The Experts below are selected from a list of 6735 Experts worldwide ranked by ideXlab platform
Francis J Doyle - One of the best experts on this subject based on the ideXlab platform.
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open loop control of particle size distribution in semi batch emulsion coPolymerization using a genetic algorithm
Chemical Engineering Science, 2002Co-Authors: Charles D Immanuel, Francis J DoyleAbstract:Abstract The open-loop generation of an optimal feed profile to attain a target particle size distribution (PSD) in the semi-batch emulsion co-Polymerization of vinyl acetate and butyl acrylate is described. A nominal model of the process based on population balancing is utilized for this purpose. A Genetic Algorithm is employed as the optimization strategy. The optimal recipes generated in these studies are implemented on an experimental emulsion Polymerization Reactor. The end-point PSD obtained in these experiments closely matches the target in spite of the model uncertainties and process disturbances. Examination of the evolution of the PSD up to the end point provides useful information for feedback control strategies.
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Polymerization Reactor control using autoregressive plus volterra based mpc
Aiche Journal, 1997Co-Authors: Bryon R Maner, Francis J DoyleAbstract:A nonlinear model-predictive control scheme based on the autoregressive-plus Volterra model is proposed. A “plant-friendly,” yet persistently exciting, input sequence is designed for model identification. A second feature of this work is that the control action is determined by solving a less computationally burdensome nonlinear programming problem than the optimization problems associated with Newton-type controllers and polynomial ARMA model nonlinear MPC. A third contribution is that semiglobal closed-loop stability conditions are derived and are shown to be less conservative than those previously published. The control scheme is shown to outperform a feedback strategy using proportional integral control and a linear model-predictive control design for the control of two Polymerization Reactor case studies. The complex features of the latter case study [multivarible (3 × 3), recycle loop, high-order (24) dynamics] motivate the applicability of this approach for industrial problems.
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nonlinear model predictive control of a simulated multivariable Polymerization Reactor using second order volterra models
Automatica, 1996Co-Authors: Bryon R Maner, Francis J Doyle, Babatunde A. Ogunnaike, Ronald K PearsonAbstract:Abstract Two formulations of a nonlinear model predictive control scheme based on the second-order Volterra series model are presented. The first formulation determines the control action using successive substitution, and the second method directly solves a fourth-order nonlinear programming problem on-line. One case study is presented for the SISO control of an isothermal Reactor which utilizes the first controller formulation. A second case study is presented for the multivariable control of a large Reactor, and uses the nonlinear programming formulation for the controller. The model coefficients for both examples are obtained by discretizing the bilinear Taylor series approximation of the fundamental model and calculating Markov parameters. The relationships between discrete and continuous-time bilinear model matrices using an explicit fourth-order Runge-Kutta method are also included. The responses to setpoint changes of both Reactors controlled with a linear model predictive control scheme and the second-order Volterra model predictive control scheme are compared to desired, linear reference trajectories. In the majority of the cases examined, the responses obtained by the Volterra controller followed the reference trajectories more closely. Practical issues, including the reduction of the number of model parameters, are addressed in both case studies.
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nonlinear model based control using second order volterra models
Automatica, 1995Co-Authors: Francis J Doyle, Babatunde A. Ogunnaike, Ronald K PearsonAbstract:Abstract A nonlinear controller synthesis scheme is presented that retains the original spirit and characteristics of conventional (linear) model predictive control (MPC) while extending its capabilities to nonlinear systems. The scheme employs a Volterra model—a simple and convenient nonlinear extension of the linear convolution model employed by conventional MPC—and gives rise to a controller composed of a conventional linear controller augmented by an auxiliary loop of nonlinear ‘corrections’. Simulation case studies involving two different examples representative of the typical spectrum of nonlinear behavior in real chemical processes—an industrial Polymerization Reactor and an isothermal Reactor exhibiting inverse response—are used to demonstrate the practical utility of the control scheme and to evaluate its performance.
Ronald K Pearson - One of the best experts on this subject based on the ideXlab platform.
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nonlinear model predictive control of a simulated multivariable Polymerization Reactor using second order volterra models
Automatica, 1996Co-Authors: Bryon R Maner, Francis J Doyle, Babatunde A. Ogunnaike, Ronald K PearsonAbstract:Abstract Two formulations of a nonlinear model predictive control scheme based on the second-order Volterra series model are presented. The first formulation determines the control action using successive substitution, and the second method directly solves a fourth-order nonlinear programming problem on-line. One case study is presented for the SISO control of an isothermal Reactor which utilizes the first controller formulation. A second case study is presented for the multivariable control of a large Reactor, and uses the nonlinear programming formulation for the controller. The model coefficients for both examples are obtained by discretizing the bilinear Taylor series approximation of the fundamental model and calculating Markov parameters. The relationships between discrete and continuous-time bilinear model matrices using an explicit fourth-order Runge-Kutta method are also included. The responses to setpoint changes of both Reactors controlled with a linear model predictive control scheme and the second-order Volterra model predictive control scheme are compared to desired, linear reference trajectories. In the majority of the cases examined, the responses obtained by the Volterra controller followed the reference trajectories more closely. Practical issues, including the reduction of the number of model parameters, are addressed in both case studies.
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nonlinear model based control using second order volterra models
Automatica, 1995Co-Authors: Francis J Doyle, Babatunde A. Ogunnaike, Ronald K PearsonAbstract:Abstract A nonlinear controller synthesis scheme is presented that retains the original spirit and characteristics of conventional (linear) model predictive control (MPC) while extending its capabilities to nonlinear systems. The scheme employs a Volterra model—a simple and convenient nonlinear extension of the linear convolution model employed by conventional MPC—and gives rise to a controller composed of a conventional linear controller augmented by an auxiliary loop of nonlinear ‘corrections’. Simulation case studies involving two different examples representative of the typical spectrum of nonlinear behavior in real chemical processes—an industrial Polymerization Reactor and an isothermal Reactor exhibiting inverse response—are used to demonstrate the practical utility of the control scheme and to evaluate its performance.
P A Tanguy - One of the best experts on this subject based on the ideXlab platform.
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retracted mass transfer characteristics by surface aeration of large paddle impeller application to a Polymerization Reactor with liquid level change
Chemical Engineering Research & Design, 2008Co-Authors: Ryuichi Yatomi, Katsuhide Takenaka, Koji Takahashi, P A TanguyAbstract:The aim of this study is to investigate the mass-transfer characteristics of a large paddle impeller by comparing with those of the down-pumping pitched blade turbine and the Rushton turbine, and also to probe the application of surface aeration to batch hydrogenation in Polymerization processes and the ethylene oxide additional reaction. Laboratory and industrial data were used for this purpose. As a result, it has been found that, with the large paddle impeller, large k L a values of surface aeration without sparging can be obtained continuously at any liquid level because of the combined effect of surface breakage, bubble entrapment and efficient liquid circulation by axial pumping capacity of the impeller. Using these experimental k L aV data, the improvement in the operation time for a 12 m 3 alkoxylation Reactor was estimated. The Reactor was then retrofitted with a large paddle impeller, and the actual operation time of 17 h was found to be strictly identical to the estimation, that is 75% reduction of the usual 70 h. This result shows the advantage of large paddle impellers in industrial processes and the accuracy of the estimation procedure.
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mass transfer characteristics by surface aeration of large paddle impeller application to a Polymerization Reactor with liquid level change
Journal of Chemical Engineering of Japan, 2007Co-Authors: Ryuichi Yatomi, Katsuhide Takenaka, Koji Takahashi, P A TanguyAbstract:The aim of this study is to investigate the mass-transfer characteristics of a large paddle impeller by comparing with those of the down-pumping pitched blade turbine and the Rushton turbine, and also to probe the application of surface aeration to batch hydrogenation in Polymerization processes and the ethylene oxide additional reaction. Laboratory and industrial data were used for this purpose. As a result, it has been found that, with the large paddle impeller, large kLa values of surface aeration without sparging can be obtained continuously at any liquid level because of the combined effect of surface breakage, bubble entrapment and efficient liquid circulation by axial pumping capacity of the impeller. Using these experimental kLaV data, the improvement in the operation time for a 12 m3 alkoxylation Reactor was estimated. The Reactor was then retrofitted with a large paddle impeller, and the actual operation time of 17 h was found to be strictly identical to the estimation, that is 75% reduction of the usual 70 h. This result shows the advantage of large paddle impellers in industrial processes and the accuracy of the estimation procedure.
Babatunde A. Ogunnaike - One of the best experts on this subject based on the ideXlab platform.
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multirate nonlinear state estimation with application to a Polymerization Reactor
Aiche Journal, 1999Co-Authors: Srinivas Tatiraju, Masoud Soroush, Babatunde A. OgunnaikeAbstract:A method of multirate nonlinear state obserer design, which can use directly a non- linear process model in the obserer design without any linear approximation, is pre- sented. The multirate nonlinear state obserer is easy to design and implement, and is computationally efficient. Furthermore, for a number of processes, it is possible to proe the global conergence of the multirate state obserer analytically. The design, imple- mentation and performance of the state obserer design method are shown by a poly- merization Reactor in which free-radical solution Polymerization of styrene takes place. The initiator concentration and three leading moments of the molecular weight distribu- () ( ) tion MWD of the polymer product are estimated continuously from: i frequent mea- surements of the Reactor temperature, jacket temperature and reacting-mixture density; () ii infrequent and delayed measurements of the leading moments of the MWD. Each infrequent measurement has a sampling period and a measurement delay of 0.5 or 1 h. In the presence of model-plant mismatch and measurement noise, the conergence of the multirate state obserer is shown by numerical simulations.
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nonlinear model predictive control of a simulated multivariable Polymerization Reactor using second order volterra models
Automatica, 1996Co-Authors: Bryon R Maner, Francis J Doyle, Babatunde A. Ogunnaike, Ronald K PearsonAbstract:Abstract Two formulations of a nonlinear model predictive control scheme based on the second-order Volterra series model are presented. The first formulation determines the control action using successive substitution, and the second method directly solves a fourth-order nonlinear programming problem on-line. One case study is presented for the SISO control of an isothermal Reactor which utilizes the first controller formulation. A second case study is presented for the multivariable control of a large Reactor, and uses the nonlinear programming formulation for the controller. The model coefficients for both examples are obtained by discretizing the bilinear Taylor series approximation of the fundamental model and calculating Markov parameters. The relationships between discrete and continuous-time bilinear model matrices using an explicit fourth-order Runge-Kutta method are also included. The responses to setpoint changes of both Reactors controlled with a linear model predictive control scheme and the second-order Volterra model predictive control scheme are compared to desired, linear reference trajectories. In the majority of the cases examined, the responses obtained by the Volterra controller followed the reference trajectories more closely. Practical issues, including the reduction of the number of model parameters, are addressed in both case studies.
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nonlinear model based control using second order volterra models
Automatica, 1995Co-Authors: Francis J Doyle, Babatunde A. Ogunnaike, Ronald K PearsonAbstract:Abstract A nonlinear controller synthesis scheme is presented that retains the original spirit and characteristics of conventional (linear) model predictive control (MPC) while extending its capabilities to nonlinear systems. The scheme employs a Volterra model—a simple and convenient nonlinear extension of the linear convolution model employed by conventional MPC—and gives rise to a controller composed of a conventional linear controller augmented by an auxiliary loop of nonlinear ‘corrections’. Simulation case studies involving two different examples representative of the typical spectrum of nonlinear behavior in real chemical processes—an industrial Polymerization Reactor and an isothermal Reactor exhibiting inverse response—are used to demonstrate the practical utility of the control scheme and to evaluate its performance.
Bryon R Maner - One of the best experts on this subject based on the ideXlab platform.
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Polymerization Reactor control using autoregressive plus volterra based mpc
Aiche Journal, 1997Co-Authors: Bryon R Maner, Francis J DoyleAbstract:A nonlinear model-predictive control scheme based on the autoregressive-plus Volterra model is proposed. A “plant-friendly,” yet persistently exciting, input sequence is designed for model identification. A second feature of this work is that the control action is determined by solving a less computationally burdensome nonlinear programming problem than the optimization problems associated with Newton-type controllers and polynomial ARMA model nonlinear MPC. A third contribution is that semiglobal closed-loop stability conditions are derived and are shown to be less conservative than those previously published. The control scheme is shown to outperform a feedback strategy using proportional integral control and a linear model-predictive control design for the control of two Polymerization Reactor case studies. The complex features of the latter case study [multivarible (3 × 3), recycle loop, high-order (24) dynamics] motivate the applicability of this approach for industrial problems.
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nonlinear model predictive control of a simulated multivariable Polymerization Reactor using second order volterra models
Automatica, 1996Co-Authors: Bryon R Maner, Francis J Doyle, Babatunde A. Ogunnaike, Ronald K PearsonAbstract:Abstract Two formulations of a nonlinear model predictive control scheme based on the second-order Volterra series model are presented. The first formulation determines the control action using successive substitution, and the second method directly solves a fourth-order nonlinear programming problem on-line. One case study is presented for the SISO control of an isothermal Reactor which utilizes the first controller formulation. A second case study is presented for the multivariable control of a large Reactor, and uses the nonlinear programming formulation for the controller. The model coefficients for both examples are obtained by discretizing the bilinear Taylor series approximation of the fundamental model and calculating Markov parameters. The relationships between discrete and continuous-time bilinear model matrices using an explicit fourth-order Runge-Kutta method are also included. The responses to setpoint changes of both Reactors controlled with a linear model predictive control scheme and the second-order Volterra model predictive control scheme are compared to desired, linear reference trajectories. In the majority of the cases examined, the responses obtained by the Volterra controller followed the reference trajectories more closely. Practical issues, including the reduction of the number of model parameters, are addressed in both case studies.