Dividing wall columns (DWCs) can effectively improve the thermodynamic efficiency of traditional distillation columns. However, DWCs have intricate structures and strong internal interactions. Numerous structural and operational variables are interrelated. This work presents an improved cellular particle swarm optimization based on the online Kriging model (KCPSO) algorithm, and applies it to the optimization of DWC with the objective of minimizing the total annual cost. The algorithm uses the information of particle swarm search to act on the online Kriging model, and reacts on the particle search through the information of the online Kriging model. Calculations demonstrate that the KCPSO algorithm is superior to standard particle swarm optimization (PSO) and cellular PSO (CPSO) algorithms due to its higher quality of iteration. The KCPSO algorithm can effectively overcome the difficulty of early convergence of the CPSO algorithm and the problem that the PSO algorithm is prone to falling into local optimal solutions.

分隔壁塔（DWCs）可以有效提高传统蒸馏塔的热力学效率。然而，DWCs 具有复杂的结构和强大的内部相互作用。许多结构和操作变量是相互关联的。这项工作提出了一种基于在线克里金模型（KCPSO）算法的改进的细胞粒子群优化，并将其应用于DWC的优化，以最小化年总成本为目标。该算法利用粒子群搜索的信息作用于在线克里金模型，并通过在线克里金模型的信息对粒子搜索做出反应。计算表明，KCPSO 算法由于其更高的迭代质量而优于标准粒子群优化 (PSO) 和蜂窝 PSO (CPSO) 算法。