Recently, graph-theoretic methods have increasingly been employed to generate near-best (n-best) heat recovery networks, aiming to maximize energy recovery efficiency. The exploration of these n-best networks has proven pivotal for making informed decisions. Nevertheless, existing studies in this domain have not attempted to study the favourability of these generated networks based on their respective dynamic control performance. This performance metric reflects the network's ability to maintain target temperature even under disturbances. The network topologies play important role in both economic (i.e., total annual cost (TAC)) and dynamic control aspects. To address this gap, this work introduces a hybrid approach. First, all combinatorically feasible heat recovery networks are generated using P-HENS. Thereafter, each network undergoes dynamic control performance evaluation through Aspen Plus simulations. The final step involves optimization of the network structures based on fuzzy method which avoids over-prioritization. To illustrate the efficacy of the proposed methodology, it is applied to solve a 5-stream problem. Results showed that Network A with the least TAC ($122,249) is not necessarily associated with the greatest dynamic performance (with failure rate of 15 %). Network C which offers the balance performance (with TAC of $122,666 and failure rate of 0 %) is chosen.

This paper aims to share the challenges encountered by the authors while exploring the significance of controllability in the early stage of heat exchanger network (HEN) design, particularly through the use of P-HENS – a graph theoretic-based HEN synthesis tool for multi-solution HEN synthesis. Presently, no existing studies have leveraged P-HENS-derived networks to reveal insight on how network topology affects its dynamic performance. This work began with a 5-stream problem as a base case, where P-HENS was used to generate four feasible HENs that meets the minimum energy requirement (MER). A preliminary screening narrowed these options to two configurations, which were then simulated in Aspen Plus. Bypass are added to the two selected HENs for further control studies in Aspen Plus Dynamics. The results indicated that both HENs could handle only some disturbances and return the outlet temperature to its nominal value, with some cases showing marginal deviations. Then, different bypass values (e.g., 0.1 and 0.5) were explored to analyze its impact on control performance but it reveals that even with a larger bypass value of 0.5, the HEN struggled to adequately adjust during disturbances. The findings from this work showed that the controllability of the HEN is collectively influenced by the bypass value, the temperature difference of the “direct inlet and outlet of the heat exchanger”, and the temperature difference of the “inlet streams of both hot and cold streams placed in the heat exchanger”. A generic workflow that would help future researchers avoid similar pitfalls has been presented.