Abstract: A process modeling technique uses a single statistical model developed from historical data for a typical process and uses this model to perform quality prediction or fault detection for various different process states of a process. The modeling technique determines means (and possibly standard deviations) of process parameters for each of a set of product grades, throughputs, etc., compares on-line process parameter measurements to these means and uses these comparisons in a single process model to perform quality prediction or fault detection across the various states of the process. In this manner, a single process model can be used to perform quality prediction or fault detection while the process is operating in any of the defined process stages or states.

Abstract: An adaptive process controller performs continuously scheduled process model parameter interpolation to determine a particular set of process model parameters which are used to develop controller tuning parameters for controller tuning. More particularly, a state-based, adaptive PID controller described herein uses a new technique to determine an appropriate process model to be used to perform adaptive tuning over the various operating regions of the plant, and in particular, uses a process model parameter determination technique that enables continuously scheduled process model parameter update over the various plant operating regions or points. The use of this continuously scheduled process model parameter update method provides for smoother transitions between tuning parameters used in the PID controller during adaptive tuning procedures which are implemented based on changes in the operating region or the operating point of the process, thereby providing for better overall control.

Abstract: Example methods, apparatuses and systems to correlate candidate factors to a predicted fault in a process control system are disclosed. Techniques may include obtaining a value associated with a particular factor corresponding to a process, and predicting a fault based on the value. A set of candidate factors corresponding to the predicted fault may be determined, and a correlation between the predicted fault and at least one factor from the set may be displayed. Different sections of the display may respectively correspond to the predicted fault and to the at least one factor, and the correlation may be indicated by time aligning the different sections. Modifications to one displayed section may result in automatic modification of other sections to maintain the correlation. A user may select one or more candidate factors to be displayed, and may indicate a particular point of a particular section to obtain additional details.

Abstract: A process is modeled by resolving the process into a plurality of process stages, including at least a first process stage and a second process stage, and developing a plurality of models, each model corresponding to a respective one of the plurality of process stages, wherein the model corresponding to each process stage is developed using data from one or more runs of that process stage and output quality data relating to the one or more runs of that process stage and wherein the model corresponding to each process stage is adapted to produce an output quality prediction associated with that process stage, and wherein the output quality prediction produced by the model of a first one of the process stages is used to develop the model of a second one of the process stages.

Abstract: A batch modeling and analysis system uses a simple and computationally inexpensive technique to align data collected from an on-going, currently running or on-line batch process with a batch model formed for the batch process so as to enable the reliable determination of the current operational state of the on-line batch process with respect to the batch model. This data alignment technique enables further statistical processing techniques, such as projection to latent sources (PLS) and principle component analysis (PCA) techniques, to be applied to the on-line batch data to perform analyses on the quality of the currently running batch. These analyses, in turn, provide useful information to a user, such as a batch operator, that enables the user to determine the quality of the batch at the present time, based on the batch model, and the likelihood that the desired batch output quality metrics will be reached at the end of the batch run.

Abstract: In a batch process control system employing storage tanks without mixers, properties of the storage tank pump out feedstock may be modeled to more accurately control the quality of a process. This model may not require the measurement of input or pump out flow or assume perfect blending. Rather, the developed model may assume that feedstock input into a storage tank may remain layered with some mixing due to continuous convection, turbulence during loading, or other factors. The model may include a projection of the properties describing a storage tank layer of input material into the model. For each new load of storage tank input feedstock, model zones may be shifted and the zone from which the feedstock is drawn may be updated with the properties from the new load.

Abstract: A system and method for controlling a process includes simulating the process and producing a simulated output of the process, developing a set of target values based on measured inputs from the process and based on the simulated output from the process simulator, and producing multiple control outputs configured to control the process based on the set of target values during each operational cycle of the process control system. The simulated outputs include one or more predicted future values up to the steady state of the process.

Abstract: A method of creating and using an adaptive DMC type or other MPC controller includes using a model switching technique to periodically determine a process model, such as a parameterized process model, for a process loop on-line during operation of the process. The method then uses the process model to generate an MPC control model and creates and downloads an MPC controller algorithm to an MPC controller based on the new control model while the MPC controller is operating on-line. This technique, which is generally applicable to single-loop MPC controllers and is particularly useful in MPC controllers with a control horizon of one or two, enables an MPC controller to be adapted during the normal operation of the process, so as to change the process model on which the MPC controller is based to thereby account for process changes.

Abstract: Example methods and apparatus to predict process quality in a process control system are disclosed. A disclosed example method includes receiving process control information relating to a process at a first time including a first value associated with a first measured variable and a second value associated with a second measured variable, determining if a variation based on the received process control information associated with the process exceeds a threshold, if the variation exceeds the threshold, calculating a first contribution value based on a contribution of the first measured variable to the variation and a second contribution value based on a contribution of the second measured variable to the variation, determining at least one corrective action based on the first contribution value, the second contribution value, the first value, or the second value, and calculating a predicted process quality based on the at least one corrective action at a time after the first time.

Abstract: A robust method of creating process models for use in controller generation, such as in MPC controller generation, adds noise to the process data collected and used in the model generation process. In particular, a robust method of creating a parametric process model first collects process outputs based on known test input signals or sequences, adds random noise to the collected process data and then uses a standard or known technique to determine a process model from the collected process data. Unlike existing techniques for noise removal that focus on clean up of non-random noise prior to generating a process model, the addition of random, zero-mean noise to the process data enables, in many cases, the generation of an acceptable parametric process model in situations where no process model parameter convergence was otherwise obtained.

Abstract: An adaptive process controller performs continuously scheduled process model parameter interpolation to determine a particular set of process model parameters which are used to develop controller tuning parameters for controller tuning. More particularly, a state-based, adaptive PID controller described herein uses a new technique to determine an appropriate process model to be used to perform adaptive tuning over the various operating regions of the plant, and in particular, uses a process model parameter determination technique that enables continuously scheduled process model parameter update over the various plant operating regions or points. The use of this continuously scheduled process model parameter update method provides for smoother transitions between tuning parameters used in the PID controller during adaptive tuning procedures which are implemented based on changes in the operating region or the operating point of the process, thereby providing for better overall control.

Abstract: A state based adaptive feedback/feedforward PID controller includes a model set component, communicatively coupled to a process input, having a state variable defining a number of process regions, and a number of models grouped into the process regions. Each of the grouped models includes a plurality of parameters having a value selected from a set of predetermined initial values assigned to the respective parameter. The adaptive controller further includes an error generator communicatively coupled to the model set component and a process output. The error generator configured to generate a model error signal representative of the difference between a model output signal and a process output signal. The error generator, communicatively coupled to a model evaluation component, is configured to compute a model squared error corresponding to a model and correlating the model squared error to parameter values represented in the model.

Abstract: A method of creating and using an adaptive DMC type or other MPC controller includes using a model switching technique to periodically determine a process model, such as a parameterized process model, for a process loop on-line during operation of the process. The method then uses the process model to generate an MPC control model and creates and downloads an MPC controller algorithm to an MPC controller based on the new control model while the MPC controller is operating on-line. This technique, which is generally applicable to single-loop MPC controllers and is particularly useful in MPC controllers with a control horizon of one or two, enables an MPC controller to be adapted during the normal operation of the process, so as to change the process model on which the MPC controller is based to thereby account for process changes.

Abstract: Disclosed is a method of controlling and managing a process control system having a plurality of control loops. The method includes implementing a plurality of control routines to control operation of the plurality of control loops, respectively. The plurality of control routines may include at least one non-adaptive control routine. Operating condition data is then collected in connection with the operation of each control loop of the plurality of control loops, and a respective process model is identified for each control loop of the plurality of control loops from the respective operating condition data collected for each control loop of the plurality of control loops. In some embodiments, the identification of the respective process models may be automatic as a result of a detected process change or be on-demand as a result of an injected parameter change.

Abstract: An auto-tuner is adapted to be used in a distributed process control network having a communications network that communicatively couples a process controller, which executes a process control routine, and one or more process devices used in a process control loop. The auto-tuner includes a first tuning element configured to cause a control entity to force the process loop to undergo an auto-tuning procedure and a tuning data stack operating within one of the process devices to receive and store a tuning signal associated with the control entity along with a time stamp indicating the time the tuning signal was acted on by the device. A measurement data stack is disposed in the same or a different process device and operates to receive and store a response or measurement signal generated by the process device along with a time stamp indicating when the response signal was generated or detected.

Abstract: A method of creating and using an adaptive DMC type or other MPC controller includes using a model switching technique to periodically determine a process model, such as a parameterized process model, for a process loop on-line during operation of the process. The method then uses the process model to generate an MPC control model and creates and downloads an MPC controller algorithm to an MPC controller based on the new control model while the MPC controller is operating on-line. This technique, which is generally applicable to single-loop MPC controllers and is particularly useful in MPC controllers with a control horizon of one or two, enables an MPC controller to be adapted during the normal operation of the process, so as to change the process model on which the MPC controller is based to thereby account for process changes.

Abstract: Disclosed is a method of controlling and managing a process control system having a plurality of control loops. The method includes implementing a plurality of control routines to control operation of the plurality of control loops, respectively. The plurality of control routines may include at least one non-adaptive control routine. Operating condition data is then collected in connection with the operation of each control loop of the plurality of control loops, and a respective process model is identified for each control loop of the plurality of control loops from the respective operating condition data collected for each control loop of the plurality of control loops. In some embodiments, the identification of the respective process models may be automatic as a result of a detected process change or be on-demand as a result of an injected parameter change.

Abstract: An adaptive multivariable process control system includes a multivariable process controller, such as a model predictive controller, having a multivariable process model characterized as a set of two or more single-input, single-output (SISO) models and an adaptation system which adapts the multivariable process model. The adaptation system detects changes in process inputs sufficient to start an adaptation cycle and, when such changes are detected, collects process input and output data needed to perform model adaptation. The adaptation system next determines a subset of the SISO models within the multivariable process model which are to be adapted, based on, for example, a determination of which process inputs are most correlated with the error between the actual (measured) process output and the process output developed by the multivariable process model. The adaptation system then performs standard or known model switching and parameter interpolation techniques to adapt each of the selected SISO models.

Abstract: A process controller that may be used to control a process having a set of process outputs effected by a set of process control input signals includes a multiple-input/multiple output controller that uses the process outputs to develop the set of process control input signals and a process model, which may be a non-linear process model, that receives the set of process control input signals to produce a prediction signal for one or more of the process outputs. The multiple-input/multiple-output control element includes another process model, which may be a standard linear process model, to develop a prediction vector for each of the process outputs and includes a correction unit that modifies the prediction vector for the one or more of the process outputs using the prediction signal for the one or more of the process outputs to thereby compensate for the non-linearities of the process.

Abstract: An apparatus is adapted to be used to create software and programming instructions for a distributed process control system having a user workstation remotely located from a distributed controller that controls one or more field devices using control modules. The apparatus includes a computer having a memory and a processing unit as well as a configuration application and a controller application stored on the computer memory to be executed on the processor. The configuration application is further capable of being executed on the user workstation of the distributed process control system to create the control modules for execution by the distributed controller while the controller application is adapted to be executed on the distributed controller to implement one of the control modules during operation of the distributed process control system.