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This paper aims to study the feasibility of municipal sewage sludge utilization as an energy source in a polygeneration system. This system offers distinctive benefits such as contribution to the principled removal of sewage sludge, simultaneous utilization of raw and digested sludge in different parts of the system, and production of renewable hydrogen from bio-waste. 4E (energy, exergy, exergoeconomic, and environmental) analyses, are performed to understand the system performance comprehensively. Then, parametric studies are examined the impact of changing the values of main parameters on the system operation. Afterward, a multi-objective optimization based on a genetic algorithm is carried out to achieve optimal values, considering a trade-off between the exergy efficiency and the total cost rate. Meanwhile, this work harnesses the potential of artificial neural networks to expedite complex and time-consuming optimization processes. According to the results, the gasifier exhibits the highest rate of exergy destruction, and the primary cost of consumption is attributed to its heat supply. The multi-objective optimization findings show that the optimum point has an exergy efficiency of 38.26 % and a total cost rate of 58.17 M$/year. The hydrogen production rate, energy efficiency, and net power generation rate for the optimal case are determined as 1692 kg/h, 35.24 %, and 4269 kW, respectively. Also, the unit cost of hydrogen in the optimal case is obtained 1.49 $/kg which offers a cost-effective solution for hydrogen production. [Display omitted] • 4E analysis and optimization performed on a bio-waste driven polygeneration system. • Plant optimized using artificial neural network and multi-objective genetic algorithm. • The feasibility of integrating a membrane for hydrogen separation investigated. • The optimum exergy efficiency and total cost rate obtained 38.26 % and 58.17 M$/year. • Hydrogen and electricity cost of 1.49 $/kg and 0.0145 $/kWh achieved for optimal case. [ABSTRACT FROM AUTHOR]

Regasification of liquefied natural gas (LNG) releases significant cooling potential, but improper usage can easily lead to wastage of resources and environmental pollution. The study developed an integrated air separation system and Organic Rankine Cycle (ORC) power generation system to effectively harness the inherent cold energy within LNG. Two distinct optimization algorithms, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Particle Swarm Optimization (PSO), were employed to determine the optimal operational parameters. The results showed the superior performance of PSO over NSGA-II, as demonstrated by the exergy efficiency, net output power, and levelized cost of energy at 78.12 %, 2.29*1011 kW, and 0.0665 $/kWh, respectively. Additionally, an assessment of various factors influencing the system's performance was conducted. The performance of both conventional and proposed systems was studied under varying regasification rates. The findings indicated that incorporating the cold recovery and storage unit enhanced the efficient utilization of LNG cold energy, resulting in an increased valley-to-peak ratio of the total power output from 0.03 in the conventional system to 0.5. The proposed system exhibits alignment with the energy consumption demands of users, and this study is anticipated to furnish a theoretical foundation for harnessing the cooling capacity of liquid hydrogen. [Display omitted] • The novel utilization of LNG cold energy by combining air separation with ORC. • PSO is superior than NSGA-II for optimization: 14.4 % decrement in LCOE. • PSO: 17.9 % and 7.0 % increment in the exergy efficiency and net output power. • Both LNG steady state and fluctuating in regasification rates were investigated. • The valley-to-peak ratio of the system's power output was increased to 0.5. [ABSTRACT FROM AUTHOR]

The extractive distillation is a commonly used method for the separation of azeotrope in the chemical industry. In this article, we investigate the safety issue of the extractive distillation process for targeted audience process engineers from the perspective of process safety. We conduct inherently safer design and multi-objective optimization of extractive distillation processes for the separation of methyl acetate/methanol via computer-aided molecular design, thermal hazard analysis, and multi-objective genetic algorithm. Firstly, the entrainers with good safety and separation performances are pre-screened by computer-aided molecular design (CAMD) based on flash point and entrainer selectivity. Then the entrainers with top-ranking separation performance are manually screened based on vapor-liquid equilibria. Next, the Pareto front solution is obtained by optimizing both safety and economic objective functions using a multi-objective genetic algorithm. Finally, the thermal hazard of the entrainer is also investigated via DSC (Differential scanning calorimetry) test. Finally, the optimization result and thermal hazard investigation demonstrate that 1,3-propanediol is inherently safer than dimethyl sulfoxide (DMSO) as a common entrainer in terms of not only a fire hazard but also a thermal hazard for the separation of methyl acetate/methanol mixture. The optimal TAC using 1,3-propanediol as an entrainer is 3.72% lower than that of dimethyl sulfoxide, and the corresponding GISI is 57.73% lower than that of dimethyl sulfoxide. As for DMSO, thermal decomposition can occur near its normal boiling point (189 ℃) and 1,3-propanediol, can be thermally stable over 330 ℃. [ABSTRACT FROM AUTHOR]

Hydrogen purification from steam methane reforming (SMR) by pressure swing adsorption (PSA) technology is a common method to obtain high-purity hydrogen. The breakthrough time can well reflect the adsorption dynamics and help PSA cycle design. In order to avoid time-consuming and labor-intensive breakthrough curve experiments, it is very necessary to develop a fast and accurate surrogate model. In this study, a genetic algorithm (GA) and artificial neural network (ANN) are combined to predict and optimize breakthrough times of adsorbates in activated carbon/zeolite layered beds. Using the Latin hypercube sampling strategy, training data sets of GA-optimized ANN (GA-ANN) obtains from the physical model of adsorption, heat and mass transfer model. The genetic algorithm (GA) optimizes the weights and biases of ANN for better performance. The ANN topology has 4 input variables (superficial velocity, activated carbon height, adsorption pressure and feed temperature) and 3 output variables (breakthrough times of CH 4 , CO and CO 2). The number of neurons in the hidden layer for the GA-ANN model was optimized as 7 to predict and optimize the maximum breakthrough time of SMR with a four-component (H 2 /CH 4 /CO/CO 2) system. The sensitivity analysis displays that relative importance to the breakthrough time is in the order of superficial velocity (40.88%) > adsorption pressure (24.55%) > activated carbon height (21.04%) > feed temperature (13.53%). To obtain high-purity hydrogen, the GA-ANN model combined with multi-objective GA optimization is used to maximize the breakthrough times of CH 4 and CO. The GA-ANN surrogate model proposed in this paper can not only accurately and quickly achieve the purpose of predicting the system breakthrough time with a high correlation coefficient (R = 0.9937) but also obtains the optimal operating conditions of PSA hydrogen purification. • A rigorous heat and mass transfer model of multi-component adsorption is established. • Artificial neural network (ANN) is well-trained by Latin hypercube sampling method. • Genetic algorithm (GA) optimizes the weights and biases of ANN for better performance. • Superficial velocity has the highest relative importance value on breakthrough time. • GA-optimized ANN model and GA combined for maximal breakthrough time of impurities. [ABSTRACT FROM AUTHOR]

With global communities being susceptible to adverse impacts of greenhouse gases, hydrogen has substantially drawn the attention of scientists to investigate it as a promising fuel. Hence, the present investigation aims to conduct an exhaustive thermodynamic analysis of an innovative biomass-assisted system driven by the Brayton cycle through multi-objective optimization to detect the different optimal scenarios for generating and storing hydrogen. This system contains a Brayton cycle as a prime mover, steam gasification of biomass and three-stage compressors to generate and store hydrogen, and multi-effect distillation to produce freshwater. The ultimate goal of this study is to use the generated steam through the produced freshwater as a steam agent of the gasifier to conclude a water-efficient scenario in producing hydrogen. To discern the impact of the chosen design parameters on the performance of the system, an exhaustive parametric study has been carried out. Afterward, multi-objective optimization via the non-dominated sorting genetic algorithm has been undertaken to find the optimal values by regarding the financial constraints. Tri-objective optimization results demonstrated that optimum values of total cost rate, exergy efficiency, and hydrogen production rate are 1.43 $/s, 47.56%. , and 0.12 kg/s. Besides, by regarding the consumption of water as an additional objective function, the results demonstrate that by reducing consumed water up to 47.5%, the generated hydrogen only decreases by 8%. • An innovative multi-generation system based on biomass is proposed. • The most influential design parameters are detected through the parametric study. • A water-saving scenario is discussed to generate efficient hydrogen. • NSGA-II is utilized to detect the optimal values of the proposed system. • The hydrogen production rate is 0.12 kg/s in the optimal design. [ABSTRACT FROM AUTHOR]

This article presents an efficient energy system to generate power via renewable sources. Biomass is the running mover of a cycle that operates on a gasification process to generate syngas. The hot syngas is then used to create hydrogen via a green and sustainable method of hydrogen production called the vanadium chlorine cycle (VCLC). The generated hydrogen is mixed with the syngas before entering the PEM fuel cell. The proposed system's energy, exergy and economic impacts are examined. Also, the effect of different variables is studied on the system performance indexes. Finally, multi-objective optimization is implemented to ensure the highest efficiency and lowest cost. Results indicate that the moister content of the biomass, the PEMFC temperature, and current density play a crucial role in the system performance. Rising the PEMFC temperature and the moister content works in favor of the system performance indices, unlike the current density of the fuel cell. The results of the multi-objective optimization represent that under optimal conditions, when moisture content, PEM current density and temperature are chosen equal to 0.314, 0.71 A/cm2 and 360.78 K respectively, system has the exergy efficiency of 32.10% and total cost rate of 52.95 $/h. • A multi-generation system consisting a Fuel cell, ORC, and VCLC is developed. • The system is analyzed from energy, exergy, economic, and environmental views. • The effect of various parameters was investigated. • Multi-objective optimization is performed using a genetic algorithm. • The results indicate that the system is effective in achieving 4E goals. [ABSTRACT FROM AUTHOR]

Cyclohexane and isopropyl alcohol are widely used in the chemical and pharmaceutical industries. There is a strong industrial need for the refinement of cyclohexane and isopropyl alcohol. In this study, the recovery of cyclohexane and isopropyl alcohol from wastewater was carried out sustainably and efficiently by extractive distillation. The suitability of ethylene glycol as entrainer was determined by relative volatility analysis. The process was optimized via multi-objective optimization, and the extractive distillation process obtained optimal parameters and process scheme. Finally, a comprehensive analysis was conducted to evaluate the economic, environmental and exergy efficiency of different process schemes. The results show that the extractive distillation coupled pervaporation technology has a 14.07% lower total annual cost than extractive distillation, and the pervaporation technology reduces gas emissions by 15.65% compared to the extractive distillation. This work explores the recovery of cyclohexane and isopropyl alcohol from wastewater by different process schemes, which has certain guiding significance for the sustainable and efficient separation of ternary azeotrope. [Display omitted] • Multi-objective optimization was used to obtain optimize parameters of process. • A pervaporation model of EG dehydration was established to realize solvent recovery. • The different energy saving intensification schemes were studied. • Detailed performance evaluation and analysis of process was done. [ABSTRACT FROM AUTHOR]

The problem of optimizing variable-specific impulse electric propulsion maneuvers is presented, proposing a novel multi-objective methodology for the computation of near-optimal planetocentric solutions. The methodology relates a parameter expressing the instantaneous maneuvering efficiency, or thrust effectivity, with the specific impulse through an arbitrary parametric function. The parameters of this function are determined by a multi-objective genetic algorithm, which minimizes propellant consumption and maneuver duration. The study includes the analysis of several scenarios, with the results demonstrating that the methodology can be successfully employed in the context of space mobility of small satellites. The findings reveal that variable-specific impulse maneuvers can achieve improvements exceeding 17% in both propellant mass and maneuver duration compared to a constant-specific impulse case. Consequently, the resultant Pareto fronts showcase enhanced trade-offs and more favorable solutions for the optimization problem. The results highlight the significance of this optimization methodology for electric propulsion technologies with throttling capabilities, particularly in systems with near-constant propulsive efficiency. [ABSTRACT FROM AUTHOR]

According to the operation parameters and the design experience parameters, a liquid hydrogen centrifugal pump was developed and tested to meet an application requirement of small flow and high head in cryogenic systems or cryogenic storage tanks. The test results showed that 6 m of hydraulic loss in the pump at the rated condition and the inlet pressure of the cryogenic fluid decreased and fluctuated, which resulted in the pump being prone to cavitation. To improve cavitation resistance and hydraulic efficiency of liquid hydrogen centrifugal pumps, the model was optimized based on the genetic algorithm of MATLAB applying 7 parameters of impeller taken as optimization variables, each sub-objective function was given corresponding weights, and the optimal solution combination at the minimum of the objective function was finally obtained, and an inducer was matched. The results showed that by reducing the angle of the inlet, increasing the diameter of the impeller, and adding an inducer, the cavitation performance of the pump can be improved and the hydraulic loss can be reduced by appropriately reducing the impeller outlet diameter, to improve the head of the pump. A comparison of the performance of the preposition inducer optimized pump with the preliminary pump shows that, at the rated condition point, the head was increased by 4.6 m resulting in an efficiency of 43%. A numerical calculation considered the thermodynamic effect of non-isothermal cryogenic cavitation on the Reynolds-averaged Navier-Stokes combined with the k - ε turbulence model was applied for getting the pump hydrodynamic performances. It was proved that the multi-objective optimization method and preposition inducers were effective, and the calculating results were by experimental data well. Feasibility of the model had been validated to a certain extent. It can bring a high level of pump optimization, integration, and quality to new and existing designs. • A LH 2 centrifugal pump is optimized by the genetic algorithm of MATLAB. • LN 2 centrifugal pump performances are carried out on the cryogenic test platform. • Multi-objective optimization and inducers can improve the LH 2 centrifugal pump. • Numerical model is feasible and prediction performance of LH 2 centrifugal pumps. • Efficiency and anti-cavitation improvement are confirmed by experiments. [ABSTRACT FROM AUTHOR]

In the process of producing isopropanol (IPA) by direct hydration of propylene, a mixture of IPA, n-propanol (NPA) and H 2 O is formed. In order to obtain high purity IPA and NPA, extractive distillation technology can be used to separate these three components. In this paper, Based on the COSMO-RS model, 1-ethyl-3-methylimidazolium dicyandiamide ([EMIM][DCA]) was selected as the extractant for extractive distillation in ionic liquids. The extractive distillation process for the separation of IPA-NPA-H 2 O system with ethylene glycol (EG) or [EMIM][DCA] as extractant was designed and optimized by multi-objective genetic algorithm. Process simulation results show that the process using [EMIM][DCA] can reduce total annual cost by 44.635% and total gas emission by 29.873% compared with the traditional organic solvent EG as extractant. These results show that [EMIM][DCA] is a good extractant, which can reduce the gas emission and energy consumption of the distillation process. In addition, we further explored the separation mechanism of the system at the molecular level by analyzing the σ-curve and the intermolecular interaction energy. • Based on COSMO-RS model, [EMIM][DCA] was selected as extractant. • The reason why EG and [EMIM][DCA] can break the IPA-H 2 O azeotrope was revealed. • Optimized the extractive distillation process with EG or [EMIM][DCA] as entrainers. • [EMIM][DCA] can realize the energy saving and emission reduction. [ABSTRACT FROM AUTHOR]

The objective of this research is to develop an energy-efficient and sustainable synthesis/separation process for the production of polyolefin elastomers, as well as identify the optimal operating conditions for this process. The proposed approach involves integrating additional reactors into the existing reactor network and implementing energy-saving strategies into the distillation process. To optimize the operating conditions, we employed three different meta-heuristic optimization techniques: non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ), multi-objective particle swarm optimization, and multi-objective grey wolf optimizer. These techniques are utilized to simultaneously minimize total annualized cost (TAC), CO 2 emissions, and condition number. To enhance the efficiency of these algorithms, we introduced the population distribution factor as a metric to enhance computational efficiency without compromising problem-solving aptitude. The results demonstrate that the improved algorithms offer enhanced computational efficiency and remarkable problem-solving ability and that NSGA-II outperforms the other algorithms in practical engineering problems. The proposed synthesis/separation sequences can reduce TAC and CO 2 emissions simultaneously, while maintaining good controllability. [Display omitted] • A sustainable synthesis/separation process for polyolefin elastomers is obtained. • Existing meta-heuristic technique is improved to enhance computation efficiency. • Different meta-heuristic techniques are investigated in solving specified problems. • Different synthesis/separation sequences are investigated for polyolefin elastomers. [ABSTRACT FROM AUTHOR]

• Energy, exergy and multi-objective optimization are performed for vortex tube integrated single-stage vapour compression refrigeration cycle (VTC). • The parametric investigation is done to assess the thermodynamic performance of VTC. • The cooling capacity of VTC is found to be 13.19% – 49.73% higher than that of VCR. • The maximum COP and exergetic efficiency of VTC are higher than those of VCR. • The evaporator temperature is the most influential input parameter in multi-objective optimization. This paper evaluates the thermodynamic performance of a vortex tube integrated single-stage vapour compression refrigeration cycle (VTC) in a subcritical region. The thermodynamic evaluation consists of an energy and exergy analyses for VTC in order to determine the effect of various design and operating parameters on its performance. Moreover, the results of VTC are compared with those of a simple vapour compression refrigeration cycle (i.e., VCR) for the considered range of evaporator and condenser temperatures using R1234yf as a refrigerant. The analysis reveals that the cooling capacity of VTC is 14.53% – 49.73% higher than that of VCR for the considered range of condenser temperature. Also, the maximum COP of VTC is 5.59% – 27.32% higher than that of VCR. The exergetic efficiency of VTC is 5.62% – 27.33% higher than that of VCR. A multi-objective optimization using a genetic algorithm has been conducted, which suggests that the evaporator temperature is the major decisive parameter to find the suitability of various applications of the system based on VTC. [ABSTRACT FROM AUTHOR]

• Two-level optimization solves the maneuver planning for multiple spacecraft close-range rendezvous. • Multi-impulse solved by trajectory planning is used in each rendezvous. • An impulsive correction method is proposed for precise rendezvous. • The surrogate model built by FNN accurately predicts the impulsive velocity increase for rendezvous. Rendezvous mission planning between service spacecraft and multiple close-range target spacecraft in the on-orbit service application is studied, using multiple impulses based on the Clohessy-Wiltshire equation as a maneuvering strategy. An impulsive correction method is proposed to avoid rendezvous errors. The optimization objectives of this work include the minimum total velocity increase and the minimum mission time. A two-level optimization approach is developed to find the Pareto-optimal set of the above multi-objective optimization problem. In the low-level optimization, the propellant-optimal impulsive trajectory for the rendezvous between the SSc and each target spacecraft at a fixed maneuver duration is solved by a proposed iterative optimization algorithm with a double loop. On the other hand, the rendezvous sequence and maneuver duration are optimized by a proposed neural-network-assisted evolutionary algorithm in up-level optimization, which combines the feedforward neural network with the elitist multi-objective genetic algorithm (NSGA-II). Finally, representative simulation examples are presented to verify the proposed optimization algorithm and demonstrate its superiority over the traditional algorithm. [ABSTRACT FROM AUTHOR]

The quadrotor represents a class of highly nonlinear dynamic systems and its controllability features are challenging. Hence, it serves as an ideal unmanned aerial vehicle platform to validate many artificial intelligence-based research investigations. Nonetheless, most of the offline tuning approaches devote efforts to find near optimal control gains to regulate individually the decoupled motion directions. This work adopts a multi-objective self-adjusting search mechanism to actuate the motions of a quadrotor via deciding the control gains of the interacting loops simultaneously. This algorithm employs a first order low pass filter transfer function as an accepting approach for the tunning mechanism. The proposed approach is compared with a Genetic Algorithm and another nonlinear Proportional-Integral-Derivative approach to highlight the usefulness of the proposed mechanism. It was founded that quadrotor follows the desired trajectory with a small tracking error of less than 2% in the X-Y plane and less than 1 % error tracking error in the altitude Z. Also, it is recorded that MONLTA can overcome the simulated wind disturbances of 0.1 N.m as a disturbance torque. [ABSTRACT FROM AUTHOR]

The increase in computer numerical control machine efficiency highly contributes to environmental emission reduction and energy-savings. Path and trajectory optimizations are used to improve machine efficiency in a coverage motion such as pocket milling, polishing, inspection, gluing, and additive manufacturing. Several studies have proposed coverage motion optimization in improving machine efficiency for time and energy consumption. Ensuring the smoothness and satisfaction of the machine constraints in coverage motion is necessary. This paper proposes a multi-objective path and trajectory optimization to obtain a trade-off between time and energy consumption for coverage motion. Jerk limited acceleration profiles describe the trajectory where velocity profiles generated for each linear segment attain desirable velocities. The energy model of an industrial two-axis feed drive system is used in finding solutions to the optimization problem. The non-dominated sorting genetic algorithm II generates a Pareto front for trade-off time and energy consumption solutions. Simulation results of the proposed method are validated through experiments using the industrial two-axis feed drive system. Experimental results show the effectiveness of the proposed approach where time reduction and energy savings are 10.05% and 2.10%, respectively. In addition, the optimized path has a lower maximum error of 76.6% compared to the constantly commanded velocity optimized path. • A method of simultaneous trajectory generation and path optimization is proposed. • Jerk limited acceleration profiles describe the trajectory. • The NSGA II is used to find the trade-off between time and energy consumption. • The best trade-off solution achieves time reduction and energy savings of 10.05% and 2.10%. • Optimized path motion with variable velocities reduces maximum error by 76.6%. [ABSTRACT FROM AUTHOR]

On Flores island (Azores, Portugal), energy production depends up to 47% on fossil fuels, namely Diesel. To minimize CO 2 emissions, the dependency on fuel prices, and to mitigate the consequences of variability and intermittency of renewable energy sources, a new energy system, based on H 2 storage was analysed. To achieve the optimal size of the system, a computer model was developed and a multi-objective genetic algorithm function was used to minimize three objectives: the difference of levelised cost of energy (△ LCOE), CO 2 emissions and the percentage of renewable energy dumped (R RES, dump). From the set of solutions obtained, one that meets R RES, dump ≤ 1%, lowest CO 2 emissions and lowest △ LCOE is chosen and an economical and energetic analysis is performed. The newly proposed system reduces Diesel consumption by 68,7% (1057487 L/year) and CO 2 emissions by 65,9% (2455,1 CO 2 tons/year) achieving a renewable energy sources (RES) penetration of 89% (36% increase), but fails to decrease the levelised cost of energy (56,62 €/MWh increase). However, a way to make the project viable through financial support is presented and an alternative to reduce the levelised cost of energy by commercialising the products of electrolysis, hydrogen and oxygen, is proposed. Finally, it is expected that with further research and development of H 2 technologies, economic and energetic results will get more advantageous, opening up new perspectives for the future. [Display omitted] • Development of a control system to simulate the energy system of an island. • Optimum design of a H 2 energy storage system. • Utilization of a multi-objective genetic algorithm (MOGA). • Economic and energetic analysis of a H 2 energy storage system. • H 2 energy storage system increases RES energy in over 35%. [ABSTRACT FROM AUTHOR]

The aim of this research is to develop continuous reactor network models to determine the reactor configuration and corresponding optimal operating conditions of a polyolefin elastomers polymerization process. To achieve this objective, a modified vapor-liquid equilibrium model is integrated with both the continuous stirred tank reactor (CSTR) model and the plug flow reactor (PFR) model to accurately predict the vaporization rates of liquid. The multi-objective problem is formulated to maximize the monomer conversion and minimize the total annualized cost simultaneously. To solve the multi-objective problem, the genetic algorithm is utilized to identify the optimal operating conditions. Furthermore, the impact of the reactor combination mode is analyzed by varying the number and sequence of CSTR or PFR. The research findings reveal that a multi-phase CSTR without circulating stream followed by a PFR is the optimal reactor configuration. The proposed configuration can enhance reactor productivity and reduce the total annualized cost simultaneously while meeting the desired polymer properties. [Display omitted] • Optimization for a polyolefin elastomers polymerization process is performed. • Modified vapor-liquid equilibrium model is adopted in reactor simulation. • The genetic algorithm is applied in solving multi-objective optimization problem. • Reactor configuration and corresponding optimal operating conditions are obtained. [ABSTRACT FROM AUTHOR]

This work deals with the optimization of an existing industrial cryogenic air separation plant with respect to exergy efficiency and argon recovery. A previously validated simulation model of the plant was used, and the operating conditions and specifications of the process correspond to those reported in the historical records of an air separation facility in Colombia. The optimization is carried out considering the variation in the historical demand and the installed capacity of the plant, using an elitist nondominated sorting genetic algorithm (NSGA-II). As exergy efficiency and argon production goals are in conflict, a Pareto front of optimal solutions is obtained for the assessed variables. It is found that under optimal conditions of the pareto fronts, exergy efficiency and argon recovery can be enhanced up to 30 % and 23 %, respectively, with respect to recommended operating conditions from the plant manual. Also, obtained results indicate that the plant operated with larger air feed flowrates than required for the corresponding specific monthly demands. Under the optimal conditions, the plant could operate with a 58 % average reduction in the specific energy consumption (0.53 kW/Nm3O 2) and larger argon recoveries. This represents a major improvement in the profitability of the operation and its sustainability indicators. [Display omitted] • Multi-objective optimization of an industrial air separation plant was carried out. • Exergy efficiency and argon recovery were optimized under fixed N 2 and O 2 production. • A variable demand with respect to historical records of the plant was considered. • Exergy efficiency and argon recovery generates an optimal pareto front. • Optimal conditions enabled up to 60 % reduction in the specific energy consumption. [ABSTRACT FROM AUTHOR]

A hydronic snow melting pavement system is an efficient and sustainable snow removal approach. However, the conventional operation method remained unclear and usually led to a waste of energy. This study developed a thermal-economic model to assess this system's economic cost and snow melting effect. The multi-objective optimization (MOO) model and genetic algorithm (GA) were adopted to optimize the operation strategy, which includes fluid temperature (FT) and idling duration (ID). The accuracy of the MOO model was verified by a convergence study. The optimal results were organized in nomograms and implemented at Beijing Daxing International Airport. The results showed that the tournament selection method performs better in MOO solving. The optimal results indicated that it would obtain a better effect to balance the economic benefit and snow melting effect by adjusting FT than ID. And the MOO model can export the appropriate strategy in response to various snowfall conditions. Specifically, the optimal strategy implementation in the case study improved the snow-free duration ratio by 126% and saved the economic cost by 7% compared to the conventional strategies. The investigation provided an alternative operation strategy to minimize the energy cost for this system while ensuring the snow melting effect. [Display omitted] [ABSTRACT FROM AUTHOR]

Canard is a cutting-edge aerodynamic attachment for lowering the vehicle's drag coefficient by efficiently directing the airflow as well as reducing the lift coefficient by enhancing down-force. This paper aims to simulate the airflow crossing over the car to investigate the effect of canards' geometric design on the rear-body wake using the application of CFD-based optimization. Hence, 7 design variables based on the geometry of the canard are considered, and the objective functions are set to be drag and lift coefficients that are aimed to be minimized. Firstly, ANSYS Fluent is utilized to generate CFD calculations for a series of Design of Experiment (DOE) points. Then, the GMDH-ANN processes the results to elicit the polynomials that demonstrate the relation of design variables and objective functions. A genetic algorithm is next implemented for multi-objective optimization using polynomials as its input, and consequently, Pareto optimal points are achieved. The numerical results show that an appropriate design for canards on the rear bumper causes a potential drag and lifts reduction of 9.62% and 9.6% in comparison to the car without canards. Moreover, the size of the wake behind the car decreases and the differences in the pressure distribution between car fore-body and rear-body is reduced. Finally, the fuel efficiency is potentially enhanced due the changes in car drag coefficient and frontal area. • This paper aimed to simulate the airflow crossing over the car. • 7 design variables based on the geometry of the canard are considered. • The GMDH type of ANN is applied for the results to elicit the polynomials. • A genetic algorithm is next implemented for multi-objective optimization. • Pareto optimal points are achieved. [ABSTRACT FROM AUTHOR]

Hybrid systems which integrate photovoltaic (PV) and thermoelectric generator (TEG) can improve the utilization of solar energy. In this study, a multi-objective genetic algorithm (MOGA) is adopted to maximize the output power and conversion efficiency for the PV-TEG system, where the CFD model is coupled in the optimization procedure and the penalty function is employed to ensure the appropriate operating temperature. Meanwhile, two geometric parameters along with two boundary conditions are selected as the design variables, while the effects of three cooling conditions are also discussed. Compared with the PV only system, the output power of the PV-TEG system is improved by up to 11.52% with the conversion efficiency also improved by 44.97%. At the same solar concentration condition, the optimal PV-TEG system could increase the output power by 36%. Based on the analysis of optimal solutions, it is demonstrated that the adopted TEG contributes significantly to the output power, while a better cooling condition also has a positive effect on the comprehensive performances. Furthermore, a multiple-criteria decision-making approach is applied to balance output power and conversion efficiency. Compared with the maximum efficiency solution, the best compromise solution increases output power by 120.02% and decreases the efficiency only by 14.83%. [ABSTRACT FROM AUTHOR]

• Various optimization design methods for high-power nanocrystalline alloy high-frequency transformer are analyzed and compared. • MOPSO has more advantages in the optimization design of high-frequency transformer. • The influence of temperature rise calculation methods on the optimization algorithm is analyzed. • The essence of improving the performance of high-frequency transformer through optimization methods is discussed. The optimization design of high-frequency transformer (HFT) is a multi-objective optimization problem owing to the mutual constraints between design parameters such as power density, efficiency and temperature rise, and traditional design methods often face difficulties in balancing and selecting multiple design parameters. Therefore, this paper derives the structural parameters of HFT, calculates the magnetic core losses and winding losses considering temperature effects, and establishes a 7-node thermal network model. Selecting power density and efficiency as optimization objectives, the HFT is optimized based on multi-objective particle swarm optimization algorithm and non-dominated genetic algorithm, and compared with traditional free parameter scanning method to analyze the advantages of intelligent optimization algorithm. Further, the influence of temperature rise calculation methods on the optimization design results of HFT is compared and analyzed. Finally, a nanocrystalline HFT with 17 MW/m3 power density and 99 % efficiency and an optimization scheme of HFT with 26 MW/m3 power density and 99.2 % efficiency are designed based on the proposed optimization design processes. The effectiveness of the calculation model and optimization method is verified through experimental measurement, and the essential principle of improving the HFT performance through optimization methods is explored through finite element simulation, providing theoretical reference and data support for the optimization design of nanocrystalline HFT. [ABSTRACT FROM AUTHOR]

• Developed an Improved Particle Swarm Optimization (IPSO) model for optimizing heavy construction equipment management. • Integrated three critical objectives (i.e., time, cost, and quality) into a holistic equipment optimization process. • Demonstrated the versatility and effectiveness of the IPSO algorithm in managing complex, real-world construction scenarios. • Enhanced methodological transparency and reproducibility by detailing the software and hardware specifications used in the study. Expert systems play a crucial role in decision-making across various industries. This study introduces a novel expert system employing a tailored multi-objective optimization (MOO) model to address the intricate demands of highway projects. Integrating an advanced Enhanced Particle Swarm Optimization (IPSO) strategy, our model emphasizes key operations like excavation, hauling, grading, and compaction. Considering factors such as equipment count, velocity, and capacity, the system provides a comprehensive set of optimal solutions and reveals the Pareto frontier. In benchmarking our Improved Particle Swarm Optimization (IPSO) model against established methods, such as Genetic Algorithms (GA) by various researchers and the Guided Population Archive Whale Optimization Algorithm (GPAWOA), our approach significantly excelled. Our model demonstrated a 35.4%-time reduction and a 39.1% cost reduction while enhancing operational quality—starkly contrasting the modest improvements seen with other methods. This showcases the IPSO model's robustness in optimizing construction equipment utilization beyond current standards. Functioning as a decision-support tool, the expert system aids stakeholders in selecting optimal equipment setups, considering diverse attributes from equipment specifications to speed and volume. This application is exemplified through a real-world highway construction case study. The Case Study showcases the proposed model's remarkable impact, yielding a 35.4% time saving and a 16.8% cost decrease with Optimal Solution (I). In contrast, Optimal Solution (III) required 144.6% more time yet reduced costs by 39.1%. Furthermore, Optimal Solution (II) achieved a 4.6%-time reduction and a 32.4% cost decrease, demonstrating the system's versatility in managing constraints and optimizing decision-making processes across various project stages. [ABSTRACT FROM AUTHOR]

Evaporative cooling systems face limitations in producing cold water due to their dependence on ambient air conditions, air-water cross-contamination, and large equipment size. This study introduces a vacuum-assisted hollow fiber membrane integrated evaporative water cooler as a potential solution. To facilitate its deployment in applications such as building air conditioning and industrial processes, a robust design optimization framework was constructed. A numerical model was established and experimentally verified. Combining the response surface method, three regression models were derived for convenient performance prediction. The six input design parameters encompass operating conditions and membrane specifications. The three output performance metrics are outlet water temperature, coefficient of performance, and cooling capacity per unit volume. Multi-objective optimization using a genetic algorithm, under two inlet water temperature conditions, yielded well-balanced cooler designs with superior cooling performance, energy efficiency, and compactness. The regression models demonstrated high prediction accuracy, with R 2 surpassing 0.93. Compared to the original designs, the parameter combinations identified through the ideal point method realized over 40 % reduction in outlet water temperature, over 66 % increase in coefficient of performance, and over 138 % enlargement in cooling capacity per unit volume. Furthermore, validation of the obtained Pareto fronts confirmed the practical reliability of the design references. [Display omitted] [ABSTRACT FROM AUTHOR]

In this study, the heat transfer and flow resistance characteristics of a trifoliate petal twisted helically coiled tube (TPTHCT) were numerically studied. First, a mathematical model of the TPTHCT was established and the enhanced heat transfer mechanism was analyzed. Secondly, the TPTHCT and the smooth helically coiled tube (SHCT) were comparatively investigated mainly on the parameters of heat transfer coefficient (h), pressure drop (|Δ p |), heat transfer effectiveness (ε), and heat transfer exergy loss number (ξ HT). Finally, a Multi-Objective Genetic Algorithm (MOGA) was utilized to optimize the TPTHCT. The results indicate that an additional torsional force exists in the TPTHCT, which causes a more complex flow state and makes the temperature distribution more uniform. For the same working condition, h increased (by a maximum of 40 %), |Δ p | increased (by a minimum of 50 %). ξ HT consistently exhibited an opposite variation trend with ε at the same inlet temperatures ratio (τ); further ξ HT can describe the effect of changes in τ , whereas ε cannot. The best result from the optimization was achieved with the objective function being maximum h , minimum |Δ p |, and minimum ξ HT ; and the optimal structural parameters were R co = 25.1 mm, P = 93.2 mm, d i = 8.8 mm, r 2 = 0.3, θ = 432°, a = 0.16, E = 151 mm. • The trifoliate petal twisted helical coiled tube (TPTHCT) is investigated. • Correlation of the transfer exergy loss number (ξ HT) for the TPTHCT is formulated. • The MOGA method is employed for the design of the TPTHCT. [ABSTRACT FROM AUTHOR]

• A digital twin model of pharmaceutical workshop with 3D visualization for solvent purification and recovery was designed. • The purity of product and solvent was effectively improved by multi-objective genetic algorithm and system evaluation. • The effectiveness of digital twin in predictive capability using CNN-LSTM regression model was demonstrated. Digital twin is to make full use of the physical model, sensor update, operation history and other data to integrate multidisciplinary, multi-physical quantity, multi-scale and multi-probability simulation process, and to complete the full life cycle process of the corresponding physical equipment in the virtual space. In this paper, digital twin technology was used to simulate the production process of a pharmaceutical workshop, thus realizing solvent recovery by an azeotrope system separation process. The separation was optimized by multi-objective genetic algorithm. separation process, wherein the polynomial curve fitting and entropy-weighted TOPSIS data evaluation algorithms were combined to optimize the operating parameters, then the solvent purity and yield reach 99.96 % and 77.9 %, respectively, which is much higher than the corporate requirements. In addition, the effectiveness of the digital twin in providing data-driven prediction capability was verified by a CNN-LSTM-based regression prediction model, and the CNN-LSTM model has an accuracy of more than 98.9 % for all prediction results. Finally, a 3D visualization model was built using the Unity engine, enabling researchers to visualize and explore changes in key metrics during the separation process. This study can provide some guidance for digital transformation and intelligent development in pharmaceutical industry. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • The syndicated optimization for engine-ORC combined system is conducted. • The power, bsfc, and BSNOx of combined system are improved by coupling ANN with NSGA. • The optimum injection timing, valve phases, pump and expander speeds are obtained. • The optimized BSNOx reduces 37.16%, without the deterioration of bsfc and power. Organic Rankine cycle (ORC) could compensate for the brake power loss caused by in-engine purification by recovering exhaust energy, thus achieving lower emissions and higher power output for the engine. Aimed at identifying optimum parameters that could maximize the performance of a diesel engine-ORC combined system, this study conducts a multi-objective optimization of dynamic performance, fuel consumption, and NOx emission of the combined system, considering the engine's injection timing, intake, and exhaust phases, ORC's working fluid pump speed, and expander speed as decision variables. Initially, a validated simulation model is used to study the effect of these parameters on the performance of the combined system, indicating the necessity of optimization. Subsequently, initial datasets are obtained, with the data of decision variables obtained using the D-optimum Latin hypercube sampling method and the performance indexes calculated by the simulation model. Following these initial datasets, artificial neural network (ANN) models predicting the performance of the combined system are constructed. And the power output, brake-specific fuel consumption (bsfc), and NOx emission are optimized by coupling ANN with non-dominated sorting genetic algorithm-III. Multi-objective optimization results indicate that overly reducing NOx emissions can degrade the dynamic performance and fuel efficiency of the combined system, with the degradation of 1.85% in power output and 1.89% in bsfc as a consequence of a maximum reduction of 43.79% in Brake-specific NOx (BSNOx). Prioritizing maximum power could substantially reduce fuel consumption but weaken the effectiveness of NOx reduction, with a 4.03% improvement in power output and a reduction of 3.87% and 16.48% in bsfc and BSNOx, respectively. A balanced approach considering both dynamic performance and NOx emission is the most suitable for enhancing performance with emission control in mind. This could ensure a 37.16% reduction in BSNOx, maintaining comparable power and fuel consumption. This study offers valuable insights for future advancements in engine performance. [ABSTRACT FROM AUTHOR]

Urban drainage system plays an important role in urban rainwater management. The design of urban drainage system is challenging for its complex constraints and expensive individual evaluation. A kind of multi-objective evolutionary algorithm driven by both structural and empirical knowledge, called structural and empirical knowledge driven multi-objective evolutionary algorithm, is proposed in this paper. A novel tree-template-based encoding schema is proposed to extract the structural knowledge of urban drainage system. The population initialization method and genetic operators (i.e., crossover and mutation) are customized to handle the constraints among decision variables caused by the structure restriction of the urban drainage system. The radial basis function (RBF) model is employed to learn the empirical knowledge from the previous evolution process, and the RBF-based approximate evaluation is used to partially replace the expensive individual evaluation to improve the efficiency of the algorithm. Besides, by combining the decomposition idea and the constrained dominance principle, the local constrained dominance principle is suggested to handle the constraints in decision space, which can effectively improve the defect that the constrained dominance principle is easy to fall into local optimum. Comprehensive experiments on two urban drainage system cases show the superiority of the proposed algorithm for the optimal urban drainage system design. [ABSTRACT FROM AUTHOR]

This study proposed a multi-objective optimization strategy to produce high-quality glass micro gratings. The applied methods include factor screening, surface response methodology (RSM), non-dominated sorting genetic algorithm (NSGA-II) and decision-making. Specifically, the significant factors were selected among several process parameters in hot embossing through screening experiments. Afterward, the regression models for the transferred ratio, uniformity of glass microstructures and warpage concerning those significant process parameters were established. Meanwhile, the significance of selected factors and their interactions was analyzed. Based on the regression models, the Pareto front was obtained by using NSGA-II program, where the top 10 solutions were applied in glass hot embossing. Finally, a decision-making strategy was employed to further choose a feasible solution that allows the fabrication of glass micro gratings with a high transfer ratio, high uniformity, low warpage, low surface roughness, and no defect. The experimental results indicated that embossing temperature has the most significant effect on the replication accuracy of glass micro gratings, followed by embossing force and embossing time. However, warpage was not significantly sensitive to any process parameters in this hot embossing process. The defect-free glass micro gratings could be eventually fabricated under the optimal process parameters, achieving a transfer ratio and uniformity of higher than 95 % and 83 %, respectively. Moreover, their Ra surface roughness and warpage can be lower than 13 nm and 1.5 μm. The satisfactory quality of embossed micro gratings revealed that the proposed multi-objective optimization strategy is feasible in glass hot embossing. [Display omitted] • A multi-objective optimization strategy was proposed for glass hot embossing process. • The effects of process parameters on transferred ratios, uniformity and warpage were experimentally investigated. • The defect-free glass micro gratings with satisfactory replication accuracy were successfully fabricated. [ABSTRACT FROM AUTHOR]

• A multidisciplinary simulation framework integrating the CCH and turboshaft engine is constructed. • A multi-objective optimization problem is designed to evaluate and quantify the flight performance and environmental impact of the CCH. • The NSGA-II multi-objective optimization method based on entropy weight evaluation is adopted. • The goal of low-emission for CCH is achieved through variable rotor speed and variable geometry adjustment. To address compatibility issues of the operational performance and environmental impact of Coaxial Compound Helicopters (CCH), a low-emission optimization control method for coaxial compound helicopter/engine based on variable geometry adjustment is proposed. Firstly, a multidisciplinary simulation framework for CCH and turboshaft engine is deployed by integrating the power characteristics of CCH, the performance of a variable geometry turbine engine, and the emission characteristics of nitrogen oxides (NO x). Subsequently, a multivariable and multi-objective optimization problem is formulated to assess and quantify the flight performance and environmental impact of CCH through a holistic optimization strategy. On this basis, a Non-dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective optimization method based on entropy weight assessment is employed to obtain the optimal control plan for variable rotor speed and adjustable mechanisms within the operation envelope, by balancing the thermodynamic losses of the engine and the aerodynamic efficiency of the CCH. Finally, the optimization control method for CCH is validated based on flight missions. The simulation results demonstrate that the combination of variable rotor speed and variable geometry adjustment of engine effectively reduces the engine NO x emission index by more than 10.7 % within the full envelope, relative to the original rotor speed plan. In the actual flight mission profile, the total NO x emissions can be reduced by 11.78 %, improving the environmental impact of CCH. [ABSTRACT FROM AUTHOR]

A multi-objective optimization method utilizing genetic algorithms is developed to optimize wind farm layout design with the dual objectives of enhancing the production of wind power and reducing hour-level intermittency in the generated power. This method introduces a novel metric for annual wind power intermittency based on the transition probability of wind conditions (i.e., speed and direction). The present multi-objective optimization method ensures that Pareto optimal layouts are evenly distributed according to multiple cost function values. To mitigate wind power intermittency, the spatial distribution of wake fields is strategically manipulated to counteract hourly fluctuations in wind conditions. In wind conditions favoring high power generation, it is found that increasing the overlap between wakes and turbines helps to minimize disparities in generated power compared to other wind conditions. • Wind power intermittency is tackled via genetic algorithm optimization. • A metric of annual wind power intermittency is defined for layout optimization. • Temporal fluctuations in generated power are mitigated effectively. • Improved layouts reduce wind power intermittency significantly. • Reynolds-averaged Navier–Stokes simulations are conducted for wake analysis. [ABSTRACT FROM AUTHOR]

Gas-phase polymerization of propylene via 4th generation Ziegler-Natta (ZN) catalysts was studied and optimized using intelligent data-based methods. The effect of co-catalyst (triethylaluminium (TEA)) and external donor (ED) compositions on the activity and PP-isotacticity for two commercial MgCl 2 /TiCl 4 /phthalate(ID)/TEA/silane(ED) catalyst systems with different Ti-contents were investigated using design-of-experiment (DOE) combined with response-surface-methodology (RSM). Sobol's sensitivity analysis was employed to determine the quantitative impact of catalyst compositions on the target variables. Both catalysts performance was optimized via multi-objective genetic algorithm (GA) to maximize the activity and PP-isotacticity. Results revealed that the activity and PP-isotacticity for both catalysts will be enhanced at low co-catalyst and high ED amounts, respectively. Increasing the ED improved the activity of low-Ti catalyst, whereas it reduced the activity of high-Ti one. Sensitivity results for both catalysts depicted the comparable influence of co-catalyst and ED on the activity and the strong influence of ED on PP-isotacticity. GA optimization offered the activity of 3.7 – 4.65 gPP/mgCat and PP-isotacticity of 95.8 – 97.5 % for low-Ti catalyst at the optimal TEA/Catalyst = 7 – 7.2 and TEA/ED = 6.0 – 7.2 and the activity of 3.9 – 4.7 gPP/mgCat and PP-isotacticity of 93 % for the high-Ti one at the optimal TEA/Catalyst = 7 – 11.2 and TEA/ED = 12. [Display omitted] • Gas-phase polymerization of propylene via Ziegler-Natta catalysts was intensified. • Effect of co-catalyst and external donor on polymerization performance was studied. • Co-catalyst and external donor showed a comparable impact on catalyst activity. • PP-isotacticity was strongly sensitive to the variations of the external donor. • Genetic algorithm was used to maximize both catalyst activity and PP-isotacticity. [ABSTRACT FROM AUTHOR]

Operating rule curves offer a promising approach to guide the operations of hydro–based power systems, such as reservoirs and hydro–photovoltaic hybrid power systems. Nevertheless, the joint operating rule curves have seldom been derived for the hydro–hydrogen–based power systems, primarily due to the complexities in simultaneously adjusting hydropower and hydrogen power. This study is developed to derive the operating rule curves for a hydro–hydrogen–wind–photovoltaic hybrid power system. First, a long-term operation model is built to maximize the guaranteed rate, as well as the total operation revenue obtained from both power generation and hydrogen production. Subsequently, eight operating rule curves are developed to guide the hybrid operation using the inflection point method. Finally, the parameters of operating rule curves are derived using the non-dominated sorting genetic algorithm II under the parameterization-simulation-optimization framework. China's Ertan hydro–hydrogen–wind–photovoltaic hybrid power system served as a case study, and the results indicated that the proposed method could effectively derive the operating rule curves. Compared with the traditional operation, the average operation revenue and guaranteed rate of the optimal operating rule curves increased to 4.50 billion CNY kWh (+17.19%) and 0.99 (+11.24%), respectively. These findings indicate that the proposed operating rule curves achieve a commendable balance between economy and reliability. • Hydrogen power is incorporated into hydro–based hybrid power systems operation. • Key variables in the rule curves are identified by considering Pearson correlation. • Proposed curves improve operation revenue by 17.19% over traditional operation. • Guaranteed rates of the rule curves outperform traditional operation by 11.24%. [ABSTRACT FROM AUTHOR]

• Apply the multi-objective optimization algorithm in solving a phase-modulated wavefront shaping problem and achieve multi-point uniform focusing. • Propose the Attenuate mutation Non-dominated Sorting Genetic Algorithm II (ANSGA-II), which is more suitable for phase modulation wavefront shaping. • Extend the multi-objective optimization algorithm to solve the imaging recovery application of light penetrating scattering medium, which provides a new idea for scattering imaging technology. With the development and advancement of interventional medical technology, multimode fibers (MMFs) are gradually knowing by public, and have been widely used in medical detection, neuroscience, optical trapping and optogenetics. However, the mode dispersion and mode coupling of MMFs result in a disordered speckle image at the output end, which cannot be used for further diagnosis and treatment. Therefore, we introduce a new multi-objective optimization genetic algorithm for phase modulation wavefront shaping. Named Attenuate mutation Non-dominated Sorting Genetic Algorithm II (ANSGA-II). Different from the previous single-objective optimization algorithm, our method optimizes the intensity enhancement and the intensity uniformity simultaneously, achieves multi-point uniform intensity focusing with higher intensity enhancement and higher intensity uniformity. In addition, we also verified the excellent performance of our method in image recovery through MMFs. Our method breaks through the limited optimization capability of single-objective optimization algorithms and provides new ideas for achieving efficient focusing and image recovery of light penetrating MMFs. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of multi-objective optimization of a composite shell structure while adhering to constraints on the number of calls to a pseudo-experimental model, simulating real experiments. Two considered objective functions are defined to determine the investigated structure's dynamic properties and material costs; the optimization involves genetic algorithms, neural surrogate model and multi-fidelity finite-element models. The results of multi-objective optimization were presented as Pareto fronts. A new strategy for preliminary result verification is proposed, significantly reducing the need for a computationally intensive complete verification that requires complex models or experimental investigations. Two different indicators are applied to assess the quality of the obtained Pareto fronts; one is a new one proposed in the paper. Moreover, a multi-fidelity approach is discussed, and three finite element models with different mesh densities are employed, together with a pseudo-experimental model constructed using high-fidelity results and incorporating a nonlinear transformation. However, challenges arise due to the arbitrarily constrained number of pseudo-experiments, limiting future experiments is crucial. The study highlights the need for further analysis of Pareto front indicators and statistical analysis of applied tools like deep neural networks and genetic algorithms. Future research directions include exploring ensemble learning in surrogate models for potential optimization benefits. • Multi-Fidelity Modeling: Demonstrated the effectiveness of a multi-fidelity approach. • Deep Network Surrogate Models: Showed the ability to capture complex data structures. • Multi-Objective Optimization: Determined dynamic properties and material costs. • Curriculum Learning: An innovative strategy for surrogate model improvement. • Pareto Front Quality: Evaluated using relevant indicators. [ABSTRACT FROM AUTHOR]

The main concern of this article is a numerical investigation of the geometrical parameters of composite gas diffusion layers (CGDLs) and their impact on effective thermal conductivity (ETC). The geometric parameters under investigation include porosity, thicknesses of the gas diffusion layer (GDL) and micro porous layer (MPL), as well as fibers diameter and orientation. Additionally, non-geometric parameters such as saturation level and operating temperature are examined. This study utilizes realistic full microstructure simulations (GDL + MPL + PolyTetraFluoroEthylene/PTFE + binder) of a paper GDL type (SGL 25BC) to examine and enhance ETC in both in-plane (IP) and through-plane (TP) directions by optimizing the parameters under investigation. To achieve this, a MATLAB code was used to generate microstructures under various conditions, which were simultaneously imported into COMSOL multi-physics via live link technique (LLT). Subsequently, the non-dominated sorting genetic algorithm II (NSGA II) was employed as an optimization method to refine the geometric/non-geometric parameters. Finally, the ETC results for both TP and IP directions were compared with experimental data from the literature. The optimized microstructure exhibits higher ETC values compared to both the initial simulated microstructure and the experimental data. This finding indicates that fabricating CGDLs within the recommended range of optimal geometric values can lead to a substantial increase in their ETC. [ABSTRACT FROM AUTHOR]

The present study comprehensively examines the thermodynamic performance of an adsorption refrigeration system considering the effect of wetness fraction and hot flushing. A detailed mathematical model analyses temporal exergy destruction and proposes an internal heat recovery mechanism to reduce the exergy losses. The non-sorting genetic algorithm II (NSGA II) along with the linear programming technique for multi-dimensional analysis of preference (LINMAP) is used to optimize six operating variables, maximizing the coefficient of performance (COP) and minimizing specific exergy destruction (e D) using MATLAB R2021b. The research findings reveal that the precooling and preheating processes together contribute more than half of total exergy destruction in the sorption reactor. The exergy losses by the flushing process, a significant contributor after the cooling tower, precooling, and thermal energy source, are substantially reduced with the implementation of the heat recovery scheme. Further, the multi-objective optimization shows that the optimal values of COP, e D , and chiller efficiency with heat recovery mode are found to be improved by +18.56%, -27.63%, and + 25.37%, respectively, compared to without heat recovery. Finally, the comparison among the optimum design variables has been presented based on NSGA II, PSO, and GA. The research and findings reported in this work have implications for optimizing the cooling performance of adsorption systems with heat recovery applications. • Multi-objective optimization of an advanced adsorption cooling system using NSGA II, PSO, and GA. • Significant amount of exergy loss is noted in hot flushing during normal operation. • Precooling and preheating account half of exergy destruction in sorption reactor. • Optimum specific exergy destruction decreases by 27.63% with internal heat recovery. • COP and chiller efficiency are found to be optimal at 0.492 and 8.4%, respectively. [ABSTRACT FROM AUTHOR]

The container loading problem (CLP) holds significant importance in logistics and supply chain management due to its direct influence on transportation costs and times, thereby impacting the competitive advantages of enterprises across industries. Although there are existing studies for CLP, there remains a gap in addressing the optimization of multiple objectives while satisfying practical constraints. Moreover, container loading methods must also meet performance requirements such as high computational speed, explainability, and implementation capability. Although meta-heuristic-based algorithms have shown effective computational capabilities and performance, such algorithms often being trapped in the local optima especially when searching in the vast solution space of the CLP, particularly as the quantity and diversity of cargo sizes and shapes increase. Motivated by realistic needs, this study aims to develop a UNISON-based framework that integrates the merging spaces algorithm, a novel simple but effective hybrid simplified swarm optimization genetic algorithm (SSO-GA), and a multi-populations co-evolution strategy to determine the loading sequence of parcels to maximize space utilization and weight balance while satisfying to practical constraints. Specifically, the merging spaces algorithm merges fragmented small spaces resulting from loaded parcels into larger spaces, thereby facilitating the accommodation of additional parcels. The hybrid SSO-GA splits encoded solutions and updates segment using novel strategies resulting in the rearrangement of a group of parcels and their corresponding layouts for better space utilization. Furthermore, the multi-populations co-evolution strategy enhances the diversity of the search space and stabilizes solution quality by simultaneously exploiting the best solutions and exploring alternative solutions during the solution update process. An empirical study was conducted by using a public benchmark dataset which demonstrated the practical effectiveness of the proposed framework by significantly improving average space utilization while ensuring center balance and satisfying practical constraints. Furthermore, this study can also serve as a digital support system capable of assisting decision-makers in optimizing container loading operations, thereby improving productivity and saving valuable time. [Display omitted] • Digital solution for optimizing container loading is developed. • Multiple objectives and constrains are considered for container loading optimization. • Multi-objectives Multi-populations Simplified swarm optimization (MOMP-SSO) is developed. • This solution integrated data preparing, meta-heuristics, and domain knowledge. • Container loading can be effectively optimized to improve space utilization and safety. [ABSTRACT FROM AUTHOR]

• The critical role of passive LCL filters in enhancing grid stability, particularly in areas with weak infrastructure. • The innovative design approach utilizes Multi-Objective Particle Swarm Optimization and Multi-Objective Genetic Algorithm for optimal filter design, effectively addressing complex challenges. • Enhanced Grid Resilience by minimizing filter parameters, particularly resonance frequency, to boost power quality and stability amid changing grid conditions. • The approach has been successfully validated through numerical simulations, demonstrating significant improvements in total harmonic distortion and stability margin. In an era characterized by the increasing integration of renewable energy sources into the power grid, the stability and resilience of the grid have become paramount, especially in areas with weak grid infrastructure. One effective approach to mitigate the adverse effects of grid disturbances and harmonics is passive LCL (inductor-capacitor-inductor) filters. However, designing these filters for optimal performance under varying conditions remains challenging. This research paper presents an effective approach to addressing this challenge by utilizing the power of evolutionary algorithms, specifically Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Genetic Algorithm (MOGA), to design optimal passive LCL filters for weak grid environments. The proposed method aims to enhance grid resilience by optimizing the filter parameters to consider a weak grid's unique characteristics and uncertainties. Minimizing the resonance frequency has succeeded in achieving power quality and stability improvements related to changes in grid impedance. The proposed approach achieves power quality and stability improvements related to changes in grid impedance and dealing with fragile conditions, which is the main scope of this paper. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

• The mechanical behaviors of LDCB were numerically simulated and verified. • A data-driven computational optimization framework was proposed. • DCQGA-GPR model with higher stability and prediction accuracy was proposed. • DCQGA-GPR-NSGA-III coupling technology proposed was determined as an excellent optimization strategy by benchmarking five algorithms. • The optimized LDCB design exhibits a significant performance improvement. The thin-walled lenticular deployable composite boom (LDCB) is promising for aerospace engineering applications due to its lightweight and compact nature, but its mechanical behaviors are the main challenges limiting its practical application. Here, the axial compression and folding behaviors of LDCB were numerically simulated and the simulation results were verified according to the experimental data in the literature. Then, a new double chains quantum genetic algorithm (DCQGA)-gaussian process regression (GPR) model and data-driven computational optimization framework were proposed and the new model was trained using a database with 400 simulation results, the superiority of which was demonstrated by prediction accuracy evaluation. Additionally, benchmarking five state-of-the-art algorithms found that the coupling technology of DCQGA-GPR-non-dominated sorting genetic algorithm III (NSGA-III) is an excellent optimization strategy to obtain a well-designed structure of LDCB that maximizes the effectiveness of the material while satisfying the lightweight. The optimized LDCB design exhibits a significant performance improvement, with a 26.3 % reduction in the folding moment, a 34.2 % reduction in the maximum Tsai-Hill failure index, a 43.6 % increase in the critical load, and an 11 % reduction in linear density of mass compared to the initial design. [Display omitted] [ABSTRACT FROM AUTHOR]

• A lightweight cable-strut antenna realizes the shape accuracy. • Active control is introduced to ensure the shape accuracy. • A shape sensing method based on stress information is adopted. • A nested genetic algorithm is used for the optimal design of actuators. • A ML framework enables optimal active control based on cable stress. This work proposes the integration of a tensegrity structure composed of struts and cables into an antenna, thus achieving a lightweight and optimized design for the large-scale system. Active control is introduced to ensure the deformation accuracy of the cable-strut antenna, while a shape sensing method based on force density information is adopted to obtain the deformation field of actual wind cases from monitoring the cable forces. Numerical simulations are performed to verify the accuracy of the method, and a multi-objective optimization framework is employed to determine the optimal positions and control of actuators for active control. A machine learning-based framework is proposed to achieve rapid response of the active control system under actual wind cases. The framework is composed of a two-stage control system that combines the shape sensing method with the multi-objective optimization approach. The machine learning framework establishes a quick response system that monitors changes in the cable forces, thus ensuring structural accuracy control under wind load without structural analysis. [ABSTRACT FROM AUTHOR]

• TPMS hybrid lattice structures based on the structural interpenetrating design methodology are completed. • The influence of geometric parameters on the mechanical and thermal properties of the designed structure is revealed by numerical simulation. • Mechanical properties are obtained by additive manufacturing and compression experiments. • A multi-objective optimization model for mechanical and thermal properties is established and solved. Hybrid lattice structures have significant potential in engineering applications owing to their outstanding mechanical and thermal properties. In this paper, an innovative design strategy for hybrid lattice structures is proposed involving the integration of primitive surfaces with truss lattice configurations. This approach allows for the full exploitation of the mechanical and thermal properties inherent in each component, leading to the development of new hybrid lattice structures. Firstly, the quasi-static compression and fluid-solid coupling heat conduction analysis of the hybrid lattice structure is carried out by numerical simulation. Then the effects of surface wall thickness, and truss diameter on the mechanical and thermal properties of the lattice structure are discussed. Additionally, the mechanical performance of the array hybrid lattice structure is evaluated through quasi-static compression experiments. These experimental results are compared with those from the numerical simulations, validating the accuracy of the simulation model. Finally, a multi-objective optimization model targeting specific elastic modulus and heat dissipation performance (indicated by Nusselt number) is established, in response to the demand of aviation and aerospace industries for lightweight, load-bearing, and heat-dissipating multi-functional integrated lattice design. The non-dominated sorting genetic algorithm is utilized to solve the optimal model, and the distribution of feasible solutions and Pareto front are obtained. The results show that the interpenetrating hybrid lattice structure has a greater improvement in mechanical and thermal properties than the primitive surfaces. This research holds significant implications for the design of multi-performance hybrid lattice structures. [ABSTRACT FROM AUTHOR]

Trimaran as a high-performance vessel, possesses excellent characteristics such as high speed, low wave-making resistance, and good seakeeping performance. Different from conventional monohull ship, the design of a trimaran requires additional consideration of the outrigger layout, as the position of two outriggers can affect its hydrodynamic performance. In this study, high-speed slender body potential flow theory (also named the 2D+t theory or the 2.5D method) was employed to calculate the heave and pitch motion in order to evaluate the seakeeping performance of trimarans with different outrigger layouts at varying speeds. Model experiments were subsequently conducted to validate the effectiveness of the 2.5D method in simulating the motion of trimarans under different outrigger layouts. Three multi-objective evolutionary algorithms, including Non-dominated sorting genetic algorithm II (NSGA-II), Non-dominated sorting genetic algorithm III (NSGA-III), and Multi-objective evolutionary algorithm based on decomposition (MOEA/D), as well as their applications in the optimization of trimaran outrigger layouts were introduced. Utilizing the 2.5D method based computational program as the solver, the efficiency and performance of these multi-objective evolutionary algorithms were compared. Discuss the advantages and disadvantages of three multi-objective evolutionary algorithms in solving trimaran outrigger layout optimization problem by comparing the optimization results from two aspects: optimization efficiency, and optimal solution performance. The results indicate that for optimization efficiency, the weighted sum approach based MOEA/D exhibits best optimization efficiency. NSGA-II and NSGA-III show a good advantage in terms of optimal solution performance, and compared to NSGA-II, NSGA-III can obtain more Pareto solutions. • The 2.5D method in motion prediction of a trimaran has been validated. • Three multi-objective algorithms are introduced in outrigger layout optimization. • The performance of three optimization methods are compared and discussed in details. [ABSTRACT FROM AUTHOR]

• Geometry optimization in schematic design phases is crucial to minimize energy needs. • Annual energy needs are highly influenced by local climatic conditions. • An in-house developed aNSGA-II is used in the optimization process. • Results among 19 European cities with different latitudes and climates are analyzed. The ongoing transition towards resilient and energy-efficient cities highlights the design of new buildings as a critical concern. With the increasing urbanization in Europe and the concurrent need for energy reduction and decarbonization, it is crucial to address energy efficiency in the construction sector, as by the recently adopted European Energy Performance of Building Directive. In this context, the article focuses on the role of geometry optimization in the schematic design phase of low-energy buildings, by considering it among different European climates. Indeed, this study aims to propose a multi-objective optimization approach (using a genetic algorithm) to optimize the "genes" of the building, e.g., shape, proportions, orientation and window-to-wall ratio of residential buildings in the early design phase, to cut off a significant portion of energy consumptions. The objective is thus to minimize heating and cooling energy needs based on the best combination of the above-mentioned genes. The research investigates optimal solutions across various European climates based on the Köppen-Geiger classification, comparing the results for 19 European cities with varying climate. The findings confirm that the energy needs of buildings are strongly influenced by the climate conditions of their location. The study, which comprehends almost 250.00 different configurations, assesses, as expected, the strong effect of climate conditions on the energy needs and highlights the cross-shaped buildings as the best performing in 17 over 19 sites investigated. The optimal solution for each site leads to total energy needs that are comprised between 4.58 kWh m-2 y-1 in Porto and 19.84 kWh m-2 y-1 in Goteborg, with an average across all cities of 12.45 kWh m-2 y-1. The research provides valuable insights for professional in the construction sector, aiding them in making informed decisions during the schematic design phase to achieve low-energy buildings that are adapted to specific climates. [ABSTRACT FROM AUTHOR]

This work introduces a novel approach based on utilizing intelligent data-based optimization techniques to fabricate high-quality and low-cost carbon electrodes for capacitive deionization (CDI). The effects of synthesis parameters of peanut shell-derived porous carbon (PSPC) (KOH to Raw ratio, pyrolysis temperature and time) on five factors (production yield, BET surface area, electrode specific capacitance, and ion transfer resistances, including interface and diffusion) were investigated using combined design-of-experiment (DOE), response-surface-methodology (RSM), and Sobol's sensitivity analysis (SA). Optimal scenarios providing maximum production yield, surface area, and specific capacitance, and minimum ion transfer resistances were found using a multi-objective genetic algorithm (GA). Results uncover that increasing the impregnation ratio, pyrolysis temperature and time enhances the PSPC porosity that causes the carbon surface area and electrode specific capacitance to promote, the production yield to reduce and ion transfer resistances to raise. From Sobol's SA, pyrolysis temperature was the most impacting parameter on all decision factors. The optimal PSPC prepared at 750 °C and minimum KOH:Raw = 1 and time = 30 min offered a developed mesoporous structure with BET surface area of 685 m2/g and the resulting electrode had an outstanding specific capacitance (127.13 at 5 mV/s), with reliable durability after 100 cycles and showing the lowest possible ion transfer resistances. [Display omitted] • Fabrication of low-cost and high quality carbon electrode for capacitive deionization • Effect of carbon synthesis parameters on its physical and electrochemical properties • Pyrolysis temperature has the greatest impact on product yield and its properties. • Carbon electrode properties were tuned via modern intelligent optimization approach. [ABSTRACT FROM AUTHOR]

• Wavy microchannel with compound rib of porous layer and solid wall is proposed. • The thermal-hydraulic performance is related to three types of structural parameters. • Multi objective genetic algorithm method is employed to optimize MCHS performance. • The 37.61% and 16.67% decreases in R and Ω with optimal compound rib compared to solid rib. The combining rib of porous layer and solid wall is set in wavy microchannel heat sink to enlarge heat transfer area and convection with smaller increase in flow resistance, in which the consumption of pump power relates to the ratios of wave amplitude to length or to width and the thickness of porous layer due to vortices generation while coolant passing through channel. The local thermal equilibrium occurs in porous layer, and the Forchheimer-Brinkman-Darcy relations were used to describe the flow inside porous ribs. The effects of channel-to-pitch width ratio, width ratio of porous layer to pitch and microchannel height on its cooling performance are investigated, and the parameter of figure of merit (FOM) is defined to evaluate thermo-hydraulic performance in wavy microchannel. Besides, the multi objective genetic algorithm method is employed to optimize structure parameters in wavy microchannel to obtain better performances, in which the increase in thermal resistance from 0.062 K/W to 0.166 K/W together with consumption of pump power decreasing from 0.00416 W to 0.00185 W can be obtained in the Pareto front set, and it relates to the heat dissipation in convection between coolant and bottom surface or vertical side ribs. Compared to wavy microchannel with solid rib, the 37.61% and 16.67% decreases respectively in thermal resistance and consumption of pump power occur in the case with optimal combining rib of porous layer and solid wall. [ABSTRACT FROM AUTHOR]

High-order mesh optimization has many goals, such as improving smoothness, reducing approximation error, and improving mesh quality. The previous methods do not optimize these objectives together, resulting in suboptimal results. To this end, we propose a multi-objective optimization method for high-order meshes. Central to our algorithm is using the multi-objective genetic algorithm (MOGA) to adapt to the multiple optimization objectives. Specifically, we optimize each control point one by one, where the MOGA is applied. We demonstrate the feasibility and effectiveness of our method over various models. Compared to other state-of-the-art methods, our method achieves a favorable trade-off between multiple objectives. • We propose a multi-objective optimization method for high-order tetrahedral meshes. • Employ the multi-objective genetic algorithm to relocate control points to achieve a favorable trade-off among objectives. • Select the best individual via the weighted TOPSIS method, with weights determined by combining multi-objective problem. [ABSTRACT FROM AUTHOR]

[Display omitted] • A efficient and environmentally friendly extractant for separating toluene-methanol-water azeotrope. • Three feasible schemes were designed by changing solvent and optimized by multi-objective genetic algorithm. • The proposed double-column extractive distillation process with a decanter presents superior performance. • The TAC and CO 2 emissions were reduced by 33.1 % and 50.5 %, respectively, compared with the existing process. Cleaner production and sustainable development are still hot topics for modern industry. Developing green technology with low energy consumption, low pollution, and low cost has always been the common pursuit of chemical, petroleum, pharmaceutical, and other industries. This article proposes an environmentally friendly and efficient solvent, glycerol, for the separation of the ternary azeotrope of toluene-methanol-water. Three feasible extractive distillation schemes were designed and optimized, while the double-column extractive distillation process with a decanter demonstrated superior performance when glycerol was used as the solvent. Additionally, the multi-objective genetic algorithm was employed to optimize these separation processes and determine the optimal operating conditions, taking total annual cost (TAC), CO 2 emissions (E CO2), and separation efficiency (E ext) as target performance indicators to achieve the objectives of energy conservation, consumption reduction, and emission reduction. Results demonstrated that the newly proposed scheme can significantly reduce the total annual cost by up to 33.1 % and CO 2 emissions by 50.5 % compared with the existing separation process. This work provides a new insight to separate and purify the multiple systems by extractive distillation in industry. [ABSTRACT FROM AUTHOR]