以年总操作费用(TAC)、CO2排放量(GEC)和精馏塔热力学效率(η)为目标,提出了基于粒子群算法(PSO)的优化方法,并将该方法应用于三氯氢硅(SiHCl3,TCS)歧化制取硅烷(SiH4,MS)反应精馏塔的优化设计.在Aspen Plus V7中建立流程进行模拟,使用平衡级模型,对RD-2IC(带有2个中间冷凝器的反应精馏塔)和高压分离塔的双塔构型建立稳态模型,考察了塔压、塔板数、回流比、进料位置、反应段持液量和中间冷凝器气相分率等影响因素,初步确定了各参数的最优值,为进一步深度优化提供了初值和可行域.结果表明,与单因素灵敏度分析结果相比,PSO算法优化后的TAC节省了54.50％、GEC减少了38.13％、η 提高了22.55％.
Abstract
Taking Annual Total Operating Cost(TAC),CO2 Emissions(GEC),and Distillation Tower Thermodynamic Efficiency(η)as objectives,an optimization method is proposed on the basis of particle swarm optimization(PSO),and applied to the optimization design of the reaction distillation tower for the production of monosilane via trichlorosilane disproportionation.A process is established in Aspen Plus V7 for simulation,and a steady-state model is established for the dual tower configuration including a reaction distillation tower and a high-pressure separation tower with an intermediate condenser by using an equilibrium stage model.Factors such as tower pressure,number of trays,reflux ratio,feed position,liquid holding capacity in reaction section,and gas phase fraction in intermediate condenser are investigated,and the optimal values of each parameter are preliminarily determined,providing initial values and feasible regions for further deep optimization.Results show that compared with the results from single factor sensitivity analysis,the TAC optimized by PSO algorithm saves 54.50％,GEC decreases by 38.13％,η improves by 22.55％.
维普
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