Development And Application Of Genetic Algorithms In Dividing Wall Columns Separation Process

With the development of science and technology,human demand for energy is increasing,and the energy crisis that make energy prices rose,so enterprises not only need to seek alternative sources of energy,but also need to solve the problem of energy-saving technologies urgently.Chemical industry is a high energy consumption process.However,distillation process is one of the most important energy consumer in oil refining,petrochemical and chemical industry.Therefore,the optimization and intensification of distillation process are very important for energy saving and economic benefit.Chemical process simulation and optimization problems have been the focuses of scientific researches and corporate researches.Traditional optimization method mostly is single objective optimization,and can resolve single objective optimization problems successfully.For a single objective optimization problem,optimal solution is obtained by adjusting an independent variable without any changes about other variables.However,the optimization problems for chemical process are normally multi-objective optimization problems that are worthy of research.Due to the iterations with single initial value,traditional optimization algorithm is easily to fall into local optimal solution and hard to converge,Such as the SQP optimization algorithm in Aspen Plus.Compared to ordinary distillation,dividing wall distillation,due to existing more variables and strong coupling effects between those variables,is more difficult to achieve optimal solution through traditional optimization algorithm.So researchers often realize the optimization of distillation process through sensitivity analysis by adjusting one or more variables to gradually,which bring a lot of troubles for the calculation of chemical simulation.Intelligent optimization algorithm can avoid above problems through global search that can break away from the local optimum and converge efficiently.Intelligent optimization algorithm(e.g,genetic algorithms)start it’s search from string collection,covering global area,and is good at getting a global optimum.Based on above advantages of intelligent,this paper proposes a multi objective optimization algorithm,names genetic algorithm,which is implemented by Matlab.And then,genetic method is used to optimize chemical process by Matlab and Aspen Plus.The main contents of this thesis are as follows:① Learning and summarizing different optimization algorithms.Reviewing the research background and significance of multi-objective optimization algorithm,and the research status at home and abroad.② Introducing some concepts and definitions of multi-objective optimizaiton,and describing the relevant knowledge of genetic algorithm in detail.And then,based on algorithm algorithms,this paper proposed a multi-objective optimization method implemented by Matlab linked with Aspen Plus.The proposed optimization method is applied to multi-objective optimization of dividing wall columns,which greatly improve the energy saving and cost reduce of dividing wall columns.This multi-objective optimization method is used to optimize dividing wall column process for separation of tert-butanol alcohol/water or BTX,compared to traditional Aspen simulation method,this method can made energy efficiency increased to more than 20%and received reliability and robustness by simulation.