The methanol synthesis via CO₂ hydrogenation (MSCH) reaction is a useful CO₂ utilization strategy, and this synthesis path has also been widely applied commercially for many years. In this work the performance of a MSCH reactor with the minimum entropy generation rate (EGR) as the objective function is optimized by using finite time thermodynamic and optimal control theory. The exterior wall temperature (EWR) is taken as the control variable, and the fixed methanol yield and conservation equations are taken as the constraints in the optimization problem. Compared with the reference reactor with a constant EWR, the total EGR of the optimal reactor decreases by 20.5%, and the EGR caused by the heat transfer decreases by 68.8%. In the optimal reactor, the total EGRs mainly distribute in the first 30% reactor length, and the EGRs caused by the chemical reaction accounts for more than 84% of the total EGRs. The selectivity of CH₃OH can be enhanced by increasing the inlet molar flow rate of CO, and the CO₂ conversion rate can be enhanced by removing H₂O from the reaction system. The results obtained herein are in favor of optimal designs of practical tubular MSCH reactors.

The thermochemical sulfur-iodine cycle is a potential method for hydrogen production, and the hydrogen iodide (HI) decomposition is the key step to determine the efficiency of hydrogen production in the cycle. To further reduce the irreversibility of various transmission processes in the HI decomposition reaction, a one-dimensional plug flow model of HI decomposition tubular reactor is established, and performance optimization with entropy generate rate minimization (EGRM) in the decomposition reaction system as an optimization goal based on finite-time thermodynamics is carried out. The reference reactor is heated counter-currently by high-temperature helium gas, the optimal reactor and the modified reactor are designed based on the reference reactor design parameters. With the EGRM as the optimization goal, the optimal control method is used to solve the optimal configuration of the reactor under the condition that both the reactant inlet state and hydrogen production rate are fixed, and the optimal value of total EGR in the reactor is reduced by 13.3% compared with the reference value. The reference reactor is improved on the basis of the total EGR in the optimal reactor, two modified reactors with increased length are designed under the condition of changing the helium inlet state. The total EGR of the two modified reactors are the same as that of the optimal reactor, which are realized by decreasing the helium inlet temperature and helium inlet flow rate, respectively. The results show that the EGR of heat transfer accounts for a large proportion, and the decrease of total EGR is mainly caused by reducing heat transfer irreversibility. The local total EGR of the optimal reactor distribution is more uniform, which approximately confirms the principle of equipartition of entropy production. The EGR distributions of the modified reactors are similar to that of the reference reactor, but the reactor length increases significantly, bringing a relatively large pressure drop. The research results have certain guiding significance to the optimum design of HI decomposition reactors.

The thermochemical sulfur-iodine cycle is one of the potential hydrogen production processes. The hydrogen iodide (HI) decomposition reaction is the key step in hydrogen production, and the HI conversion rate determines the level of hydrogen production efficiency. To further analyze and optimize the thermodynamic performance of the HI decomposition process from the perspective of the second law of thermodynamics, this paper establishes a one-dimensional plug flow model of the HI decomposition tubular reactor and conducts performance optimization through the minimization of entropy generation rate (EGR) in the decomposition reaction system as an optimization objective based on the finite-time thermodynamic theory. Considering the total EGR of the heat transfer, fluid flow, and chemical reaction processes, the optimization analyses are performed under the given constraints of the mixed gas inlet temperature and pressure and hydrogen production rate. Two cases are considered, first, the tube length is fixed and variable; second, the optimal control theory is used to solve the optimal distribution of the temperature of the heat source outside the tube. The results show that there is a relatively constant sub-interval between the temperature of the heat source outside the tube and temperature of the mixed gas in the optimized reactor. The range of the variation is wider than that of a reference reactor where the heat source varies linearly. Compared with the reference reactor, the total EGR of the optimal reactor with a fixed tube length (L = 3 m) is reduced by 35.9%, and there is a twice optimal total EGR when the tube length is freely changed. The optimal reactor length L_opt = 4.91 m, and the total EGR is reduced by 40.4% compared with the reference reactor. The reduction of the total EGR is mainly achieved by reducing the irreversibility of the heat transfer process. The total EGR can be further reduced during the reaction process by properly extending the tube length. Simultaneously, how to achieve the optimal distribution of heat source temperature is discussed along with the actual industrial production. The results obtained can provide theoretical guidelines for selecting the size parameters and process conditions of HI decomposition reactors.

A numerical simulation model of embedded liquid microchannels for cooling 3D multi-core chips is established. For the thermal management problem when the operating power of a chip changes dynamically, an intelligent method combining BP neural network and genetic algorithm is used for distribution optimization of coolant flow under the condition with a fixed total mass flow rate. Firstly, a sample point dataset containing temperature field information is obtained by numerical calculation of convective heat transfer, and the constructed BP neural network is trained using these data. The “working condition–flow distribution–temperature” mapping relationship is predicted by the BP neural network. The genetic algorithm is further used to optimize the optimal flow distribution strategy to adapt to the dynamic change of power. Compared with the commonly used uniform flow distribution method, the intelligently optimized nonuniform flow distribution method can further reduce the temperature of the chip and improve the temperature uniformity of the chip.