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Additive manufacturing (AM) has been a vital element of smart manufacturing. The high energy intensity or environmental sustainability issue of AM, however, has posed a great challenge to the future massive application, particularly laser-based direct energy deposition (L-DED). This study aims to determine the optimal processing parameters for energy-saving without compromising the geometrical appearance. A quantification model for energy efficiency at the process level was established, with two energy efficiency indicators of AM process. Then, a meta-heuristic Mayfly algorithm, augmented with Bayesian technique and mutation strategies, was proposed to improve hyperparameters in a typical machine learning model (XGBoost). Based on the full factorial L-DED experiments, this study compared the improved XGBoost with four types of XGBoost derivatives via four algorithm evaluation metrics. Non-dominated sorting genetic algorithm II was adopted to optimize the processing parameters subject to the constraints of geometrical appearance. Results indicated that the proposed algorithm outperformed other XGBoost derivatives in terms of prediction accuracy and convergence rate. The energy efficiency could be improved by 76.35 J/g or 6.78 % on average while ensuring the geometry of the deposited layers. This study could help enhance energy-efficient additive manufacturing via proper processing parameters selection and facilitate the sustainability in AM domain. [ABSTRACT FROM AUTHOR]

In the current research of electro-hydraulic actuators for heavy-duty automatic transmissions, achieving accurate output is proven to be insufficient to meet growing demand. Aiming at this deficiency, this paper proposes a dynamic model, which combines detailed loss analysis and multi-objective optimization to further improve energy efficiency. Firstly, considering the key resistance factors, including viscous damping coefficient, spring constant and supply pressure, a dynamic model is established. Subsequently, the overall response of the model is verified based on the existing experimental data. In addition, the accuracy and stability time are used as evaluation indexes to analyze the influence of resistance on the function pattern. Finally, a multi-objective optimization method is used to balance the relationship between energy utilization efficiency, accuracy and shift quality. The slight changes in mechanical energy and stable time lead to a significant decrease in jerk by 22.11 %, which makes the shifting process smoother and effectively improves the shifting performance. [Display omitted] • A fitting formula about flow rate coefficient is put forward by simulation. • Resistant factors effecting the shifting performance are discussed. • Energy distribution in pressure reducing valve is analyzed. • Energy utilization is improved by using genetic algorithms. [ABSTRACT FROM AUTHOR]

To promote the continuous operating of solar energy systems and increase the proportion of renewable energy consumption, solar devices are often integrated with conventional energy systems to reduce greenhouse gas emissions. In this study, a solar-natural gas coupling system is constructed, utilizing solar photovoltaics, thermal collectors, and high-efficiency energy conversion devices. After modelling the energy system, the operating modes are illustrated considering working sequences of water-cooled devices. A modified genetic algorithm, which incorporates reverse learning, controlled elite, and dynamic crowding distance theory, is constructed to optimize the configurations of the proposed energy system, with the aim of minimizing carbon and economic prices of the products. The performance of the energy system is compared with different algorithms and cases, and the results demonstrate that the proposed algorithm addresses the limitations of conventional methods, albeit with increased optimization times. Moreover, the proposed operating mode (Case 1) exhibits a stable performance, leading to a higher environmental and economic performance compared to conventional modes, with carbon and economic prices of 0.242 kg/kWh and 0.067 $/kWh, respectively. This study provides new insights into carbon footprints and economic performance evaluation of high-efficiency energy systems, offering valuable directions for future research in this field. [Display omitted] • A solar-natural gas coupling system to fulfill building multiple demands. • Comparisons of fours cases considering operating sequences of two types of chillers. • Modified genetic algorithm using reverse learning, elite controlled, dynamic crowding distance theory. • Carbon and economic prices of the products are illustrated. [ABSTRACT FROM AUTHOR]

This paper explores the influence of different turbocharging modes and multi-parameter coordinated control on the performance of compression-ignition (CI) aviation piston engine (APE). Firstly, based on a constructed one-dimensional thermodynamic model of a CI APE, the study investigates the effects of different turbocharging modes and combinations of high and low-pressure stage variable geometry turbines (VGT) on the operational performance of the engine. Subsequently, a novel stepwise approximate multi-objective optimization algorithm is proposed, combining backpropagation neural networks and the non-dominated sorting genetic algorithm II. This algorithm evaluates the influence of multiple control parameters on a two-stage turbocharged engine's performance, achieving a balance between fuel economy and reliability. The research shows that equipping CI APEs with twin VGT for supercharging can notably enhance engine performance, enabling the achievement of favorable power recovery objectives at an altitude of 8000 m. The proposed optimization algorithm exhibits strong predictability and reliability, substantially accelerating the computation speed and reducing the data volume by approximately 95%. At an engine speed of 3887 rpm, compared to the unoptimized conditions, the brake specific fuel consumption of the best scenario at altitudes of 2000 m, 4000 m, 6000 m, and 8000 m is reduced by 7.1%, 7.2%, 8.6%, and 6.9%, respectively. • Influences of different turbocharging modes on the performance of a CI APE are investigated. • A novel stepwise approximate multi-objective optimization method based on a hybrid model is proposed. • Explored the altitude-varying control strategy under coordinated multi-parameter optimization. • Increased the altitude ceiling for the CI APE, reaching 82% power recovery target at 8000 m. [ABSTRACT FROM AUTHOR]

The aim of this study is to optimize the exterior wall insulation of different orientations to further reduce the heating and cooling energy consumption of office buildings, and to analyze its economic, energy and carbon saving potential in China. A six-story office building was selected as the case building and its energy consumption was evaluated using TRNSYS, considering variations in latitude, window-to-wall ratio, aspect ratio, and exterior wall U -values of four orientations. In addition, the exterior wall insulation in four directions was optimized by coupling artificial neural network and genetic algorithm. The main results showed that in the 20°N to 40°N region of China, the GA-optimized exterior wall U -values were highest in the southern direction, followed by the eastern and western directions, and finally the northern direction. In addition, overall, the optimal combination of exterior wall U -values showed a trend of high at low latitudes and low at high latitudes, and increased with increasing WWR and decreased with increasing AR. Finally, compared to the exterior wall requirements for energy efficient buildings in GB 55015-2021, the optimized office buildings at low altitude in China showed a 3.26 %, 6.77 % and 6.59 % reduction in ALCC , total energy consumption and total carbon emissions respectively. • An economic optimization method for the exterior wall insulation based on wall orientation was proposed. • The optimal combination of exterior wall U -values was obtained. • The variation of the optimal exterior wall U -value with latitude was obtained by polynomial curve fitting. • The economic, energy and carbon saving potentials of the proposed method were analyzed by comparing with GB 55015-2021. [ABSTRACT FROM AUTHOR]

To address the low density of high-pressure gaseous hydrogen and evaporation issues associated with liquid hydrogen storage, cryogenic supercritical hydrogen storage method is presented in this paper. Based on a dual parallel mixed refrigerant cycle (DPMR) and a dual cascade mixed refrigerant cycle (DCMR), the density of supercritical hydrogen product is 70.74 kg/m3, closely approaching liquid hydrogen. The processes are simulated in Aspen HYSYS, optimized by genetic algorithm for lower specific energy consumption (SEC). Thermodynamic analysis is conducted to compare with hydrogen liquefaction processes, demonstrating the feasibility and superiority of proposed process. The results show that the DPMR process has an SEC of 6.422 kWh/kg H2 and exergy efficiency of 52.66%, while DCMR is 6.872 kWh/kg H2 and 49.24%. Additionally, the inclusion of ortho-para convertors in DPMR process is investigated to further analyze the impact of heat release for ortho-para conversion. Furthermore, a techno-economic analysis is conducted on three processes, validating the economic feasibility. The study indicates that cryogenic supercritical hydrogen storage system is of great significance to the development of high-density hydrogen storage technology. The DPMR and DCMR presented in this paper can significantly improve the energy efficiency and provide valuable information for the design of high-density hydrogen storage system. • Two high-density cryogenic supercritical hydrogen storage systems are proposed. • Mix refrigerant cycles are applied to reduce energy consumption. • The processes have lower SEC and higher FOM than conventional LH 2 processes. • The volume storage density of hydrogen can reach 70.74 kg/m3 in the systems. • OPC is considered to analyze the thermodynamic performance. [ABSTRACT FROM AUTHOR]

Organic Rankine cycle (ORC) can improve engine power by recovering exhaust energy. This paper co-optimizes the engine-ORC combined system's power and NOx emission, with decision variables of the engine's excess air ratio, spark advance angle, as well as ORC's pump and expander speeds. Firstly, a simulation model of the combined system is established and validated. Then, the initial dataset is generated by the D-optimum Latin hypercube method and simulation model. The artificial neural network (ANN) prediction models of NOx emission and power are established based on these datasets. Finally, the co-optimization is conducted using the ANN prediction model and genetic algorithm. Focusing on maximizing the combined system's power results in an 18.30 % increase in power, and a significant reduction in brake-specific fuel consumption (BSFC) and brake-specific NOx (BSNOx) by 10.10 % and 71.30 %, respectively, compared to the unoptimized basis. Targeting the lowest BSNOx leads to a limited 1.20 % increase in power output; however, it results in a 19.50 % increase in BSFC. When optimizing for both system output and BSNOx, the output remains 13.5 % above the unoptimized basis. Meanwhile, up to 89.8 % of BSNOx can be eliminated with negligible deterioration in BSFC. This study could be used for engine performance enhancements. • A data-driven ANN prediction model of engine-ORC combined system is established. • ANN cooperates with GA to co-optimize Combined system's power and NOx emission. • The optimum engine and ORC parameters for the different goals are obtained. [ABSTRACT FROM AUTHOR]

The difficulty of cold start at low temperatures has been a major technical barrier to the commercialization of proton exchange membrane fuel cells (PEMFCs). In this context, this paper proposes a strategy that combines passive and active start to achieve fast, safe, and efficient start-up at −40 °C. Firstly, a simulation model of the PEMFC system is developed using experimental data to analyze the cold start performance and energy consumption of the stack. Moreover, a multi-stage cold start strategy for heating power and current is proposed to enable the cooperative control of the positive temperature coefficient (PTC) heater and the stack. To achieve this, the dual crucial but conflicting coefficients, heating energy consumption and start-up time, are used as the optimization objectives. And the genetic algorithm (GA) is used for optimization. The results show the weighting coefficient of the optimal cold start control strategy is 0.7. Compared to the passive cold start with weighting coefficient of 0, an increase in PTC heater energy consumption of 497 kJ reduces start-up time by 32.39 % and hydrogen required for reaction by 36.17 %. Compared with other active strategies, the start time is shortened by more than 115.4s without significantly improving the energy generated by stack. • The cold start model including the 140 kW PEMFC stack and TMS is presented. • A heating power and current control strategy based on stack temperature is raised. • The strategy combines active and passive cold start, and is optimized by GA. • Dual crucial objectives, heating energy consumption and cold start time are balanced. • The strategy achieved a cold start at −40 °C in less than 80 s. [ABSTRACT FROM AUTHOR]