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Heat exchanger network (HEN) synthesis has been acknowledged as an effective design technique to achieve significant energy savings in a wide range of industrial and engineering applications. In this regard, heuristic methods have been demonstrated as a powerful means to solve the HEN synthesis problem. However, because the new structure is generated at random, the structures that can effectively make the objective cost descend are limited in comparison to the entire evolutionary process. In addition, such heuristic methods are usually time-consuming processes. In this work, an intelligent search strategy is proposed to increase the efficiency of heuristic methods. Based on the defined objective cost, this strategy could rapidly and effectively find a route that could satisfy cost minimization. It can also achieve further evolution aimed at highlighting potential structures. The proposed search strategy can increase the ability of searching and reduce time consumption. Considering the random walk algorithm with compulsive evolution as an example, the principle and impact of intelligent search were clearly demonstrated. Furthermore, applying the intelligent search strategy to the random walk algorithm with compulsive evolution to some cases in the existing literature was found to yield better solutions.

The rapid development of computational technology and the increasing energy demand have improved heat exchanger network (HEN) synthesis. The HEN synthesis involves several optimizations of matches, distributions of heat loads, and stream splitting of heat units. Thus, obtaining good results at high efficiency has been the main standard for evaluating the techniques in the research area of HEN synthesis This paper first summarizes and analyzes the main contributions of the existing HEN synthesis techniques. To compare related data quantitively, information on ten typical cases is presented in this paper. Furthermore, recently improved solutions for commonly encountered existing literature cases demonstrate the evolution and competition trends in the field of HEN synthesis. The comparison data presented in this paper not only provide a useful reference for future research but also present the optimization directions. Based on the findings of this study, it is noted that there is still a large room for improvement, and current approaches are incapable of dealing with all HEN cases. Moreover, it is still difficult to escape a local optimum and overcome structural constraints when seeking the global optimum. As a follow-up to the current work, the parallel computing mode and adaptively coordinating the ratio of global and local searching abilities are major development trends for future investigation.

Phase change materials (PCMs) have remarkable energy storage capacity and promising applications in the field of thermal control of electronic products. The problem of thermal property improvement and heat transfer of PCMs in metal-foam heatsinks is an important task for thermal management of electronic components. Mixed paraffin samples were prepared by mixing appropriate proportions of paraffin (mass) at various temperatures. Differential scanning calorimetry analysis revealed that the maximum enthalpy of 206.3 J/g is obtained by mixing 20% of 17°C liquid paraffin and 80% of 29°C solid paraffin. Heating and cooling cycling tests revealed that mixed paraffin exhibits excellent thermal stability and that the regulation method marginally affects thermal stability. Moreover, composites were prepared by embedding PCM into a copper foam by melt impregnation. The thermal conductivity of the composites increased to 4.35 W/(mK), corresponding to 20 times its original value. In addition, density functional theory and experimental results were in good agreement, indicating that the regulation method is practical and effective.

In order to investigate the control mechanism of composite phase change materials (PCM), a series of composite nano-phase change emulsion (NPCE) were prepared in this paper with high thermal conductivity, high heat storage and low supercooling using hexadecane, octadecane, hexadecanol, octadecanol and various metal nano-particles. Through the control mechanism, it is expected to adjust the phase transition range of NPCE to a desirable range. The NPCEs were characterized by particle size analyser, cryogenic transmission electron microscope (Cryo TEM), differential scanning calorimeter (DSC), thermal conductivity meter and rheometer. The results showed that the NPCEs was successfully prepared with uniform dispersion, great stability, low viscosity and narrow particle size distribution. DSC results showed that the latent heat of NPCE with 20 wt. % hexadecane was 55.86 kJ/kg. The supercooling degree of prepared NPCEs using 1.25 wt. % of hybrid nucleating agents was reduced by 81%. Metal oxides effectively improved the thermal conductivity of NPCEs. The thermal conductivity of the NPCEs with 1wt. % nano Al2O3was 0.70 W/(m ⋅ K), which was increased by 21%. The viscosity of the NPCEs increased with the increase of metal oxide concentrations and decreased with the increase of temperature. Importantly the NPCEs presented a shear thinning effect and can be considered as Newtonian fluid after shear rate of 2 s−1, which had great potential in the thermal energy storage system.

Obtaining precise and efficient solutions of large-scale cases is one of the main obstacles of heat exchanger network synthesis (HENS) technologies. Due to the absence of abundant local minima in the solution domain, searching a sufficient solution domain and decreasing the amount of calculations are two vital tasks for finding a globally optimal solution for HENS. In order to address the above-mentioned problems, a flexible model with an adjustable number of nodes is developed in this paper. In the premise of sufficient solution space, the developed model dynamically adjusts the number of calculation nodes during the optimization process based on the current configuration. The effectiveness of the developed model is demonstrated and verified using four large-scale cases with different conditions. The obtained results demonstrate that the optimal solutions produced by the proposed model for cases 1–3 with stream splits are 0.16, 0.34, and 2.97 better, while for cases 3–4 without stream splits are 0.1 and 0.6% better than the best optimal solutions reported in the literature, respectively. Moreover, the computational times for cases 1–3 are 29.5, 44.86, and 51.8% lower, while for cases 3–4 without stream splits are 25.20 and 25.24% lower than the ones without the proposed method, respectively. In addition, the overall computational efficiency of the proposed model can be improved by a maximum of 51.8%.

In this study, enhanced kinetics of methane hydrate formation in the sodium dodecyl sulfate (SDS) solution with different concentrations of suspended graphite nanoparticles (GNPs) were investigated at 6.1–9.0 MPa and 274.15 K. The GNPs with rough surfaces and excellent thermal conductivity not only provided a considerable number of microsites for hydrate nucleation but also facilitated the fast hydrate heat transfer in the suspension system. At a relatively low pressure of 6.1 MPa, the suspension with 0.4 wt% of GNPs exhibited the minimum induction time of 22 min and maximum methane uptake of 126.1 cm3·cm−3. However, the methane storage performances of the suspensions with higher and lower concentrations of GNPs were not satisfactory. At the applied pressure, the temperature increase arising from the hydrate heat in the suspension system with the optimized concentration (0.4 wt%) of GNPs was more significant than that in the traditional SDS solution. Furthermore, compared with those of the system without GNPs, enhanced hydration rate and storage capacity were achieved in the suspensions with GNPs, and the storage capacities were increased by 3.9%–17.0%. The promotion effect of GNPs on gas hydrate formation at low pressure is much more obvious than that at high pressure.

Grand canonical Monte Carlo (GCMC) simulations were used to analyse the effect of -O-Li, -NH2, -SH, -F, -COOH, and -NO2functional groups on the adsorption and separation of pure CO2and a mixture of CO2and CH4. The metal–organic framework (MOF) skeleton was structurally optimized using the Forcite and Dmol 3modules. First, the GCMC method was employed to simulate the adsorption of pure CO2by functionalized Mg-MOF-74 at 298 K and 10-3000 kPa. The results illustrated that -O-Li, -NH2, and -SH groups increased the CO2adsorption below 101 kPa and followed the order Li-O-Mg-MOF-74 > NH2-Mg-MOF-74 > SH-Mg-MOF-74. When the pressure was >101 kPa, the adsorption capacity of Mg-MOF-74 was higher than that of functionalized Mg-MOF-74. Then, the adsorption capacity and selectivity of functionalized Mg-MOF-74 in the CO2/CH4mixture were investigated at 298 K and 10–3000 kPa. The results showed that the introduction of -O-Li, -NH2, and -SH groups increased the CO2selectivity of Mg-MOF-74 within the range of 10–1440 kPa and followed the order Li-O-Mg-MOF-74 > NH2-Mg-MOF-74 > SH-Mg-MOF-74.

An efficient battery thermal management system (BTMS) can undoubtedly improve the performance and lifetime of lithium-ion batteries. In this study, a novel battery thermal cooling module (BCM) consisting of composite phase change material (CPCM) with carbon foam skeleton support material and a carbon fiber thermally conductive gasket (CFT) are proposed. Experimental results show that the combination of CPCM loaded on carbon foam and CFT provides significant cooling and temperature balancing benefits to the battery components, with peak temperature reductions of 25.13% and 27.83% at 1.3 C and 1.5 C, and maximum temperature differences for 1.5 C remaining within 2.7 °C. The high enthalpy of the CPCM is the key to battery cooling. The addition of CFT reduces the interfacial thermal resistance and enhances phonon heat transfer, effectively controlling the uniformity of the battery surface temperature. Therefore, this study shows that the combined BTMS with BCM and CFT has a good temperature response and cooling effect. The BTMS achieves the dual modulation of CPCM with high enthalpy and carbon material with enhanced heat transfer, enabling intelligent and long-term stable operation. This material combination is expected to have a wide range of applications in battery materials, energy storage and other fields.

In this study, a novel chessboard model with stream splits is proposed for mass exchange network synthesis (MENS) to achieve the minimum total annual cost (TAC). The proposed chessboard model presents a streamlined and concise representation of mass exchange networks (MENs), enabling adaptable configurations of streams. This model exhibits structural diversity and reduces the computational complexity of the MEN calculation. An improved stochastic algorithm called the random walk algorithm with compulsive evolution (RWCE) is applied to solve MENs represented by the chessboard model. The simultaneous method is employed to address three instances of single-component MEN examples and a multicomponent MEN case study. The obtained optimal solutions outperformed the solutions previously reported in the literature. These findings demonstrate the viability and usefulness of the chessboard model with stream splits and the modified RWCE algorithm for addressing MENS.