Real‐Time Optimization Using Machine Learning Models Applied to the 4,4′‐Diphenylmethane Diisocyanate Production Process

Iterative real-time optimization using modifier adaption with quadratic approximation is applied to the MDI production process. The plant model for the optimization is a surrogate model that was trained on data obtained from a rigorous steady-state simulation model. The real-time optimization scheme is tested on the operator training simulator of the plant and converges to the optimal operating point despite substantial plant model mismatch.

Abstract

In this work, the optimal time-varying allocation of steam in a large-scale industrial isocyanate production process is addressed. This is a problem that falls into the category of real-time optimization (RTO). The application of RTO in practice faces two problems: First the available rigorous process models may not be suitable for use in real-time connected to the process. Second, there is always a mismatch between the predictions of the model and the behavior of the real plant. We address the first problem by training a neural net model as a surrogate to data generated by a rigorous simulation model so that the model is simple to implement and short execution times result. The second problem is tackled by adapting the optimization problem based on measured data such that convergence to the optimal operating conditions for the real plant is achieved.

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