In sugar production, model parameter estimation and controller tuning of the nonlinear clarification process are major concerns. Because the sugar industry's clarification process is difficult and nonlinear, obtaining the exact model using identification methods is critical. For regulating the clarification process and identifying the model parameters, this work presents a state transition algorithm (STA). First, the model parameters for the clarifier are estimated using the normal system identification process. The STA is then utilized to improve the accuracy of the system parameters that have been identified. Metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and State Transition Algorithm are used to evaluate the most accurate model generated by the algorithms. By capturing the principal dynamic features of the process, the clarifier model produced from State Transition Algorithm (STA) acts more like the actual clarifier process. According to the findings, the controllers provided in this paper may be used to achieve greater performance than the standard controller design during the control of any nonlinear procedure, and STA is extremely helpful in modeling a nonlinear process.
This paper concentrates on the modeling and control of the sugar industry's nonlinear clarifier process. Since the sugar industry's clarification mechanism is complex and nonlinear, it is therefore important to obtain the exact model with identification methods. Using the normal modeling technique, the basic model of the complex process is obtained and further improved to make the model act like the actual system. The most accurate model from the algorithms is analysed using metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and State Transition Algorithm (STA). The proposed STA design to the clarifier model provides the maximum fitness. The clarifier model derived from State Transition Algorithm (STA) behaves more similar to the actual clarifier process by capturing the principle dynamic qualities of the process. Simulations have demonstrated that STA is an optimum algorithm for the clarifier process than the other algorithms. From the results, it is inferred that the controllers introduced in this study, can be utilized to accomplish a better performance than the standard controller design, and during the control of any nonlinear procedure and STA is extremely helpful in modeling a nonlinear process.
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