Patent Description:
Industrial process control and automation systems are often used to automate large and complex industrial processes. These types of systems routinely include sensors, actuators, and controllers. Some of the controllers typically receive measurements from the sensors and generate control signals for the actuators. Other controllers often perform higher-level functions, such as planning, scheduling, and optimization operations.

The quality of products produced in industrial processes typically depends strongly on having the automatic control system for the process operating well. Most automatic control loops or multivariable controllers have tuning parameters that are adjusted to the dynamic behavior of the process. When these are adjusted correctly, the controller takes good actions, the process is well controlled and high quality products result. However, should the dynamic behavior of the process change, the controller may need to be re-tuned to avoid a degradation of control performance and consequent poor quality production.

Examples of currently used systems can be found in the following:.

This disclosure provides a system and method for monitoring changes in process dynamic behavior by mapping parameters to a lower dimensional space.

In a first embodiment, the invention relates to a method according to independent claim <NUM>.

In a second embodiment, the invention relates to an apparatus according to independent claim <NUM>.

In a third embodiment, the invention relates to a non-transitory computer readable medium according to independent claim <NUM>.

Other technical features may be readily apparent to one skilled in the art from the following figures, descriptions, and dependent claims.

The figures discussed below and the various embodiments used to describe the principles of the present invention in this patent document are by way of illustration only and should not be construed in any way to limit the scope of the invention. Those skilled in the art will understand that the principles of the invention may be implemented in any type of suitably arranged device or system.

<FIG> illustrates an example industrial process control and automation system <NUM> according to this disclosure. As shown in <FIG>, the system <NUM> includes various components that facilitate production or processing of at least one product or other material. For instance, the system <NUM> can be used to facilitate control over components in one or multiple industrial plants. Each plant represents one or more processing facilities (or one or more portions thereof), such as one or more manufacturing facilities for producing at least one product or other material. In general, each plant may implement one or more industrial processes and can individually or collectively be referred to as a process system. A process system generally represents any system or portion thereof configured to process one or more products or other materials in some manner.

In <FIG>, the system <NUM> includes one or more sensors 102a and one or more actuators 102b. The sensors 102a and actuators 102b represent components in a process system that may perform any of a wide variety of functions. For example, the sensors 102a could measure a wide variety of characteristics in the process system, such as pressure, temperature, or flow rate. Also, the actuators 102b could alter a wide variety of characteristics in the process system. Each of the sensors 102a includes any suitable structure for measuring one or more characteristics in a process system. Each of the actuators 102b includes any suitable structure for operating on or affecting one or more conditions in a process system.

At least one network <NUM> is coupled to the sensors 102a and actuators 102b. The network <NUM> facilitates interaction with the sensors 102a and actuators 102b. For example, the network <NUM> could transport measurement data from the sensors 102a and provide control signals to the actuators 102b. The network <NUM> could represent any suitable network or combination of networks. As particular examples, the network <NUM> could represent at least one Ethernet network, electrical signal network (such as a HART network), pneumatic control signal network, or any other or additional type(s) of network(s).

The system <NUM> also includes various controllers <NUM>. The controllers <NUM> can be used in the system <NUM> to perform various functions in order to control one or more industrial processes. For example, a first set of controllers <NUM> may use measurements from one or more sensors 102a to control the operation of one or more actuators 102b. A second set of controllers <NUM> could be used to optimize the control logic or other operations performed by the first set of controllers. A third set of controllers <NUM> could be used to perform additional functions. The controllers <NUM> can communicate via one or more networks <NUM> and associated switches, firewalls, and other components.

Each controller <NUM> includes any suitable structure for controlling one or more aspects of an industrial process. At least some of the controllers <NUM> could, for example, represent proportional-integral-derivative (PID) controllers or multivariable controllers, such as controllers implementing model predictive control or other advanced predictive control. As a particular example, each controller <NUM> could represent a computing device running a real-time operating system, a WINDOWS operating system, or other operating system.

Operator access to and interaction with the controllers <NUM> and other components of the system <NUM> can occur via various operator consoles <NUM>. Each operator console <NUM> could be used to provide information to an operator and receive information from an operator. For example, each operator console <NUM> could provide information identifying a current state of an industrial process to the operator, such as values of various process variables and alarms associated with the industrial process. Each operator console <NUM> could also receive information affecting how the industrial process is controlled, such as by receiving setpoints or control modes for process variables controlled by the controllers <NUM> or other information that alters or affects how the controllers <NUM> control the industrial process. Each operator console <NUM> includes any suitable structure for displaying information to and interacting with an operator. For example, each operator console <NUM> could represent a computing device running a WINDOWS operating system or other operating system.

Multiple operator consoles <NUM> can be grouped together and used in one or more control rooms <NUM>. Each control room <NUM> could include any number of operator consoles <NUM> in any suitable arrangement. In some embodiments, multiple control rooms <NUM> can be used to control an industrial plant, such as when each control room <NUM> contains operator consoles <NUM> used to manage a discrete part of the industrial plant.

The control and automation system <NUM> also includes at least one historian <NUM> and one or more servers <NUM>. The historian <NUM> represents a component that stores various information about the system <NUM>. The historian <NUM> could, for instance, store information that is generated by the various controllers <NUM> during the control of one or more industrial processes. The historian <NUM> includes any suitable structure for storing and facilitating retrieval of information. Although shown as a single component here, the historian <NUM> could be located elsewhere in the system <NUM>, or multiple historians could be distributed in different locations in the system <NUM>.

Each server <NUM> denotes a computing device that executes applications for users of the operator consoles <NUM> or other applications. The applications could be used to support various functions for the operator consoles <NUM>, the controllers <NUM>, or other components of the system <NUM>. Each server <NUM> could represent a computing device running a WINDOWS operating system or other operating system. Note that while shown as being local within the control and automation system <NUM>, the functionality of the server <NUM>, the historian <NUM>, or both, could be remote from the control and automation system <NUM>. For instance, the functionality of the server <NUM> and/or the historian <NUM> could be implemented in a computing cloud <NUM> or a remote server communicatively coupled to the control and automation system <NUM> via a gateway <NUM>.

In general, the quality of products produced in industrial processes typically depends strongly on having the automatic control system for the process operating well. Most automatic control loops or multivariable controllers have tuning parameters that are adjusted to the dynamic behavior of the process. When these are adjusted correctly, the controller takes good actions, the process is well controlled and high quality products result. However, should the dynamic behavior of the process change, the controller may need to be re-tuned to avoid a degradation of control performance and consequent poor quality production.

The behavior and operation of industrial process control and automation systems, such as the system <NUM>, can be modelled using one or more process models. Accurate process models may be beneficial or required for many reasons. For example, a process model may be used to make predictions about a process or for tuning a controller. It is often a difficult problem to determine when process behavior has changed significantly. For many industrial processes, process model parameters are estimated from data, and the data used in the estimation typically comes from a planned experiment designed to create information rich data. If the process changes over time, it may be necessary to re-identify the process model. However, planned experiments can be time consuming and may risk product quality. Therefore, there is a strong motivation to be able to determine when process behavior has changed, so that experiments can be performed only when necessary.

Some process behavior monitoring techniques involve periodic estimation of process model parameters based on routine process data. These parameter estimates are monitored over time, and when significant deviations from nominal values are detected, it is concluded that process behavior has changed. However, when process data is noisy, the model parameter estimates may also be noisy. When there are multiple parameters, the variability of the estimates can make it difficult to determine when there have been significant shifts in values. In general the more parameters there are, the harder it can be to detect changes.

To address these and other issues, embodiments of this disclosure provide a system and method for monitoring changes in process dynamic behavior by mapping parameters to a lower dimensional space. The disclosed embodiments take into account that, when there is correlation between parameters, dimensional reduction can be used to make shifts in behavior easier to detect. For this reason, the dimension of the parameter space is reduced to simplify the task of detecting a deviation from nominal values. The disclosed embodiments employ one or more techniques to determine when process behavior has changed by monitoring process model parameter estimates in a lower dimensional space.

Multiple techniques of parameter space reduction can be used. In a first technique according to the present invention, the time delay and time constant parameters can be added together to create a resulting combined response time parameter. This combined parameter has been found to be less variable than the individual parameters. In a second technique not part of the invention, the frequency response of the process models can be examined. One may look at the gain and phase shift at a key frequency, such as the cut-off frequency of the nominal model. This reduces the parameter space to two parameters that can be monitored over time. Using either of these techniques, the parameter space of the models is reduced to two parameters that are less noisy. In some embodiments, one or more of the components in <FIG> (such as the operator consoles <NUM>, the historian <NUM>, the server <NUM>, or the computing cloud <NUM>) could be configured to perform these operations.

The disclosed embodiments result in having fewer and more stable parameters for monitoring, which makes the monitoring easier and more likely to detect changes in process behavior. Once a change in process behavior is detected, human or automatic intervention can be triggered to undertake a plant experiment to generate a more accurate process model for controller retuning and maintenance of good process control. These benefits represent a technical advantage over conventional systems in which noisy data hides changes in process behavior. Additional details regarding the disclosed embodiments are provided below.

Although <FIG> illustrates one example of an industrial process control and automation system <NUM>, various changes may be made to <FIG>. For example, the system <NUM> could include any number of sensors, actuators, controllers, networks, operator stations, control rooms, historians, servers, and other components. Also, the makeup and arrangement of the system <NUM> in <FIG> is for illustration only. Components could be added, omitted, combined, further subdivided, or placed in any other suitable configuration according to particular needs. Further, particular functions have been described as being performed by particular components of the system <NUM>. This is for illustration only. In general, control and automation systems are highly configurable and can be configured in any suitable manner according to particular needs. In addition, <FIG> illustrates one example operational environment in which changes in process dynamic behavior can be monitored by mapping parameters to a lower dimensional space. This functionality can be used in any other suitable system.

The first and second techniques discussed above will now be described in greater detail.

Technique <NUM>: Using process response time to represent higher order process dynamics.

In the first technique, process response time may be used to represent higher order process dynamics. Process response time can be defined as the time it takes for a process step response to reach <NUM>% of its final value. In the case of first-order plus deadtime models, process response time is the sum of the time constant and the time delay. In the case of higher order models, the step response can be simulated to determine the process response time. In particular, the higher order model can be reduced to the response time.

A process P may be modeled by, for example, a linear system of differential equations, which can be written in Laplace transfer function form as: <MAT> where y is the process output, m is the number of process inputs, ui is the ith process input, each Pi takes the form <MAT> and bi and fi are polynomials in s, the Laplace domain variable, and di is a pure time delay.

First-order plus deadtime transfer functions are simple, but powerful, models that can be used to describe many industrial processes well enough for the purposes of controller design. A first-order plus deadtime transfer function is a specific form of Equation (<NUM>) and takes the form: <MAT> where ki is the process gain and τi is the process time constant.

One way to obtain process models is from process model identification based on process data. <FIG> illustrates charts <NUM>, <NUM> showing an example of routine process data for a single-input single-output system, according to this disclosure. As shown in <FIG>, the chart <NUM> shows the process input u over time for the example system. The chart <NUM> shows the process output y over time for the system. The other plot in the chart <NUM> shows r over time, where r is the target output or set point.

When estimating the parameters for a process model, an optimization such as the following may be performed: <MAT> where y(i) are the measured process values, yθ(i) are the predictions based on the model with parameters θ, and N is the number of data points.

This optimization finds the process model parameter values that minimize the sum of squares of errors between the measured process values and the values predicted by the process model. Techniques for process model parameter estimation are well-known and will not be described in further detail herein.

If information rich data is available (for example, data from a planned plant experiment), then it is possible to obtain accurate process model parameter estimates. Typically, data from routine process operations is not information rich, and so process model parameter estimates from routine operating data tend to have large variances and other potential inaccuracies. However, while the routine process data may not allow accurate process model estimation, if process model parameters are periodically re-estimated, changes in process behavior may be detected as a shift in the estimates.

The objective is to monitor parameter estimates made from routine operating data and to look for changes. For example, consider the case of a paper mill that has a process for producing paper. One of the most important parameters is that the produced paper have the right basis weight. To adjust the basis weight of the paper, one method is to adjust a stock flow valve at the headbox. This can be represented by three parameters: gain, time delay, and time constant.

An example of gain is, if the stock flow valve is opened x amount, the weight of the paper changes by y amount. In a simplified example, the gain could be y/x. The time delay indicates how long after the stock flow valve is adjusted that any change in the weight of the paper occurs. The time constant measures a "ramp time" of a gradual change; that is, the time constant indicates how long from when a change is first measured until a new steady state value is measured. In the case of the paper mill, the time constant may indicate how long it takes, after adding more stock to the headbox, for the concentration in the headbox to increase from a previous steady state value to a new steady state value.

For first-order plus deadtime models, both the time constant and time delay affect the speed of the process response, and there can be a correlation between the estimates of these parameters. For example, the time constant and time delay tend to move in opposite directions. For this reason, it can be advantageous to simply add these two parameters together and monitor the sum, which is typically a more stable parameter. It should be noted that for first order plus time delay processes, the sum of time constant plus time delay is the <NUM>% process response time. This technique can also be generalized to higher order processes. A simple method is to simulate the step response to determine the process response time.

This can be viewed as a dimension reduction technique in that, using this technique, one may monitor the two parameters, gain and response time, to detect changes in process behavior, as opposed to monitoring gain, time constant, and time delay, or an even larger number of parameters depending on the process model order. There are many other techniques of dimensional reduction that may be used for this problem.

Technique <NUM>: Using phase at model cutoff frequency to represent process dynamics.

The second process model parameter dimensional reduction technique for monitoring for changes in process behavior is to look at the frequency response of the process model. The frequency response of the process model, as represented by a Bode plot, shows the process gain and phase as a function of frequency.

Bode plots are derived as follows. For a process model in transfer function form, such as in Equation (<NUM>) above, Pi(jω) is obtained by substituting s = jω). The polar form of this complex valued function is: <MAT> where |Pi(jω)| is the amplitude ratio and ϕi is the phase angle as functions of ω. A Bode plot is simply a plot of these two functions. Bode plots and frequency domain analysis are well-established fields and further details will not be included herein.

<FIG> illustrates an example Bode plot <NUM> according to this disclosure. From examination of the Bode plot <NUM>, a cutoff frequency <NUM> and a phase <NUM> at the cutoff frequency may be determined. The cutoff frequency <NUM> is determined by finding the frequency at which the amplitude ratio is equal to <NUM>, or -<NUM> if the magnitude is expressed in decibels. The phase <NUM> at the cutoff frequency <NUM> is simply the value of the phase at the cutoff frequency. In the Bode plot <NUM>, the cutoff frequency <NUM> is roughly <NUM> rad/s and the phase <NUM> at that frequency is roughly -<NUM> degrees. No matter the order of the polynomials making up the transfer function in Equation (<NUM>), the model may be summarized by the Bode plot <NUM>. The phase plot is associated with the dynamics of the model and large changes in the phase, particularly around the cutoff frequency may be associated with important changes in the process dynamics.

For this reason, the phase at the cutoff frequency may be used to represent the dynamics of the process model, regardless of the order of the process model, for monitoring for changes in the process behavior. To be clear, if a series of models are being compared, then the frequency of interest can be chosen as the cutoff frequency for the nominal model. The dynamics of subsequent models can then be summarized by their gain, and by their phase at the frequency of interest (the cutoff frequency of the nominal model).

Instead of monitoring all individual parameters associated with the process dynamics, one may monitor the phase at a key frequency, such as the cutoff frequency of the nominal model. This is demonstrated in the example below.

Consider the routine process data as shown in <FIG>. To monitor this process, process model parameter estimates could be made periodically, such as every <NUM> minutes. Then, each time <NUM> minutes is elapsed, the most recent window of data, for example the last four hours, can be used to estimate the process model parameters. The parameter estimates may then be transformed to a reduced dimensional space. For example, the response time of the model could be calculated, or the phase at the cutoff frequency of the nominal model could be calculated. The reduced dimensional estimates can then be monitored over time, and shifts in values beyond thresholds around the nominal value indicate one or more changes in process behavior.

When it is determined that process behavior has changed, then excitation (e.g., one or more disturbances, perturbations, etc.) can be added to the process to generate an information rich data set so that an accurate model of the new process behavior may be obtained.

<FIG> illustrates a chart <NUM> showing first order plus deadtime process model parameter estimates made periodically over a period of time during a process, according to this disclosure. The gain, time constant, and time delay are all estimated over a period of time (here, sixteen hours) and plotted in plots <NUM>, <NUM>, and <NUM> respectively, along with the nominal model values and thresholds for change detection. In addition, the estimated process response time and the phase at the nominal cutoff frequency are also plotted in plots <NUM> and <NUM> respectively. Here the estimated response time plot <NUM> is the sum of the estimated time constant plot <NUM> and the estimated time delay plot <NUM>, as determined using Technique <NUM> described above. The plot <NUM> showing the phase at the nominal cutoff frequency is obtained using Technique <NUM> described above.

In the data set shown in the chart <NUM>, there is a change in the process (in particular, a change in the time constant to a value outside a threshold) starting at approximately <NUM> hours. The objective is to detect the change by examining the data in the different plots <NUM>-<NUM>. The estimated gain plot <NUM> shows no estimates going beyond a threshold around the nominal value at any time, which is correct. The estimated time constant plot <NUM> shows significant swings in the estimate over time, with the estimate incorrectly going outside of the detection thresholds several times prior to the <NUM> hour mark (even though the actual value in within the thresholds prior to the <NUM> hour mark). Also, the estimates are incorrectly within the thresholds from <NUM> to <NUM> hours (even though the actual value of the time constant moves outside the threshold starting at approximately the <NUM> hour mark). Thus, the time constant estimate data (as represented by the estimated time constant plot <NUM>) is not reliable for monitoring for change detection. The time delay estimate data has similar problems.

However, the estimated response time plot <NUM> more clearly indicates the change that occurs at approximately <NUM> hours. The estimated response time plot <NUM> shows the estimates to be within the threshold until approximately the <NUM> hour mark. After the <NUM> hour mark, the plot <NUM> shows the estimates are outside of the threshold. This kind of clear pattern is what is desired for reliable change detection. The plot <NUM>, indicating the phase at nominal cutoff frequency, shows the estimates moving outside of the threshold only a few times prior to the <NUM> hour mark. After the <NUM> hour mark the estimates are outside of the threshold except for the estimate at <NUM> hours. So, in this data set, Technique <NUM> (using the phase at the nominal cutoff frequency) is not quite as reliable for detecting change as Technique <NUM> (the process response time); however, Technique <NUM> is still better than separately monitoring the time constant and time delay.

This example shown in <FIG> demonstrates that when monitoring for changes in process behavior by examining process model parameter estimates over time, it is helpful to reduce the number of parameters to monitor. In many cases, reducing the dimension of the parameter space can improve the reliability of the monitoring. The techniques described herein provide different methods for summarizing the parameters associated with process dynamics (those other than the gain), either by the process response time, or by the phase at the cutoff frequency.

<FIG> illustrates an example device <NUM> supporting a method for monitoring changes in process dynamic behavior by mapping parameters to a lower dimensional space, according to this disclosure. The device <NUM> could, for example, represent the operator consoles <NUM>, the historian <NUM>, the server <NUM>, or a device in the computing cloud <NUM> of <FIG>. However, these components could be implemented using any other suitable device or system, and the device <NUM> could be used in any other suitable system.

As shown in <FIG>, the device <NUM> includes at least one processor <NUM>, at least one storage device <NUM>, at least one communications unit <NUM>, and at least one input/output (I/O) unit <NUM>. Each processor <NUM> can execute instructions, such as those implementing the processes and methods described above that may be loaded into a memory <NUM>. Each processor <NUM> denotes any suitable processing device, such as one or more microprocessors, microcontrollers, digital signal processors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or discrete circuitry.

The memory <NUM> and a persistent storage <NUM> are examples of storage devices <NUM>, which represent any structure(s) capable of storing and facilitating retrieval of information (such as data, program code, and/or other suitable information on a temporary or permanent basis). The memory <NUM> may represent a random access memory or any other suitable volatile or non-volatile storage device(s). The persistent storage <NUM> may contain one or more components or devices supporting longer-term storage of data, such as a read only memory, hard drive, Flash memory, or optical disc. The memory <NUM> or the persistent storage <NUM> may be configured to store information and data associated with monitoring changes in process dynamic behavior by mapping parameters to a lower dimensional space.

The communications unit <NUM> supports communications with other systems or devices. For example, the communications unit <NUM> could include a network interface card or a wireless transceiver facilitating communications over a wired or wireless network (such as the network <NUM>). The communications unit <NUM> may support communications through any suitable physical or wireless communication link(s).

The I/O unit <NUM> allows for input and output of data. For example, the I/O unit <NUM> may provide a connection for user input through a keyboard, mouse, keypad, touchscreen, or other suitable input device. The I/O unit <NUM> may also send output to a display, printer, or other suitable output device.

Although <FIG> illustrates one example of a device <NUM> supporting a method for monitoring changes in process dynamic behavior by mapping parameters to a lower dimensional space, various changes may be made to <FIG>. Also, computing devices can come in a wide variety of configurations, and <FIG> does not limit this disclosure to any particular configuration of computing device.

<FIG> illustrates an example method <NUM> for monitoring changes in process dynamic behavior by mapping parameters to a lower dimensional space, according to this disclosure. The method <NUM> is used for monitoring process changes in an industrial process control and automation system, such as the system <NUM> of <FIG>. In some embodiments, the method <NUM> could be performed by one or more components of the system <NUM>, such as an operator station <NUM>, a server <NUM>, or a computing cloud <NUM>. However, the method <NUM> could be used with any other suitable system. For ease of explanation, the method <NUM> will be described as being performed by a computing device, such as the device <NUM> of <FIG>.

At step <NUM>, the device acquires process data collected during performance of a process in an industrial process control and automation system. This could include, for example, the device acquiring process data from a historian. The process data may be output from one or more controllers in the industrial process control and automation system. In some embodiments, the process data is stored in the historian after the process data is output from the one or more controllers. In some embodiments, the data is acquired somewhat in real-time, in that batches of data are uploaded to the device every minute or every few seconds.

At step <NUM>, the device reduces a dimension space of the process data by combining two or more parameters of the process data or examining a frequency response of the process data. The device reduces the dimension space of the process data by combining a time constant parameter and a time delay parameter into a response time parameter. In some embodiments, the device reduces the dimension space of the process data to a gain and a phase shift of the process data at a cut-off frequency.

At step <NUM>, the device determines a change in the process based on a change in the process data in the reduced dimension space. In some embodiments, the device determines the change in the process by inputting the process data in the reduced dimension space into an algorithm and receiving an output of the algorithm indicating that the process data has exceeded a predetermined threshold.

At step <NUM>, the device outputs a result based on the determined change in the process. In some embodiments, the result that is output comprises an alarm or warning to a user, where the alarm or warning indicates to the user to re-tune or calibrate a component of the industrial process control and automation system. In some embodiments, the result that is output comprises a signal or instruction transmitted by the device to a component of the industrial process control and automation system, where the signal or instruction is configured to automatically change a setting of the component.

Although <FIG> illustrates one example of a method <NUM> for monitoring changes in process dynamic behavior by mapping parameters to a lower dimensional space, various changes may be made to <FIG>. For example, while shown as a series of steps, various steps shown in <FIG> could overlap, occur in parallel, occur in a different order, or occur multiple times. Moreover, some steps could be combined or removed, and additional steps could be added according to particular needs.

In some embodiments, various functions described in this patent document are implemented or supported by a computer program that is formed from computer readable program code and that is embodied in a computer readable medium.

It may be advantageous to set forth definitions of certain words and phrases used throughout this patent document. The terms "application" and "program" refer to one or more computer programs, software components, sets of instructions, procedures, functions, objects, classes, instances, related data, or a portion thereof adapted for implementation in a suitable computer code (including source code, object code, or executable code). The term "communicate," as well as derivatives thereof, encompasses both direct and indirect communication. The phrase "associated with," as well as derivatives thereof, may mean to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, have a relationship to or with, or the like.

The description in the present application should not be read as implying that any particular element, step, or function is an essential or critical element that must be included in the claim scope. The scope of patented subject matter is defined only by the appended claims.

Claim 1:
A method comprising:
acquiring (<NUM>) process data collected in an industrial process control and automation system (<NUM>);
reducing (<NUM>) a dimension space of the process data by combining two or more parameters (<NUM>, <NUM>, <NUM>) of the process data,
wherein the dimension space of the process data is reduced by combining a time constant parameter (<NUM>) and a time delay parameter (<NUM>) into a response time parameter (<NUM>);
determining (<NUM>) a change in a process based on a change in the process data in the reduced dimension space; and
outputting (<NUM>) a result based on the determined change in the process to generate a process model for re-tuning and maintenance of process control in the industrial process control and automation system.