2017Unpublished venueRequires access

Comprehensive optimization of batch process based on particle swarm optimization algorithm

Yang Lan, Haipeng Pan, Yibo Zhang

Open publisher page 4 citations

Abstract

Based on Iterative Particle Swarm Optimization (PSO) Algorithm, comprehensive optimization problem of batch process is discussed in this paper. Batch process optimization problems in general to product concentration as optimization goal, if optimization object is changed to the sum of the product concentration, the reciprocal of reaction time and the reciprocal of energy loss and then optimization method will be more complex. Based on the iterative particle swarm optimization algorithm, optimal solution is obtained by searching for optimal trajectory. Results of study are applied to classical chemical reaction cases. Simulation results show that this algorithm is better than single objective algorithm in speed of optimization and efficient use of energy.

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What this paper is about

Based on Iterative Particle Swarm Optimization (PSO) Algorithm, comprehensive optimization problem of batch process is discussed in this paper. Batch process optimization problems in general to product concentration as optimization goal, if optimization object is changed to the sum of the product concentration, the reciprocal of reaction time and the reciprocal of energy loss and then optimization method will be more complex. Based on the iterative particle swarm optimization algorithm, optimal solution is obtained by searching for optimal trajectory. Results of study are applied to classical chemical reaction cases. Simulation results show that this algorithm is better than single objective algorithm in speed of optimization and efficient use of energy.

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Available abstract

Based on Iterative Particle Swarm Optimization (PSO) Algorithm, comprehensive optimization problem of batch process is discussed in this paper. Batch process optimization problems in general to product concentration as optimization goal, if optimization object is changed to the sum of the product concentration, the reciprocal of reaction time and the reciprocal of energy loss and then optimization method will be more complex. Based on the iterative particle swarm optimization algorithm, optimal solution is obtained by searching for optimal trajectory. Results of study are applied to classical chemical reaction cases. Simulation results show that this algorithm is better than single objective algorithm in speed of optimization and efficient use of energy.

Key concepts: Multi-swarm optimization, Particle swarm optimization, Derivative-free optimization, Meta-optimization, Mathematical optimization, Metaheuristic, Computer science, Process (computing)

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