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The safety and stability of metal hydride reactor (MHR) structure are significantly important for normal operations of hydrogen isotope storage and supply system. Moreover, considering that the experiments of studying the fatigue and creep properties of MHR structure are always time, money, difficulty and energy consuming. To further improve the service life of MHR as well as lower the failure risk, this paper proposes a system integration method based on ASME code validation, which combines genetic algorithm (GA) and back propagation neural network (BPNN) for rapid assessment of creep-fatigue life (CFL) and optimal design of structural parameters. First, numerical simulation is applied to perform thermal-mechanical coupled finite element analysis and verified by ASME code. Then, parametric sensitivity analysis and experimental design of parameter-based training set based on Taguchi method were performed, and response values are obtained from physical models combined with numerical simulations. Finally, a parametric optimization procedure combining BPNN and GA is developed based on the training data. In the workflow, parametric data flow is passed through the experimental design, numerical simulation, prediction and optimization processes. The results show that the proposed BPNN proxy model based on ASME code validation has good performance in predicting the life of MHR. Based on this, the life of the MHR is improved by 8% compared to the reference sample. • The correlation between creep-fatigue life (CFL) of metal hydride reactor (MHR) and design parameters is established. • Numerical models combining BPNN with ASME Code are available for assessment of CFL of MHR. • The sensitivity analysis is applied to measure the effect of design parameters on CFL of MHR. • Optimal design scheme of MHR is performed through an integrated design optimization method. [ABSTRACT FROM AUTHOR]

The mesh reflector suffers inevitably from complicated environmental interferences during long-term on-orbit operation and consequently the reflector may deviate significantly from its initial state. It is thus crucial to identify the deformed reflector on-orbit in order to evaluate timely the mesh reflector accuracy. However, the existing reflector identification methods are difficult to achieve this object as the most of them need to install additional equipment on antenna or satellite. Aiming at realizing on-orbit mesh reflector identification, a novel identification approach based on tie cable tensions sensing (TCTS) is proposed in the paper. The TCTS method leverages real-time measurements of tie cable tensions through embedded force sensors to calculate the remaining cable tensions required to address reflector deformations. For this purpose, an optimization problem is established based on tension balance equation and then solved using a proposed iteration solution algorithm incorporating the genetic algorithm. The actual configuration of mesh reflector is then determined by all cable tensions according to the force density method and the reflector can be identified ultimately. The simulated studies on reflector identification of a mesh reflector with 8 m aperture diameter are conducted under temperature loads and cable stiffness degradations respectively. The results indicate that the TCTS method possesses high reflector identification accuracy and good anti-noise capability under different environmental interferences. Furthermore, a reflector identification method based on partial tie cable tensions sensing (PTCTS) is developed in order to reducing system complexity and cost. The sensitivity analyses of tie cable tensions are performed in order to provide a reference for location placement of force sensors in the PTCTS method. The simulated results indicate that the optimized location placement scheme can enhance significantly the reflector identification accuracy of PTCTS method. Besides, the PTCTS method exhibits equal anti-noise capability with the TCTS method. Finally, the experimental studies are conducted on reflector identification of a mesh reflector test model with 1.5 m diameter and 0.5 m height under cable stiffness degradations. Through comparing with the reflector identification results obtained by the photogrammetry method under different cable stiffness degradation degrees, the accuracy and effectiveness of the TCTS/PTCTS methods are demonstrated experimentally. • The mesh reflector is identified based on tie cable tension sensing (TCTS). • Partial tie cable tensions sensing (PTCTS) can reduce system complexity. • The TCTS/PTCTS method provides a promising way to identify mesh reflector in orbit. • The TCTS/PTCTS approach is demonstrated numerically and experimentally. [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]

In the literature, the reliability analysis of one-shot devices is found under accelerated life testing in the presence of various stress factors. The application of one-shot devices can be extended to the bio-medical field, where we often evidence that inflicted with a certain disease, survival time would be under different stress factors like environmental stress, co-morbidity, the severity of disease etc. This work is concerned with a one-shot device data analysis and applies it to SEER gallbladder cancer data. The two-parameter logistic-exponential distribution is applied as a lifetime distribution. For robust parameter estimation, weighted minimum density power divergence estimators (WMDPDE) is obtained along with the conventional maximum likelihood estimators (MLE). The asymptotic behaviour of the WMDPDE and the robust test statistic based on the density power divergence measure are also studied. The performances of estimators are evaluated through extensive simulation experiments. Later those developments are applied to SEER gallbladder cancer data. Citing the importance of knowing exactly when to inspect the one-shot devices put to the test, a search for optimum inspection times is performed. This optimization is designed to minimize a defined cost function which strikes a trade-off between the precision of the estimation and experimental cost. The search is accomplished through the population-based heuristic optimization method genetic algorithm. • A robust inference based on weighted density power divergence. • Elaborated study of the asymptotic property. • Testing of hypothesis based on a robust statistic. • Search for optimum inspection times using genetic algorithm. • Application to SEER gallbladder cancer data. [ABSTRACT FROM AUTHOR]

Grain boundary (GB) segregation of Al in ZnO plays an important role in lowering its thermal conductivity for thermoelectric applications. However, the effect of Al concentration on the GB complexions and their transition is not well understood. Herein, a genetic algorithm assisted multiscale modelling framework was used to study the role of GB concentration on the GB segregation of Al on five special twin GBs of ZnO. A critical concentration of 5–6 atoms/nm2 was determined for the complexion transition from single layer to multilayer. Calculated segregation energies were used in a phenomenological model to link GB concentration with the nominal concentration of dopants. The model was used to calculate the nominal solubility of Al in ZnO as a function of grain size, which was validated with the experimental data from the literature. The proposed framework provides a path for establishing GB-structure – property correlation and thereby, predictive dopant engineering of ceramics. [ABSTRACT FROM AUTHOR]

Fischer Tropsch synthesis (FTS) is a promising catalytic solution to achieve ultra-clean fuel in which catalyst has a significant role. Even though several kinetic models have been rendered for it, representing a new one based on a meticulous mechanism is noteworthy, particularly when employing a novel catalyst. Hence, in this work, molecular dynamic (MD) simulation is utilized to identify the appropriate pathway from the viewpoint of minimum energy of the elementary reactions. In this regard, we consider a majority of plausible FTS elementary reactions over Ru–CO@C(Z-d)@void@CeO 2 (cobalt carbon-MOF based plus ruthenium and ceria with porous structure) catalyst, and by employing MD computations forward and reverse activation energy barrier values for each reaction are evaluated. Then, based on the minimum energy pathway (MEP), the appropriate mechanism is derived, which is inferred from the H-assisted CO dissociation pathway for the beginning step. Subsequently, a kinetic model is developed based on this mechanism, and the model parameters are correlated and optimized employing the genetic and Levenberg-Marquardt algorithms, respectively. In addition, we use various statistical methods to assess the significance of the model. For instance, the Mean Absolute Percentage Deviation (MAPD) of the correlated values from the experimental value is 4.46%. Additionally, the relative residual percentage (RR%) plots illustrate that 99% of the correlated data have less than a 10% deviation. [Display omitted] • Rendering a comprehensive kinetic model based on an evaluated mechanism for Fischer Tropsch Synthesis. • Presenting a mechanism by evaluating the minimum energy pathway using Molecular dynamic computations. • Evaluating the H-assisted CO dissociation pathway meticulously. • Assessing the significance of the model employing statistical methods. [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]

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]

We propose a new method, called a deep-genetic algorithm (deep-GA), to accelerate the performance of the so-called deep-BSDE method, which is a deep learning algorithm to solve high dimensional partial differential equations through their corresponding backward stochastic differential equations (BSDEs). Recognizing the sensitivity of the solver to the initial guess selection, we embed a genetic algorithm (GA) into the solver to optimize the selection. We aim to achieve faster convergence for the nonlinear PDEs on a broader interval than deep-BSDE. Our proposed method is applied to two nonlinear parabolic PDEs, i.e., the Black-Scholes (BS) equation with default risk and the Hamilton-Jacobi-Bellman (HJB) equation. We compare the results of our method with those of the deep-BSDE and show that our method provides comparable accuracy with significantly improved computational efficiency. • We introduce a deep-GA method. • The method provides a better accuracy and efficiency than the deep-BSDE solver. • Efficiency and accuracy is obtained by embedding a genetic algorithm (GA). • We solve the Black-Scholes with default risk and Hamilton-Jacobi-Bellman equations. [ABSTRACT FROM AUTHOR]

The optimization of the membrane electrode assembly (MEA) is a crucial aspect in improving the performance of high-temperature proton exchange membrane fuel cells (HT-PEMFCs). Through experimental validation, a three-dimensional HT-PEMFC computational fluid dynamics (CFD) model was established to simulate the transport of charge, mass, and momentum across various porous layers. A deep neural network (DNN) was designed to quantify the influence of porous layer structures on cells performance and determined the optimal solution of the MEA parameters for different Pt loadings. The training data of DNN were generated using 11 porous layer structural parameters selected via a Monte Carlo method, and their corresponding current densities predicted by the validated HT-PEMFC CFD model. The DNN model showed an average relative error of only 0.76% in predicting current density when compared to CFD simulations. Using a bi-objective genetic algorithm (GA) in conjunction with the DNN model, the optimized cell power densities at a voltage of 0.4 V and Pt loadings of 0.3 mg/cm2 and 0.5 mg/cm2 are 18% and 9% higher than the maximum values reported in the literature, respectively. The proposed approach combining DNN model and GA can serve as a useful guide for the design of HT-PEMFC in practical applications. • HT-PEMFC CFD model is validated by published experimental data. • Data-driven DNN model achieves 0.76% mean relative error for predictions. • DNN model couples with genetic algorithm to optimize porous layer structures. • Cell performance can improve 18% and 9% at Pt loadings of 0.3 and 0.5 mg/cm2. [ABSTRACT FROM AUTHOR]

The optimisation of green hydrogen production systems is challenging. Moreover, an accurate simulation of the system is required for effective optimisation. This study presents a novel method for optimising utility-scale hybrid photovoltaic–wind systems for hydrogen production using accurate simulation models. The optimisation objective is to minimise the levelised cost of hydrogen (LCOH) using genetic algorithms. Different types of systems (such as islanded systems, grid-connected systems with or without the possibility of purchasing electricity from the grid, and grid-connected systems considering power curtailment), are evaluated and optimised. Each combination of components and control strategy is simulated during the system lifetime (20 yrs) in time steps of 5 min, considering the degradation of renewable generators during the system lifetime and different real-time pricing curves and renewable resource curves for each year of the system lifetime. Accurate models are used in the simulations, including electrolyser efficiency dependent on the input power and cold-start extra ageing. An application example located in Zaragoza (Spain) is shown, obtaining LCOH from 4.74 to 16.06 €/kg, depending on the type of project and electrolyser. • Utility-scale PV-wind-hydrogen optimisation of components and control. • Minimisation of the levelised cost of hydrogen (LCOH). • Four different types of systems, isolated or grid-connected. • Advanced models including electrolyser variable efficiency. • Each year different electricity price and irradiation; components degradation. [ABSTRACT FROM AUTHOR]

The flow field dominates the process of mass transport and distribution inside the proton exchange membrane water electrolysis cell (PEMEC). Initially, a three-dimensional and non-isothermal PEMEC channel model was estimated to investigate the impact of channel height and width. Based on these results, a complete PEMEC model with a parallel flow-field pattern was developed, and the channel heights and widths of the model were optimized using a genetic algorithm (GA). Compared to the original model, the optimized model decreased the pressure drop by 34.97% at a lower voltage. In addition, because of the optimized flow-field pattern, the optimized model improves the current density on the proton exchange membrane as well as the gas holdup situation in a partial position in the cathode gas diffusion layer. • PEMEC channel height influences the gas fraction, voltage, and pressure drop. • The channel width has a stronger influence on voltage. • GA was used to optimize PEMEC channel structure with two objectives. • The optimized model was characterized by a decrease in pressure drop of 34.97%. [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]

Microinjection, which is widely employed in modern biology and medicine research, is a crucial method for transferring biomaterial in a single cell. Despite substantial research into microinjection automation technology, how to achieve high precision, which is crucial for quantitative analysis, has gotten scant attention. This paper offers a dual-loop control syringe-based picoliter-scale injection system, with the inner being a pneumatic servo loop with a baroreceptor providing pressure feedback and the outside being a position servo loop with a microscope providing visual feedback. Specifically, significant crawling phenomenon are found throughout the characteristic test trial, rendering the gas-liquid interface uncontrollable. To mathematically characterize this phenomenon, a theoretical model is developed that accounts for static friction, capillary force, capillary cone angle, and fluid viscous force. The genetic algorithm is used to identify difficult-to-measure and calibrate parameters. Based on the established model, an integral sliding-mode controller (ISMC) with saturation function and a PID controller for the inside loop control were established, both of which have a fast convergence rate and do not overshoot. Finally, experimentation is used to validate the designed cell injection system, demonstrating that the suggested system can regulate the smallest droplet in the tip of the needle to within 0.25 pL. • A syringe-based picoliter-scale injection system with injecting accuracy of 0.25 pL is developed. • Severe crawling phenomenon is observed, and a mathematical model is established to describe this phenomenon. • An integral sliding-mode control with saturation function is designed, demonstrating fast convergence rate and non-overshoot. [ABSTRACT FROM AUTHOR]

This study introduces a data-driven surrogating modeling approach for determining thermal parameters in undetermined thermal mathematical models (TMM) used in spacecraft systems. Maintaining appropriate temperature levels for onboard components is crucial for the success and safety of space missions. However, accurately determining thermal parameters in the undetermined TMM presents a challenge. To address this, we propose an approach that combines TMM correlation with test data and data-driven surrogate modeling. Our approach minimizes an objective function that measures the deviation between predicted temperatures from a surrogate model, incorporating thermal parameters, and steady state temperatures obtained from thermal vacuum balance test results. This optimization is performed using a genetic algorithm while imposing constraints to ensure the physical validity of the thermal parameters and correlation criteria related to temperatures. We demonstrate the effectiveness and validity of our approach by applying the obtained thermal parameters to the TMM. The resulting calculated temperatures exhibit excellent agreement with the test results. This methodology offers a user-friendly and reliable way to determine thermal parameters in undetermined TMM within spacecraft systems, ultimately enhancing temperature control and mission success. • Efficient thermal parameter determination in spacecraft TMMs through data-driven surrogate models. • TMM correlations achieved by minimizing temperature deviation with surrogate models and test data. • Reliable TMM correlations achieved by considering realistic constraints, overcoming thermal parameter non-uniqueness. • Reasonable selection of thermal parameters, enhancing reliability and reducing user knowledge requirements. [ABSTRACT FROM AUTHOR]

Arginine deiminase (ADI) has been widely employed for the biological production of nutraceutical citrulline. However, sub-optimal enzyme properties, operational instability, and difficulty in reuse impede its industrial usage as well as increase the production cost. The present study describes a simple, cost-effective carrier-free immobilization technology that can circumvent these limitations. ADI was precipitated and cross-linked with glutaraldehyde to obtain novel cross-linked arginine deiminase aggregates (ADI-CLEA). A neural network-based multiobjective genetic algorithm (MOGA-NN) was employed to optimize the immobilization of ADI. MOGA-NN optimization yielded ADI-CLEA with 0.30 IU/mL activity and 61 % recovery. Structural characterization using TEM, SEM, FTIR, DLS/ZETA revealed that the developed ADI-CLEA were monodisperse, ultra-porous, and highly stable. ADI-CLEA showed an improved pH /temperature profile and resistivity against denaturants than free enzyme. The efficiency of ADI-CLEA in the industrial production of nutraceutical citrulline was also studied. ADI-CLEA produced 215.4 g/L citrulline in first cycle of usage. Reusability and storage stability analysis further showed that ADI-CLEA could be reused for more than seven consecutive cycles and stable for up to 50 days. In conclusion, this novel formulation increases the stability/robustness of ADI, eases the product separation process and could be reused for economically viable bioproduction of nutraceutical citrulline in industries. [Display omitted] • Novel cross-linked enzyme aggregates of arginine deiminase (ADI-CLEA) were reported. • Immobilization conditions were optimized by network-based multiobjective genetic algorithm. • ADI-CLEA exhibited enhanced catalytic efficiency, stability and robustness. • Improved citrulline yield (215 g/L) was obtained by ADI-CLEA. • ADI-CLEA could be recycled for seven cycles without losing its activity. [ABSTRACT FROM AUTHOR]

To handle the impact of solar radiation fluctuations on the solar reactor, a new proportional-integral (PI) hybrid control strategy based on back propagation neural network (BPNN) is designed in this study to achieve stable operation. Firstly, the BPNN model optimized by genetic algorithm is trained using reactor model simulation data. Then, the BPNN model is combined with PI feedback control to obtain a BPNN-based PI feedforward-feedback hybrid control strategy for regulating the inlet flow rate. Finally, the effectiveness of the control strategy during transient operation under different disturbances is tested. The results show that during actual solar radiation disturbance, the integral square errors (ISE) of outlet hydrogen fraction and methane conversion for BPNN-based PI hybrid control are 7.03 × 10−6 and 1.05 × 10−4, respectively. Compared to traditional PI feedback control, the new hybrid control strategy represents a reduction of 94% and 91% in ISE, indicating that the strategy can significantly improve production stability. • BPNN based PI hybrid control strategy is designed for solar methane reformer. • Different control strategies are examined with various solar fluctuation conditions. • Stabilities of outlet hydrogen fraction and methane conversion are clearly improved. • The proposed BPNN based PI hybrid control strategy reduces ISE by 90%. • Dynamic tracking performance of proposed control strategy is revealed. [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]

Cryogenic supercritical hydrogen storage technology is a high-density physical hydrogen storage method, which is a highly promising hydrogen storage approach. However, existing research has mainly focused on the development of storage devices for cryogenic supercritical hydrogen, with limited research on the cooling process of supercritical hydrogen. Therefore, a novel cooling process for cryogenic supercritical hydrogen production based on helium expansion cycle with liquid nitrogen (LN 2) pre-cooling is proposed in this paper. In this process, after being compressed and cooled in multiple stages, the feed hydrogen is pre-cooled by LN 2. Then, cryogenic supercritical hydrogen (10 MPa, −235.15 °C, 63.36 g/L) is obtained by helium multi-stage expansion refrigeration. The process is simulated by Aspen HYSYS and optimized by genetic algorithm (GA) to obtain lower specific energy consumption (SEC). Thermodynamic analyses are conducted to evaluate the process. And compared with the conventional hydrogen liquefaction process, the results show that the proposed process is reasonable and superior. The SEC 0 , figure of merit (FOM), and the total exergy loss of the system are 5.432 kWh/kg H2 , 43.88%, and 289.69 kW respectively. This study indicates that the proposed process has significant implications for the hydrogen industry and provides valuable information for cryogenic supercritical hydrogen storage systems. • A novel high-density cryogenic supercritical hydrogen storage system is proposed. • Twelve key variables in the process are optimized to reduce the energy consumption. • The process has higher exergy efficiency than conventional LH 2 plants. • The volume storage density of hydrogen can reach 63.36 kg/m3 in the system. [ABSTRACT FROM AUTHOR]

Sustainable management of water distribution networks (WDNs) is essential to ensure the continuous supply of water. However, the water pipes in WDNs often experience unprecedented failure, which causes disruption in services, flooding, increased maintenance costs, and reduced water quality. Although researchers have developed models to predict the failure of water pipes, the literature lacks fully optimized and robust models. Therefore, this study proposes a new methodology to develop optimized models for predicting the failure probability of water pipes by fusing logistic regression with genetic algorithms. The methodology was applied to the data of the Hong Kong WDN, and experiments were conducted to optimize the hyperparameters and features of logistic regression models. The performance of the proposed methodology is evaluated using five key metrics: accuracy, precision, recall, F1 score, and Area Under the Curve (AUC). The results show significant improvement over conventional approaches, with the best model achieving an F1 score of 0.868 and an AUC of 0.944. These results show that the model can effectively predict the failure probability of water pipes. The relative contribution of each feature to the model's outcome was investigated using the SHapley Additive exPlanations. Additionally, a web application based on the proposed methodology in this study was developed for Hong Kong that other water utility management can benefit from, which can facilitate reliable decision-making for the management of WDNs. [ABSTRACT FROM AUTHOR]

Bundle AI is a marketing tool project motivated to provide solutions for manual product bundling, a popular marketing strategy that boosts sales of low-selling products and encourages mass purchases with promising customer satisfaction. It is a cost-effective solution as it does not depend on large volumes of transactional data, which is suitable for small businesses. The bundling of products was defined as a Multiple Constraints Knapsack Problem where the best solution is determined using Genetic Algorithm. Bundle AI delivers the fit product bundle with the incorporation of the value and reward system. [ABSTRACT FROM AUTHOR]

Due to the rapid development of confidential digital communication over international networks, security is the most significant factor. Steganography is the science of concealed communication and is used to conceal the presence of secret information among the various methods of hiding data in communications. Steganography techniques are usually implemented either in spatial or frequency domains. Some spatial domain methods fundamentally utilize the absolute difference value between neighbor pixels to encode the secret message, categorized as Pixel-Value Differencing Steganography methods (PVDS). In PVDS, the hiding capacity of the adjacent pixels depends on their difference value. Consequently, the secret data will be embedded with a high visual quality of the stego-image. Indicator-based PVDS (IPVDS) technique is derived from the PVDS method. It depends on an Indicator pixel (IP) to extract the secret data correctly. The Genetic Algorithm (GA) approach to optimization depends on the concept of survival of the fittest. This paper introduces two proposals, GA-based steganography schemes (GA-IPVD) and (GA-IPVDM). These new techniques encoded secret data using the IPVDS method. Before embedding data in the cover image, the order of the pixels corresponding to the secret data is modified and rearranged. GA controls the operation of rearranging and modifying the order of pixels and all parameters. The current paper addresses steganography as an optimization problem and seeks to determine the optimal order of the pixels that enhance the matching between the cover image and stego-image to reduce distortion despite high embedded capacity. In the first proposed technique GA-IPVD, GA concentrates on the imperceptibility of embedded data and maximizes the quality of the stego-image according to the given secret data. In contrast, the GA maximizes the capacity of embedded data while maintaining an acceptable amount of stego-image quality in the second proposed technique GA-IPVDM. The proposed systems have embedding key that increase the security of proposed schemes. The experimental results demonstrate that the proposed methods can embed a large quantity of secret data while maintaining an acceptable visual quality of the stego-images. The proposed technique has also demonstrated excellent resistance to a variety of stego-attacks, including the pixel difference histogram (PDH) as well as regular and singular (RS) analyses. Additionally, the out-of-boundary pixel issue (BI), which endures in the majority of modern data hiding algorithms, has been successfully resolved. [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]

• A Cascade 2-stage condensation ORC is integrated with LNG vaporizing at 6 bar. • Simultaneous optimization in 3 levels is implemented using Genetic algorithm. • Novel variable definition method accelerates accurate result achievements. • Structure, working fluids, and variables optimized considering vital constraints. • A structure is proposed based on the exergy analysis result. One of the most efficient methods of exergy recovery during the regasification process is integrating the LNG stream with an Organic Rankine Cycle (ORC) to generate electricity. This study employs a cascade structure of ORC consisting of two double-stage condensations operating at 6 bar regasification pressure. To obtain an optimal ORC system integrated with an LNG, three optimization levels for a specific heat source are addressed, including process variables, working fluids, and structure. Ten process variables and two working fluid variables for the top and bottom cycle among 13 candidates of refrigerants were optimized to provide the maximum power output. The results determined that refrigerants of R41 and R1150 have the best performance in the optimized process conditions for the top and bottom cycles, respectively. For the regasification of 36 tonnes per hour of an LNG stream, the produced net power and the exergy efficiency are achieved by 2116.76 kW and 0.29, respectively. The exergy analysis revealed that employing the double-stage condenser ORC is not a good choice. Accordingly, a cascade-parallel structure is proposed for further studies. [ABSTRACT FROM AUTHOR]

Recovering ammonia from anaerobic digestion (AD) effluent offers the opportunity to produce a renewable fertilizer and reduce environmental impact. A novel process developed for such an application was evaluated during this study. The feature of the process is integrating biogas stripping and gypsum (CaSO 4 ·2H 2 O) absorption. The result revealed that the lower CO 2 content and the absorption temperature contributed the higher efficiency of ammonia recovery. The gypsum solution recovered around 95% of the stripped ammonia when the stripping gas contained 10% CO 2 and it rose to 100% when the gas contained more than 30% CO 2. Furthermore, a statistical model and genetic algorithm were applied to estimate the total ammonia recovery and optimize the operating parameters. The experimental and modeling results indicated that integrating biogas stripping with gypsum absorption can effectively harvest ammonia nitrogen and reduce CO 2 emissions while producing (NH 4) 2 SO 4 fertilizer from AD effluent. [Display omitted] [ABSTRACT FROM AUTHOR]

A two-step optimization algorithm is proposed to redesign a precision reference repeating sun-synchronous orbit for interferometric synthetic aperture radar satellites with injection error. This purpose is to design a new reference orbit for the satellite that deviates from the original preset orbit. Manoeuvring satellites to this new orbit consumes less propellant than manoeuvring to the original orbit. In addition, the repeatability of the orbit is a guarantee for obtaining good interference images, which puts higher requirements on the revisit accuracy of the redesign reference orbit. In the first step, the initial guess for reference orbital elements is designed by considering the global coverage of the Earth and the propellant consumption, taking into account the J 2 and J 4 perturbations. A search table of feasible solutions in the neighbourhood of the injection orbit is established. This approach, which is based on a search table, transforms the optimization of continuous variables in infinite space into the traversal of discrete variables in finite space, thus greatly reducing the optimization time. In the second step, the revisit accuracy of the satellite's full-dimensional state vector is optimized under the orbit propagation of a high-order gravity field. Unlike traditional methods that only optimize the revisit error of position, this paper also optimizes that of velocity. A new optimization method for full-dimensional revisit error of state vector is proposed, which combines nested differential correction with non-dominated sorting genetic algorithm-II. Compared with the traditional genetic algorithms, the proposed nested differential correction method provides the non-dominated sorting genetic algorithm-II with more accurate initial values, which reduces the iterations. Finally, the simulation results show that the total velocity change from the actual injection orbit to the redesigned reference orbit is halved compared to the original preset orbit. Moreover, the revisit errors of position and velocity are within 1 m and 1 m / s , respectively. [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]

In this study, response surface methodology (RSM) and artificial neural network-based genetic algorithm (ANN-GA) were utilized to predict two crucial output parameters of membrane performance, namely separation efficiency and oil flux, derived from experimental investigations. The central composite design (CCD) screening approach of RSM was employed to evaluate the influence of important process input parameters, such as oil concentration (ranging from 50 to 10,000 ppm), feed flow rate (ranging from 150 to 300 mL/min), and pH of the feed (ranging from 4 to 10), as well as their synergistic effects on the output variables. The constructed RSM model and ANN-GA were effectively employed to estimate the optimum conditions for maximizing the output variables. Statistical analysis using the determination coefficient (R2) and standard error of prediction (SEP), along with analysis of variance (ANOVA) and the t-test, demonstrated the accurate description of the membrane performance process by both models. For the oil flux, the RSM model showed an estimated R2 of 0.9916 and SEP of 3.54%, while the ANN model exhibited an R2 of 0.9933 and SEP of 3.31%. In terms of separation efficiency, the RSM model yielded R2 = 0.9929 and SEP = 1.31%, whereas the ANN model achieved R2 = 0.9961 and SEP = 0.99%. Remarkably, the ANN-GA approach revealed the best optimum conditions for both responses. Furthermore, the sensitivity analysis of the developed ANN model indicated the order of significance of the variables as follows: oil concentration > feed pH > feed flow rate. These findings substantiate the efficacy of the proposed approach, making it viable for implementation in diverse industries to facilitate sustainable monitoring and management practices. • Ceramic membranes were manufactured using inversion and sintering methods experimentally. • Genetic-based neuro-computing was proposed for modelling oil flux and oil-water separation. • Classical RSM technique was employed based on the feature selection approach. • The best optimum condition for both responses was obtained with ANN-GA. • Reliable results were obtained using ANN-GA with 99% accuracy. [ABSTRACT FROM AUTHOR]

[Display omitted] • An end-to-end inverse framework achieves fast identification of 3D microstructures from tailored strength, stiffness, and fatigue property. • A deep learning and genetic algorithm framework that is capable of both forward and inverse predictions. • We demonstrated the performance of this framework in the context of Ti-6Al-4V in both forward/inverse directions and achieved tailored mechanical properties with large variance. • The framework is highly transferrable to other materials and scientific fields. Materials-by-design has been historically challenging due to complex process-microstructure-property relations. Conventional analytical or simulation-based approaches suffer from low accuracy or long computational time and poor transferability, limiting their applications in solving the inverse material design problem. Here, we establish a deep learning and genetic algorithm framework that integrates forward prediction and inverse exploration. This framework provides an end-to-end solution to achieve application-specific mechanical properties by microstructure optimization. In this study, we select the widely used Ti-6Al-4V alloy to demonstrate the effectiveness and efficiency of this framework. We realize this by tailoring its 3D microstructures to achieve various yield strengths and elastic moduli across a large design space, while minimizing stress concentration factors. Compared with conventional methods, our framework is efficient, versatile, and readily transferrable to a wide range of materials and properties. Paired with emerging additive manufacturing techniques in controlling local microstructural features, our method offers far-reaching potentials for the accelerated development of application-specific, high-performing materials. [ABSTRACT FROM AUTHOR]

Considering the benefits of geothermal energy in low-temperature combined processes, this study intends to present, examine, and optimize a novel thermal integration process incorporated into a two-stage geothermal flash cycle. In order to meet this objective, a modified bi-evaporator refrigeration technology using an ejector has been designed, with the capability of generating coolant for air-conditioning and industrial requirements. Furthermore, the entire system utilizes a low-temperature water electrolysis process for hydrogen production. In addition to designing a new process, this study incorporates advanced methodologies to comprehensively analyze the proposed system's thermodynamic, economic, and exergoenvironmental aspects. In this context, an extensive parametric study is undertaken, followed by the definition of two distinct multi-objective optimization scenarios: the energy-environment and the energy-cost scenarios. Based on the outcomes derived from the first scenario, the corresponding objective functions, namely energy efficiency and exergoenvironmental index, are determined to be 52.89% and 0.5, respectively. This condition is also responsible for generating net output electricity, cooling load, and hydrogen production rate of 3.897 MW, 26.88 MW, and 4.02 kg/h, respectively. Moreover, it exhibits an exergy destruction rate of 5493 kW. In the second scenario, the objective functions, i.e., energetic efficiency and payback period, are calculated to be 39.37% and 3.94 years, respectively. The aforementioned products demonstrate capacities of 3.697 MW, 17.66 MW, and 5.0 kg/h, respectively. Besides, the exergy destruction rate is determined to be 4667 kW. [ABSTRACT FROM AUTHOR]

The 0–1 Knapsack problem is a non-deterministic polynomial-time-hard combinatorial optimization problem that cannot be solved in reasonable time using traditional methods. Therefore, researchers have turned to metaheuristic algorithms for their ability to solve several combinatorial problems in a reasonable amount of time. This paper adapts the Kepler optimization algorithm using eight V-shaped and S-shaped transfer functions to create a binary variant called BKOA for solving the 0–1 Knapsack problem. Several experiments were conducted to compare the efficacy of the binary Kepler optimization algorithm to several competing optimizers when solving 20 well-known knapsack instances with dimensions ranging from 4 to 75. The experimental results demonstrate the superiority of this algorithm over other metaheuristic algorithms, except for the genetic algorithm, which is marginally superior. To further improve the binary Kepler optimization algorithm, it is combined with an enhanced improvement strategy to create a new hybrid variant. This hybrid variant, termed HBKOA, has superior exploration and exploitation capabilities that make it better than genetic algorithm and other optimizers for all performance metrics considered. The enhanced improvement strategy is also integrated with several competing optimizers, and the experimental results show that HBKOA, hybrid binary manta ray foraging optimization, and hybrid binary equilibrium optimizer are competitive for small and medium-dimensional instances and superior for the higher dimensions. [ABSTRACT FROM AUTHOR]

After the completion of the launch mission, the soft landing recovery and reuse of the carrier rocket's first-stage can effectively control the landing area to ensure safety and significantly reduce the cost of space launch transportation. The trajectory optimization of the whole process from the first section to the landing site is significant to fuel saving. In the entire process, the first-stage needs to go through three startup ignitions of course adjustment, power deceleration, and vertical landing. For this segmented fuel optimization problem, optimizing the thrust size and direction during three sections and the switching time between each section is necessary. Optimizing the vertical landing section is essential to ensure the accuracy of landing position and speed, which greatly complicates the whole process's optimization. Therefore, the optimization process of the vertical landing is substituted by a neural network predictor, which is used to obtain the mapping relationship between the initial state of the vertical landing section and the fuel consumption of the segment. Besides, the thrust profiles of the first two startup sections are reasonably handled based on physical cognition. The combination of convex optimization and neural network successfully converted the multi-stage optimal control problem into a parameter optimization problem and solved it by a genetic algorithm. Optimization results were compared with the conventional method, which indicated its superiority. [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]

The development of deep space exploration has led to new exploration demands that cannot be met by traditional planetary rovers. This paper proposes a new wheeled rover that combines the advantages of passive all-wheel attachment and vibration reduction at high speeds. This rover can achieve vibration reduction in the vertical direction from ground excitation, preventing the instability of pitch and roll. Flexible wheels were used to reduce the high-frequency excitation of the ground on the rover. The design concept and mechanical principle of the proposed rover were comprehensively analyzed. A quasi-static kinematic model was developed to analyze the overstepping performance of the rover with this new design. Further, the response characteristics of the system to the ground excitation were investigated by vibration equations established using the Lagrangian method. The optimal parameters were obtained using a genetic algorithm to optimize the system with multiple objectives. Furthermore, the overstepping performance, all-wheel attachment, and vibration reduction of the planetary rover were verified through multi-body simulation. The proposed rover has considerable potential for missions such as the Mars sample return and planetary station construction. • A planetary rover with adaptive suspension for high-speed driving is proposed. • The theoretical analysis of the climbing ability of a planetary rover is provided. • A vibration modeling and evaluation method for proposed rover is established. • A variety of objective functions are proposed and optimized accordingly. [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]

The ejector, as one of the core components of the CO 2 trans-critical ejection refrigeration system, plays an important role in improving refrigeration capacity and reducing compressor power consumption. Although the ejector can be optimized by experiment and Computational Fluid Dynamics (CFD) simulation to improve its performance, these methods are time-consuming and complicated. This study aims to propose a method to improve the performance of the CO 2 trans-critical two-phase ejector assisted by using CFD, artificial neural network (ANN), and genetic algorithm (GA). Firstly, the influence of geometric parameters on ejector efficiency was analyzed by using CFD technology, and the database was generated. Next, the complex and time-consuming CFD model was replaced by the ANN surrogate model established to predict the ejector performance. Finally, the GA method was used to optimize the ejector to maximize the ejector efficiency. The results show that the efficiency of the optimized ejector is 35.39%. The optimized ejector increases the secondary flow velocity and eliminates the vortex in ejector, and the efficiency of the optimized ejector is raised by more than 8% on average than that of initial ejector under different primary and secondary flow conditions. The research shows that the combination of CFD, ANN, and GA has higher reliability and better performance in the optimization design of CO 2 two-phase ejector. [ABSTRACT FROM AUTHOR]

The corrosion-fatigue degradation is a significant concern causing subsea pipeline failure. This paper presents an inspection planning methodology for subsea pipelines subject to corrosion-fatigue degradation using dynamic Bayesian network (DBN) and improved Adaptive Genetic Algorithm (IAGA). The classical decision tree is used to represent the inspection process of pipeline. The corrosion-fatigue degradation is simulated using DBN to estimate the failure probability of subsea pipeline. IAGA is used to automatically present the inspection schemes and determine the optimal solution due to its strong optimization ability. A case study is utilized to illustrate the feasibility of the methodology. The results show that the cost is the lowest when the pipeline is inspected three times (11, 19 and 25 years) in its service, which is 58.0235 million dollars. The methodology can be used to guide the daily inspection planning of subsea pipeline subject to corrosion-fatigue degradation. [ABSTRACT FROM AUTHOR]

An Inertial Navigation System (INS) is a standalone system that provides continuous navigation information on the position, velocity, and attitude in various conditions. Gyroscopes and accelerometers are essential components of INS. However, these sensors are prone to errors that accumulate over time and degrade the accuracy of the INS. Therefore, enhancing the performance of INS is a challenging problem. This paper aims to address this problem by proposing a method to reduce errors in real-time processing. The method uses Lifting Wavelet Transform (LWT), a popular denoising technique that belongs to the wavelet family and has an advantage over the classic wavelets in terms of time and computation complexity. Moreover, the method employs an optimization technique based on the Genetic algorithm as an intelligent averaging for combining different multi-level LWTs, which significantly improves the accuracy and efficiency of INSs. Finally, a new and implementable structure is introduced for denoising inertial sensors. The results show that the proposed method achieves an 83% and 59% improvement in gyroscope and accelerometer data for dynamic data, compared with raw data. Besides, for static data, there are 71% and 36% improvements in gyroscope and accelerometer data, respectively. [ABSTRACT FROM AUTHOR]

This study presents a new geothermal-based system for power generation and liquid hydrogen production. Depending on the network requirements, part of the generated power is supplied to the network and the remaining part enters the PEM electrolyzer to produce hydrogen. The produced hydrogen is then pre-cooled by the ejector cooling cycle before entering the liquefaction cycle. The ejector refrigeration cycle not only uses geothermal waste heat but also facilitates the liquefaction process through precooling hydrogen; therefore, it significantly improves the overall performance of the integrated system. In addition, the modified liquefaction cycle is suggested, which is superior to common ones. After simulating, some key parameters are identified and a comprehensive parametric research is carried out to investigate the thermodynamic, economic and environmental performance in different conditions. The optimization procedure is implemented through a genetic algorithm approach and the objective functions are defined. The results reveal that the double flash power cycle has the greatest effect on exergy destruction rate and total cost rate. In the optimal case, the total cost rate, the liquefied hydrogen rate, the total exergy efficiency, net power generation, and C O 2 reduction rate are determined as 181.71 $/h, 59.92 kg/h, 25.27%, 4.03 MW, and 1421.2 kg/h, respectively. Also, coupling an artificial neural network with genetic algorithm significantly lessens optimization time. [Display omitted] • A novel geothermal-based system for hydrogen liquefaction is proposed. • A comprehensive energy, exergy, economic and environmental (4E) analysis is applied. • Performing a machine learning optimization method to reduce the calculation time. • The double flash cycle has the highest exergy destruction rate among the subsystems. [ABSTRACT FROM AUTHOR]

The "IADC space debris mitigation guidelines" requires that the post-mission orbit lifetime of low Earth orbiting (LEO) satellites should not be longer than 25 years, which is the key to debris mitigation. To analyze the degree of global compliance to this 25-year guideline, a fast and relatively accurate orbit life estimation algorithm is required. The traditional method uses numerical orbit propagation to accomplish the task, although its accuracy is high when a high-precision force model is used, the calculation is time-consuming. This paper proposes a new method for orbit lifetime estimation without relying on orbit propagation. A back propagation (BP) neural network is used to directly fit the relationship from the satellite parameters to the orbit lifetime. The space objects that have reentered the Earth's atmosphere are selected from the catalog database of the US Space Surveillance Network (SSN), and are used to generate samples for network training. The genetic algorithm is combined with the traditional back propagation algorithm for the training of the neural network. The results show that for the specific application background of identifying whether a satellite complies with the 25-year guideline, the orbit lifetime estimation accuracy of the BP network meets the demand to some extent. Moreover, because it does not involve orbit propagation, the BP network has a high computational efficiency in estimating orbit lifetime, showing a good application prospect. [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]

Demand Side Management (DSM) plays a significant role in the smart grid to minimize Electricity Cost (EC). Home Energy Management Systems (HEMSs) have recently been studied and proposed explicitly for HEM. In this paper, we propose a novel nature-inspired hybrid Genetic Flower Pollination Algorithm (GFPA) to minimize cost with an affordable delay in appliance scheduling. Our proposed GFPA algorithm combines elements of the Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) to create a hybrid approach. To assess the effectiveness of the proposed algorithm, we consider a scalable town consisting of 1, 10, 30, and 50 homes, respectively. The proposed solution finds an optimal scheduling pattern that simultaneously minimizes EC and Peak to Average Ratio (PAR) while maximizing User Comfort (UC). We assume that all homes are homogeneous in terms of appliances and power consumption patterns. Simulation results show that our proposed scheme GFPA performs better when applying Critical Peak Pricing (CPP) signal using different Operational Time Intervals (OTIs) and compared with unscheduled, GA, and FPA-based solutions in terms of reducing cost since they achieve on average 98%, 36%, 23%, and 22%, respectively. Similarly, PAR averages 98%, 36%, 59%, and 55%, respectively. While, UC comparing to GA and FPA, are around 88%, 48%, and 63%, respectively. Our proposed scheme achieves better results by applying Real Time Pricing (RTP) signals and different OTIs. As these schemes, i.e., unscheduled, GA, FPA, and GFPA, achieve cost on average 92%, 50%, 29%, and 28%, respectively. While PAR on average 94%, 39%, 62%, and 56%, and UC for GA, FPA, and GFPA on average 98%, 52%, and 49%, respectively. Overall, our proposed GFPA algorithm offers a more effective solution for minimizing EC with an affordable delay in appliance scheduling while considering PAR and UC. [ABSTRACT FROM AUTHOR]

Partial differential equations (PDEs) are essential mathematical models for describing a wide range of physical phenomena. Numerically, Physic-Informed Neural Networks (PINNs), a variant of artificial neural networks, present a promising method for solving PDEs. However, due to limitation in accuracy and stability, various adaptive PINN variants have been proposed. We have designed a novel approach that adopted self-adaptive PINN (SA-PINN) with two optimization techniques: the genetic algorithm (GA) and the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm. Self-adaptive PINN modifies the weights in the loss function to be fully trainable, enabling the ANN to learn and stabilize the PINN in approximating the difficult regions of the solution. GA initializes the population of ANN trainable parameters to optimize the training process with less number of iterations, while L-BFGS is used to find the best solution accurately. Our proposed approach, named SA-PINN-GA-LBFGS, is tested on solving several benchmark PDE problems including elliptic, parabolic, and hyperbolic types. We compare our results with state-of-the-art methods, demonstrating that SA-PINN-GA-LBFGS provides higher accuracy and greater efficiency. [ABSTRACT FROM AUTHOR]

In the present study, dynamic modeling, optimal operation and control of a reactive batch distillation process is illustrated based on experimental and simulation studies using methyl acetate production case study. An equilibrium stage model, incorporating non-ideal VLE and start-up region of heating, is developed based on data generated on an experimental unit by identifying five input parameters representing uncertainties. The optimal control problem is solved using genetic algorithm, considering the objective function identified on the basis of trend analysis using the developed dynamic model, to find the optimal reflux ratio, heat input to the reboiler, and mole ratio of methanol to acetic acid in the initial reaction mixture. Open-loop and closed-loop implementations clearly illustrate the improved performance with respect to the quantity of methyl acetate in the distillate product with a reasonably high conversion and product purity within reasonably short batch duration, illustrating the successful optimal control implementation experimentally. [Display omitted] • Dynamic model for methyl acetate production in RBD unit is developed from cold start. • 5 uncertainty parameters are identified based on open-loop and closed-loop operation. • Objective function is chosen by trend analysis with respect to independent variables. • Optimal input variables and set point profiles are determined by genetic algorithm. • Optimal policies are implemented through open-loop and closed-loop experimentation. [ABSTRACT FROM AUTHOR]

The growing demand for industrial water has motivated techniques for water reuse within the ambit of production scheduling. In batch processes, these seek to minimize freshwater usage together with an economic objective, such as makespan. It may, however, be possible to increase reuse potential by compromising on the makespan objective and intentionally delaying the start of stages so as to enable better time synchronization. This work seeks to develop a scheduling methodology for batch processes that reduce freshwater requirements by intentionally waiting between stages. A genetic algorithm-based formulation is proposed to develop such a production schedule where delays up to a maximum allowed waiting duration are introduced if it enables water reuse. The proposed scheduling strategy is demonstrated using a textile industry case study. Our results show that, on average, a 20% decrease in freshwater usage can be obtained for a single-batch campaign while for a multi-batch campaign, the savings in freshwater is nearly 59% for a 19% increase in makespan. • A strategy for minimizing freshwater in multistage, multiproduct batch processes is proposed. • Intentional waiting increases potential opportunities for discharge water from a stage to be re-used. • Production schedules with intentional waiting are generated using a genetic algorithm. • The genetic algorithm has the twin objectives to minimize freshwater use and makespan. • The strategy is demonstrated using an industrial case study from the textile industry. [ABSTRACT FROM AUTHOR]