Surrogate modeling-based multi-objective optimization for the integrated distillation processes

Lu, Jiawei; Wang, Qiong; Zhang, Zhuxiu; Tang, Jihai; Cui, Mifen; Chen, Xian; Qing, Liu; Fei, Zhaoyang; Qiao, Xu

doi:10.1016/j.cep.2020.108224

Cited by 24 publications

(8 citation statements)

References 39 publications

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“…The surrogate-based optimization is a stochastic optimization algorithm designed for global optimization problems with high computational costs. It has been widely utilized in the discipline of chemical engineering, such as design and operation optimization for chemical production processes. − Figure gives the schematic diagram of the surrogate-based parameter identification method. As is depicted, the algorithm randomly generates several initial points of parameters at first.…”

Section: Methodsmentioning

confidence: 99%

Integrated Experimental and Modeling Approach to Evaluate Surface Crystallization on Polymer Coatings

Shen

et al. 2022

Ind. Eng. Chem. Res.

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Resistance to surface crystallization of polymer building coatings is a significant performance index for coating design. However, the high time expense of conventional surface crystallization experiments limits the efficiency of coating evaluation. In this study, an integrated experimental and modeling approach is proposed to evaluate surface crystallization on polymer coatings. Two models are developed as well as the surrogate-based optimization method is adopted to identify model parameters, and a transport-crystallization coupled model is constructed to predict the surface crystallization using the identified parameters. The effectiveness of the integrated approach is validated in three different coatings. It shows that the parameter identification method can estimate model parameters properly and provide good fitting results. In addition, the coupled model shows satisfactory accuracy when predicting surface crystallization. Furthermore, the analysis of the simulation results indicates that the coupled model could be instructive for coating design since it reveals the quantitative relationship between the surface crystallization resistance and macroscopic coating properties.

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Section: Methodsmentioning

confidence: 99%

Integrated Experimental and Modeling Approach to Evaluate Surface Crystallization on Polymer Coatings

Shen

et al. 2022

Ind. Eng. Chem. Res.

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“…An additional research focused on determining the operating conditions of an industrial phosphoric acid process, minimizing the chemical losses of phosphate and maximizing the productivity of the digestion tank [100]. Other examples of the use of multi-objective optimization in process intensification include works that focus on the optimization of thermodynamics, kinetics and heat/mass transfer [101][102][103][104][105][106][107], using strategies for model identification, data processing and design and synthesis of process flowsheets and parameters, which result in frameworks that can be applied in industries to obtain algorithms that present optimal economical and sustainable solutions. A study used genetic algorithms to tune process parameters in order to improve absorption and overall performance in the industrial gas-sweetening process [108]; by evaluating trade-offs, in different scenarios, between the global warming potential and acidification potential as environmental indicators and the net profit as the economic objective, they found that increasing the net profit from USD 60 to 65 million per year increased the GWP by 0.95%.…”

Section: Application Of Moo To Process Intensificationmentioning

confidence: 99%

Applications of Multi-Objective Optimization to Industrial Processes: A Literature Review

2022

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Industrial processes provide several of the products and services required for society. However, each industry faces different challenges from different perspectives, all of which must be reconciled to obtain profitable, productive, controllable, safe and sustainable processes. In this context, multi-objective optimization has become a powerful tool to aid the decision-making mechanism in the synthesis, design, operation and control of such processes. The solution to the mathematical models provides the necessary tools to asses the system performance in terms of different metrics and evaluate the trade-offs between the objectives in conflict. The number of applications of multi- objective optimization in industrial processes is ample and each application has its own challenges. In the present literature review, a broad panorama of the applications in multi-objective optimization is presented, including future perspectives and open questions that still need to be addressed.

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“…The genetic algorithm, one of the most popular metaheuristics, has been widely used in much of the literature in the chemical engineering field recently. ,,− In this study, the Nondominated Sorting Genetic Algorithm II (NSGA-II) was implemented to the EB production to find the best design for the optimal performance of the process. It has a nondominated fast sorting algorithm to reduce the computational complexity and a crowding operator along with Pareto dominance to ensure the diversity of the population and offspring.…”

Section: Multiobjective Modeling and An Optimization Algorithmmentioning

confidence: 99%

Multiobjective Economic-Environmental-Selectivity Optimization of the Dry Gas Based Ethylbenzene Production Process

Han

Zhao

2021

Ind. Eng. Chem. Res.

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The ethylbenzene production process from dry gas has been an energy-intensive process that will inevitably bring about massive carbon emissions. With increasingly stringent regulations to mitigate the effect of greenhouse gas emissions, decision-makers need to make a trade-off among the objectives of environmental impact, economic benefit, and process indicators. An integrated framework combining simulation and multiobjective optimization is proposed to address this issue. The model of the dry gas based ethylbenzene production process is developed by Aspen Plus. The multiobjective optimization (MOO) model, including the profit before tax (PBT), greenhouse gas emissions (GHGs), and alkylation reaction selectivity (S AR ), is then derived considering the operational flexibility and mass and energy balance constraints. The Nondominated Sorting Genetic Algorithm II (NSGA-II) is used to solve this problem, and the three-dimensional Pareto front is obtained. The TOPSIS method is employed to select a specific solution from the Pareto front. Then, the sensitivity analysis is performed to assess the effects of the decision variables on the objectives. Compared with the original operating conditions, a substantial reduction in GHGs (23%) and a slight increase in PBT (4.1%) are achieved at the expense of specific S AR (5.3%).

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Surrogate modeling-based multi-objective optimization for the integrated distillation processes

Cited by 24 publications

References 39 publications

Integrated Experimental and Modeling Approach to Evaluate Surface Crystallization on Polymer Coatings

Integrated Experimental and Modeling Approach to Evaluate Surface Crystallization on Polymer Coatings

Applications of Multi-Objective Optimization to Industrial Processes: A Literature Review

Multiobjective Economic-Environmental-Selectivity Optimization of the Dry Gas Based Ethylbenzene Production Process

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