X (i-l) jk

ijk

I

Equation 13

[0157] If some measurements of the process input X_jk are available for the current Process Set but the number is insufficient to be indicative of the process input for the Segment, a combination of historic values from previous Process Sets and/or Segments, and the set of available measurements for the current Process Set can be used to calculate the estimated value (as per Equation 13). Those skilled in the art would be able to design a weighting method to combine these data sets to obtain the most relevant and accurate estimate of X_jk for a given process.

[0158] In the described example, for situations where either the width or the frequency of bucket-wheel overload alarms have not yet been measured or the number of measurements are not yet sufficient to provide an overall

measurement with high confidence, the performance impact can be determined using a prediction process. For the described example, it would be typical for a prediction of performance impact to be used when the expected output performance of a stockpile is required, prior to the commencement of reclaiming to inform the choice of which stockpile to reclaim from (in the case where multiple stockpiles exist). It could also be used when reclaiming has commenced but insufficient time has been spent reclaiming in a given Segment for the measured value of width to be considered reliable, and/or the number of bucket-wheel overload alarms to be indicative of the final value.

[0159] In these instances the estimated process impact can be calculated in the same way as actual process impacts, except using estimates for the process inputs rather than the measured values, or a combination of measured and estimates. For stockpile width and bucket-wheel overload alarms the estimates would typically be based on a short term average of widths and alarms for the given Segment, as shown in Equation 13. The most appropriate number of elements to be included in the average can be determined by a person skilled in the art based on the historical analysis of input value variations and patterns identified. In the instance where actual measured values are combined with estimated values, the measured values are simply included in the average calculation. In the exemplary implementation, the overall estimated process input value is calculated using a weighting process which produces the most relevant and accurate values for the given process.

[0160] The system 110 is operable to enable the first operator to enter details regarding the predicted impact or effect on the output for each of the

(indeterminable) input(s) via the HMI. The inputted details are stored in the process record as part of the second set of variables, wherein each variable in the second set of variables is associated with a variation in the measured actual process output from the output value attributed to the influence of a respective variable of the first set of variables.

[0161] In embodiments of the invention, the predicted impact method may be entered as a configuration and the actual identification of the impact of each input on the process output then automated.

11. Performance Indication - Calculating API by Combining Actual And Predicted Performance Impacts

[0162] An indication or measure of performance may be generated. This may be done, for example, via an algorithm that may be implemented by the control application. Generating the performance indication may comprise combining predicted and measured performance impacts to calculate the API.

[0163] To determine aggregated performance indicators for each Segment and the overall API the estimated process impacts M_% calculated in step 9 can be substituted into Equation 10 and Equation 12. [0164] In the described example, the API can be calculated where the value of one or both of the process inputs, width and frequency of bucket-wheel overload alarm occurrences, has been estimated and used to estimate the performance impact for the Segment (as in the previous step). In these situations, the overall Process Set's API can be calculated in the same way as for actual impacts where the estimated impact replaces any Segment impacts not yet known.

[0165] In the embodiment described, the processor 122, under control of the control application, is operable to generate an overall API for the process, via processing as described above of the relevant data and information held in the process record, and to store details of the generated overall API in the process record as an API variable.

[0166] In an alternative embodiment of the invention, the system 110 is operable to enable the first operator to enter details regarding the overall API via the HMI. In such a case, the inputted details of the overall API are stored in the process record as the API variable.

12. Validating the Relationship

[0167] The relationship between the API and the actual performance output of the system may be validated for accuracy and consistency, for example.

[0168] The accuracy and consistency of the relationship between the calculated API and the actual performance output can be determined and analysed using statistical processes and error calculation methods known to persons skilled in the art. One such method is linear regression, where, in the embodiment described, the API is converted to the expected tonnes per hour output by multiplying it with the benchmark process output for the Process Set and plotted versus the actual performance output (also in tonnes per hour). The higher the correlation coefficient for the relationship, the closer the calculated line of regression is to having a gradient of one and a vertical axis intercept of zero, the more accurate the relationship. In instances where the correlation is less than optimal the cause of the variance can be investigated and used to improve the relationship. Particularly, the cause of inaccuracy (low correlation) between the calculated/estimated production output and the historical actual production output can be investigated. Alternative methods include, but are not limited to, error calculations which analyse the difference between the calculated and actual values.

[0169] Since the example used only consists of two process inputs the accuracy and consistency of the relationship is unlikely to be very high; however, for a realistic implementation a relationship close to one will be realistically achievable.

[0170] In the embodiment described, the processor 122, under control of the control application, is operable to validate the relationship, via processing according to an appropriate method as described above, to store details of the validation in the process record as a relationship validation variable, and to display the same via the HMI.

13. Identifying Cause(s) of Performance Variation

[0171] Cause(s) of actual or predicted performance variations and associated production impact may be identified. This may be done, for example, via an algorithm that may be implemented by the control application.

[0172] For any actual or system predicted performance variation, the cause of the variation can be identified by ranking the performance impacts of the various input factors from highest to lowest (as calculated using Equation 14). In the embodiment described, the input factors with the highest impacts are for most situations considered to be the most likely causes of the performance variation.

ί

The value of the performance impact itself is a measure of the impact that the potential causal input is having on the system output. The process location(s) of the potential causal input can be identified by ranking the performance impact for the input factors of each process Segment. The highest ranking, and therefore impacting, Segments are likely to be the primary location of the cause unless the issue is systemic and permeates throughout all/most of the process Segments.

-ioo%) + 100 % Equation 14

[0173] In instances where there is a negative performance variation for a

Segment, as indicated by an API less than 100%, especially when the variation is significantly large, the cause can be identified by ranking the performance impacts as calculated using Equation 14, for each input factor from highest to lowest. The input factor with the highest impact is then considered to be the most likely cause of the performance variation, in the embodiment described.

[0174] In the embodiment described, the processor 122, under control of the control application, is operable to automatically identify the potential causes and their locations, via processing according to an appropriate method as described above and to display the same via a report to appropriate personnel on the HMI.

14. Difficulty Ranking

[0175] The identified process input, its impact, and process location may be used to determine a difficulty ranking for overcoming or reducing the impact of a performance deficiency. This may be done, for example, via an algorithm that may be implemented by the control application.

[0176] A person skilled in the art would be able to identify most of the potential causes of performance variation either based on prior process knowledge or from the historical analysis required to set-up the system 110. Each input variable or factor represents at least one potential causal event. The number of causal events is dependent on the process and associated systems and subsystems, and the independence/interdependence of the input variables or factors. In the instance where a sufficiently exhaustive set of potential causal factors have been identified, to be effective, each causal factor can be

categorized by its characteristic nature and the level of difficulty or effort to resolve.

[0177] The characteristic nature of a causal event can be broadly separated into two categories in the embodiment described; a first or "special cause event" which results from a fundamental change in the process, and a second or "common cause event", which result from variations that are considered normal with respect to inherent system/process variability. Classification using this method or other more applicable event categories for a given or particular process can be determined by those skilled in the art and is useful in determining the priority of addressing the issue. For example, in a particular instance the resolution of special cause events may be of higher priority than the resolution of common cause events, because of the relative effectiveness in process optimisation for that process.

[0178] As well as the characteristic nature of the causal event it is valuable to classify each cause by the difficulty to resolve the cause or prevent the causal event from occurring again. In the embodiment described, the scale extends from "immediately correctable", correctable for the next process Segment, correctable for the next Process Set, minor system changes required, to significant system changes required, and uncorrectable.

[0179] By defining the characteristic nature and the level of difficulty or effort or resources required to resolve the cause of identifiable performance variations it is then possible to set up database to allow the nature and the difficulty of identified causal events to be reported.

[0180] For each process input, based on a given impact level and process location (as determined by the process Segment), a difficulty ranking can be determined for the procedure of correcting a given event (such as a negative performance variation). The first step in establishing a difficulty ranking process is to define a preferably exhaustive set of potential sources, or root causes, of performance variation. Each input factor represents at least one potential causal event. For the given example, a deficiency in output performance caused by a Segment width less than benchmark levels may be caused by either the stacking of a smaller stockpile (with respect to volume and height), a change in stockpiling method (such as a change from a chevron ply stacking method to a chevron stacking method), or a change in the geometry of the retaining area of the stockpile (often referred to as a canyon). Similarly, for bucket-wheel overload alarms, an increase in the number of alarms for a given Segment may be caused by either an increase in bench height, a change in stockpiled product, greater moisture content within the stockpiled product, definable mechanical issues with the drive arrangement, and a change in the control parameters of the automated process. These sources of performance variation have been provided as examples and in practice they would preferably be defined with much greater technical precision by persons skilled in the art.

[0181] The characteristic nature of the causal event can then be broadly defined as either "special cause" if it results from a fundamental change in the process and "common cause" if the variation in input value is considered normal, with respect to the inherent system/process variability. Using the stockpile width process input as an example, a reduction in Segment width is a result of a special cause event if the change is not common and can occur within or between Process Sets, such as physical changes in the retaining area of the stockpile. Changes in stacking method may also be defined as special cause, if the production process does not regularly experience changes in stacking method based on natural variability in the production process, such as the volume of material mined per day. All non-special cause events are likely to be common cause events, such as the size of a stacked stockpile which has a natural variability inherent to the production system. However, if the size of the stockpile changes more than the level of natural variability then it is also defined as special cause. A person skilled in the art would be able to develop a mapping process for determining whether a causal event is special or common cause from the process input, its location within the process, and its variance relative to the historic variance of the input.

[0182] As well as the ability to define each potential causal event as a special or common cause using pre-defined classification criteria, a person skilled in the art would be similarly able to define classification criteria which rank the difficulty of overcoming or reducing the impact of a performance deficiency. For the given example, a simple difficulty classification such as immediately correctable (easy), correctable in-between Process Sets (medium), or correctable with significant system changes (hard) could be used. Using these classifications performance degradation related to a reduced Segment width and caused by the natural variation in stockpile size would generally be of medium difficulty since the next train could be loaded from another larger stockpile. On the other hand, changes in the stockpile retaining area usually require structural changes that result in a hard classification. Although not represented in the given example, an easy difficulty ranking would be assigned to corrections such as settings changes that can be applied either during Segments or at least between them.

[0183] In the embodiment described, the system 110 is operable to enable the first operator to enter classification details regarding the characteristic nature and level of difficulty classifications via the HMI. The inputted classification details are stored in association with the process record. The processor 122, under control of the control application, via processing according to an appropriate method as described above, is operable to automatically retrieve and report appropriate classification details for identified causal events via the HMI.

15. Procedure to Resolve

[0184] The identified causal process input, its impact, and process location may be used to determine a relevant procedure to follow or action to take on the occurrence of an event, such as to overcome or reduce the impact of a performance deficiency, for example. This may be done, for example, via an algorithm that niay be implemented by the control application.

[0185] Similar to the previous step, procedures to correct all potential root causes can be developed by those skilled in the art such that they are immediately available when the corresponding root cause, or potential root cause, has been identified. To set up correction procedures for bucket-wheel overload alarms, the first step is to develop procedure for correcting each possible root cause. The identification of the root cause or potential root causes is as per the root cause determination/mapping process described in the previous step. Where multiple potential root causes are identified the procedure is accompanied by a method of establishing the most likely root cause. In an exemplary implementation or embodiment the system 110 is operable, where possible, to automatically evaluate any further criteria and establish the most likely root cause.

[0186] Using the example of bucket-wheel overload alarms, the root cause can be determined by those skilled in the art using, amongst other information, the Segment location, the products type and density, and additional sensor readings from the drive mechanism. For the established root cause or refined list of potential root causes, a correction procedure can be provided to enable a rapid corrective response and sustain the benchmark process output performance levels.

[0187] In the embodiment described, the system 110 is operable to enable the first operator to enter resolve details regarding actions, such as processes and procedures, corresponding to each identifiable causal event type and to be implemented on the identification thereof by the system 10 via the HMI. The inputted resolve details are stored in association with the process record. The processor 122, under control of the control application, via processing according to an appropriate method as described above, is operable to automatically retrieve and report appropriate resolve details for identified causal events via the HMI. In this manner, information, processes, and procedures are immediately accessible and/or reported via the HMI in conjunction with the identification of the potential causes of performance variation. This advantageously makes the corrective action more effective and more likely to occur. It also increases the rate of process improvement and its level of sustainability.

16. Automated Benchmark Calculations

[0188] In embodiments of the invention, the system 110 is operable to

automatically or semi-automatical!y determine equipment performance benchmark(s) for the production process output(s) and input(s). This can be set up by implementing and automating the algorithms used by the first operator to determine the benchmarks in the first instance, within the system 110.

17. Automated relationship calculation

[0189] In embodiments of the invention, the system 1 0 is operable to automatically or semi-automatically determine input-output relationships

(relationship calculations of the production process output(s) as derived from the input(s)). This can be set up by implementing and automating the algorithms used by the first operator to determine the relationship between process input(s) and output(s) in the first instance, within the system 110.

18. Automated Calibration Check [0190] In embodiments of the invention, the system 100 is operable to

automatically detect and report via the HMI when equipment performance benchmark(s) and performance relationship(s) are no longer valid.

[0191] As described previously, in embodiments of the invention processes of determining benchmark(s) for process input(s) and output(s), and performance relationships between input(s) and output(s), are automated. As a consequence of such automation, changes in these values over time can be recorded and processed by the system 110. The system 110 is operable to process the changes in these values and their effect on performance impact, performance indices and the API and on the basis of such processing identify situations when this variation is greater than a previously determined allowable threshold. In these situations, the system 110 is operable to report an appropriate action requirement (such as a need to check the variables which define the benchmarks and relationships) via the HMI.

[0192] In an embodiment of the invention, the calibration process described above is automated utilising a calibration control system.

[0193] Although the algorithms implemented in the control application of the described embodiment largely use the derivation and definition of static parameters, or configuration constants, in alternative embodiments of the invention the system 110 is operable to monitor the optimal change in these values and utilise dynamic relationship derivations.

[0194] Once the record has been generated and the information required to set up or initialise the system 110 has been determined and saved (following input by the first operator as in the described embodiment or automatic generation by the control application of the system 110 in alternative embodiments of the invention), the system 110 enters the second mode of operation, the ongoing control mode. In the second mode, a second operator or user, having limited technical skills, is able to supervise the ongoing control of the process by the system 110. Of course, there is no requirement that the second operator does not possess technical skills, and may even be the first operator. [0195] In the second operating mode, whilst the process is being controlled, data or information gathered or sensed by sensors of the set of sensors, including detected actual values of the process state, process input(s), and process output(s) at any or prescribed or preselected moments in time is communicated via signals to the controller computer 114. The required sensory data is provided at a sufficient level of accuracy and adequate resolution to enable receipt and further processing by the processor 122 of the computer 114 under control of the control application. The communicated data is extracted from the received signals by the system 110 which is operable to analyse each individual input and output to determine their actual value or performance level versus a target or threshold value or performance target. In the embodiment described, the performance targets, or thresholds, utilised are based on both the required production level to meet or satisfy the production target(s), as well as benchmark or best practice equipment performance levels (as determined during the set up or initialisation phase). In the embodiment described, benchmark values are considered to be the maximum demonstrated, or sustainable, rate of the equipment or components of the process machinery (i.e. the reclaimer 112) as determined from historical performance data. In alternative embodiments of the invention, other benchmark values, determined on the basis of other criteria, may be used.

[0196] It can be appreciated that, in the embodiment described, rather than relying solely on production target rates the calculation of overall production performance is calculated utilising a systematic analysis of historical

performance. The result of comparing performance to an equipment performance benchmark rather than a production target is that variations indicate either spare or inadequate performance capacity and is therefore more meaningful when optimising the actual process capacity.

[0197] As described previously, the system 110 is operable such that the performance level utilized for each input and output is normalized to a

percentage where 100% represents benchmark performance; or within the inventive methodology, "expected performance" i.e. it would deliver the

benchmark equipment performance for the components of the process system machinery (the reclaimer 112). Any positive improvement in performance beyond . the baseline is represented as 100% + x% where x represents the percentage increase in output performance that the input level produces above and beyond the benchmark level.

[0198] In its normalised format all the performance deviations/impacts from 100% are aggregated by operation of the system 110 and added to 100% to produce the performance indicator or API in the embodiment described. This aggregation represents an aggregation of the performance for each process segment and/or batch, and indicates the overall deviation from the benchmark level of output performance that is expected from the equipment. For instance, an API of 105% would indicate that the input factors affecting performance are at levels that would be expected to produce a production output 5% greater than the benchmark level. Conversely, an API of 95% would indicate that at least one input level is below benchmark and therefore is expected to reduce output performance by 5%.

[0199] If a sufficient number of the key input factors or influencing variables that affect output performance are included in its derivation, the API closely correlates with output performance. This is because there will be a greater likelihood that the influence of changes in a given input factor, on the process' output performance, will be included in the API and therefore reflected in its value.

[0200] Of particular advantage is that the system 110 operates as a predictive and therefore leading indicator of performance. This is achieved by calculating the API prior to the commencement of the process utilising all available input values (e.g., stockpile geometries) (to be considered in the implementation) and for the remaining inputs performing, as described previously, a prediction calculation utilising historical levels of each of these factors where the most recent recorded performance has the highest significance and therefore weighting.

[0201] The system 110 is operable to provide uninterrupted API reporting between the predictive and actual performance by the use of an algorithm implemented in the control application and operable to combine the available results in such a way that the predictive index is incrementally improved in accuracy, as more recorded data becomes available (via sensors of the sensor set) to replace the forecast estimates or values in the relevant process record. This allows the accuracy and relevance of the API to improve up until the final calculation of actual performance achieved, for the process for a given sequence of operation.

[0202] The system 110 is operable to perform the API calculations at a frequency that allows it to be meaningful and therefore for use prior to and during actual operation to allow timely decisions and corrective actions to be undertaken.

[0203] When operating the second mode or state, the GUI of the HMI has a first or primary view which provides an indication of the value of the performance indicator. In the embodiment described, this comprises a traffic light type visual reporting of the API's value on the display 116, displaying, for example, red if the API is below expectation and green if the API is above or equal to expectation. The system 110 is operable to provide the current or real time actual value being sensed, long term average, and calculated performance for each process input factor, based on its relevant process segment/batch, through interface of the GUI in an expandable tree structure or similar. However, for the purposes of providing the most important information in the simplest format, for an unskilled operator, the main focus is visual reporting and providing a list of performance exceptions which provides the potential root causes, their locations in the production sequence, their impact (performance index), and the ability to view the exception with any other relevant and available performance information, which the system 110 is operable to determine as described previously. Examples of the format of available information include time or frequency based graphs, which are useful for investigating and understanding the origin of any performance issues and interpretations.

[0204] Another key advantage provided by the system 11.0 and methodology is that it is operable to verify the accuracy and meaningfulness of the API calculation by running the calculations on any set of any set of historical input and output data. This allows the calculated API to be evaluated by determining the strength of the correlation with the corresponding actual system output. The same methodology can be utilised to perform calibration checks over time and ensure that the accuracy of the API is maintained, especially in instances where significant permanent changes are made to the system and/or the process.

[0205] Embodiments of the system and method of the present invention as described can be implemented for manufacturing processes, for example the casting of components. In these scenarios the output(s) may include, but are not limited to: definable quality metrics, number of defects, extent of each type of defect, production throughput, material properties, dimensions, and/or

tolerances. Similarly the input(s) consist of the factors which affect the chosen output(s) of interest. The measurement of both inputs and outputs occurs through means which include but are not limited to; physical observations and measurements, sensory feedback, non-destructive testing, destructive testing and customer feedback. Depending on the characteristics of the input and output variables, a method of determining a benchmark for each may be generated as per the embodiment of the invention hereinbefore described. The relationship(s) may be established by a method known to a person skilled in the art and may include first principles, regression models, decision trees, genetic algorithms and/or neural networks. Based on this embodiment the resultant physical actions or responses allow the desired output to either be sustained or improved over time.

[0206] Embodiments of the system and method of the present invention as described can also be implemented for sales and marketing, for example the consumption of a chosen good or product. In these scenarios the output(s) may include, but are not limited to: sales or per capita consumption. Similarly, the input(s) consist of the factors which affect the chosen output of interest. These inputs may include, but are not limited to: season, price, price of alternatives, time in the market, trend cycle length, customer perception, customer

expectations, marketing activity measures, packaging, retail availability, public image, supply and trends, government programs and activities, economic factors, social factors, size of market, consumer habits and trends, and consumer mobility. Both input and output measures are established in a standardised and quantifiable form that can either be determined manually, semi- automatically or automatically. Depending on the characteristics of the input and output variables, a method of determining a benchmark for each may be generated as per the embodiment described. Since the level of consumption, or other sales and marketing metric, consists of a function of many interrelated variables and circumstances, the controllability of each input factor needs to be taken into consideration when developing the input/output relationship and the method of deriving the resultant physical action or response. Some variables will be directly controllable, others can be influenced, and the remaining ones independent and uncontrollable. The relationship(s) may be established by a method known to a person skilled in the art and may include first principles, regression models, decision trees, genetic algorithms and/or neural networks. Based on this embodiment the resultant physical actions or responses allow the desired output to either be sustained or improved over time.

[0207] Embodiments of the system and method of the present invention as described can also be implemented for retail sales, for example the sales of given products within a department store. In these scenarios, the output(s) may include, but are not limited to: overall sales, sales per customer or product movement. Similarly, the input(s) consist of the factors which affect the chosen output of interest. These input(s) may include, but are not limited to: product locations, customer movements within the store, relative positions and price of alternative products, relative positions and price of non-rivalling products, physical setup factors such as lighting, and external factors (for example, location, traffic, accessibility, visibility, signage, competition, neighbours, parking, weather and advertising). Both input and output measures are established in a standardised and quantifiable form that can either be determined manually, semi- automatically or automatically. Methods of tracking movements using sensors, inventory records and or other records or mechanisms are required for the tracking of product and customer movements. Depending on the characteristics of the input and output variables, a method of determining a benchmark for each may be generated as per the embodiment described. The relationship(s) may be established by a method known to a person skilled in the art and may include first principles, regression models, decision trees, genetic algorithms and/or neural networks. Based on this embodiment the resultant physical actions or responses allow the desired output to either be sustained or improved over time. [0208] Advantageous outcomes that can be achieved using embodiments of the present invention include the ability to:

• Achieve production targets by enabling a rapid response to predicted production variations, by providing leading indicators of poor performance, its cause, its impact, the difficulty of the required response, and procedures and other information necessary to perform the corrective action;

• Determine the system's demonstrated capability and capacity;

• Determine accurate and achievable short, medium and long term production targets;

• Improve the system control and therefore reduce the variation in levels of production rate, quality and/or reliability that can be achieved;

• Either enable or improve the responsiveness to factors that detrimentally affect performance;

• Identify areas for system improvement and the corresponding increase in production rates, quality and/or reliability;

• Improve the rate of accumulation of business knowledge regarding the optimal running of the production system;

• Enable a faster response to performance deficiencies to sustainably increase production output performance;

• Increase the sense of immediacy of corrective actions, whether preemptive or otherwise, and therefore enable increased production through more rapid response times;

• Allow the reallocation of resources from delayed and reactive responses to predictive and proactive system improvement;

• Reduce the quantity of reactive data analysis and facilitate the reallocation of resources to the actioning of analysis outcomes and their timely response;

• Allow the prioritising of resources to the issue(s) with the greatest capacity for production impact; and

• Allow informed production and business decisions to be made.

[0209] Embodiments of the invention can be applied to or implemented in any production system that either has or can be setup to record the factors which affect performance and the corresponding system output. Such production systems include, but are not limited to: Stacking and reclaiming stockyard systems;

Railroad bulk material freighting car dumping and loading systems;

Shiploading systems;

Retail systems; and

Manufacturing production systems.

[0210] The implementation of embodiments of the system and method of the present invention within any relevant production system may enable an increase in the consistency and value of process inputs, and an associated overall increase in process output(s) consistency and value.

[0211] It should be noted that correlation does not guarantee causation." For actual causation to be established to a defined level of confidence an appropriate experimental scenario must be run and evaluated. In most cases this is not possible, and as such embodiments of the invention may rely on determined causal relationships typically obtained from analysis of the system or process.

[0212] It can be appreciated that embodiments of the invention allows for optimising the throughput rate of an automated or semi-automated production process or system through the use of an intelligent systems based methodology. Embodiments of the invention detect and report on the cause(s) and quantified impact of any current and future potential negative production variations to allow effective, rapid and even pre-emptive responses. This has positive impacts on production performance, organisational learning and advancement, and organisational approaches to production process improvement.

[0213] Embodiments of the invention facilitate control of a process automatically, semi-automatically or manually, by affecting input factors affecting output performance.

[0214] It will be appreciated by those skilled in the art that variations and modifications to the invention described herein will be apparent without departing from the spirit and scope thereof. The variations and modifications as would be apparent to persons skilled in the art are deemed to fall within the broad scope and ambit of the invention as herein set forth. [0215] It will be clearly understood that, if a prior art publication is referred to herein, that reference does not constitute an admission that the publication forms part of the common general knowledge in the art in Australia or in any other country.

Claims

THE CLAIMS DEFINING THE INVENTION ARE AS FOLLOWS:

1. A system for controlling a physical process, the system comprising a

processor means and a storage means, the storage means having a software application stored thereon, whereby the processor means is operable, under control of the software application, to:

receive data associated with an expected performance of the process; receive data associated with a desired performance of the process;

process the received data to transform the received data from a first state to a second state providing an indication of performance of the process; and perform an action on the basis of the indication of performance, the action resulting in a physical effect.

2. A system for controlling a process, the system comprising processor means and a storage means, the storage means having a software application and a database stored thereon, whereby the processor means is operable, under control of the software application, to: determine an expected performance of the process;

determine a desired performance of the process;

process the expected performance of the process and the desired performance of the process to generate an indication of performance; and perform an action on the basis of the indication of performance.

3. The system of claim 2, wherein the expected performance and/or the desired performance is or is related to an actual performance of the process.

4. The system of claim 3, wherein the actual performance comprises an actual output of the process.

5. The system of any one of claims 2 to 4, wherein to determine an expected performance of the process, the processor means is operable, under control of the software application, to: generate a first set of variables, wherein each variable in the first set of variables is able to influence the performance of the process;

allocate a respective value to each of the variables in the first set of variables;

generate a performance variable associated with the performance of the process; and

allocate a performance value to the performance variable.

6. The system of claim 5, wherein the allocation comprises one or more of: assigning the value(s); generating the value(s); and/or a sensory measurement or calculation.

7. The system of claim 5 or 6, wherein each variable in the first set of

variables is associated with, relates or corresponds to an input to the process.

8. The system of any one of claims 5 to 7, wherein the value allocated to each of the variables in the first set of variables is associated with, relates or corresponds to a benchmark of the respective variable.

9. The system of any one of claims 5 to 8, wherein the performance of the process is associated with, relates or corresponds to an output of the process.

10. The system of any one of claims 5 to 9, wherein the performance value allocated to the performance variable is associated with, relates or corresponds to a benchmark of the performance variable.

11. The system of any one of claims 5 to 10, wherein the processing

comprises determining a relationship between the value of each variable in the first set of variables and the performance value of the performance variable.

12. The system of any one of claims 5 to 1 1 , comprising at least one sensor operable to sense data associated with the desired performance of the process and further operable to determine an actual performance of the process via processing of said sensed data.

13. The system of claim 12, wherein the at least one sensor belongs to a first set of sensors and data from sensors in the first set of sensors is associated with the desired performance of the process and the first set of variables and the system is operable to determine the expected

performance of the process via processing of the sensed data.

14. The system of claim 12, wherein to perform the processing, the processor means is operable, under control of the software application, to:

determine a second set of variables, wherein each variable in the second set of variables is associated with a variation in the determined actual performance of the process from the performance value attributed to the influence of a respective variable in the first set of variables;

allocate a respective value to each of the variables in the second set of variables on the basis of the determined relationship between the value of the respective variable in the first set of variables and the performance value of the performance variable; and

generate the indication of performance on the basis of the allocated values of each of the variables in the second set of variables. 5. The system of any one of claims 2 to 14, wherein the performed action comprises controlling the process to result in a physical effect.

16. A method for controlling a process, the method comprising:

determining an expected performance of the process;

determining a desired performance of the process;

processing the expected performance of the process and the desired performance of the process to generate an indication of performance; and performing an action on the basis of the indication of performance.

17. The method of claim 16, wherein determining an expected performance of the process comprises:

generating a first set of variables, wherein each variable in the first set of variables is able to influence the performance of the process;

allocating a respective value to each of the variables in the first set of variables;

generating a performance variable associated with the performance of the process; and

allocating a performance value to the performance variable.

18. The method of claim 17, wherein the allocation comprises one or more of: assigning the value(s); generating the value(s); and/or a sensory measurement or calculation.

19. The method of claim 17 or 18, wherein the performance variable is

determined from the first set of variables, using the relationship between process input(s) and/or output(s) and the corresponding benchmark(s).

20. The method of any one of claims 7 to 19, wherein each variable in the first set of variables is associated with, relates or corresponds to an input to the process.

21. The method of any one of claims 17 to 20, wherein the value allocated to each of the variables in the first set of variables is associated with, relates or corresponds to a benchmark of the respective variable.

22. The method of any one of claims 17 to 21. wherein the performance of the process is associated with an output of the process.

23. The method of any one of claims 17 to 22, wherein the performance value allocated to the performance variable is associated with a benchmark of the performance variable.

24. The method of any one of claims 17 to 23, wherein the processing comprises determining a relationship between the value of each variable in the first set of variables and the performance value of the performance variable.

25. The method of any one of claims 17 to 24, wherein determining a desired performance of the process comprises measuring the actual performance of the process.

26. The method of any one of claims 17 to 25, wherein the processing

comprises:

determining a second set of variables, wherein each variable in the second set of variables is associated with a variation in the determined actual performance of the process from the performance value attributed to the influence of a respective variable in the first set of variables;

allocating a respective value to each of the variables in the second set of variables on the basis of the determined relationship between the value of the respective variable in the first set of variables and the performance value of the performance variable; and

generating the indication of performance on the basis of the allocated values of each of the variables in the second set of variables.

27. The method of any one of claims 17 to 26, wherein the performed- action comprises controlling the process to result in a physical effect.

28. A computer-readable storage medium on which is stored instructions that, when executed by a computing means, causes the computing means to perform the method for controlling a process according to any one of claims 16 to 27.

29. A computing means programmed to carry out the method for controlling a process according to any one of claims 16 to 27.

30. A computer program, including at least one instruction capable of being executed by a computer system, which implements the method for controlling a process according to any one of claims 16 to 27.

31. A data signal including at least one instruction being capable of being received and interpreted by a computing system, wherein the instruction implements the method for controlling a process according to any one of claims 16 to 27.

32. A system for controlling a process substantially as hereinbefore described with reference to the accompanying drawings.

33. A method for controlling a process substantially as hereinbefore described with reference to the accompanying drawings.