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• Fusion of advanced deep-learning models CNN, LSTM, DNN and RNN with GA optimizes GWPZ mapping. • Overlaying GWPZ with drought maps unveils links between drought levels and groundwater potential. • CMIP6 climate models project changing GWPZ under different socioeconomic scenarios. • Workshops and local collaboration empower communities for sustainable water management. This study presents a novel approach to groundwater resource assessment and drought vulnerability, achieved through integrating cutting-edge deep-learning algorithms into a comprehensive framework. In regions susceptible to drought, ensuring groundwater availability is of paramount importance. Addressing this critical need, our research employs an ensemble of advanced algorithms, including long short-term memory, convolutional neural network, deep neural network, and recurrent neural network. These algorithms are further enhanced through optimization using a genetic algorithm to map groundwater potential zones (GWPZ). Leveraging model validation based on the area under the receiver operating characteristic curve (AUCROC), the long short-term memory-genetic algorithm model emerges as the superior algorithm, boasting the highest values of AUCROC: 0.995 for training and 0.996 for testing). Utilizing this optimized model, this study developed the GWPZ map, subsequently overlaying it with established drought maps developed by the Bangladesh Agricultural Research Council across distinct periods—Pre-Kharif, Kharif, and Rabi. The derived baseline GWPZ spatial distribution revealed five categories—very low (34.99%), low (27.67%), moderate (13.26%), high (11.71%), and very high (12.37%)—and their intersection with drought-prone regions, indicative of probabilities of drought occurrence ranging from very severe to low (15.14% to 24.69%). Moreover, employing the best-predicted model, this study projected future GWPZ for 2050 and 2100 using the coupled model intercomparison project 6 general circulation model data under the ambit of three distinct shared socioeconomic pathways (SSPs): SSP1-2.6, SSP2-4.5, and SSP5-8.5. Our findings suggested a contraction in the groundwater potential area by the end of the twenty-first century (2100). This pioneering integration sheds light on the intricate relationship between groundwater availability and drought susceptibility, furnishing invaluable insights for formulating targeted water resource management strategies. By amalgamating advanced computational techniques with geospatial analyses, this research contributes to a more comprehensive grasp of water resource dynamics within the context of escalating climate challenges. Consequently, it offers a foundation for informed decision-making and implementing sustainable water management practices in regions grappling with the dual challenges of groundwater scarcity and recurrent droughts. [ABSTRACT FROM AUTHOR]

To improve the cell productivity of Corynebacterium glutamicum , its initial specific growth rate was improved by medium improvement using deep neural network (DNN)-assisted design with Bayesian optimization (BO) and a genetic algorithm (GA). To obtain training data for the DNN, experimental design with an orthogonal array was set up using a chemically defined basal medium (GC XII). Based on the cultivation results for the training data, specific growth rates were observed between 0.04 and 0.3/h. The resulting DNN model estimated the test data with high accuracy (R2 test ≥ 0.98). According to the validation cultivation, specific growth rates in the optimal media components estimated by DNN-BO and DNN-GA increased from 0.242 to 0.355/h. Using the optimal media (UCB_3), the specific growth rate, along with other parameters, was evaluated in batch culture. The specific growth rate reached 0.371/h from 3 to 12 h, and the dry cell weight was 28.0 g/L at 22.5 h. From the cultivation, the cell yields against glucose, ammonium ion, phosphate ion, sulfate ion, potassium ion, and magnesium ion were calculated. The cell yield calculation was used to estimate the required amounts of each component, and magnesium was found to limit the cell growth. However, in the follow-up fed-batch cultivation, glucose and magnesium addition was required to achieve the high initial specific growth rate, while appropriate feeding of glucose and magnesium during cultivation resulted in maintaining the high specific growth rate, and obtaining a cell yield of 80 g/L ini. • A novel growth medium for Corynebacterium glutamicum was developed using deep neural networks and optimization algorithms. • During the validation cultivation, high specific growth rates were observed in the proposed media. • The limiting factors for high biomass yield were identified in the proposed medium. • To optimize productivity and yield, a fed-batch culture was designed using the proposed medium. [ABSTRACT FROM AUTHOR]

This article introduces and analyzes an integrated energy system that may generate electricity and potable water at a low cost, speeding up the desired clean transition process. The proposed system uses flame-assisted fuel cells to improve upon the inefficiencies of traditional power generation setups by increasing fuel utilization and reducing waste heat. The product's energy cost and overall efficiency are improved by using fuel gas condensation combined with a multi-effect desalination process. The engineering equation solver software examines the feasibility of the proposed system using energy, exergy, exergoeconomic, environmental, and sustainability analyses. The parametric analysis also compares the effects of changing key design variables and performance metrics. Then, the genetic algorithm integrated with an artificial neural network model is applied to minimize the power cost while enhancing the exergy efficiency. According to the results, the proposed efficient hybrid system has an affordable energy cost of 0.123 $/kWh and an environmental impact of 524 kg/MWh. The parametric study demonstrates that increasing the current density improves potable water production while reducing the efficiency and increasing the unfavorable power costs. Also, it can be shown that the sustainability indices improve significantly when a lower fuel utilization factor and equivalence ratio are chosen. The results further reveal that the optimization achieves a higher exergy efficiency of 6.2% and a reduced power cost of 0.03 $/kWh than the design condition. The scatter dispersal of key variables indicates that the fuel utilization factor lacks effectiveness, and keeping the current density and fuel temperature close to their upper limits is techno-economically favorable. Due to the significant temperature increase, mixing of the fuel and air, and chemical interactions between the reactants, the irreversibility analysis shows that the mixer and the flame-assisted fuel cell have the greatest destruction rate under the optimal conditions. • A new hybrid system is introduced based on the flame-assisted solid oxide fuel cell. • The proposed system's feasibility is assessed from all aspects. • A genetic algorithm optimizer integrated with machine learning is applied. • Optimization achieves higher efficiency of 6.2% and a lower cost of 0.03 $/kWh. • Choosing a lower fuel utilization factor improves sustainability indices. [ABSTRACT FROM AUTHOR]

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]

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]

Accurate isotherm models contribute to predicting the amount of adsorbate adsorbed in the adsorbent within a certain range of pressure and temperature. Experimental adsorption isotherms of five components (CO 2 , CO, N 2 , CH 4 and H 2) on pelletized zeolite 13X from publication data were correlated with common-used fundamental isotherm models, including Langmuir, Sips, dual-site Langmuir (DSL), and temperature-dependent (TD) isotherm models, including TD Langmuir, TD Sips and TD DSL. Overall, the average relative errors (AREs) of TD models are higher than those of common-used fundamental isotherm models, but TD models have better applicability. In addition, three surrogate artificial neural networks (ANNs) optimized by genetic algorithm (GA) of single- and multi-component gas adsorption isotherm models are proposed. Three GA-ANN models set the adsorption amount as the output and take one, two or three potential inputs, such as adsorption pressure, temperature and adsorbate components. Three GA-ANN models generally have lower AREs and better applicability than fundamental isotherm models and TD isotherm models. The proposed GA-ANN3 model, which has three inputs, makes a new attempt at adsorption isotherms and can fit the multi-component gas adsorption at different temperatures with the change of adsorption pressure. The adsorber dynamics for multi-component adsorption (CO 2 : CO: N 2 : H 2 = 19.9: 0.1: 44.6: 35.4 mol%) in zeolite 13X bed are compared by the extended TD Langmuir model and extended TD DSL model. Since the AREs of the TD DSL model are smaller than that of the TD Langmuir, the breakthrough curves of multi-component gas simulated by the TD DSL model are better than that of TD Langmuir. These results can contribute to relevant researchers choosing suitable adsorption isotherm models for numerical simulations of adsorption processes. • Fitted dual-site Langmuir model performs better in breakthrough curve simulation. • Artificial neural network (ANN) enables construction of adsorption isotherm models. • Genetic algorithm (GA) optimizes the weights and biases of ANN for better performance. • Three GA-ANN models have lower average relative errors and better applicability. • GA-ANN3 attempts to express adsorption isotherms for multi-component mixtures. [ABSTRACT FROM AUTHOR]

• An optimal path planning is proposed for spacecraft fleet to asteroids detumbling. • Optimal low-thrust trajectories for the spacecraft fleets in a 3D high perturbed asteroid environment is designed through a combined GA-Collocation method. • A deep neural network is used to rapidly approximate the gravitational acceleration of the asteroid. • The optimal problem utilized optimization method, as GA for guessing systematically the optimal initial costates, with high speed and accuracy be able to it yield consistent and reliable results. Following space discoveries, asteroids were found as rich sources of minerals and organic matter that can be exploited. In this paper, we present a precise, fast, and robust energy-optimal soft landing with a combined GA-Collocation method for a spacecraft fleet aimed at an irregular asteroid detumbling mission. It is assumed that the spacecraft fleet carried to the asteroid's equilibrium points introduced as the start locations of the mission. Here, the 433 Eros asteroid has been considered as the stony target known for its irregular elongated shape and nearly large dimension. We modeled the gravitational potential field by polyhedron as the most accurate one for such asymmetric objects. Hence, to decline the high burden computational of related equations in gravitational acceleration calculation, a Deep Neural Network with seven hidden layers and a relatively large dataset covering 6000 pairs has been developed. We utilized a Genetic Algorithm to guess systematically more optimal and reliable initial costates of some key parameters to raise the speed and accuracy of estimation. The acceptable efficiency and accuracy in optimal path prediction of the presented approach for a fleet of spacecraft approved throughout precise simulations. Eventually, the feasibility of the proposed approach is demonstrated through the corresponding results. [ABSTRACT FROM AUTHOR]

In recent years, Deep Learning models have shown a great performance in complex optimization problems. They generally require large training datasets, which is a limitation in most practical cases. Transfer learning allows importing the first layers of a pre-trained architecture and connecting them to fully-connected layers to adapt them to a new problem. Consequently, the configuration of the these layers becomes crucial for the performance of the model. Unfortunately, the optimization of these models is usually a computationally demanding task. One strategy to optimize Deep Learning models is the pruning scheme. Pruning methods are focused on reducing the complexity of the network, assuming an expected performance penalty of the model once pruned. However, the pruning could potentially be used to improve the performance, using an optimization algorithm to identify and eventually remove unnecessary connections among neurons. This work proposes EvoPruneDeepTL, an evolutionary pruning model for Transfer Learning based Deep Neural Networks which replaces the last fully-connected layers with sparse layers optimized by a genetic algorithm. Depending on its solution encoding strategy, our proposed model can either perform optimized pruning or feature selection over the densely connected part of the neural network. We carry out different experiments with several datasets to assess the benefits of our proposal. Results show the contribution of EvoPruneDeepTL and feature selection to the overall computational efficiency of the network as a result of the optimization process. In particular, the accuracy is improved, reducing at the same time the number of active neurons in the final layers. [ABSTRACT FROM AUTHOR]

The synergistic effects of two or more carrier species significantly intensify the performance of liquid membranes. However, this process is difficult to model as different carrier species make different complexes with permeating species. Artificial Neural Network (ANN) based modeling is helpful in this case. First number of neurons in the hidden layer was optimized to reduce mean squared error (MSE). Minimum MSE (10.96) was achieved with ten neurons. Simulations showed that neodymium transport initially increased with increasing carrier species TOPO concentration but later decreased due to increased viscosity of liquid membrane. Similarly, higher neodymium transport was found at a moderate concentration of H 2 SO 4 in receiving phase and a low concentration of HCl in the feed phase. Trained ANN was coupled with the Genetic Algorithm to determine the optimum operating parameters in their given range. 0.12 M TOPO concentration in the liquid membrane, 4.2 M of H 2 SO 4 concentration in the receiving phase, and 0.5 M of HCl concentration in the feed phase were optimum values of the operating parameters to achieve 100% neodymium transport through the liquid membrane in 360 min operation. [Display omitted] • Liquid Membrane was modeled using Artificial Neural Network. • Artificial Neural Network was coupled with Genetic Algorithm for optimization. • 0.12 M TOPO was found optimum carrier species concentration. • 0.42 M H 2 SO 4 was found optimum concentration of stipend agent. • 100% Neodymium transport was achieved after 360 min of operation. [ABSTRACT FROM AUTHOR]

This study proposes a systematic methodology for improving PEMFC's performance combining computational fluid dynamic (CFD), artificial neural network (ANN), and intelligent optimization algorithms. Firstly, a three-dimensional (3-D) multiphase PEMFC CFD model with 3-D fine-mesh flow field is developed. Then the key structural features of the fine-mesh flow field are extracted as optimization decision variables, and the sampling points are selected by using the Latin hypercube sampling (LHS) experimental method. The power density and oxygen uniformity index of sampling points are calculated by CFD modeling to form the database, which is used to train the artificial neural network (ANN) surrogate model. Finally, the single-objective optimization (SOO) and multi-objective optimization (MOO) are implemented by using genetic algorithm (GA) and non-dominated sorting genetic algorithm (NSGA-II), respectively. It was found that using trained ANN surrogate models can get a high prediction precision. The maximum power density of SOO is increased by 7.546% than that of base case and is 0.562% larger than that of MOO case. However, the overall pressure drop in cathode flow field of SOO case is greater than that of MOO case and the base case. Furthermore, the oxygen concentration, the oxygen uniformity index and the water removal capacity of MOO case are better than that of SOO case. It is recommended that the improved flow field structure optimized by MOO is more beneficial to improve the overall performance of PEMFC. [Display omitted] • A novel three-dimensional fine-mesh flow field of PEMFC is designed. • Proposing a co-simulation model for improving the novel PEMFC's performance. • The single-objective and multi-objective optimizations are performed using genetic algorithm. [ABSTRACT FROM AUTHOR]

Hydrogen production from the supercritical water gasification (SCWG) of sewage sludge (SS) is a sustainable and efficient process. However, the challenging and intricate task for the experimental technique is to find out the correlation between proximate, ultimate analysis and gasification conditions with H 2 production. This process is complicated, expensive and requires many experimental techniques. To accurately predict and analyze the effect of input parameters on SCWG of SS process economically, an efficient model must be developed. The novelty of this study includes the consideration of four different machine learning (ML) methods integrated with Genetic Algorithm for the prediction, analysis, and evaluation of Hydrogen yield from the supercritical water gasification of sewage sludge. The ML methods included Support Vector Machine, Ensembled Tree, Gaussian Process Regression, and Artificial Neural Network. The results suggests that GPR is favored for predicting Hydrogen yield (Coefficient of determination (R2) = 0.997, Root Mean Square Error (RMSE) = 0.093, and is highly recommended for dealing with complex variable-target correlation. On the other hand, the performance of Support Vector Machine (SVM) was poor with R2 = 0.761 and RMSE = 2.479. The R2 and RMSE for Ensembled Tree (ET) and Artificial Neural Network (ANN) was 0.994, 0.560 and 0.943, 1.521 respectively. The partial dependence plot shows that temperature, moisture content and pressure are among the effective parameters of SCWG. Furthermore, optimization techniques such as genetic algorithms are incorporated to optimize H 2 production by tuning the ML hyperparameters. Additionally, a Graphical User Interface was developed by utilizing the optimized GPR model for ease in computing H 2 yield.The optimum ML method integrated with GA will be beneficial for researcher to predict the H 2 yield for the experimental work. [Display omitted] • Machine learning methods integrated with Genetic Algorithm are analyzed for prediction, analysis & evaluation of H 2 yield. • Partial dependence plot shows that temperature, moisture content and pressure are among the effective parameters. • Graphical User Interface was developed by utilizing the optimized GPR model for ease in computing H 2 yield. [ABSTRACT FROM AUTHOR]

[Display omitted] • A GP-based design framework is proposed for compound distillation sequences (CDS). • Exhaustive method and elitist GA are used to optimize the SDC and DWC respectively. • ANN models are used to predict the steady state characteristics of the SDC and DWC. • N-butane/n-pentane/benzene/toluene/o-xylene/2,4-methyl-benzene separation is studied. • The CDSs achieve smaller TAC than the direct and indirect SDC sequences. For the separation of multi-component mixtures, compound distillation sequences (CDSs) comprising simple distillation columns (SDCs) and dividing-wall columns (DWCs) offer the advantage of significant energy-saving potential and relatively straightforward industrial implementation. This work proposes a design framework to facilitate the development of optimal CDSs. In this framework, genetic programming is employed to explore the optimal sequence; simultaneously, the exhaustive search method and the elitist genetic algorithm are separately employed to search for the optimal design of, respectively, SDCs and DWCs involved. Furthermore, to efficiently estimate the total annual cost (TAC) and both the flow rates and compositions of products of the SDCs and DWCs during the optimization process, artificial neural network (ANN)-based performance estimators were developed for them. An illustrative case study on n-butane/n-pentane/benzene/toluene/o-xylene/2,4-methyl-benzene mixtures is conducted. Analysis of the optimization results demonstrates a significant improvement in economic performance for the resulting CDS compared to the direct and indirect SDC sequences. [ABSTRACT FROM AUTHOR]

• Filling model of zero-dimensional gas and one-dimensional wall is built and verified. • Modified formula and artificial neural networks (ANN) models for MC are proposed. • Latin hypercube sampling, Genetic algorithm and Sobol sensitivity analysis are used. • Modified formula and ANN models for MC parameter improve MC's calculation accuracy. • ANN model of MC extends applicability to situations of filling time less than 30s. The MC method was officially released in SAE J2601 protocol in 2016 to refuel light-duty hydrogen fuel cell vehicles quickly and safely. For the non-communication MC method, the pressure target for controlling the end of filling is calculated based on the formula of the final hydrogen temperature, which contains a key parameter, MC. SAE has proposed two formulas to estimate the MC parameter. This article proposes a modified formula and artificial neural networks (ANN) model for MC parameter. To generate simulated data for fitting the modified formula and training the ANN model, a hydrogen filling model (0D1D) with a zero-dimensional gas and a one-dimensional tank wall is established and thoroughly verified. In establishing the ANN model, Latin hypercube sampling (LHS) is used to sample independent variables randomly, and genetic algorithms (GA) are used to optimize the initial weights and biases of the ANN. The Sobol sensitivity analysis is used to determine the importance of various initial conditions on the MC parameter. Research shows that the final fueling time and initial pressure have a greater impact on the MC parameter , while the ambient and precooling temperatures have little impact. The modified formula of the MC parameter improves R2 from the original 0.76073 to 0.98132 when fitting the MC simulated data. In situations of short filling time, the modified formula and ANN models of the MC parameter reduce the relative error between the 0D1D model by 13.8 % − 58.8 % and 16.6 % − 82.3 %, respectively, thus improving the calculation accuracy of the final hydrogen temperature and ending pressure target. Moreover, the ANN model can predict the MC parameter during the whole fueling process, while the MC formula utilizes the fixed value when the fueling time is less than 30 s. This work has positive significance for improving refuelling protocol. [ABSTRACT FROM AUTHOR]

Failure assessment is one of the fundamental tasks for optimization of composite pressure vessels (CPVs). However, the extensive design space of composites usually leads to costly and repetitive work of failure assessment that hinders the design and optimization of CPVs. In this work, a combined FEM simulation and machine learning approach is used to predict the failure factors R of CPVs for further optimization. CPVs with various design parameters are automatically generated and analyzed in ABAQUS to obtain failure factors under various external loadings. Then, an assembled deep neural network (DNN) namely Multi-DNN is trained to map relations between design parameters and R of CPVs. Further, transfer learning (TL) is introduced to improve the extensibility of our approach by fine-tuning the pre-trained Multi-DNN using scarce data of new design space. Such a TL-Multi-DNN model can precisely predict R of CPVs with new design parameters. Additionally, combining the trained models and genetic algorithm, the optimization of CPVs is carried out with high efficiency and low computational cost. As a result, R of CPV with various given and new design parameters can be estimated directly by the trained models and the optimal layer sequence of CPV can be obtained efficiently. [ABSTRACT FROM AUTHOR]

The ejectors are crucial in enhancing the efficiency of the CO 2 -based refrigeration cycle. Although the ejector can be optimized by experimental and computational fluid dynamic simulation to improve its performance, these methods are time-consuming and complicated. This research aims to present a fast and systematic approach for optimizing CO 2 ejector by combining variance analysis, surrogate model, and non-dominated sorting genetic algorithm. Firstly, the key geometric structures affecting the performance of ejector were determined by variance analysis. Secondly, the influence of key geometric structures and boundary conditions on ejector efficiency and entrainment ratio was analyzed by using computational fluid dynamic simulation, and the database was generated. Next, the artificial neural network surrogate model was established to forecast the ejector performance. Finally, the non-dominated sorting genetic algorithm was involved to optimize the ejector for maximizing ejector efficiency and entrainment ratio. The results show that the efficiencies of the optimized ejectors are above 42%, and the entrainment ratios exceed 0.7. Compared with the basic model, the average efficiencies have increased about 13.20%. When the pressure lift is 0.7 MPa, the entrainment ratios of the optimized ejectors are 0.696 (−20 °C ≤ T e < −15 °C), 0.82(-15 °C ≤ T e < −5 °C), 0.60 (−5 °C ≤ T e < 5 °C) and 0.90 (5 °C ≤ T e < 15 °C), respectively. The above research demonstrates that the success of this approach in addressing time-consuming multi-optimization problems. It offers a fast and systematic approach for the multi-objective optimization of CO 2 ejector as a valuable reference for engineering applications. • Homogeneous relaxation model is established to simulate the CO 2 ejector. • The key geometric structures are determined by variance analysis method. • A fast and systematic multi-objective optimization framework is proposed. • The ejector efficiency and entrainment ratio are optimized simultaneously. • The boundary conditions of the ejector are considered as independent variable. [ABSTRACT FROM AUTHOR]

[Display omitted] • Bi 2 WO 6 /g-C 3 N 4 /ZIF-67 is a green hybrid dual S-scheme heterojunction photocatalyst. • Artificial neural networks/genetic algorithms modeled and optimized the reactions. • Density functional theory was employed to study the degradation pathway of Cefixime. • Antibacterial tests/seed cultures proved that the degradation of Cefixime is green. • Bi 2 WO 6 /g-C 3 N 4 /ZIF-67 is also an efficient photocatalyst for other pollutants. This study investigated the photocatalytic adsorption, degradation, and mineralization of Cefixime (CFX) through hydrothermally synthesized Bi 2 WO 6 (36 %)/g-C 3 N 4 (54 %)/ZIF-67(10 %) dual S-scheme heterojunction (BCZ). Full characterization analysis for the fresh and reused photocatalyst was explored to study the formation of heterojunction, stability, and reusability of the BCZ. VB-XPS, Mott-Schottky plots, and UV–Vis DRS defined the band structure and electron transfer mechanism of BCZ. The effects of ten operating condition factors, including reaction time, initial concentration of CFX, the dosage of photocatalyst, reaction temperature, initial pH of the reaction, visible and UV intensity, and concentration of Na 2 SO 4 , NaOH, and NaCl in the reaction solution were experimentally investigated. These factors were then modeled through artificial neural networks (ANN). The number of neurons, training algorithm, and the type of activation functions in the ANN were optimized by mean squared error metric followed by the visualizations of the ANN predictions. Two cost functions (the ratio of instantaneous CFX concentration to initial CFX concentration ( C / C 0) and the ratio of photocatalyst dosage to the amount of CFX removed) were employed to optimize the value of these operating condition factors separately and simultaneously through single and multi-objective genetic algorithms. Coupling LC-MS results and DFT calculations, a degradation pathway for Cefixime was proposed and then analyzed by QSAR. The toxicity of the treated solution was investigated using the wheat seed culture, along with MIC and MBC testing conducted by E. coli and S. aureus, and its eco-friendliness was confirmed by TOC and COD. Furthermore, ICP-OES confirmed that BCZ is a green photocatalyst. [ABSTRACT FROM AUTHOR]

[Display omitted] • P. ostreatus mycelium biomass and EPS were optimized by GA-RSM. • Biomass (36.45 g/L) and EPS (6.72 g/L) were obtained under optimized conditions. • ANN showed better prediction performance results than RSM. • A user-friendly GUI was developed for computing mycelium biomass and EPS production. This study aimed to enhance the production of mycelium biomass and exopolysaccharides (EPS) of Pleurotus ostreatus in submerged fermentation. Response Surface Methodology (RSM) sought to optimize culture conditions, whereas Artificial Neural Network (ANN) aimed to predict the mycelium biomass and EPS. After optimization of RSM model conditions, the maximum biomass (36.45 g/L) and EPS (6.72 g/L) were obtained at the optimum temperature of 22.9 °C, pH 5.6, and agitation of 138.9 rpm. Further, the Genetic Algorithm (GA) was employed to optimize the cultivation conditions in order to maximize the mycelium biomass and EPS production. The ANN model with an optimized network structure gave the coefficient of determination (R2) value of 0.99 and the least mean squared error of 1.9 for the validation set. In the end, a graphical user interface was developed to predict mycelium biomass and EPS production. [ABSTRACT FROM AUTHOR]

In minerals processing, the froth flotation is one of the widely used process that separates valuable mineral components from their associated gangue materials. The efficiency of this process relies on several factors, such as feed characteristics, particle size, pulp flow rate, pH, conditioning time, aeration, reagents system and many other affecting parameters. These processing parameters significantly impact the overall performance of the flotation process and influence the quality of the final concentrate. For instance, improper pulp flow and reagent dosing systems can result in metal loss and waste, particularly when dealing with frequently changing ore compositions. In this work, we established an Artificial Intelligence-based system which goal is to intelligently monitor flotation circuits and to recommend set-points for the process's manipulated variables in order to achieve optimal performance. The system has been developed and evaluated within an industrial flotation plant that processes complex Pb-Cu-Zn sulfide ores. Leveraging an Artificial Neural Network-based Mixture of Experts (MoEs) predictive model, the system accurately estimates the mineral grades in the final concentrate and tailing of the flotation circuit. Moreover, using a Genetic Algorithms-based optimization pipeline, the system recommends set-points for the manipulated variables of the process for a maximum recovery and optimal product quality. The industrial validation of the predictive component demonstrated a 94% accuracy with a rapid 3s response time. Furthermore, the hypothetical simulation of the optimization component indicated a potential 5% increase in circuit recovery and a 4% increase of lead (Pb) grade in the circuit's final concentrate. This developed system aims to enhance the control of froth flotation process, stabilize the product quality, and improve the overall economic benefits of production efficiency. This research contributes to the field of manufacturing systems by providing practical data-driven application for the advanced monitoring, optimization and control of industrial processes with a specific emphasis on the froth flotation process. • Development of an AI-based system for monitoring and optimizing froth flotation circuits in mineral processing. • Industrial-scale operational and laboratory data collection from a industrial flotation plant. • Circuit monitoring achieves 94% accuracy and 3-second response time during one-month industrial evaluation. • Circuit optimization simulations show potential for improved recovery and final concentrate quality. [ABSTRACT FROM AUTHOR]

Unsteady motion resulting from the coupling of uniform revolution and variable rotational motion in the Voith-Schneider Propeller (VSP) causes nonlinear characteristics in hydrodynamics, and the rapid changes in the rotational angle increase torque and reduce efficiency. To effectively improve the efficiency of the VSP, herein, the periodic characteristics of coupled VSP motions were employed to transform the complex spatial movement of the blades into a parameterized study of two-dimensional plane curves. A parametric scheme is developed for the asymmetric distribution of blade rotational angles, and a numerically validated computational method is used to train a radial-basis neural network model. Based on this, an optimization scheme for different control points is obtained using a multi-island genetic algorithm. The optimized solution obtained for the case of a single blade is applicable to cases that consider the coupled interference among blades and changes in the advance coefficient. The efficiency of the six-blade configuration with an eccentricity of 0.7 increased by 16%. When the eccentricity is 0.9, which corresponds to high eccentricity and a low-efficiency operating condition, the efficiency of the six-blade configuration is maximally improved by 75%. The method based on motion curve parameter control has certain significance for VSP efficiency optimization. • The research on parameterization of two-dimensional plane curves for complex spatial motion of blades. • An asymmetrically distributed parametric scheme for rotational motion curves has been designed. • Effective reduction in rotational torque has led to an increase in efficiency at an eccentricity of 0.9. • The single blade scheme remains applicable to conditions involving changes in the number of blades and advance coefficient. [ABSTRACT FROM AUTHOR]

Towards establishing low-carbon bio-economy, the energy-rich syngas is considered a global energy carrier. Targeting hydrogen as a promising advanced fuel, the significance of methane also increases due to its direct conversion capability into hydrogen. The current study aims to use a co-pyrolysis-based valorization of de-oiled microalgae and low-rank coal blend to generate H 2 and CH 4 rich syngas. Pyrolysis kinetic models, Kissinger-Akahira-Sunose (KAS) and Starink (STK) are used to evaluate apparent activation energy (E a). The gradual addition of microalgae (0⎼100%) in coal reduces E a from 189.11⎼55.87 kJ/mol and 180.16⎼54.61 kJ/mol by KAS and STK method, respectively. The maximum hymethane carrying ratio is observed 2.51 and 3.51 at optimized conditions of response surface methodology (RSM) and artificial neural network-based multi-objective genetic algorithm (ANN-MOGA), respectively. Maximum H 2 (54.5 %) in the syngas is observed at mid pyrolysis stage (451 °C) using ANN-MOGA optimized conditions (blending ratio - 42.25 % and heating rate-13.8 °C/min). This study highlights the advantage of ANN-MOGA optimization over statistical based optimization. Hence, incorporating of the evolutionary algorithm as integrated ANN-MOGA optimization could be an efficient way for hymethane rich syngas emission in co-pyrolysis approach to gain carbon-neutral energy with zero waste discharge. [Display omitted] [ABSTRACT FROM AUTHOR]

There is an increasing interest in investigating the effects of input uncertainties on dynamic systems. The probabilistic analyses for a vibrator-ground (VG) interaction system are rare and the effects of system uncertainties need to be revealed. This study aims to present an approach for the probabilistic performance-based analysis of the VG system under multi-source uncertainties. The probabilistic model of the VG system is constructed on the basis of the Monte Carlo (MC) simulation combined with the Latin Hypercube Sampling (LHS) method, while the artificial neural networks optimized by the genetic algorithms are employed to reduce the large computational expenses in the MC simulation. Then, a multi-criteria sensitivity analysis is presented by using a technique for order preference by similarity to ideal solution (TOPSIS) to evaluate the effects of input uncertainties on the dynamic performance of the vibrator. Finally, a probabilistic simulation analysis of the VG system is conducted by implementing the presented approach. The results demonstrate the effectiveness of the presented probabilistic performance-based analysis approach for the VG system and evaluate the effects of input uncertainties on the dynamic performance of the system. [ABSTRACT FROM AUTHOR]

Solid oxide fuel cell system is widely acknowledged as the leading alternative energy generation system in the field. Due to their high efficiency, low emissions, low noise, and various other advantages, solid oxide fuel cell systems are being considered for use in automobiles as a replacement for traditional internal combustion engines. However, prolonged operation and abnormal shutdowns can lead to performance degradation, which affects the efficiency, stability, and lifespan of stack. In the context of prolonged operation, considering the fluctuations in stack performance parameters and balance of plant, several regression models based on voltage parameters are established to accurately predict changes in stack performance. The results reveal that the genetic algorithm optimized backpropagation neural network model is highly sensitive in predicting the system performance degradation. Further analysis reveals that abnormal shutdowns can cause system performance fluctuations. As a result, the number and duration of shutdowns are incorporated into genetic algorithm optimized backpropagation neural network model for analysis. This model exhibits the best performance degradation evolution prediction compared to the long short-term memory and particle swarm optimization backpropagation neural network methods. This finding is important for monitoring and controlling the operational status of stack. [Display omitted] • This paper develops multiple models to predict performance degradation. • The models consider prolonged operation and abnormal shutdowns. • The proposed method reduces the prediction error by 68.47%. [ABSTRACT FROM AUTHOR]

There is a coupling effect between the liquid and the solid in the hydraulic engine mount (HEM). The accurate estimation and optimization of the dynamic characteristics including dynamic stiffness and lag angle for HEM in the frequency domain are still intractable problems. To this end, a novel model of a deep neural network (DNN)-based on dynamic modeling method is developed by using dataset to estimate the dynamic characteristics, and coupled with a genetic algorithm (GA) for the optimization design. Here, the dataset can be divided into two parts. One part is the input of DNN model and contains feature parameters from simulation; other part is the output of DNN model and composed of dynamic stiffness and lag angle from experiment. They are applied to train, test and validate the DNN model. Besides, the conventional model based on the lumped parameter is also presented to achieve the dynamic characteristics and used for comparison. The performed experiments demonstrate that the estimation accuracy of the DNN model is higher than that of the lumped parameter model. Finally, an optimal design method for the lag angle corresponding to frequency is proposed by combining the DNN model and the GA under the prescribed cost function and constraint conditions. The optimization results are approximately close to the desired values and verify the effectiveness of the proposed method which can improve the isolation performance of HEM. • A novel model of a deep neural network (DNN)-based on dynamic modeling method is developed by using dataset to estimate the dynamic characteristics for hydraulic engine mount (HEM). • A unique data driven-based approach by integrating genetic algorithms into DNN for optimizing HEM is presented. • The integration algorithm allows for fast and effective optimization of dynamic characteristics, providing a practical solution for real-world industrial applications. [ABSTRACT FROM AUTHOR]

The study aims to optimize the geothermal and solar-assisted sustainable energy and hydrogen production system by considering the genetic algorithm. The study will be useful by integrating hydrogen as an energy storage unit to bring sustainability to smart grid systems. Using the Artificial Neural Network (ANN) based Genetic Algorithm (GA) optimization technique in the study will ensure that the system is constantly studied in the most suitable under different climatic and operating conditions, including unit product cost and the plant's power output. The water temperature of the Afyon Geothermal Power Plant varies between 70 and 130 °C, and its mass flow rate varies between 70 and 150 kg/s. In addition, the solar radiation varies between 300 and 1000 W/m2 for different periods. The net power generated from the region's geothermal and solar energy-supported system is calculated as 2900 kW. If all of this produced power is used for hydrogen production in the electrolysis unit, 0.0185 kg/s hydrogen can be produced. The results indicated that the overall energy and exergy efficiencies of the integrated system are 4.97% and 16.0%, respectively. The cost of electricity generated in the combined geothermal and solar power plant is 0.027 $/kWh if the electricity is directly supplied to the grid and used. The optimized cost of hydrogen produced using the electricity produced in geothermal and solar power plants in the electrolysis unit is calculated as 1.576 $/kg H 2. The optimized unit cost of electricity produced due to hydrogen in the fuel cell is calculated as 0.091 $/kWh. • An integrated system consists of geothermal and solar energy, an electrolyzer, and a fuel cell unit. • Thermoeconomic analysis and the ANN-based GA optimization are evaluated. • The overall system energy and exergy efficiencies are calculated at 4.97% and 16.0%. • The optimum cost of produced hydrogen and electricity are calculated as 1.576 $/kg H 2 and 0.091 $/kWh, respectively. [ABSTRACT FROM AUTHOR]

Gravity assist is crucial for tour exploration of Jupiter's Galilean moons. To achieve high accuracy and low cost gravity-assisted transfer trajectories, the circular, restricted, three-body problem (CR3BP) model is adopted. Since there is no analytical solution for CR3BP, the orbit propagation must be calculated by numerical integration, which requires considerable time for the design of complex Jupiter exploration missions. Artificial neural networks (ANNs) that can fully approximate complex nonlinear relationships are used to model accurate and computationally efficient maps between the orbit states before and after gravity assist. Thereafter, a new framework based on the ANN model is developed for the fast design of Jovian-moon gravity-assisted transfers, by which optimal trajectories are obtained using a genetic algorithm (GA). Finally, the accuracy and performance of the proposed method are verified by two representative test cases of the Jupiter-Europa and Jupiter-Ganymede systems. The simulation results show that using the trained ANN model can significantly improve the convergence speed of the genetic algorithm, and high-precision, optimal solutions can be obtained rapidly. [ABSTRACT FROM AUTHOR]

Compared with single-stage hydrogen storage refuelling, cascade storage refuelling has more advantages and significantly reduces cooling energy consumption. In the cascade system, the parameters of cascade storage tanks are critical, especially the initial pressure and volume. This article analyzes the thermodynamic processes in a cascade hydrogen refuelling station (HRS) and establishes the simulation model in Matlab/Simulink platform. The state of charge (SOC) of the onboard storage tank and the cooling energy consumption of the refuelling system are obtained from different initial pressures and volumes of the cascade storage tanks by using the simulation model. These data are introduced into the artificial neural networks in Matlab to generate a relationship between the decision variables and objective functions. The decision variables are optimized to minimize the cooling energy consumption and maximize the SOC through the genetic algorithm and Pareto optimization. So that optimal initial pressure and volume of the cascade storage tanks are determined. The research shows that when the ambient temperature is 293.15 K, and the SOC is 0.98–0.99, using the optimal initial pressure and volume of the cascade storage tanks can reduce the cooling energy consumption by up to 11.43%, compared with the baseline situation. Among the factors affecting cooling energy consumption and SOC, initial pressure is more sensitive than volume, so optimizing initial pressure, especially for the high-pressure cascade storage tank, seems more meaningful than volume. This research is instructive for the construction of the cascade HRS. • A thermodynamic model for cascade hydrogen refuelling is developed and well validated. • Effect of initial pressure and cascade tank volume on SOC and cooling energy is studied. • Artificial neural network (ANN) is trained by orthogonal test data from physical model. • The ANN model is used to determine objective functions and constraints of optimization. • Genetic algorithm and Pareto algorithm are combined for multi-objective optimization. [ABSTRACT FROM AUTHOR]

Compared with single-stage hydrogen storage refuelling, cascade storage refuelling has more advantages and significantly reduces cooling energy consumption. In the cascade system, the parameters of cascade storage tanks are critical, especially the initial pressure and volume. This article analyzes the thermodynamic processes in a cascade hydrogen refuelling station (HRS) and establishes the simulation model in Matlab/Simulink platform. The state of charge (SOC) of the onboard storage tank and the cooling energy consumption of the refuelling system are obtained from different initial pressures and volumes of the cascade storage tanks by using the simulation model. These data are introduced into the artificial neural networks in Matlab to generate a relationship between the decision variables and objective functions. The decision variables are optimized to minimize the cooling energy consumption and maximize the SOC through the genetic algorithm and Pareto optimization. So that optimal initial pressure and volume of the cascade storage tanks are determined. The research shows that when the ambient temperature is 293.15 K, and the SOC is 0.98–0.99, using the optimal initial pressure and volume of the cascade storage tanks can reduce the cooling energy consumption by up to 11.43%, compared with the baseline situation. Among the factors affecting cooling energy consumption and SOC, initial pressure is more sensitive than volume, so optimizing initial pressure, especially for the high-pressure cascade storage tank, seems more meaningful than volume. This research is instructive for the construction of the cascade HRS. • A thermodynamic model for cascade hydrogen refuelling is developed and well validated. • Effect of initial pressure and cascade tank volume on SOC and cooling energy is studied. • Artificial neural network (ANN) is trained by orthogonal test data from physical model. • The ANN model is used to determine objective functions and constraints of optimization. • Genetic algorithm and Pareto algorithm are combined for multi-objective optimization. [ABSTRACT FROM AUTHOR]

• Study of two airfoils in tandem configuration placed into an incoming flow at low Reynolds numbers. • Machine learning with artificial neural networks was used to replace CFD solver in genetic algorithm optimization. • Upstream airfoil was pitching at a fixed frequency, while the downstream airfoil was kept at a fixed angle of attack. • Both high-amplitude and low-amplitude pitching of forefoil were considered. • Low-amplitude pitching was found to be more efficient for longer flight durations. Optimization was used to find the best configuration of two airfoils in tandem placed into an incoming flow. Upstream airfoil (forefoil) was pitching at a fixed frequency, while the downstream airfoil (hindfoil) was kept at a fixed angle of attack. Study was performed at a low Reynolds number of 30,000 based on chord length. Selig-Donovan 7003 (SD7003) was used for both airfoils, which is a high-performance airfoil specially designed for low Reynolds number flows. The optimization studies were conducted using a genetic algorithm (GA) to maximize aerodynamic performance. The design variables in this study were: horizontal and vertical spacing between the airfoils and hindfoil's angle of attack. Since the optimization process is time-consuming, machine learning was used to train four artificial neural networks (ANNs) to be coupled with genetic algorithm to reduce the computational cost. Two separate optimization cases were considered at two different orders of magnitude in pitching amplitudes of the forefoil, while the pitching frequency was kept at constant value. We found that in both cases, optimum tandem configurations had a smaller combined drag coefficient in comparison with the addition of two separate airfoils. The case with high pitching amplitude produced higher magnitude of lift, while the low amplitude case resulted in a significant improvement in aerodynamic performance. [ABSTRACT FROM AUTHOR]

The operation control plays a great role in the running performance of an industrial process. To achieve a successful control, the key point is searching for the target value of the control parameters. The relationship between operation parameters and performance parameters is usually nonlinear and complex, so it is hard to control an industrial process excellently with workers' experience. Based on a large amount of actual operation data, a bridge between control parameters and performance parameters might be built by the neural network. Prediction model is established by using neural network, genetic algorithm was then used to refine these parameters, the optimal condition can be further achieved by combining the relation bridge and genetic algorithm (GA). Paying attention to a cement clinker production process, this article established an optimizing approach based on the neural network and genetic algorithm and one-year operation data (615 sets). The feeding mass rate of raw meal and coal at the precalciner and the feeding coal mass rate at the rotary kiln are selected as the main independent variable and control parameters, and the specific standard coal consumption is determined as the key performance parameter and the optimization objective function. The mean square error and the correlation coefficient of the established neural network model are 12.84 and 0.89, respectively. The relative errors of approximately all prediction data (92.52 %) are within ±5 %. The optimal values for the raw meal feeding rate, the coal feeding rate into precalciner, and the coal feeding rate into rotary kiln are 259.57 t/h, 7.84 t/h, and 7.40 t/h, respectively. In that optimal condition, the specific standard coal consumption reaches 80.00 kg standard coal /t clinker (This is a relative value, as there is a slight drift in zero point of the factory's instruments). A highly accurate neural network model is developed, which can significantly reduce standard coal consumption and improve industrial energy efficiency. • Operating parameters of a cement plant were optimized by BP neural network and GA. • Feeding quantity of raw meal, coal at kiln inlet and outlet were the main objects. • The three optimal independent variables are 259.6, 7.8 and 7.4 t/h, respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • Machine learning model (ANN) was trained to estimate the performance of Fischer–Tropsch synthesis perfectly. • The effect of different parameters such as temperature, pressure, H 2 /CO, and space velocity on CO conversion and products selectivity was studied. • Genetic Algorithm optimization technique was applied to maximize the selectivity of liquid fuel by manipulating the operating parameters. This study aimed to increase liquid fuel (C 5+) product selectivity from CO and H 2 conversion over a Cu/Fe-based catalyst and followed by artificial neural network simulation to enable a profound visualization strategy for Fischer–Tropsch (FT) process. The experiments were performed in a fixed-bed reactor over temperatures (T) of 483–553 K, pressures (P) of 14.5–203 psig, space velocities (SV) of 1000–2900 hr−1, and H₂/CO feed ratios of 1–6.2. Mathematical modeling (MM) and artificial neural network (ANN) were utilized to simulate the nonlinear CO conversion and product selectivity of the FT process. Nine simultaneous reaction pathway was developed to evaluate the reactant and product behaviors under varying FT conditions. In addition, a multilayered parameter processed by a neural network was used to predict the performance of the CO hydrogenation process. A comparison between these two approaches demonstrated that ANN more closely mimicked the nonlinear conversion and selectivity of the FT process than MM, and the correlation coefficients for the predicted mole fractions and CO conversion were 0.991 and 0.986, respectively. A hybrid Genetic Algorithm–ANN procedure was constructed to predict the optimum operating conditions to increase liquid fuel over the catalyst. The optimum C 5+ selectivity (75.1 %), which meets the FT process objective, was achieved under operating conditions of T = 483 K, P = 203 psig, SV = 1303 hr−1, and H₂/CO feed ratio = 1.01 for process design optimization. [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]

The objective of this paper is to optimize the energy consumption and performance of fuel cell electric bicycles (FCEBs) under specific key input parameters. The paper applied an integrated method that includes an artificial neural network (ANN) and genetic algorithm (GA) to forecast and identify an optimal performance and energy consumption of FCEBs. The simulation model of FCEBs is established and simulated in MATLAB-Simulink environment to generate 1000 data points, that are used for training, validating, testing artificial neural network, the ANN architecture containing five input neurons, two hidden neurons and two output neurons, respectively. Furthermore, the GA is integrated to find the maximum performance and energy consumption once the ANN has exactly been trained. The study found that the FCEB configuration can achieve an effective performance at 30.3 km/h with required power of 210.4 W under speed level_5, radius of wheel 0.39 m, frontal area 0.423 m2, slope grade 0%. In order to validate and verify the simulated results, the experimental approach method was conducted in the same condition. The experimental results fit well with the simulated results in the same initial input parameters. • A method integrating the artificial neural network with genetic algorithm is applied. • The consumed power and fuel cell voltage are optimized by combining artificial neural network and genetic algorithm (ANN-GA). • The effective performance area of fuel cell electric bicycle under various a structure and operating parameters is identified. • Deep-learning results reflect major physical mechanism of fuel cell electric bicycle operation. [ABSTRACT FROM AUTHOR]

This study aimed at using α-L-arabinofuranosidase CcABF to improve the clarity and active substances in fermented ginkgo kernel juice by artificial neural network (ANN) modeling and genetic algorithm (GA) optimization. A credible three-layer feedforward ANN model was established to predict the optimal parameters for CcABF clarification. The experiments proved the highest transmittance of 89.40% for fermented ginkgo kernel juice with this understanding, which exhibited a 25.56% increase over the unclarified group. With the clarification of CcABF, the antioxidant capacity in juice was enhanced with the increase of total phenolic and flavone contents, and the maximum DPPH and hydroxyl radical scavenging rates were increased by 89.71% and 26.65%, respectively. The contents of toxic ginkgolic acids declined markedly, while the active ingredients of ginkgetin and ginkgolide B showed a modest increase. Moreover, changes in free amino acids and volatile compounds improved the nutritive value and flavor of clarified fermented ginkgo kernel juice. • The fermented ginkgo kernel juice was clarified by α-L-arabinofuranosidase CcABF. • The transmittance of ginkgo kernel juice reached 89.40% by ANN and GA optimization. • Beneficial substances of clarified ginkgo kernel juice increased to varying degrees. • The antioxidation function of clarified ginkgo kernel juice was enhanced obviously. • The nutritive values and flavor of the clarified ginkgo kernel juice were optimized. [ABSTRACT FROM AUTHOR]

Owing to the inherent characteristics of ground beef, adulteration presents a substantial risk for suppliers and consumers alike. This study developed a robust and novel method for identifying replacement fraud in ground beef with beef liver, beef heart, and pork using Near Infrared-Hyperspectral Imaging (NIR-HSI) coupled with chemometric and other statistical methods. More specifically, NIR-HSI provided an efficient and accurate means of identifying each type of adulteration using the classification model Genetic Algorithm (GA) - Backpropagation Artificial Neural Network (BPANN), showing perfect sensitivity and specificity (a value of 1.00) for the calibration and the validation sets for all types of adulteration. As an alternative to chemometric analysis, Hyperspectral Imaging-Root Mean Square (HSI-RMS) value, based on the RMS cut-off calculation, was determined to discriminate types of adulterations without the need of resource-intensive modelling. This HSI-RMS approach provides a simple-to-use method that avoids the complexity of HSI data processing and aims to directly understand the similarity between different spectra of one sample in the pixel level. Different types of adulteration show noticeable differences reflected in the HSI-RMS value (varying from 55 to 1439), which demonstrate the potential of HSI-RMS concept as a novel and valuable alternative for assessing the HSI data and facilitating the identification of adulterants. [Display omitted] • Endogenous and exogenous adulteration were detected simultaneously using NIR-HSI. • Sensitivity and specificity exhibit a value of 1.00 for validation set using raw average spectra from HSI data. • HSI-RMS was first developed for discovering the internal differences between pixels/spectra within the HSI image. • HSI-RMS value can be a valuable parameter for rapidly identifying meat adulteration without the need of modelling. [ABSTRACT FROM AUTHOR]

Indirect evaporative cooling has garnered considerable attention and research owing to its notable advantages, especially high cooling efficiency and low energy consumption. However, the complexity and diversity of parameters affecting indirect evaporative cooler (IEC) performance necessitate the development of a fast and efficient performance prediction model, which remains a critical research objective. To address the particular challenge, this paper introduces a novel approach that utilizes the principles of neural network theory to construct a predictive model based on backpropagation artificial neural networks (BP-ANN). The developed model is employed to investigate the impact of six main operating parameters of the water spray system on the IEC performance. Wet-bulb efficiency and pressure drop of secondary air channel are utilized as the performance evaluation indexes. The model is rigorously validated by relevant experimental data, enabling a comprehensive analysis of the influence of each parameter on the operational performance of the whole system. The significance of the influential parameters is evaluated through grey relational analysis. Furthermore, a multi-objective optimization approach employing a genetic algorithm (GA) is introduced to effectively determine the optimal operating parameters of the IEC system. The results demonstrate that the whole system can efficiently operate with low energy consumption when the wet-bulb efficiency and pressure drop weights are balanced. The optimal operating parameters include a nozzle pressure of 0.15 MPa, spacing of 85 mm, aperture of 6 mm, count of 11, spray density of 0.85 kg/(s ∙ m2) and air velocity of 2 m/s, at which time the wet-bulb efficiency was 0.85, the pressure drop was 210 Pa, and the coefficient of performance (COP) was 12.4, which demonstrated an efficiency increase of 23.2% in comparison with the base system. The findings of this research significantly contribute to a comprehensive understanding of the influence of water-spray operating conditions on IEC performance, and provide valuable insights for the design and operation of high-efficient IEC systems in practical applications. • The datasets were generated by a laboratory-scale prototype of the IEC system. • The ANN model for IEC considering spray operation was trained and validated. • Effects of nozzle and operation characteristics parameters were investigated. • The significance of the influential parameters was evaluated. • The optimal parameters for IEC were determined based on genetic algorithms. [ABSTRACT FROM AUTHOR]

This work presents a novel hybrid (physics- and data-driven) model for short-term (intra-day and day-ahead, 3h-24h) wind power forecasting (STWPF). Traditionally, STWPF predictors admitted very few meteorological variables only from the grid points closest to the turbines. Here, with the aim to further capture the underlying atmospheric processes ruling the wind variability in the wind farm, the approach relies on drastically expanding the predictor space, composed of numerous meteorological variables throughout a large geographical domain, retrieved from a weather forecasting model (COSMO-1). An ad-hoc genetic algorithm that optimizes the selection of predictors is designed and combined with feed-forward artificial neural networks for its cost function evaluation. The introduced model is compared to multiple benchmark models in a 16-turbine wind farm in the Swiss Jura mountains. For +12h and +24h lead times, the new approach shows a root-mean squared error normalized to the installed wind farm capacity of 11% and 11.6%, respectively. These values entail ∼ 16% higher forecasting skill compared to state-of-the-art predictor frameworks. Results highlight the ability of the presented approach to systematically select as predictors different variables with a well-known impact on the wind farm performance, such as the turbulent kinetic energy or the vertical wind shear. Clustering the data according to the wind direction provides substantial benefit. In addition, it provides a better understanding of the attained improvement: largest performances occur in those wind directions affected by highly complex terrain. This indicates that the proposed model can be especially suitable for wind farms in complex terrain. • A new physics- and data-driven model for 3h-24h wind power prediction is presented. • It relies on massively expanding the predictor space, set by weather model outputs. • It shows capable to select variables with well-known impact on the wind power signal. • It shows 16% higher forecasting skill compared to state-of-the-art predictor spaces. • The proposed model can be especially suitable for wind farms in complex terrain. [ABSTRACT FROM AUTHOR]

Microchannel heat sinks provide the solution to the ever-increasing heat flux generated from micro-electric components. In this study, performance optimization of a microchannel heat sink with delta winglet vortex generators was carried out based on the data obtained from numerical CFD simulations. A total of 192 design points were generated by altering the fluid velocity in terms of Reynolds number (Re), winglet width (W d), length (L d), and the angle of attachment (β) of the winglet. The Artificial Neural Network (ANN) model coupled with Non-dominated Sorting Genetic Algorithm NSGA-II was used simultaneously to minimize the friction factor and increase the Nusselt number. The ANN model predicted the output values within the error limit of 10%. The Pareto optimal front generated by the algorithm contains the input parameters in the range 982 < Re < 988, 177 μ m < L d < 233 μ m , 10 μ m < W d < 25 μ m , 57° < β < 64°. Decision-making methods TOPSIS, LINMAP, and Shannon entropy were employed to calculate the optimal solution from the data set and the obtained points showcased 70%, 120% and 158% surge in Nusselt number while an increase of friction factor by 35%, 109%, and 140% respectively is reported. The Performance Evaluation Criteria (PEC) values obtained from the best solutions were 1.52, 1.72, and 1.92, respectively. Furthermore, the accuracy of the optimal solutions was verified numerically. The flow and thermal field of the microchannel are also analyzed, and results showed that the angle of attachment and width of the winglet played a crucial role in the overall performance. [Display omitted] • Investigated the hydrothermal performance and optimization of the microchannel heat sink with delta winglets. • Analyzed 192 design points obtained by controlling four input variables. • Optimized solution points generated by the combination of ANN and NSGA-II. • Employed decision-making criteria to select the best solution in terms of overall performance. • PEC values of 1.52, 1.72, and 1.92 were achieved from the optimized best solution set. [ABSTRACT FROM AUTHOR]

Spherical pressure-resistant shells, as a universal structural component of deep-sea submersibles, provide a safe and normal operating environment for personnel and internal equipment. In the paper it presented and optimized the BP neural network model based on a genetic algorithm (GA) accordingly, and the method and accuracy are validated through by a beam model. Simultaneously focusing on steel spherical shells, the study proposed a dataset that captures the influence of the primary dimension of the shell (radius-to-thickness ratio, R / t) on the critical pressure response. The genetic algorithm is employed to optimize the back propagation (BP) neural network model for predicting critical pressure. The structural reliability is adopted as a design criterion to determinate and optimize the geometric parameters and critical pressure of the spherical shell structure. Finally, an ultra-high-strength steel spherical model is designed, constructed and meanwhile collapse pressure tests are accomplished to verify the accuracy of the presented improved BP neural network model based on the computational reliability method. The results reveal that the machine learning optimization design method proposed in this paper can effectively enhance the accuracy of critical pressure predictions and the precision of reliability assessments for deep-sea spherical shells. • An optimized BP neural network model based on a genetic algorithm accordingly is proposed, and its accuracy is validated by a beam model. • An improved BP neural network model based on the reliability method (CV-GA-BP-MCS) is presented and confirmed accordingly. • A deep-sea ultra-strength steel spherical model designed by CV-GA-BP-MCS has been designed, tested and verified respectively. [ABSTRACT FROM AUTHOR]

The high degree-of-freedom (DOF) optimized design of the liquid cooling structure embedded in the 3D-IC is critical to fully releasing the cooling potential for heat sinks. For the embedded hybrid heat sink with finned microchannels, this study sets constraints on the total volume of the heat sink and the volume ratio of the internal fins based on the constructal theory. A conditional generative adversarial network (cGAN) was used to predict the physical parameter distribution of the heat sink. A five-degree-of-freedom (5-DOF) evolutionary design of the fin cross-section shape to minimize the maximum temperature of heat sinks was performed by incorporating a genetic algorithm. The trained cGAN had desirable prediction accuracy and much lower computational cost for high-DOF design than the full CFD model. The fin cross section with a larger width and length, and the fins inwardly contract on four sides or produce ribs on four sides, could reduce the maximum and average temperatures of the heat sink. The 5-DOF optimal fin cross-section shape to minimize the maximum temperature of the heat sink is similar to a butterfly wing. During the constructal evolution that minimizes the maximum temperature, the overall thermal performance of the heat sink is also promoted significantly. • A cGAN was used to reconstruct the heat sink physical fields in the high-DOF constructal design. • Trained cGAN had much lower computational cost for high-DOF design than the full CFD model. • High-DOF constructal evolutionary pattern of fin cross-section shapes in heat sink was revealed. • The 5-DOF optimal fin cross-section shape with minimized heat sink T max is like a butterfly wing. • During the constructal evolution that minimizes the T max , the PEC of the heat sink is also promoted. [ABSTRACT FROM AUTHOR]

Fuel chemistry represents a typical complex system involving thousands of intermediate species and elementary reactions. Traditional mechanism reduction methods, such as sensitivity analysis and graph-based approaches, fail to explore global correlations of the sub-systems, thereby compromising their efficiency and accuracy. A novel machine learning-based approach called deep mechanism reduction (DeePMR) has been developed to address this issue. The current method transforms mechanism reduction into an optimization problem in the combinatorial space of chemical species while mitigating the curse of dimensionality inherent in the high-dimensional space. We propose an iterative sampling–training–predicting strategy combining deep neural networks with genetic algorithms to learn the landscape of the combinatorial space and locate the targeted subspace. Applying DeePMR to fuel chemistry mechanisms has led to much more compact mechanisms than traditional methods, including directed relation graph (DRG) or path flux analysis (PFA) methods, with three to four orders of magnitude acceleration in numerical simulation. In addition, reduced mechanisms by DeePMR indicate a principal-satellite formulation for constructing chemical reaction mechanisms, providing a straightforward yet effective alternative to hierarchy-based construction methods. The DeePMR method provides a general framework for model reduction across various fields. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach to address the challenge posed by large datasets in the context of antenna optimization through machine learning (ML). The approach involves a modified artificial neural network (ANN) technique. To tackle the issue of training with limited data, we introduce a method that leverages prior knowledge to create initial small datasets. Throughout the training process, these datasets are iteratively enhanced by incorporating new simulation data obtained from optimization results. In the applications presented here, ANN is used to establish a relationship model between antenna geometric variables and transmission coefficients (S 21). Subsequently, a genetic algorithm (GA) is used to optimize the decoupling of the antenna array. By implementing wideband and narrowband optimization of the S 21 between two antennas, the accuracy and efficiency of this method are verified. The initial design and optimized antennas were fabricated and measured. In summary, this proposed method not only can effectively mitigate the expenses associated with design but also generates antenna optimization solutions of high quality. [ABSTRACT FROM AUTHOR]

An artificial neural network (ANN) and genetic algorithm (GA) coupled with fermentation kinetics were employed to establish an innovative L-lysine fermentation control process. [Display omitted] • Artificial neural network (ANN) coupled genetic algorithm (GA) was established. • ANN-GA was utilized to establish fermentation control strategy. • ANN-GA coupled fermentation kinetics was employed to regulate lysine fermentation. • Optimal parameters were achieved using ANN-GA coupled fermentation kinetics. • The ANN-GA optimized model showed significantly enhanced lysine yield. Fermentation plays a pivotal role in the industrialization of bioproducts, yet there is a substantial lag in the fermentation process regulation. Here, an artificial neural network (ANN) and genetic algorithm (GA) coupled with fermentation kinetics were employed to establish an innovative lysine fermentation control. Firstly, the strategy of coupling GA with ANN was established. Secondly, specific lysine formation rate (q p), specific substrate consumption rate (q s), and specific cell growth rate (μ) were predicted and optimized by ANN-GA. The optimal ANN model adopts a three-layer feed-forward back-propagation structure (4:10:1). The optimal fermentation control parameters are obtained through GA. Finally, when the carbon to nitrogen ratio, residual sugar concentration, ammonia nitrogen concentration, and dissolved oxygen were [2.5, 4.5], [6.5, 9.5] g·L−1, [1.0, 2.0] g·L−1 and [20, 30] %, respectively, the lysine concentration reaches its peak at 213.0 ± 5.10 g·L−1. The novel control strategy holds significant potential for optimizing the fermentation of other bioproducts. [ABSTRACT FROM AUTHOR]

Biochar can be used to improve the anaerobic digestion (AD) of agricultural wastes for higher methane production. However, the interaction of biochar addition with other factors of the anaerobic co-digestion (Co-AD) process has rarely been investigated. In this study, process models based on response surface methodology (RSM) and artificial neural network (ANN) were compared in modeling the methane yield (MY, mL CH 4 /g VS added) from the Co-AD of poultry litter and wheat straw with biochar addition. Box-Behnken design was applied, with the controlling parameters being carbon to nitrogen ratio (C/N), total solids (TS, %), and biochar addition (Biochar, % TS). Numerical optimization and genetic algorithm (GA) were used as optimization tools for RSM and ANN, respectively. A significant second-order quadratic model was built by RSM (R2 = 0.9981 and RMSE = 0.91), which demonstrated significant interactions between C/N and TS (p < 0.0001), and between C/N and Biochar (p < 0.05). The trained ANN (3−3−1) was less accurate (R2 = 0.9926, RMSE = 1.80) compared to RSM. The optimization results by RSM and ANN coupled with GA (ANN-GA) were both validated with prediction errors <0.5%. The optimization results by ANN-GA should be used since it generated a higher maximum MY of 290.7 ± 0.2 mL CH 4 /g VS added , under the optimal conditions of C/N ratio 24.46, TS 5.03%, and Biochar 8.73% TS, showing an improvement of 20.6% (compared to the control) through process optimization. The methods can also be applied in other scenarios for process modeling and optimization. The optimized results could support real applications of using additives including biochar, active carbon, nanoparticles, etc., to promote the bioenergy production from AD of agricultural wastes. [Display omitted] • Poultry litter and wheat straw were co-digested for bioenergy (methane) production. • Biochar-improved anaerobic co-digestion was modeled based on a Box-Behnken design. • Response surface methodology and artificial neural network (ANN) were compared. • RSM indicated a significant interaction between biochar addition and substrate carbon-to‑nitrogen ratio. • Optimal methane yield was determined by ANN coupled with genetic algorithm. [ABSTRACT FROM AUTHOR]

Genetic algorithms (GA) and artificial neural networks (ANN) as intelligent algorithms have been widely applied in engineering fields such as structural design. The GFRP-concrete-steel composite column (GCS), known for its corrosion resistance, has garnered significant attention in its design. However, the application of GA and ANN methods in the context of GCS has primarily been confined to enhancing the predictive accuracy of ANN. This paper introduces a novel approach for the structural optimization of GCS using GA and ANN. A total of 1050 finite element models were generated and analyzed using ANSYS for the purpose of training ANN. The validity of these finite element models was confirmed through comparative analysis with experimental data. The trained ANN was then utilized to predict the loading capacity of GCS for each generation in the GA. By collecting pricing data for GFRP tubes, steel tubes, and concrete under various parameters, pricing fitting formulas for the three materials were derived. Subsequently, the cost of GCS under different parameter sets was calculated. The maximization of the ratio of GCS loading capacity to its cost (LP) was established as the optimization objective. Through the iterative optimization process of the GA-ANN algorithm, the structural optimization was achieved to maximize the economic efficiency of GCS. Comparing the optimization results with numerical simulation results showed that errors were mostly contained within 10 % or even as low as 5 %, thereby validating the accuracy of the GA-ANN algorithm. The applicability of the GA-ANN algorithm to GCS structural optimization was demonstrated by altering a single concrete variable. The method proposed in this paper can be effectively applied to the structural design and optimization of composite columns with a sandwich structure, such as GCS. [ABSTRACT FROM AUTHOR]

Optimal aiming strategies are essential for efficient solar power tower technology operation. However, the high calculation complexity makes it difficult for existing optimization methods to solve the optimization problem in real-time directly. This work proposes a real-time optimal heliostat aiming strategy generation method via deep learning. First, a two-stage learning scheme where the neural network models are trained by genetic algorithm (GA) benchmark solutions to produce an optimal aiming strategy is presented. Then, an end-to-end model without needing GA solutions for training is developed and discussed. Furthermore, a robust end-to-end training method using randomly sampled flux maps is also proposed. The proposed models demonstrated comparable performance as GA with two orders of magnitude less computation time through case studies. Among the proposed models, the end-to-end model shows significantly better generalization ability than the pure data-driven two-stage model on the test set. A robust end-to-end model with data enhancement has better robustness on unseen flux maps. [ABSTRACT FROM AUTHOR]

The main objectives of this study were to 1) review the literature on the applications of soft computing concepts to the field of human factors and ergonomics (HFE) between 2013 and 2022 and 2) highlight future developments and trends. Multiple soft computing methods and techniques have been investigated for their ability to address various applications in HFE effectively. These techniques include fuzzy logic, artificial neural networks, genetic algorithms, and their combinations. Applications of these methods in HFE have been highlighted in one hundred and four articles selected from 406 papers. The results of this study help address the challenges of complexity, vagueness, and imprecision in human factors and ergonomics research through the application of soft computing methodologies. • We reviewed the literature on the applications of soft computing concepts to the field of HFE. • Multiple soft computing methods have been investigated for their ability to address various applications in HFE effectively. • One hundred and four articles selected from 406 papers. • The results of this study can help address the challenges of complexity, vagueness, and imprecision in HFE. [ABSTRACT FROM AUTHOR]