Closed-loop Subspace Identification Algorithms for 2-D Separable-in-Denominator Systems**This study was supported by the National Natural Science Foundation of China under Grant 61374099, the Program for New Century Excellent Talents in University under Grant NCET-13-0652, and Beijing Higher Education Young Elite Teacher Project under Grant YETP0505.

Batch process reactors are often used for products where quality is of paramount importance. To this end, this work addresses the problem of direct, data-driven, quality control for batch processes. Specifically, previous results using subspace identification for modeling dynamic evolution and making quality predictions are extended with two key novel contributions: first, a method is proposed to account for midbatch ingredient additions in both the modeling and control stages. Second, a novel model predictive control scheme is proposed that includes batch duration as a decision variable. The efficacy of the proposed modeling and control approaches are demonstrated using a simulation study of a poly(methyl methacrylate) (PMMA) reactor. Closed loop simulation results show that the proposed controller is able to reject disturbances in feed stock and drive the number-average molecular weight, weight-average molecular weight, and conversion to their respective set-points. Specifically, mean absolute percentage errors (MAPE) in these variables are reduced from 8.66%, 7.87%, and 6.13% under traditional PI control to 1.61%, 1.90%, and 1.67%, respectively.

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Cited by 2 publications

References 11 publications

Data-Driven Modeling and Quality Control of Variable Duration Batch Processes with Discrete Inputs

Data-Driven Modeling and Quality Control of Variable Duration Batch Processes with Discrete Inputs

Data-Driven Modeling and Quality Control of Variable Duration Batch Processes with Discrete Inputs

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