An efficient and random synthesis method for mass exchange networks with multi-component using a node-based vertical non-structural model

Abstract

Mass exchange network (MEN) synthesis is an important technique to ensure efficient purification of industrial waste gas sources and water streams. However, most existing models for MEN synthesis are not capable of dealing with multi-component problems and achieving optimal designs. In this study, an innovative process integration strategy based on a node-based vertical non-structural model (NV-NSM) is proposed. The developed strategy allows for a more random representation of process configuration and aids in handling both single- and multi-component problems. The NV-NSM arranges several groups of main nodes in the process streams and the sub-nodes in their branches and results in design configurations by connecting any two sub-nodes on the rich and lean streams at the same main node number. When dealing with multi-component synthesis, the number of tray column is directly optimized to balance the mass transfer needs of different components. As part of the current investigation, the random walk algorithm with compulsive evolution (RWCE) is established to realize the simultaneous optimization of multiple variables, including the mass transfer loads, the number of trays, the stream split ratios, and the flowrates of lean streams. Furthermore, the fine-search strategy based on the RWCE is applied to improve global and local optimization performance. Finally, the proposed method is applied to four MEN cases featuring different exchanger specifications and considering both single and multiple components. The implementation and analysis result in more optimal solutions compared to those published in the literature, demonstrating the feasibility and effectiveness of the proposed method in analyzing and evaluating different MEN problems, including multi-component ones.