A novel stochastic algorithm-based non-structural model for combined heat and mass exchanger network synthesis

Combined heat and mass exchanger network (CHMEN) synthesis plays a vital role in the chemical industry with a wide spread of applications due to its effectiveness in enhancing energy recovery and reducing emissions. However, in the optimization of CHMEN, obtaining the optimal solution is challenging because the complex heat/mass exchanger matches in addition to the numerous variables in both the mass and heat transfer processes. Therefore, a novel simultaneous optimization method is proposed in this paper to resolve the CHMEN synthesis problem. First, a general node-based non-structural model (G-NNM) is established employing mathematical programming. In this regard, the potential mass or heat exchange units are represented by alternative nodes in the rich/poor or hot/cold streams. This allows for realizing all the possible matches. Specifically, the concept of superposition streams is utilized to design the coupling model of subsystems, linking their interaction by the mass exchange temperature. Additionally, a random walk algorithm with compulsive evolution is introduced to handle complex computations. Then, the optimal solutions are obtained using the accepting inferior solution strategy. Finally, the validity and effectiveness of the proposed simultaneous optimization method are demonstrated in this work. The results show that the proposed method effectively addresses industrial challenges and achieves an optimal total annual cost (TAC) compared to existing methods in the literature. Overall, by taking into account the coupling modes between subsystems, this study provides a feasible scheme to address the issue of other process integration.