Multiobjective Stochastic Optimization of Dividing-wall Distillation Columns Using a Surrogate Model Based on Neural Networks

Abstract

Surrogate models have been used for modelling and optimization of conventional chemical processes; among them, neural networks have a great potential to capture complex problems such as those found in chemical processes. However, the development of intensified processes has brought about important challenges in modelling and optimization, due to more complex interrelation between design variables. Among intensified processes, dividing-wall columns represent an interesting alternative for fluid mixtures separation, allowing savings in space requirements, energy and investments costs, in comparison with conventional sequences. In this work, we propose the optimization of dividing-wall columns, with a multiobjective genetic algorithm, through the use of neural networks as surrogate models. The contribution of this work is focused on the evaluation of both objectives and constraints functions with neural networks. The results show a significant reduction in computational time and the number of evaluations of objectives and constraints functions required to reaching the Pareto front.

Deterministic Global Optimization with Artificial Neural Networks Embedded

“…In biochemical engineering for example, ANNs are commonly used to model fermentation processes [7] and optimize their output to identify promising operating points for further experiments [8][9][10]. ANNs are also used as surrogate models for the optimization of chemical processes [11][12][13][14][15][16][17][18][19][20][21][22][23][24].…”

Regions of Optimality for Separation System Synthesis Using Rigorous Modeling, Stochastic Optimization, and Column Stacking

“…Ge et al 34 used the combination of the radial basis function neural network and genetic algorithm optimal design. Gutieŕrez-Antonio and Briones-Rami ́rez 35 proposed optimization with a multiobjective genetic algorithm with neural networks as surrogate models. Franke 36 determined that DWCs are well suited for mixed-integer nonlinear programming optimization with modified generalized Benders decomposition algorithm.…”

Meta-modelling in chemical process system engineering

“…Meta-model optimization has already been extensively used in design and optimization of many different processes. A wide variety of applications include flowsheeting 10,140,141,142,143,144 ; boiler and combustion processes 145,146,147 ; separation processes such as simulated moving bed chromatography 148 , pressure swing adsorption 149 heated integrated column 144 , divided wall column 150 , CO2 capture process 151 ; reactor operation such as iron oxide reduction 152 , nano-particle synthesis 153 , bacteria cultivation 154 ; polymer processing 155 ; chemical processes in semiconductor industry 156,157,158 ; etc. In should be pointed out that in some of these work, actual experiments instead of higher fidelity simulations were used.…”