Optimal System Research Articles

Mathematical modeling and strategy for optimal control of diphtheria

This research introduces a novel approach to combating diphtheria by presenting a comprehensive optimal control strategy focused on awareness campaigns to avoid direct contact with infected individuals and promote vaccinations. These campaigns highlight the severe complications of diphtheria, such as acute respiratory issues, myocarditis, and neurological paralysis. Additionally, the campaigns emphasize the negative impacts of an unbalanced lifestyle and environmental factors, as well as the importance of timely treatment and psychological support. The model aims to improve control strategies by reducing the number of infected individuals I(t) and exposed individuals E(t), as well as asymptomatic carriers A(t), which we have integrated into the model as an aspect that has been relatively unexplored in diphtheria transmission. The optimal controls are meticulously determined using Pontryagin’s maximum principle. The resulting optimality system is solved iteratively, ensuring precision and clarity in the results. Extensive numerical simulations rigorously support and confirm the theoretical analysis using MATLAB, providing concrete evidence of the strategy’s effectiveness. The broader implications and potential applications of this optimal control strategy extend to other infectious diseases, enhancing its relevance and utility in public health.

Development of Polyamide-Modified Membranes for Solar-Driven Seawater Desalination System

In response to the escalating global water crisis, this study introduces the development of polyamide-modified membranes (PA-PES, PA-PP, and PA-PTFE) through interfacial polymerization to enhance the efficiency of a passive solar desalination system. FTIR analysis and morphological characterization showed that a thin polyamide film formed above the modified membranes using m-phenylene diamine (MPD) and trimesoyl chloride (TMC). Notable improvements were observed in its productivity and distillate salinity by integrating these modified membranes into the membrane distiller of the system. Mainly, the PA-PES membrane achieved productivity of 764.56 ml/m2-h and reduced salinity to as low as 2 g NaCl/L. Despite challenges in salinity reduction, possibly due to residual chlorides, this study demonstrates the potential of polyamide-modified membranes in advancing solar-driven desalination, offering a promising solution to mitigate global water scarcity. This research paves the way for further advancements in sustainable desalination technology, emphasizing the need for continued optimization and exploration of membrane-based systems.

Extrinsic parameters optimization for fringe projection system based on standard components

Fringe projection profilometry (FPP) is a wildly used three-dimension reconstruction method, where the reconstruction accuracy is closely related to the calibration precision. However, various sources of error in the calibration process can result in inaccurate calibration results. In particular, the calibration results of extrinsic parameters are more likely to deviate from the true values, which in turn leads to low-frequency errors in the reconstruction results. In order to obtain the extrinsic parameters closer to the true values, a simple but effective method is proposed in this paper. The proposed method utilizes the standard plane and the dumbbell bat, which are commonly used in measurements, to optimize the extrinsic parameters. First, the standard plane and dumbbell bat are reconstructed using the initial calibration results to transfer the errors in the extrinsic parameters to the plane fitting residuals, as well as to the fitting residuals for the radius and the center distance of dumbbell bat. The residuals and errors are then minimized using nonlinear optimization, resulting in the more accurate extrinsic parameters. Experimental results demonstrate that the proposed method can effectively compensate the calibration error of the extrinsic parameters. The root mean square (RMS) error of planar reconstruction is reduced from 0.0291 mm to 0.0106 mm, which is reduced by about 63.6 %. The RMS error of spherical reconstruction is reduced from 0.0358 mm to 0.0166 mm, which is about 53.6 %. The measurement accuracy of the FPP system is further improved.

Pharmacological and toxicological characteristics of baicalin in preventing spontaneous abortion and recurrent pregnancy loss: A multi-level critical review

RelevanceSpontaneous abortion (SAB) and recurrent pregnancy loss (RPL) occur alone or concurrently with increasing incidences recently. Scutellaria baicalensis Georgi (SBG) has been used to prevent pregnancy loss for thousands of years, which is recognized as a “pregnancy-stabilizing herb” in ancient China. Baicalin (BA) and its metabolite baicalein (BE) are the main bioactive flavonoids in the root of SBG. MethodsIn this study, we focused particularly on the metabolism, toxicology, and pharmacological effects of BA at the maternal-fetal interface based on the biological process prediction by network pharmacology. Focused on the systematic review of BA's regulatory mechanisms of immune homeostasis, cell proliferation and invasion, programmed cell death, inflammatory microenvironment, angiogenesis, oxidative stress and vascular remodeling at the maternal-fetal interface, it was found that BA exerts its biological effects to treat SAB and RPL through multiple perspectives and targets. We also critically elucidated the limitations of using BA from a clinical perspective. ResultsWe explored the bioavailability, targeting and efficacy of BA from a new perspective (optimization of the BA delivery system, organoid studies based on BA, potential effects of BA on uterine flora and bioactive components). Finally, we propose a multimodal stereo sequencing study of biologically active components based on pathological dynamics incorporating single-cell RNA sequencing, spatially resolved transcriptomics, and single-cell multimodal omics to delve deeper into the fetal-preserving mechanism of BA and to promote the application of BA in clinical practice.

Deep learning for solving initial path optimization of mean-field systems with memory

We consider the problem of finding the optimal initial investment strategy for a system modelled by a linear McKean–Vlasov (mean-field) stochastic differential equation with delay, driven by Brownian motion and a pure jump Poisson random measure. The goal is to determine the optimal initial values for the system in the period [ − δ , 0 ] , where δ > 0 is a delay constant, before the system starts at t = 0. Due to the delay in the dynamics, the system will, after startup, be influenced by these initial investment values. It is known that linear stochastic delay differential equations are equivalent to stochastic Volterra integral equations. By utilizing this equivalence, we can find implicit expressions for the optimal investment. Moreover, we propose a deep neural network-based algorithm to solve the stochastic control problem with delay. Specifically, we employ a multi-layer feed-forward neural network for control modelling in the interval [ − δ , 0 ] , and use back-propagation to train the feed-forward neural network. The gradient of the loss function is computed using stochastic gradient descent (SGD) with respect to the weights of the network.

Analysis of various system designs for adsorption cycle in building heating applications

Buildings are major energy consumers, highlighting the need for efficient thermal energy storage to reduce reliance on non-renewable sources. Adsorption-based systems offer significant energy savings for heating applications. This research compares four critical adsorption-based cycles: adsorption thermal energy storage, adsorption heat transformer, adsorption mass recovery with heating and cooling, and a novel combined cycle. The study uncovers new insights into temperature and pressure control challenges through lumped parameter modeling. These dynamic modeling results advance our understanding and optimization of adsorption systems, enhancing their feasibility and efficiency for building heating. The adsorption mass recovery cycle demonstrates superior energy storage density (289 kW h/m³) compared to the adsorption heat transformer cycle (240 kW h/m³) and the adsorption thermal energy storage cycle (157 kW h/m³). It also has the highest water uptake (0.56 kg/kg), surpassing the other two cycles. Despite its pressure fluctuations and temperature instability challenges, the adsorption mass recovery cycle outperforms the adsorption heat transformer and adsorption thermal energy storage cycles in energy storage density. The novel combined cycle optimizes heat storage and release, achieves the highest energy storage density (302 kW h/m³), and outperforms the individual cycles. The combined cycle's performance was evaluated in a home in South Korea, revealing significant energy savings and proving an effective solution for enhancing energy efficiency in building heating systems. This research addresses a crucial gap and provides a foundation for future advancements in thermal energy storage.

Multi-sensor fusion for biomechanical analysis: evaluation of dynamic interactions between self-contained breathing apparatus and firefighter using computational methods

Understanding the complex three-dimensional (3D) dynamic interactions between self-contained breathing apparatus (SCBA) and the human torso is critical to assessing potential impacts on firefighter health and informing equipment design. This study employed a multi-inertial sensor fusion technology to quantify these interactions. Six volunteer firefighters performed walking and running experiments on a treadmill while wearing the SCBA. Calculations of interaction forces and moments from the multi-inertial sensor technology were validated against a 3D motion capture system. The predicted interaction forces and moments showed good agreement with the measured data, especially for the forces (normal and lateral) and moments (x- and z-direction components) with relative root mean square errors (RMSEs) below 9.4%, 7.7%, 7.7%, and 7.8%, respectively. Peak pack force reached up to 150 N, significantly exceeding the SCBA’s intrinsic weight during SCBA carriage. The proposed multi-inertial sensor fusion technique can effectively evaluate the 3D dynamic interactions and provide a scientific basis for health monitoring and ergonomic optimization of SCBA systems for firefighters.

Evaluating the techno-economic potential of large-scale green hydrogen production via solar, wind, and hybrid energy systems utilizing PEM and alkaline electrolyzers

The study evaluates the potential of solar, wind, and hybrid PV/WT renewable energy systems for green hydrogen production in four Iraqi cities. Through a comparative analysis of six distinct scenarios involving the deployment of 60 MWp solar panels, 30 MWp wind turbines, and 45 MWp hybrid PV/WT systems, the research aims to ascertain the most energy-efficient and cost-effective strategy for hydrogen generation. This evaluation is aligned with the operational capacities of two types of water electrolyzers: Alkaline (AWE) and proton exchange membrane (PEM), each with a 17.5 MWp capacity. Employing the HOMER Pro software for system simulation and optimization, and considering a project timeline from 2022 to 2042, the study identifies Anbar City as the prime location for green hydrogen production, highlighting solar PV panels as the most economical option with the lowest levelized cost of energy at US $4.5/MWh. The analysis further demonstrates that hydrogen production costs are US $1.98/kg for AWE electrolyzers and US $2.72/kg for PEM electrolyzers, with net present costs of US $26.31 million and US $35.91 million, respectively. Moreover, the annual hydrogen output is estimated at 1.11 million kg for AWE and 1.19 million kg for PEM electrolyzers. These insights significantly contribute to the strategic planning and development of Iraqi green hydrogen sector, offering a valuable framework for policymakers and stakeholders invested in sustainable energy transitions.

Review of public transportation integration and modeling strategies: Toward seamless urban mobility

To solve the issues of expanding urbanization, traffic congestion, and environmental sustainability, public transportation system integration has become a crucial area of concern for urban planners and legislators. This paper discusses the concept of integrating different modes of transportation and the necessity of using a reliable modeling strategy to enhance and optimize urban mobility networks generally. The implementation and efficiency of integrated public transportation systems in several cities are examined in this research paper. The study focuses on a number of notable case studies of various cities that have made substantial efforts to integrate multiple modes of transportation to enhance urban mobility. This study provides a thorough overview of the advantages, difficulties, and potential solutions associated with integrated public transportation as it exists today and as it is currently being modeled. The research also examines several modeling strategies for integrated transport system design and optimization. This study seeks to provide significant insights into the current status of integrated public transportation, identify research gaps, and propose viable pathways for future research and development by synthesizing literature and case studies. Information for the analysis was gathered from the Web of Science, Scopus, Science direct, MDPI Publisher database spanning the years 2000 to 2024. The article collected a total of 624 bibliometric records of publications. Both quantitative and qualitative analyses were conducted as part of the study. This paper conducts a literature review focusing on the latest research concerning the Integration of public transportation-related subjects. It employs scientific databases, a specified list of keywords, and established inclusion criteria to gather data, assess content, and conduct a bibliometric analysis.

High-uniformity liquid-cooling network designing approach for energy storage systems by graph-coupled genetic algorithm

Electrochemical battery energy storage stations have been widely used in power grid systems and other fields. Controlling the temperature of numerous batteries in the energy storage station to be uniform and appropriate is crucial for their safe and efficient operation. Thus, effective thermal management is required. In this work, an approach for rapid and efficient design of the liquid cooling system for the stations was proposed. A hydraulic solution model for the liquid-cooling network was established based on graph theory principles, and the genetic algorithm was employed for automatic system optimization to achieve high cost-effectiveness and ideal temperature distribution. Experiments were conducted to validate the accuracy of the hydraulic model and evaluate the efficacy of the optimization strategy. The flow rate calculated from the hydraulic model agreed well with the experiments, with a discrepancy smaller than 3.5 %, and the uniformity of flow rates was improved to as high as 70 % after the optimization. Furthermore, the applicability of this approach was also confirmed across various sizes of energy storage systems.

Energy system optimization based on fuzzy decision support system and unstructured data

To address the complex challenges in energy systems, this study proposes a novel optimization framework that integrates fuzzy decision support and unstructured data processing technologies. This framework aims to improve efficiency, reduce costs, decrease environmental impact, increase system flexibility, and enhance user satisfaction, thereby promoting sustainable development in the energy industry. The framework combines the innovative Energy Semantic Mapping Model (ESMM) and the advanced deep learning architecture ResNet to process textual and visual data effectively. ESMM enables accurate prediction of energy demand, while ResNet significantly reduces equipment maintenance costs and improves energy distribution efficiency. These advancements are critical as they address the limitations of existing approaches in handling large-scale unstructured data and making informed decisions under uncertainty. The Environmental Impact Assessment (EIA) confirms the model's effectiveness in reducing carbon emissions. A comprehensive economic analysis demonstrates substantial cost savings in energy procurement and operations and maintenance, with overall savings exceeding 25%. Enhanced user satisfaction and reduced system response times further validate the practical utility of the proposed approach. Additionally, a genetic algorithm is used to optimize the fuzzy rule base, enhancing the robustness and adaptability of the model. Experimental results show superior performance compared to traditional systems, providing strong empirical evidence for the intelligent transformation of energy systems. This research contributes to the field by offering a more sophisticated and flexible solution for managing energy systems, particularly in terms of leveraging unstructured data and improving decision-making processes.

Optimization of an autaptic culture system for studying cholinergic synapses in sympathetic ganglia.

An autaptic synapse (or 'autapse') is a functional connection between a neuron and itself, commonly used in studying the molecular mechanisms underlying synaptic transmission and plasticity in central neurons. Most previous studies on autonomic synaptic functions have relied on spontaneous connections among neurons in mass cultures. However, growing evidence supports the utility of microcultures cultivating autaptic neurons for examining cholinergic transmission within sympathetic ganglia. Despite these advancements, standardized protocols for culturing autaptic sympathetic neurons have yet to be established. Drawing on historical literature, this study delineates optimal experimental conditions to efficiently and reliably produce cholinergic synapses in sympathetic neurons within a short time frame. Our research emphasizes five key factors: (i) the generation of uniformly sized microislands of growth permissive substrates; (ii) the addition of nerve growth factor, ciliary neurotrophic factor (CNTF), and serum to the culture medium; (iii) independence from specific serum and neuronal medium types; (iv) the reciprocal roles of CNTF and glial cells; and (v) the promotion of cholinergic synaptogenesis in SCG neurons through indirect glia co-cultures, rather than direct glial feeder layer cultures. In conclusion, glia-free monocultures of SCG neurons are relatively simple to prepare and yield robust and reliable synaptic currents. This makes them an effective model system for straightforwardly addressing fundamental questions about neurogenic mechanisms involved in cholinergic synaptic transmission in autonomic ganglia. Furthermore, autaptic culture experiments could eventually be implemented to investigate the roles of functional neuron-satellite glia units in regulating cholinergic functions under physiological and pathological conditions.

Energy, Exergy, and flow fields analysis to enhance performance potential of a heat-driven thermoacoustic refrigerator

This study focuses on the design, optimization, and analysis of a heat-driven thermoacoustic refrigerator system to be used in a medium-size Combined Heat and Power (CHP) system, using its exhaust hot gases. Two configurations are investigated to maximize the total Coefficient of Performance (COPTot). Several approaches, including energy, sensitivity, displacement, and exergy analyses were conducted to understand the underlying physics, identify more effective performance configurations, and to find areas with the most potential for improvements. In an energy analysis, the engine and resonator efficiency, and the refrigerator COP (COPRF) were examined. The effects of engine and refrigerator stacks and their heat exchanger lengths and spacings on engine efficiency and the COPTot were investigated. Work and heat transfer dynamics are investigated across the system. Component interactions were explored through studying the acoustic intensity and pressure and velocity phase difference (θPU) distribution, and variations in velocity and pressure amplitudes in the engine and refrigerator stacks. Displacement analysis was introduced to assess the impact of stack length variations on refrigerator and engine displacement amplitudes. The analysis revealed the significant influence of the hot heat exchanger in the engine (HHXe) and the cold heat exchanger in the refrigerator (CHXRF) on design optimality. Sensitivity analysis identifies that the performance indices of the refrigerator are mainly sensitive to the stack length and spacing. Additionally, based on three new performance indices, an exergy analysis identified why and how the refrigerator heat exchanger closest to the engine performs better.

Hybrid intelligent algorithm aided energy consumption optimization in smart grid systems with edge computing

The rapid proliferation of smart grid systems necessitates efficient management of energy resources, particularly in the context of mobile edge computing (MEC) networks. This paper presents a novel approach to optimize the energy consumption in smart grid systems with the integration of edge computing, employing a hybrid intelligent algorithm (HIA) empowered by particle swarm optimization (PSO). The primary objective is to enhance the sustainability and operational efficiency of smart grid infrastructures by minimizing the energy consumption in the MEC networks. The proposed HIA utilizes PSO to dynamically allocate computational tasks and manage resources among edge devices based on real-time demand fluctuations. This adaptive approach aims to achieve the optimal load balancing and energy efficiency across the smart grid ecosystem. By leveraging the PSO’s ability to iteratively refine solutions and adapt to changing environmental conditions, the algorithm optimizes the energy consumption while maintaining requisite service levels and reliability. Simulation experiments and case studies validate the effectiveness of the proposed PSO-based HIA in reducing the energy consumption without compromising system other performances. The results demonstrate substantial improvements in the energy efficiency, illustrating the feasibility and benefits of employing intelligent algorithms tailored for edge computing environments within smart grid systems. This research contributes to advancing sustainable smart grid technologies by introducing a robust framework for energy optimization through hybrid intelligent algorithms.

Numerical investigation and optimization of battery thermal management systems based on phase change material coupled with heating plates in low temperature environment

Low temperature environment has a significant impact on the performance and reliability of lithium-ion batteries (LIBs), particularly in terms of capacity, posing challenges to their safety and availability. However, existing studies primarily focus on cooling technologies for high temperatures, while less attention is paid to the preheating and heat preservation performance under low temperatures. In this study, a simple and effective battery thermal management system (BTMS) combines phase change material (PCM) and heating plates (HPs) is developed. The influence of structures, PCMs, composite phase change material (CPCM), and battery spacing is investigated. Additionally, the essential of insulation is emphasized. Furthermore, the utilization of natural thermal insulation material in BTMS is explored, and the influence of insulation thickness during both preheating and insulation stages are researched. The results indicate that the proposed BTMS can preheat the batteries to 20 °C under four typical low temperatures of 0 °C (480 s), −10 °C (720 s), −20 °C (900 s), and − 30 °C (1200 s). The temperature differences can be controlled below 3 °C. Moreover, the temperature can be maintained above 10 °C for approximately 4 h in the parking stage. A thickness of 10 mm is found to be sufficient. Overall, the energy compensation value above 5 is achieved under most low temperatures.

VOF-DEM study of particle entrainment behaviors in the gas–solid-liquid system with multi-inlet configuration

Enhancing the entrainment capability of particles by bubbles is pivotal for facilitating particle elutriation and augmenting reaction efficiency in gas–liquid-solid systems. The current work utilizes the volume of fluid method in conjunction with discrete element method (VOF-DEM) to study particle entrainment in gas–liquid-solid three-phase reactors featuring different gas inlet layouts. Following model validation, this study delves into the multiphase flow characteristics and particle behaviors in detail. The results reveal that the merging of adjacent channels leads to the formation of larger combined bubbles, thereby increasing particle force, velocity and entrainment height. The average entrainment height during the merging of adjacent bubble channels is 42% higher compared to the four-channel configuration. However, this amalgamation diminishes the channel count and particle entrainment count. Specifically, the highest particle entrainment occurs in the two-channel configuration, which is 26% greater than that in the adjacent-channel merging configuration. Conversely, increasing the channel number decreases bubble size and overall particle entrainment capacity. With consistent bubble sizes, an increase in particle entrainment is associated with reduced entrainment height, indicative of diminished momentum transfer from bubble wakes to individual particles. Additionally, maintaining an optimal number of channels ensures high gas inlet velocity and bubble size, thereby enhancing particle entrainment efficiency. Lastly, appropriately reducing channel distance fosters interaction between adjacent channels, resulting in a meandering bubble path that further promotes particle entrainment and elution efficiency. The results obtained from this study serve as a useful reference for the design and optimization of similar systems.

Design and Testing of a Fruit Tree Variable Spray System Based on ExG-AABB

This paper addresses the issue of pesticide waste and low utilization rates resulting from traditional plant protection via spraying operations, which apply equal dosages to different targets or to different parts of the same target. To tackle this problem, we designed a variable fruit tree spraying system based on the ExG-AABB (excess green and axis-aligned bounding box) algorithm. We used a Kinect depth camera to capture information about the fruit tree canopy and constructed a spray flow model using pulse width modulation and variable spray control technology. Variable multi-nozzle spraying was guided by combining this canopy data. We evaluated the accuracy of each model in calculating canopy volume by comparing the coefficient of determination (R2) and root mean square error (RMSE) of the ExG-AABB with the slice convex hull method, voxel method, three-dimensional alpha-shape method, and QuickHull method. The ExG-AABB algorithm had the highest R2 value (0.9334) and the lowest RMSE value (0.0353 m3) among the five models, indicating that it most accurately reflects the true volume of the fruit tree canopy. This validates the effectiveness of the ExG-AABB algorithm in calculating canopy volume. We established a correlation model between canopy volume and spray volume, designed a canopy-adaptive layering method based on point cloud processing, and achieved precise calculation of nozzle flow. Comparative field experiments were conducted to analyze the spray coverage rate and observed flow, thereby evaluating the spraying effect of this variable spraying system. The experimental results showed that compared to conventional continuous spraying, this variable spraying system not only achieves more uniform spray coverage but also significantly reduces pesticide usage by 48.1%. Furthermore, through system optimization, the average coverage rate of the middle layer of the canopy decreased by 17.53%, effectively reducing the phenomenon of overlapping spraying from multiple nozzles and improving spraying efficiency.

Evaluation of activated carbon fiber packed-bed for the treatment of gas-to-liquid wastewater: experimental, modeling and ASPEN Adsorption simulation

AbstractThis study investigates the continuous adsorption treatment of gas-to-liquid (GTL) wastewater from the Fischer-Tropsch process using activated carbon fiber (ACF) as the adsorbent. ACF, characterized by a high surface area of 1232 m²/g, was utilized to treat actual GTL wastewater, which contains long and short-chain alcohols, fatty acids, and other hydrocarbons. Experimental analysis, packed-bed modeling and simulation using ASPEN Adsorption were employed to understand the dynamics of the adsorption process. The experimental setup involved a bench-scale column packed with specified masses of ACF, with GTL wastewater pumped upward through the column at varying flow rates. Breakthrough curves were constructed to assess column performance, with parameters, such as feed flow rate (5 and 10 mL/min) and packing mass (5 and 10 g) systematically varied. The results demonstrate a significant influence of these parameters on column performance, with higher flow rates initially accelerating adsorption kinetics. Conversely, increasing packing mass extends the duration of column saturation, improving efficiency. Empirical models, including the Yoon-Nelson and El-Naas et al. models were applied to fit the experimental data, with the latter showing superior performance in representing the adsorption mechanism within the column. Quantitative analysis of model fitting using Akaike Information Criteria (AIC) identified the Yoon-Nelson and El-Naas et al. model as the most suitable for describing the GTL wastewater/ACF system, with an AIC weight parameter of 0.33 and R2 averaging 86.5%. Furthermore, simulation results from ASPEN Adsorption exhibited strong agreement with experimental data, validating its efficacy for simulating liquid adsorption processes. The study provides valuable insights into the design and optimization of large-scale wastewater treatment systems, offering practical solutions to address global water challenges.

Cathode-less RF plasma thruster design and optimisation for an atmosphere-breathing electric propulsion (ABEP) system

Atmosphere-breathing electric propulsion (ABEP) is a concept that ingests residual atmospheric gases as a source of propellant for an electric thruster, removing the need for onboard propellant storage. This would enable continuous low-thrust drag compensation, extending the lifetime of spacecraft in Very-Low Earth Orbit (VLEO); <250 km. VLEO is an appealing region for spacecraft operations, enabling new remote sensing missions with improved radiometric performance and spatial resolution, whilst reducing size, mass and power requirements, as well as mission cost. A preliminary design review and optimisation is therefore conducted for an ABEP system that uses the cathode-less radio frequency (RF) plasma thruster from Technology for Innovation & Propulsion (T4i) S.p.A. This removes the issue of thruster erosion by means of magnetic confinement and offers reduced susceptibility to varying atmospheric composition. A semi-empirical oxygen-nitrogen global source model (GSM) has been developed which considers the volume-averaged flux, momentum, and energy balance of the RF discharge. This includes a detailed chemistry model for the complex electron-molecular reactions and energy-loss channels of air plasma in the ionisation chamber. The GSM is coupled to an analytical model of flux balance for an air intake, verified by Direct Simulation Monte-Carlo (DSMC) simulation, to consider its design for maximum collection efficiency. This is then utilised in a robust multi-objective optimisation of the ABEP system, accounting also for spacecraft aerodynamics and power requirements.

Optimization of biomass-fueled multigeneration system using SOFC for electricity, hydrogen, and freshwater production

The rapid depletion of fossil fuel resources and their detrimental impact on the environment necessitates the exploration of renewable energy alternatives. Biomass stands out as a reliable renewable source, especially in multigeneration systems. This study suggests a biomass-driven multigeneration configuration for the production of electricity, heating, hydrogen, and freshwater. The configuration is evaluated from thermodynamic and economic perspectives, utilizing an Artificial Neural Network to predict key outcomes. The system optimization process integrates Machine Learning with decision-making methods to achieve optimal conditions. The findings highlight that an anode-cathode gas recycling design is the most effective configuration for the Solid Oxide Fuel Cell unit, achieving a cycle efficiency of 67.68% and a product cost of $11.41/GJ. Municipal Solid Waste biomass and CO2 were identified as the most efficient fuel and gasification agents, respectively. The evaluations also determined that the integration of the multi-objective grey wolf optimizer and the CatBoost method is the most reliable machine learning model for optimization. Furthermore, by combining the Multi-Objective Harris Hawks Optimization algorithm with TOPSIS, LINMAP, and Fuzzy decision-making approaches, the system achieves optimal performance across multiple scenarios. In one scenario, the system reached an exergy efficiency of 67.68% and a hydrogen production rate of 22.34 kg/h. The optimum recycling ratios for the anode and cathode were determined to be 0.16 and 0.38, respectively. The study demonstrates the feasibility of the proposed configuration, offering significant potential for sustainable and cost-effective energy generation.