Honeywell International Inc.

Honeywell G.m.b.h.

Gas Research Institute

J Ward MacArthur

Eric W Grald

Ranganathan Srinivasan

Konstantinos Tsakalis

Charles Q Zhan

Sachindra K Dash

Wendy K Foslien

Sriram Hallihole

Dinkar A Mylaraswamy

Lorne W Nelson

Dipak J Shah

Jeffrey M Hammer

Zhuxin J Lu

Madhukar Madhavamurthy Gundappa

Michael A Woessner

Gobinath Pandurangan

David A Wahlstedt

Brian C Horn

Robert L Buchholz

Sanjay Venugopal

Venkata K P Reddy Vurubindi

Paul A Anderson

Murugesh Palanisamy

James V Radomski

Mohan N Srinivasrao

A method includes obtaining a process model representing an industrial process and obtaining controller specifications for an industrial process controller. The method also includes identifying a controller design having one or more parameters for the industrial process controller using the process model, an uncertainty associated with the process model, and the one or more parameters. The method further includes validating the controller design of the industrial process controller for use in a closed-loop control system and deploying the controller design if validated to the industrial process controller.

Robust control design approach for chemical processing industries and other industries

This disclosure provides adaptive control techniques for pH control or control of other industrial processes. For example, in one method, a robust stability condition (RSC) value is determined during operation of a process controller, and a characteristic of the process controller is adaptively modified based on the RSC value. The RSC value provides an estimate of performance of the process controller in controlling the industrial process. In another method, one of multiple process controllers is selected based on RSC values associated with the process controllers, and one or more control signals are output from the selected process controller to an industrial process in order to control the industrial process. The RSC values provide estimates of performances of the multiple process controllers in controlling the industrial process.

Adaptive control techniques for pH control or control of other industrial processes

A method includes receiving data associated with operation of an industrial process system. The method also includes identifying a model defining a behavior of the industrial process system using the data, an orthonormal bases function, and an ordinal spline bases function. The orthonormal bases function can be generated using estimated poles of the industrial process system. The ordinal spline bases function can be generated using a specified set of cubic splines. The ordinal spline bases function can also be generated using a distribution of knots and multiple ordinal spline functions associated with the knots. More knots can be associated with a more nonlinear portion of the industrial process system, and fewer knots can be associated with a less nonlinear portion or a linear portion of the industrial process system.

Apparatus and method for nonlinear process identification using orthonormal bases and ordinal splines in a process control system

A system includes a process controller and an equation evaluation apparatus. The equation evaluation apparatus includes an equation editor, a model factory, and an equation evaluation engine. The equation editor is adapted to receive equations describing a process to be controlled by the process controller. The equation editor is also adapted to generate model information representing the equations. The model factory is adapted to receive the model information and generate an equation stack representing the equations. The equation evaluation engine is adapted to receive evaluation information from the process controller, evaluate at least one of the equations using the evaluation information and the equation stack, and send a result of the evaluation to the process controller. The model information could include information representing algebraic equations, differential equations, algebraic states, differential states, inputs, parameters, constants, and/or expressions.

System and method for solving chemical engineering equations and model development using equation editor

One method includes obtaining a preliminary model associated with a system to be controlled and constructing a weighted model using one or more weighting factors. The method also includes identifying a final model of the system using the preliminary and weighted models, where the final model has a stability margin that is greater than an uncertainty associated with the final model. The method further includes controlling the system using a controller designed based on the final model. Another method includes identifying a first model associated with a system to be controlled, performing model order reduction to identify a second model, and controlling the system using a controller designed based on the second model. Performing the model order reduction includes reducing a weighted coprime factor model uncertainty between the first and second models.

Apparatus and method for system identification and loop-shaping controller design in a process control system

An apparatus, method, and computer program are provided for automated closed-loop identification of an industrial process in a process control system. Multiple models (such as multiple model structure-model order combinations) can be identified, where the models are associated with a process to be controlled. One or more metrics (such as a prediction metric or rank) can be determined for each of the models. At least one of the models can be selected based on the one or more metrics. A final model for controlling the process can be provided (such as to a controller), where the final model is based on the at least one selected model. A band pass filter could be designed using some of the identified models. The band pass filter could be used to identify at least one other of the models or to determine at least one of the one or more metrics.

Apparatus and method for automated closed-loop identification of an industrial process in a process control system

A method and system for process control using a model predictive controller. The control system can have one or more control devices operably coupled to a processing system for controlling a process of the processing system; a modeling tool to provide a non-linear model based at least in part on the process and to provide a plurality of linearized models based at least in part on the non-linear model, where the plurality of linearized models are linearized at different linearization rates; and a controller operably coupled to the modeling tool. The controller can select one of the plurality of linearized models based on a comparison of the plurality of linearized models with a reference model. The controller can send one or more control signals to at least one of the one or more control devices. The one or more control signals can be determined using the selected one of the plurality of linearized models.

Method and system for process control

A method and system for process control. The control system can be operably coupled to a processing system. The control system can include control devices operably coupled to the processing system; a modeling module to provide a linear model based at least in part on the processing system; a computational module to provide controller algorithms associated with the control devices; a user interface module to present at a user interface controller information based at least in part on the linear model and the controller algorithms; and a separate coordination module for establishing communication between the modeling module, the computational module and the user interface module. One or more control signals can be provided to at least one of the control devices for controlling the processing system. In one embodiment, the modeling module can generate the linear model from a non-linear process.

Various methods, devices, systems, and computer programs are disclosed relating to the use of models to represent systems and processes (such as manufacturing and production plants). For example, a method may include generating a first model and a second model using operating data associated with a system or process. The method may also include using the first and second models to predict one or more events associated with the system or process. The one or more events are predicted by generating one or more initial event predictions using the first model and adjusting the one or more initial event predictions using the second model. The first model may represent a Principal Component Analysis (PCA) model, and the second model may represent a Fuzzy Logic model.

System, method, and computer program for early event detection

A process control system which includes at least one manipulated variable and at least one controlled variable, provides a method for robust control of a process. Predetermined constraints of the manipulated variables and the controlled variables, and the present values of the manipulated variables are obtained. New values are calculated for the controlled variables for a predetermined number of points in the future, such that the values of the controlled variables are within the predetermined range thereby obtaining an optimal robustness of the resultant controller. The manipulated variables are also calculated to be within predetermined constraints, and the controlled variables to fall within a predetermined range when controllable. From a plurality of solutions, a most robust solution is selected. Then the manipulated variables are adjusted to cause the process control system to drive the values of the controlled variables to the calculated values.

Method of multivariable predictive control utilizing range control

The present invention describes an apparatus and method for controlling the temperature in a plurality of comfort zones within a house or building. The invention contemplates a series of zone control devices which are selectively opened or closed to allow heating and/or cooling to individual zones. The temperature in each zone is sensed and compared to a preferred setpoint temperature to determine a temperature error for each zone. The respective temperature error signals for each zone are used to determine the zone of greatest thermal error. The zone of greatest thermal error is used to turn the heating/cooling plant on and off. The remaining zone or zones of lesser thermal error have their zone control devices closed or opened, depending upon the temperature error signal for their respective zones. In this manner, all zones are synchronized to the zone of greatest thermal error and to the cycle of the heating/cooling plant.

Error based zone controller

Disclosed is an adaptive and predictive controller having self tuning capabilities. The receding horizon window and sample interval are determined by the tunning mechanism automatically, based on the relationship between the controlled and manipulated variables. The interval and window may be set by the user as well. Constraints on the manipulated and controlled variables at the end of the horizon window are accommodated automatically. In addition to providing servo regulatory control the invention teaches simultaneous minimization of operating costs.

Receding horizon based adaptive control having means for minimizing

An adaptive cycle rate thermostat control. A thermostat including a switch and apparatus for heat anticipation operates responsively to an anticipator time constant. Apparatus for sensing room temperature provides an electronic signal proportional to the room temperature sensed. Apparatus for providing an acitivation signal to the switch responsive to the heat anticipation apparatus and sensed temperature cycles a variable-speed heat pump. Apparatus for modifying the anticipator time constant in response to room temperature swings provides the modified anticipator time constant to the heat anticipation means so as to keep room temperature swings within desirable limits while operating the heat pump more efficiently.

Adaptive anticipator mechanism for limiting room temperature swings

An acutioneering control adapted to be connected to a thermostat control for temperature modifying apparatus wherein the thermostat control includes a temperature sensor which provides a sensed temperature signal. The auctioneering control includes apparatus for sensing absolute humidity and providing a first output signal corresponding to the sensed absolute humidity; first apparatus for comparing the sensed absolute humidity with a predetermined value wherein the first comparing apparatus receives the first output signal and provides a humidity error signal; apparatus for modifying a predetermined temperature setpoint signal responsive to the humidity error signal; apparatus for limiting the modified temperature and providing a limited temperature setpoint signal; and second apparatus for comparing the limited temperature setpoint signal with the sensed temperature signal. The modifying apparatus may further comprise a third apparatus for comparing the predetermined temperature setpoint value with the humidity error signal so as to provide a modified temperature setpoint signal corresponding to the difference between humidity error signal and the predetermined temperature setpoint value.

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Scottsdale, AZ, United States of America

J Ward MacArthur