Abstract We present a new method for the synthesis of mass exchanger networks (MENs) involving packed columns. Simultaneous synthesis of MENs is typically done through the use of mixed-integer nonlinear program (MINLP) optimization, with simplifications made in the mathematical representations of the exchangers due to computational difficulty in solving large non-convex mixed-integer problems. The methodology proposed in this study makes use of the stage-wise based superstructure MINLP formulation for the network synthesis. This stage-wise superstructure model incorporates fixed mass transfer coefficients, fixed column diameters, no pressure drops, and unequal compositional mixing for models. In this paper, the simplified MINLP model is further improved by including a detailed individual packed column design in a non-linear programming (NLP) sub-optimization step, where orthogonal collocation is utilized for the partial differential equations, and optimal packing size, column diameter, column height, pressure drops, and fluid velocities. Detailed designs are then used to determine correction factors that update the simplified stage-wise superstructure models to more accurately portray the chosen design. Once the MINLP is updated with these correction factors, the model is re-run, with new correction factors obtained. This iterative procedure is repeated until convergence between the objective function of the MINLP and that of the NLP sub-optimization is achieved, or until a maximum number of iterations is reached. The methodology is applied to two examples and is shown to be robust and effective in generating new topologies, and in finding superior networks that are physically realizable.

中文翻译：

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两步混合优化策略中的大规模交换网络的综合

摘要 我们提出了一种合成涉及填充柱的质量交换网络 (MEN) 的新方法。MEN 的同时合成通常是通过使用混合整数非线性程序 (MINLP) 优化来完成的，由于解决大型非凸混合整数问题的计算困难，因此对交换器的数学表示进行了简化。本研究中提出的方法利用基于阶段的超结构 MINLP 公式进行网络合成。这种分阶段上层结构模型包含固定的传质系数、固定的柱直径、无压降和不等的模型成分混合。在本文中，简化的 MINLP 模型通过在非线性规划 (NLP) 子优化步骤中包含详细的单个填充柱设计得到进一步改进，其中偏微分方程使用正交搭配，以及最佳填充尺寸、柱直径、柱高、压降和流体速度。然后使用详细设计来确定修正系数，更新简化的逐级上部结构模型，以更准确地描绘所选设计。一旦用这些校正因子更新了 MINLP，模型就会重新运行，并获得新的校正因子。重复这个迭代过程，直到实现 MINLP 的目标函数和 NLP 子优化的目标函数之间的收敛，或者直到达到最大迭代次数。