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For high-purity distillation processes,it is difficult to achieve a good direct product quality control using traditional proportional-integral-differential(PID)control or multivariable predictive control technique due to some difficulties,such as long response time,many un-measurable disturbances,and the reliability and precision issues of product quality soft-sensors.In this paper,based on the first principle analysis and dynamic simulation of a distillation process,a new predictive control scheme is proposed by using the split ratio of distillate flow rate to that of bottoms as an essential controlled variable.Correspondingly,a new strategy with integrated control and on-line optimization is developed,which consists of model predictive control of the split ratio,surrogate model based on radial basis function neural network for optimization,and modified differential evolution optimization algorithm. With the strategy,the process achieves its steady state quickly,so more profit can be obtained.The proposed strategy has been successfully applied to a gas separation plant for more than three years,which shows that the strategy is feasible and effective.
For high-purity distillation processes, it is difficult to achieve good direct product quality control using conventional proportional-integral-differential (PID) control or multivariable predictive control technique due to some difficulties, such as long response time, many un-measurable disturbances , and the reliability and precision issues of product quality soft-sensors. This paper, based on the first principle analysis and dynamic simulation of a distillation process, a new predictive control scheme is proposed by using the split ratio of distillate flow rate to that of bottoms as an essential controlled variable. Responsively, a new strategy with integrated control and on-line optimization is developed, which consists of model predictive control of the split ratio, surrogate model based on radial basis function neural network for optimization, and modified differential evolution optimization algorithm. With the strategy, the process achieves its steady steady quickly, so more profit can be The proposed strategy has been successfully applied to a gas separation plant for more than three years, which shows that the strategy is feasible and effective.