Multiobjective Optimization for Synthesizing Compressor-Aided Distillation Sequences with Heat Integration

Abstract

Various column structures, configurations, and energy conservation methods have been proposed to reduce the cost and energy consumption in distillation sequences, such as thermally coupling or heat integration. In this work, a synthesis method of optimal separation sequences including thermally coupled columns is proposed. In the proposed method, vapor recompression of the flow to the condenser and pressure change within thermally coupled columns are taken into account to increase the possibility of heat integration. Furthermore, the energy requirement (ER) was adopted as the objective function as well as the total annual cost (TAC). By solving the multiobjective optimization problem, the relationship between the required annual cost and the amount of energy can be explicitly evaluated, i.e., the necessary cost increase per unit of energy input to the sequence can easily be assessed. A superstructure approach was adopted to enumerate all the candidate distillation sequences which consisted of conventional and thermally coupled columns. Rigorous simulations of each column were effectively used to assign reliable data to the optimization stage. Then, the process synthesis problem was formulated as a mixed integer linear programming (MILP) problem whose objective is to minimize TAC and ER. For two case study problems, Pareto-optimal solutions have been derived. The results of two case studies indicated that nonconventional sequences became the optimal for both cases. Vapor recompression was effective for minimizing the energy requirement.

Multi-objective Optimization and Control of Self-Heat Recuperative Azeoropic Distillation for Separating an Ethanol/Water Mixture

“…The multi-objective genetic algorithm has been widely used in the optimization process of distillation systems. , The optimization of the SHRAD process is a mathematical problem of multi-objective optimization with complex calculation and high non-linearity. Alcántara-Avila et al used the multi-objective optimization technology to optimize the compressor-aided distillation sequences with heat integration, which provided a new idea for the optimization of the SHRAD process. In this section, the multi-objective genetic algorithm is used to optimize the SHRAD process.…”

“…The process optimization of the SHRD is a highly nonlinear multi-objective optimization problem. Since the multi-objective optimization technique has more advantages than single-objective optimization, especially when conducting the heat-integrated distillation system with compressors, 22 this paper will use the multi-objective genetic algorithm to optimize the SHRD process of ethanol dehydration. The conventional azeotropic distillation (CAD) and self-heat recuperative azeotropic distillation (SHRAD) are proposed to separate the ethanol/water mixture, and their performances are discussed.…”

An Overview of the Application of Harmony Search for Chemical Engineering Optimization

“…Several chemical engineering problems can involve several objectives that are usually in conflict and should be optimized simultaneously [1,190]. e optimal design of conventional and intensified separation sequences to maximize the purity of target compound(s) and to minimize the energy consumption [191,192], the integration of mass and energy in different processes [193][194][195], and the simultaneous minimization of economic (i.e., capital and operating costs) and environmental objectives [196] are examples of multi-objective optimization problems in the context of chemical engineering. e resolution of these problems can be performed using multi-objective HS algorithms.…”

Advancements in Optimization and Control Techniques for Intensifying Processes

“…Thus, the combination of rigorous simulations and MILP optimization reduces the computational time and avoids the possibility of becoming trapped in infeasible or local minima/maxima solutions. This approach has been applied for the synthesis of compressor-aided distillation sequences [65], heat-integrated distillation sequences [66], reactive distillation columns with intermediate heat exchangers [67][68][69], and reactive distillation sequences combining heat integration and thermally coupling [70]. The last example shows a very interesting synergistic effect because the adoption of heat integration in thermally coupled reactive distillation can lessen the remixing effect, recirculate less flow between columns, and lead to composition profiles that are more distant from the chemical equilibrium.…”