Multi-objective optimization of non-isothermal simulated moving bed reactor: Methyl acetate synthesis

Wang, Jian; Tian, Yiming; Yan, Li; Xu, Jinkai; Yu, Weiyong; Ray, Ajay K.

doi:10.1016/j.cej.2020.125041

Cited by 13 publications

(17 citation statements)

References 38 publications

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“…Mathematical model to describe non-isothermal 4-zone SMBR and numerical schemes used in this study were developed and described in detail in our previous study [31,32]. Some of the model parameters used in the model were experimentally measured by Yu et al [36].…”

Section: Modeling Of Smbrmentioning

confidence: 99%

“…. In our previous publications [31,32], the conventional equilibrium-dispersion (ED) model had been extended to account for the catalytic reaction. Due to the simplification of negligible radial gradients and instantaneous adsorption equilibrium, the reactor model is 1-dimensional.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Effects of non-isothermal operation on a 4-zone SMBR for the synthesis of methyl acetate catalyzed by Amberlyst15 have been investigated under optimal conditions in our previous studies [31,32]. In the first non-isothermal article [31], extensively used "Equilibrium Theory" [33,34] for the design of SMB separation processes followed by "restrictive optimization" [35] was applied to maximize the unit throughput for practically complete conversion and complete separation.…”

Section: Introductionmentioning

confidence: 99%

“…The search for suitable operating conditions were restricted to the (mII-mIII) plane. In the subsequent non-isothermal article [32], "non-restrictive" multiobjective optimization [35] was carried out allowing reduced purity requirement, solvent consumption, and product yield into consideration. Optimal operating conditions for various optimization problems were identified in the parametric space consisting of all mvalues.…”

Section: Introductionmentioning

confidence: 99%

“…Optimal operating conditions for various optimization problems were identified in the parametric space consisting of all mvalues. Since methanol, one of the reactants, is used as the mobile phase and in large excess, methyl acetate synthesis in our studies [31,32] falls in the catalogue of…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Optimizations of Non-Isothermal Simulated Moving Bed Reactor: Parametric Analyses

et al. 2021

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Simulated moving bed reactor (SMBR), a multicolumn multifunctional integrated reactor system, which can be exploited with on-site adsorptive separation to enhance conversion of equilibrium-limited reversible chemical reaction. In this article, for generality, a dimensionless SMBR model was developed and effects of five representative temperature distributions among different zones on the performance of an SMBR for reversible reaction in the general form of a reactant decomposed to two products were evaluated based on simultaneous maximization of unit throughput and product purity. Multipliers were applied to adjust some of the model parameters such that different operation modes can be compared under various conditions in the parametric space. The multiobjective optimization problems were solved using the non-dominated sorting genetic algorithm. All simulations were carried out using FORTRAN codes. The results showed that both kinetics and adsorptive separation play important roles in SMBR. When kinetics is fast or adsorptive potency of the reactant is higher than the desired product (B) but lower than byproduct (C), non-isothermal operation can significantly improve unit throughput. On the contrary, feed concentration and reaction enthalpy have minor effects on the optimal solutions. Decision variables based on flow rate ratios and internal concentration profiles were used to explain the trends of Pareto optimal solution.

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Section: Modeling Of Smbrmentioning

confidence: 99%

Section: Mathematical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Optimizations of Non-Isothermal Simulated Moving Bed Reactor: Parametric Analyses

et al. 2021

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Parameter optimization of the simulated moving bed system based on the IMOSCSO algorithm

Chen,

2024

Can J Chem Eng

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The optimization of operating parameters for the simulated moving bed (SMB) is complex. A parameter optimization method for the SMB system was proposed based on the improved multi‐objective sand cat swarm optimization (IMOSCSO) algorithm. The multi‐objective sand cat swarm optimization (MOSCSO) algorithm integrated the update and selection mechanism of the repository in the multi‐objective algorithm. Three strategies were proposed to improve the traditional MOSCSO algorithm for increased population diversity, global search capability, and convergence speed. First, the cubic chaotic map was used to initialize the population, which improved the uniformity of the population distribution. Second, including a variable spiral search strategy in the prey search phase enabled the sand cat swarm to explore more search paths to adjust its position. Third, the convergence speed was enhanced by incorporating the alert mechanism of the sparrow search algorithm. The improved algorithm was tested with standard test functions. The IMOSCSO algorithm outperformed other algorithms in terms of convergence and accuracy. Finally, the IMOSCSO algorithm optimized the system parameters of the SMB, demonstrating its practical applications.

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Using a machine learning model for the optimal design of simulated moving bed processes and its application to separate rebaudioside A and stevioside

Yao

Zheng

Chen

et al. 2021

J of Chemical Tech & Biotech

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BACKGROUND Solving a mechanistic model of simulated moving bed (SMB) is extremely time consuming, therefore, it is inefficient for online control using the optimization method based on such a mechanistic model. To this end, the machine learning model can be regarded as a potential substitution to accelerate the optimization process. RESULTS Several machine learning algorithms were applied to predict the product purities under various operation conditions using two SMB processes as case studies: i.e., a sugar separation of rebaudioside A and stevioside, as well as an enantioseparation of 1,1′‐bi‐2‐naphthol racemate. The results indicate that the random forest (RF) model and the deep neural network (DNN) model provide a satisfactory accuracy with a mean absolute error (MAE) lower than 0.19% (RF) and 0.08% (DNN), respectively. During the optimization process to maximize the feed flowrate under specific purity requirements, among the two selected models, DNN model showed a better generalization ability than RF model and gave a feed flowrate 10% higher than the highest value in the training dataset, which was consistent with the result obtained by using the mechanistic model. The optimized operation conditions for sugar separation were verified experimentally and the achieved purities of rebaudioside A and stevioside were 99.2% and 98.8%, respectively. CONCLUSION The DNN model was successfully used to substitute the mechanistic model to reach a rapid optimization of SMB processes with an improved efficiency of 103 ~ 104 times. © 2021 Society of Chemical Industry (SCI).

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Multi-objective optimization of non-isothermal simulated moving bed reactor: Methyl acetate synthesis

Cited by 13 publications

References 38 publications

Multi-Objective Optimizations of Non-Isothermal Simulated Moving Bed Reactor: Parametric Analyses

Multi-Objective Optimizations of Non-Isothermal Simulated Moving Bed Reactor: Parametric Analyses

Parameter optimization of the simulated moving bed system based on the IMOSCSO algorithm

Using a machine learning model for the optimal design of simulated moving bed processes and its application to separate rebaudioside A and stevioside

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