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Description:
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Model Predictive Control (MPC ) is an optimal -control based method to select control inputs by minimizing the predicted error from setpoint for the future . Industrially popular MPC algorithms use linear convolution models for predicting controlled variable response in future . For highly nonlinear processes , the linear MPC might not provide satisfactory performance . Nonlinear Model Predictive Control (NLMPC ) employs nonlinear models of the process in the control algorithm for controlled variable response in future .
Reactive distillation modeling and control poses a challenging problem because the simultaneous separation and reaction leads to complex interactions between vapor -liquid equilibrium , vapor -liquid mass transfer and chemical kinetics . Hence , reactive distillation processes are highly nonlinear in nature . Application of reactive distillation for the production of ethyl acetate is considered for this dissertation . A detailed steady -state and dynamic mathematical model of reactive distillation is developed . This model is used for control studies of the reactive distillation column . Nonlinear Model Predictive Control algorithm is developed for centralized multivariable control of reactive distillation column . The performance of NLMPC is compared with decentralized PI control structure . |