Constrained Run-to-Run Optimization for Batch Process Based on Support Vector Regression Model

Introduction Batch and semi-batch processes are very im-portant in the fine chemicals industry to manufac-ture the low-volume and high-value products suchas biochemicals, crystals and speciality chemicals.In the control of batch process, the major objectiveis to improve the end-of-batch product properties.When the control objective is to optimize a perfor-mance index at the end of batch cycle, the problemis called the end-point optimization problem. Tosolve the problem,the traditional approach…

Constrained Run-to-Run Optimization for Batch Process Based on Support Vector Regression Model

An iterative (run-to-run) optimization method was presented for batch processes under input constraints. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Because support vector machine is powerful for the problems characterized by small samples, nonlinearity, high dimension and local minima, support vector regression models were developed for the end-point optimization of batch processes. Since there is no analytical way to find the optimal trajectory, an iterative method was used to exploit the repetitive nature of batch processes to determine the optimal operating policy. The optimization algorithm is proved convergent. The numerical simulation shows that the method can improve the process performance through iterations.