基于粒子群算法的隔离壁精馏塔的综合与设计

隔离壁精馏塔（dividing-wall distillation column，DWDC）是提高两个或者多个传统精馏塔热力学效率的有效手段。由于隔离壁精馏塔内部结构复杂、相互作用强，传统的序贯优化方法计算时间长，很难达到全局最优解。标准粒子群算法应用广泛、易于实现，但易于早熟、易于陷入局部极值点。因此，本工作采用改进的元胞粒子群算法对Kaibel隔离壁精馏塔进行综合与设计研究。元胞粒子群算法通过改进粒子的学习策略，采用元胞邻域的方法可有效地将粒子分散在多个子空间。对比标准粒子群优化和元胞粒子群优化两种方法的50次优化效果，结果表明，两种粒子群算法能够对内部结构复杂、相互作用强的四组分Kaibel隔离壁精馏塔这一复杂分离系统进行优化，优化效果显著。

Synthesis and design of dividing-wall distillation column based on particle swarm optimization

Dividing-wall distillation column (DWDC) is an effective way to enhance the thermodynamic efficiency of two or more traditional distillation columns. Because of the complicated inner structure and strong interactions of DWDCs, traditional sequential optimization requires much calculation time and it is difficult to achieve a global optimal solution. Standard particle swarm optimization is widely used and easy to be implemented, but it shows premature convergence and easy to drop into the local extremum. Therefore, the improved particle swarm optimization is studied in this paper for the synthesis and design of the Kaibel DWDC. Particle swarm optimization changes the learning strategy of particles and divides the particles into several subgroups employing the concept of the cellular neighborhood. 50 Steps of the optimization process based on standard and particle swarm optimization are compared. Results show that two-particle swarm optimization methods are able to complete the synthesis and design of the Kaibel DWDC, which has complicated inner structures and strong interactions. Moreover, the optimized results are significantly effective.