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Information Journal Paper

Title

REJECTION OF THE FEED-FLOW DISTURBANCES IN A MULTI-COMPONENT DISTILLATION COLUMN USING A MULTIPLE NEURAL NETWORK MODEL-PREDICTIVE CONTROLLER

Pages

  13-23

Abstract

 This article deals with the issues associated with developing a new design methodology for the NONLINEAR MODEL-PREDICTIVE CONTROL (MPC) of a chemical plant. A combination of multiple NEURAL NETWORKS is selected and used to model a nonlinear multi-input multi-output (MIMO) process with time delays. An optimization procedure for a neural MPC algorithm based on this model is then developed. The proposed scheme has been tested on a model of an 18-plate MULTI-COMPONENT DISTILLATION COLUMN. The algorithm provides excellent disturbance rejection for this process.

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    • Cite

    APA: Copy

    JAZAYERI RAD, H.. (2004). REJECTION OF THE FEED-FLOW DISTURBANCES IN A MULTI-COMPONENT DISTILLATION COLUMN USING A MULTIPLE NEURAL NETWORK MODEL-PREDICTIVE CONTROLLER. IRANIAN JOURNAL OF CHEMISTRY AND CHEMICAL ENGINEERING (IJCCE), 23(2), 13-23. SID. https://sid.ir/paper/530503/en

    Vancouver: Copy

    JAZAYERI RAD H.. REJECTION OF THE FEED-FLOW DISTURBANCES IN A MULTI-COMPONENT DISTILLATION COLUMN USING A MULTIPLE NEURAL NETWORK MODEL-PREDICTIVE CONTROLLER. IRANIAN JOURNAL OF CHEMISTRY AND CHEMICAL ENGINEERING (IJCCE)[Internet]. 2004;23(2):13-23. Available from: https://sid.ir/paper/530503/en

    IEEE: Copy

    H. JAZAYERI RAD, “REJECTION OF THE FEED-FLOW DISTURBANCES IN A MULTI-COMPONENT DISTILLATION COLUMN USING A MULTIPLE NEURAL NETWORK MODEL-PREDICTIVE CONTROLLER,” IRANIAN JOURNAL OF CHEMISTRY AND CHEMICAL ENGINEERING (IJCCE), vol. 23, no. 2, pp. 13–23, 2004, [Online]. Available: https://sid.ir/paper/530503/en

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