Feasibility Analysis in Batch Process: A Machine Learning Approach

Abstract

Scheduling is a major issue in process operations, and it is essential for maximizing production output. Short-term scheduling is used in batch operations to allocate a set of restricted resources across time to manufacture one or more items according to a batch recipe. For such processes, a feasible design space can be described by the decision-maker as per the process constraints and objectives. A machine learning approach using support vector machines is implemented for the classification of the feasible region from a data set. An algebraic equation is obtained using support vectors (data points participating in creating classification boundary), which could be incorporated into the mathematical model. The equation will lead to attaining several objectives in the finite obtained region. In this paper, kernel-based support vector classification is tailored to a continuous-time formulation for the short-term scheduling of batch processes. First, using a classification SVC over a data set, it was observed that the mathematical model is infeasible for a set of production combination of two different products. Using the proposed methodology, a design space is presented graphically for feasible production targets. In the second case, the study is applied to perform optimization operations to target feasible region within a specified limit as a choice of decision-maker.