Continental Controls Corporation

General Electric Company

Peter N Berkowitz

Michael N Papadopoulos

Paul Burnham Smith

Larry W Colwell

William A Poe

Martin K Moran

A multivariable control method and system suitable for manipulating process variables based on observed changes in controlled objectives without extensive data analysis or involved model development. The method includes operating the process at an initial set of process variables and an initial set of controlled objectives, monitoring the set of process variables and the set of controlled objectives while continuously operating the process, adjusting one or more members of the set of process variables based upon a non-linear optimization with respect to a desired set of controlled objectives, and utilizing the monitored set of process variables and the monitored set of controlled objectives in the non-linear optimization.

Multivariable control method and system without detailed prediction model

On-line control of a process is achieved by feedforward computations of manipulated variable setpoints modified during each of frequent optimization cycles by feedback trim determined by actual versus predicted effects of setpoint changes on the process controlled variables. Process, economic, contractual and equipment parameter values are continuously polled and collected and used to compute the optimal setpoints for the manipulated variables before the feedback trim is applied. Drift factors are added to the feedback trim to provide on-line calibration of key measuring instruments. Drifts in process performance are updated on-line periodically through self-tuning routines computed as calibration factors for predictor and control equations based on rigorous process simulations and actual plant performance. The equations take the form of polynomials in which each term contains an adjustable coefficient and one or more variable process parameter. The tuning factors are automatically applied at chosen time intervals to minimize the size of the feedback corrections computed during every optimization cycle. Tuning changes are effected by multiplying all terms of the predictor polynomials by the same factor. Effects of changes in process equipment are also accounted for on-line through calibration routines wherein individual coefficients in the polynomials are modified to reflect the equipment change in the apparatus. Optimization cycles typically occur every thirty seconds, and the system is capable of handling wide fluctuations in operational and economic parameters.

Multivariable process control method and apparatus

On-line control of a process is achieved by feedforward computations of manipulated variable setpoints modified during each of frequent optimization cycles by feedback trim determined by actual versus predicted effects of setpoint changes on the process controlled variables. Process, economic, contractual and equipment parameter values are continuously polled and collected and used to compute the optimal setpoints for the manipulated variables before the feedback trim is applied. Drift factors are added to the feedback trim to provide on-line calibration of key measuring instruments. Drifts in process performance are updated on-line periodically through self-tuning routines computed as calibration factors for predictor and control equations based on rigorous process simulations and actual plant performance. The equations take the form of polynomials in which each term contains an adjustable coefficient and only a single variable process parameter. The tuning factors are automatically applied at chosen time intervals to minimize the size of the feedback corrections computed during every optimization cycle. Tuning changes are effected by multiplying all terms of the predictor polynomials by the same factor. Effects of changes in process equipment are also accounted for on-line through linearization routines wherein individual coefficients in the polynomials are modified to reflect the equipment change in the apparatus. Optimization cycles typically occur every thirty seconds, and the system is capable of handling wide fluctuations in operational and economic parameters.

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Houston, TX, United States of America

Peter N Berkowitz