Machine Learning for Modeling and Control of Industrial Clarifier Process

In sugar production, model parameter estimation and controller tuning of the nonlinear clarification process are major concerns. Because industry’s is difficult nonlinear, obtaining exact using identification methods critical. For regulating identifying parameters, this work presents a state transition algorithm (STA). First, parameters for clarifier estimated normal system process. The STA then utilized to improve accuracy that have been identified. Metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), State Transition used evaluate most accurate generated by algorithms. By capturing principal dynamic features process, produced from (STA) acts more like actual According findings, controllers provided in paper may be achieve greater performance than standard design during control any procedure, extremely helpful modeling