(363o) Nonlinear Model Predictive Control for the Dividing Wall Column
AIChE Annual Meeting
2022
2022 Annual Meeting
Computing and Systems Technology Division
Interactive Session: Systems and Process Control
Tuesday, November 15, 2022 - 3:30pm to 5:00pm
Pyomo[4, 5] has a flexible environment with inherent object-oriented aspects, along with advanced tools to automatically discretize general Differential-Algebraic Equation (DAE) optimizations. For a general Pyomo DAE model, Control and Adaptation with Predictive Sensitivity Enhancements (CAPRESE[6, 7]) is a nonlinear optimization-based framework in Python for sensitivity-based NMPC and Moving Horizon Estimation (MHE) strategies. The NMPC scheme for the three-product Petlyuk DWC is illustrated in Figure 1. The controlled variables of the MPC are five input compositions, including impurity compositions of two outflows of the prefractionator (yDp,C, and xBp,A), and three product compositions of the corresponding product streams of the main section (xD,A, xS,B, and xB,C). The manipulated variables of the MPC are five outputs, containing the reboiler duty to feed ratio (QR/F), reflux flow rate, reboiler duty, side product flow rate, and thermally coupled liquid and vapor flow rates to the prefractionator. The separation of a ternary equimolar saturated liquid mixture of ethanol (A), n-propanol (B), and n-butanol (C) is used as the studied case. Dynamic and economic performances show that the NMPC scheme are significantly improved. These demonstrates that the NMPC is a very feasible and effective scheme to control the three-product Petlyuk DWC.
References
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