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.

Energy-intensive industries contribute large amounts of greenhouse gas emissions. An effective strategy to decarbonise these industries is by applying process integration tools to enhance energy efficiency and reduce overall energy consumption. Recent studies showed that thermal energy storage offers significant benefits in energy efficiency enhancement, as it can amplify the energy recovery potential. Despite its potential, studies that applied process integration tools to address heat recovery problems with consideration of heat storage remain limited. This work develops an optimisation framework that aims to determine optimal heat storage type and size based on the total annualised cost (i.e., costs associated with storage facilities and utilities) to form a feasible heat recovery network between plants. The proposed framework is demonstrated through a case study that focuses on optimising the sensible heat storage selection for indirect heat integration between a mixed plastic waste treatment plant and a steel mill. By analysing the performance and effectiveness of the storage media studied, nitrate salt storage medium is selected due to its greatest energy and cost savings of 12.7 % and 20.7 %, when compared to direct Heat Integration. Insights from this provide information on the feasibility of implementing a storage-supported heat recovery network in the energy-intensive industry.

The rise in biodiesel production results in an excess of crude glycerol, which further leads to environmental concerns. Consequently, transforming crude glycerol into valuable products is deemed an effective way to address this issue. Process Integration techniques are introduced to enhance the overall economic viability by maximizing the energy recovery in the biodiesel plant. However, most of the existing studies merely focused on a single optimal heat exchanger network (HEN) generated. In this study, P-HENS software is utilized to generate viable HENs for a glycerol-derived 1,3-propanediol plant. Subsequently, piping costs of each HEN are estimated to determine the optimal HEN by assuming the respective heat exchanger is placed at the centroid. Finally, the optimal HEN is identified based on the total annualized costs (TAC) (which include the capital cost of the heat exchanger, utility cost, and piping costs) and energy-related carbon emissions of the network. The results show that, among the 4,188 feasible networks generated, network #623 possesses the best overall performance when both cost and environmental aspects are considered. The carbon emissions of network #623 is 16.7% lower than that of the case without heat recovery. This work demonstrates the usefulness of the generated near-best HENs in enabling a more comprehensive HEN optimization. By application of the proposed methodology, the most economical and environmentally friendly HEN can be determined. This contributes to both cost savings and sustainability in HEN design.

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.

Energy-based industrial symbiosis is a potential decarbonisation strategy for energy-intensive industries, which contribute significantly to carbon emissions. Thermal energy storage (TES) can be integrated to enhance energy efficiency and operational flexibility, while addressing issues related to supply–demand fluctuations. Nonetheless, the economic feasibility of TES-supported interplant heat recovery depends on the costs and properties of the storage media incorporated. Therefore, this work presents a systematic framework for optimising TES selection across a spectrum of storage options for interplant indirect heat integration. The objective is to minimise the total annualised cost (TAC), comprising energy and storage capital costs. The optimal TES option can then be identified based on its respective TAC ranking. A case study that compares the effectiveness of the indirect method against the intraplant and direct methods is conducted. The results show that among the 33 TES options evaluated, silica fire brick offers the lowest TAC and energy-related carbon emissions, leading to a reduction of 21.60% and 13.16%, respectively, as compared to the intraplant method. Subsequently, a sensitivity analysis is performed to explore the impacts of varying stream flowrates and storage capacity redundancy allocation on the TES selection. This provides insights into the performance of various TES options under intraplant, direct, and indirect heat integration methods. Finally, the threshold (i.e., stream flowrate required to provide economic gain under a given redundant allocation scenario) aligned with the strategic planning can be determined. This work demonstrates that TES integration can improve the economic feasibility and sustainability of industrial symbiosis in energy-intensive industries.