Pareto-optimal solutions based multi-objective particle swarm optimization control for batch processes

Jia, Li; Cheng, Dashuai; Chiu, Min‐Sen

doi:10.1007/s00521-011-0659-6

Cited by 21 publications

(7 citation statements)

References 11 publications

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“…For the optimal design of the bioreactors, Mandli and Modak developed a computational methodology based on genetic algorithms to solve the changing multiobjective optimization problem with multiple decision variables . To maximize the yield of the final product and reduce the formation of byproducts in a batch process, Jia et al proposed a novel diversity preservation strategy in multiobjective particle swarm optimization (MOPSO) to enhance the convergence and diversity of the attained Pareto optimal set . We herein propose a hierarchical structure multiobjective differential evolution (MODE) algorithm to solve the multiobjective optimization problems of the CCR process.…”

Section: Introductionmentioning

confidence: 99%

“…15 To maximize the yield of the final product and reduce the formation of byproducts in a batch process, Jia et al proposed a novel diversity preservation strategy in multiobjective particle swarm optimization (MOPSO) to enhance the convergence and diversity of the attained Pareto optimal set. 16 We herein propose a hierarchical structure multiobjective differential evolution (MODE) algorithm to solve the multiobjective optimization problems of the CCR process.…”

Section: Introductionmentioning

confidence: 99%

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Integrated Dual-Production Mode Modeling and Multiobjective Optimization of an Industrial Continuous Catalytic Naphtha Reforming Process

Wei

Yang

Qian

et al. 2016

Ind. Eng. Chem. Res.

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Catalytic naphtha reforming is a key process both in refineries and in the production of aromatic compounds. However, the characteristics of dual-production modes render the process model difficult to adapt to changing production needs. Because catalytic naphtha reforming has a complicated reaction mechanism along with multiple operation variables and objectives, its optimization is challenging. We herein report the modeling and optimization steps employed to resolve these issues. A detailed continuous catalytic regenerative (CCR) reforming process model was established, integrating the reaction kinetic model, reactor model, heater model, compressor model, and separator model. On the basis of the CCR model, multiobjective optimizations were performed, and a hierarchical structure of stochastic algorithm was proposed, thus reducing computation costs during model calculations. Three multiobjective optimization problems were solved using the proposed algorithm, with these cases being based on refinery production, the production of aromatic compounds, and energy conservation. Optimization results were consistent with the industrial process and identified improvements through tuning the key operational parameters, such as inlet temperature, pressure, and hydrogen-to-oil molar ratio. Optimal operating points were also listed for different requirements of the reforming process.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated Dual-Production Mode Modeling and Multiobjective Optimization of an Industrial Continuous Catalytic Naphtha Reforming Process

Wei

Yang

Qian

et al. 2016

Ind. Eng. Chem. Res.

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show abstract

“…Sarkar and Modak [35] used a multiobjective genetic algorithm to evolve a set of nondominated solutions distributed along the Pareto front for optimal control problems. Jia et al [24] proposed an improved multi-objective particle swarm optimization based on Pareto-optimal solutions and then applied it to optimize 3 batch processes. The results showed that the quality at the end of each batch can approximate the desire value sufficiently.…”

Section: A Brief Review Of the Literature On Deand Ea-based Dynamic Omentioning

confidence: 99%

Self-adaptive differential evolution with multiple strategies for dynamic optimization of chemical processes

Cheng

Qian

et al. 2019

Neural Comput & Applic

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Dynamic optimization has become an increasingly important aspect of chemical processes in the past few decades. To solve such chemical dynamic optimization problems (DOPs) effectively, we put forward a modified differential evolution algorithm named XADE in this paper, which integrates the self-adaptive principle and multiple mutation strategies. In XADE, four mutation strategies with different characteristics are introduced instead of using a single strategy. Meanwhile, the mutation strategies and DE's two control parameters are gradually adjusted adaptively based on the knowledge learned from the previous searches in generating improved solutions. The advantageous performance of XADE is validated by comparisons with several state-of-the-art adaptive DE variants on 24 complex test instances. Experimental results show that XADE is an effective approach to solving global numerical optimization problems. Moreover, the effectiveness of XADE is validated by applying the approach to 4 real-world complex DOPs with different characteristic in the chemical engineering field.

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“…It has two main advantages; first, the proposed method can be applied to any realworld problem and second, it shows better performance in terms of error, sensitivity, specificity and accuracy for benchmark data sets. Jia et al [58] improve multi-objective PSO by employing a novel diversity preservation strategy that combines the information of distance and angle into similarity judgment to select global best and thus the convergence and diversity of the Pareto front is guaranteed. In this manner, they can distribute enough Pareto solutions in the Pareto front evenly.…”

Section: Particle Swarm Optimization-based Schedulingmentioning

confidence: 99%

A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem

Torabi

Sahebjamnia

Mansouri

et al. 2013

Applied Soft Computing

108

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Pareto-optimal solutions based multi-objective particle swarm optimization control for batch processes

Cited by 21 publications

References 11 publications

Integrated Dual-Production Mode Modeling and Multiobjective Optimization of an Industrial Continuous Catalytic Naphtha Reforming Process

Integrated Dual-Production Mode Modeling and Multiobjective Optimization of an Industrial Continuous Catalytic Naphtha Reforming Process

Self-adaptive differential evolution with multiple strategies for dynamic optimization of chemical processes

A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem

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