Abstract
BackgroundA novel advanced control scheme for azeotropic distillation (AD) shows superiority in improving product quality and economic benefit. MethodsA soft sensor model predictive control (SS-MPC) strategy was developed for the separation of the diisopropyl ether (DIPE)/ isopropyl alcohol (IPA)/water mixture via AD. The soft sensor model was built based on time-delayed neural networks (TDNN), while the weight of the SS-MPC controller was optimized by multi-objective genetic algorithm. Significant findingsProduct purity of two distillation columns was controlled simultaneously by the proposed soft sensor model predictive controller, meanwhile, avoiding the measurement of composition in practice. The dynamic performance was compared with the conventional PID scheme and MPC scheme in the face of ±10 % feed disturbances. The results show that SS-MPC can better control the product purity than PID without using the composition detection instruments.
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