Abstract: Flow Shop Scheduling Problems (FSSP) solved using combination of Reinforcement Learning (RL), Genetic Algorithm (GA) and Heuristics is effective if can provide makespan as minimum as possible. Embodiments herein provide a method and system for evolved State-Action-Reward-State-Action (evolved SARSA) RL for flow shop scheduling, which is a hybrid framework of hierarchical RL with evolutionary techniques and heuristics method to solve FSSP. An optimum job sequence is estimated that minimizes the makespan thereby achieving maximum utilization of the resources. The evolutionary and heuristics strategy is applied in a reinforced way of learning for estimating the optimal schedule. The framework refines FSSP solution provided by Reinforced-SARSA (R-SARSA) using the evolutionary Genetic Algorithms (GAs), which is further guided by heuristic in moving towards the optimal solutions and prevents from being stuck at a local optimum.

Abstract: State of the art techniques used for Flue Gas Desulpharization (FGD) process monitoring fail to comprehend the relationship between various process parameters, which is crucial in determining the performance of a FGD process being monitored. The disclosure herein generally relates to industrial process monitoring, and, more particularly, to a method and system for performance optimization of Flue Gas Desulphurization (FGD) Unit. The system identifies Key Performance Indicators (KPIs) associated with the process being monitored, and identifies parameters associated with each KPI. This information is used to generate several predictive models, from which a predictive model having the highest value of composite model score amongst the predictive models is selected as the predictive model for processing the input data, which is then used to perform optimization of the FGD process.