Recommending optimum configurations in industrial control systems for improving quality of product

Abstract

The embodiments of present disclosure herein address unresolved problem of getting optimum quality of product while changing operating conditions and raw material frequently in an industrial manufacturing process. The disclosure herein generally relates to a deep learning based approach for a multi-objective constrained optimization. Embodiments provide a method and system for recommending optimum configurations in industrial control systems for improving quality of product. The system is configured to automate improvement over existing golden batch with a data driven approach and replace need of random experimentation with very minimal systematic experiments. The system ensures that no constraint violations are made, and the system remains stable even when data is noisy. Further, the system recommends values of parameters so that improvement in quality is achieved. The changes in parameter value should be gradual even if the historical data received from feedback is noisy.