Reliability-Based Design Optimization for Polymer Electrolyte Membrane Fuel Cells: Tackling Dimensional Uncertainties in Manufacturing and Their Effects on Costs of Cathode Gas Diffusion Layer and Bipolar Plates.

Polymer Electrolyte Membrane Fuel Cells (PEMFCs) have emerged as a pivotal technology in the automotive industry, significantly contributing to the reduction of greenhouse gas emissions. However, the high material costs of the gas diffusion layer (GDL) and bipolar plate (BP) create a barrier for large scale commercial application. This study aims to address this challenge by optimizing the material and design of the cathode, GDL and BP. While deterministic design optimization (DDO) methods have been extensively studied, they often fall short when manufacturing uncertainties are introduced. This issue is addressed by introducing reliability-based design optimization (RBDO) to optimize four key PEMFC design variables, i.e., gas diffusion layer thickness, channel depth, channel width and land width. The objective is to maximize cell voltage considering the material cost of the cathode gas diffusion layer and cathode bipolar plate as reliability constraints. The results of the DDO show an increment in cell voltage of 31 mV, with a reliability of around 50% in material cost for both the cathode GDL and cathode BP. In contrast, the RBDO method provides a reliability of 95% for both components. Additionally, under a high level of uncertainty, the RBDO approach reduces the material cost of the cathode GDL by up to 12.25 $/stack, while the material cost for the cathode BP increases by up to 11.18 $/stack Under lower levels of manufacturing uncertainties, the RBDO method predicts a reduction in the material cost of the cathode GDL by up to 4.09 $/stack, with an increase in the material cost for the cathode BP by up to 6.71 $/stack, while maintaining a reliability of 95% for both components. These results demonstrate the effectiveness of the RBDO approach in achieving a reliable design under varying levels of manufacturing uncertainties.

Inverse Identification of Constitutive Model for GH4198 Based on Genetic-Particle Swarm Algorithm.

A precise Johnson-Cook (J-C) constitutive model is the foundation for precise calculation of finite-element simulation. In order to obtain the J-C constitutive model accurately for a new cast and forged alloy GH4198, an inverse identification of J-C constitutive model was proposed based on a genetic-particle swarm algorithm. Firstly, a quasi-static tensile test at different strain rates was conducted to determine the initial yield strength A, strain hardening coefficient B, and work hardening exponent n for the material's J-C model. Secondly, a new method for orthogonal cutting model was constructed based on the unequal division shear theory and considering the influence of tool edge radius. In order to obtain the strain-rate strengthening coefficient C and thermal softening coefficient m, an orthogonal cutting experiment was conducted. Finally, in order to validate the precision of the constitutive model, an orthogonal cutting thermo-mechanical coupling simulation model was established. Meanwhile, the sensitivity of J-C constitutive model parameters on simulation results was analyzed. The results indicate that the parameter m significantly affects chip morphology, and that the parameter C has a notable impact on the cutting force. This study addressed the issue of missing constitutive parameters for GH4198 and provided a theoretical reference for the optimization and identification of constitutive models for other aerospace materials.

Clarifying the impact of engine operating parameters of heavy-duty diesel vehicles on NOx and CO2 emissions using multimodal fusion methods.

The issue of air pollution from transportation sources remains a major concern, particularly the emissions from heavy-duty diesel vehicles, which pose serious threats to ecosystems and human health. China VI emission standards mandate On-Board Diagnostics (OBD) systems in heavy-duty diesel vehicles for real-time data transmission, yet the current data quality, especially concerning crucial parameters like NOx output, remains inadequate for effective regulation. To address this, a novel approach integrating Multimodal Feature Fusion with Particle Swarm Optimization (OBD-PSOMFF) is proposed. This network employs Long Short-Term Memory (LSTM) networks to extract features from OBD indicators, capturing temporal dependencies. PSO optimizes feature weights, enhancing prediction accuracy. Testing on 23 heavy-duty vehicles demonstrates significant improvements in predicting NOx and CO2 mass emission rates, with mean squared errors reduced by 65.205 % and 70.936 % respectively compared to basic LSTM models. This innovative multimodal fusion method offers a robust framework for emission prediction, crucial for effective vehicle emission regulation and environmental preservation.

Qualitative and quantitative reservoir characterization using seismic inversion based on particle swarm optimization and genetic algorithm: a comparative case study.

Accurate reservoir characterization is necessary to effectively monitor, manage, and increase production. A seismic inversion methodology using a genetic algorithm (GA) and particle swarm optimization (PSO) technique is proposed in this study to characterize the reservoir both qualitatively and quantitatively. It is usually difficult and expensive to map deeper reservoirs in exploratory operations when using conventional approaches for reservoir characterization hence inversion based on advanced technique (GA and PSO) is proposed in this study. The main goal is to use GA and PSO to significantly lower the fitness (error) function between real seismic data and modeled synthetic data, which will allow us to estimate subsurface properties and accurately characterize the reservoir. Both techniques estimate subsurface properties in a comparable manner. Consequently, a qualitative and quantitative comparison is conducted between these two algorithms. Using two synthetic data and one real data from the Blackfoot field in Canada, the study examined subsurface acoustic impedance and porosity in the inter-well zone. Porosity and acoustic impedance are layer features, but seismic data is an interface property, hence these characteristics provide more useful and applicable reservoir information. The inverted results aid in the understanding of seismic data by providing incredibly high-resolution images of the subsurface. Both the GA and the PSO algorithms deliver outstanding results for both simulated and real data. The inverted section accurately delineated a high porosity zone ( > 20 % ) that supported the high seismic amplitude anomaly by having a low acoustic impedance (6000-8500 m/s ∗ g/cc). This unusual zone is categorized as a reservoir (sand channel) and is located in the 1040-1065 ms time range. In this inversion process, after 400 iterations, the fitness error falls from 1 to 0.88 using GA optimization, compared to 1 to 0.25 using PSO. The convergence time for GA is 670,680 s, but the convergence time for PSO optimization is 356,400 s, showing that the former requires 88 % more time than the latter.

Inverse design of chemoenzymatic epoxidation of soyabean oil through artificial intelligence-driven experimental approach.

This paper presents an inverse design methodology that utilizes artificial intelligence (AI)-driven experiments to optimize the chemoenzymatic epoxidation of soyabean oil using hydrogen peroxide and lipase (Novozym 435). First, experiments are conducted using a systematic 3-level, 5-factor Box-Behnken design to explore the effect of input parameters on oxirane oxygen content (OOC (%)). Based on these experiments, various AI models are trained, with the support vector regression (SVR) model being found to be the most accurate. SVR is then used as a fitness function in particle swarm optimization, and the suggested optimal conditions, upon experimental validation, resulted in a maximum OOC of 7.19 % (∼98.5 % relative conversion of oil to epoxy). The results demonstrate the superiority of the proposed approach over existing methods. This framework offers a general intensified process optimization strategy with minimal resource utilization that can be applied to any other process.

DCWPSO: particle swarm optimization with dynamic inertia weight updating and enhanced learning strategies.

Particle swarm optimization (PSO) stands as a prominent and robust meta-heuristic algorithm within swarm intelligence (SI). It originated in 1995 by simulating the foraging behavior of bird flocks. In recent years, numerous PSO variants have been proposed to address various optimization applications. However, the overall performance of these variants has not been deemed satisfactory. This article introduces a novel PSO variant, presenting three key contributions: First, a novel dynamic oscillation inertia weight is introduced to strike a balance between exploration and exploitation; Second, the utilization of cosine similarity and dynamic neighborhood strategy enhances both the quality of solution and the diversity of particle populations; Third, a unique worst-best example learning strategy is proposed to enhance the quality of the least favorable solution and consequently improving the overall population. The algorithm's validation is conducted using a test suite comprised of benchmarks from the CEC2014 and CEC2022 test suites on real-parameter single-objective optimization. The experimental results demonstrate the competitiveness of our algorithm against recently proposed state-of-the-art PSO variants and well-known algorithms.

Optimizing Nutritional Decisions: A Particle Swarm Optimization-Simulated Annealing-Enhanced Analytic Hierarchy Process Approach for Personalized Meal Planning.

BACKGROUND/OBJECTIVE: Nutritionists play a crucial role in guiding individuals toward healthier lifestyles through personalized meal planning; however, this task involves navigating a complex web of factors, including health conditions, dietary restrictions, cultural preferences, and socioeconomic constraints. The Analytic Hierarchy Process (AHP) offers a valuable framework for structuring these multi-faceted decisions but inconsistencies can hinder its effectiveness in pairwise comparisons. METHODS: This paper proposes a novel hybrid Particle Swarm Optimization-Simulated Annealing (PSO-SA) algorithm to refine inconsistent AHP weight matrices, ensuring a consistent and accurate representation of the nutritionist's expertise and client preferences. Our approach merges PSO's global search capabilities with SA's local search precision, striking an optimal balance between exploration and exploitation. RESULTS: We demonstrate the practical utility of our algorithm through real-world use cases involving personalized meal planning for individuals with specific dietary needs and preferences. Results showcase the algorithm's efficiency in achieving consistency and surpassing standard PSO accuracy. CONCLUSION: By integrating the PSO-SA algorithm into a mobile app, we empower nutritionists with an advanced decision-making tool for creating tailored meal plans that promote healthier dietary choices and improved client outcomes. This research represents a significant advancement in multi-criteria decision-making for nutrition, offering a robust solution to the inconsistency challenge in AHP and paving the way for more effective and personalized dietary interventions.

Mother-Daughter Vessel Operation and Maintenance Routing Optimization for Offshore Wind Farms Using Restructuring Particle Swarm Optimization.

As the capacity of individual offshore wind turbines increases, prolonged downtime (due to maintenance or faults) will result in significant economic losses. This necessitates enhancing the efficiency of vessel operation and maintenance (O&M) to reduce O&M costs. Existing research mostly focuses on planning O&M schemes for individual vessels. However, there exists a research gap in the scientific scheduling for state-of-the-art O&M vessels. To bridge this gap, this paper considers the use of an advanced O&M vessel in the O&M process, taking into account the downtime costs associated with wind turbine maintenance and repair incidents. A mathematical model is constructed with the objective of minimizing overall O&M expenditure. Building upon this formulation, this paper introduces a novel restructuring particle swarm optimization which is tailed with a bespoke encoding and decoding strategy, designed to yield an optimized solution that aligns with the intricate demands of the problem at hand. The simulation results indicate that the proposed method can achieve significant savings of 28.85% in O&M costs. The outcomes demonstrate the algorithm's proficiency in tackling the model efficiently and effectively.

A Novel Invasive Weed Optimization and its Variant for the Detection of Polycystic Ovary Syndrome.

INTRODUCTION: This study intends to provide a novel Invasive Weed Optimization (IWO) algorithm for the detection of Polycystic Ovary Syndrome (PCOS) from ultrasound ovarian images. PCOS is an intricate anarchy described by hyperandrogenemia and irregular menstruation. Indian women are increasingly finding reproductive disorders, namely PCOS. METHODS: The women having PCOS grow more small follicles in their ovaries. The radiologists take a look into women's ovaries by use of ultrasound scanning equipment to manually count the number of follicles and their size for fertility treatment. These may lead to error diagnosis. RESULTS: This paper proposed an automatic follicle detection system for identifying PCOS in the ovary using IWO. The performance of IWO is improved in Modified Invasive Weed Optimization (MIWO). This algorithm imitates the biological weeds' behavior. The MIWO is employed to obtain the optimal threshold by maximizing the between-class variance of the modified Otsu method. The efficiency of the proposed method has been compared with the well-known optimization technique called Particle Swarm Optimization (PSO) and with IWO. CONCLUSION: Experimental results proved that the MIWO finds an optimal threshold higher than that of IWO and PSO.

The impact of managed aquifer recharge on the fate and transport of pesticides in agricultural soils.

Groundwater aquifers worldwide experience unsustainable depletion, compounded by population growth, economic development, and climate forcing. Managed aquifer recharge provides one tool to alleviate flood risk and replenish groundwater. However, concerns grow that intentional flooding of farmland for groundwater recharge, a practice known as Ag-MAR, may increase the leaching of pesticides and other chemicals into groundwater. This study employs a physically based unsaturated flow model to determine the fate and transport of residues of four pesticide in three vadose zone profiles characterized by differing fractions of sand (41 %, 61 %, and 84 %) in California's Central Valley. Here, we show that the complex heterogeneity of alternating coarse and fine-grain hydrogeologic units controls the transit times of pesticides and their adsorption and degradation rates. Unsaturated zones that contain a higher fraction of sand are more prone to support preferential flow, higher recharge rates (+8 %), and faster (42 %) water flow and pesticide transport, more flooding-induced pesticide leaching (about 22 %), as well as more salt leaching correlating with increased risks of groundwater contamination. Interestingly, considering preferential flow predicted higher degradation and retention rates despite shorter travel times, attributed to the trapping of pesticides in immobile zones where they degrade more effectively. The findings underscore the importance of considering soil texture and structure in Ag-MAR practices to minimize environmental risks while enhancing groundwater recharge. The study also highlights that selecting less mobile pesticides can reduce leaching risks in sandy areas.

A hybrid, nonlinear programming approach for optimizing passive shimming in MRI.

BACKGROUND: In magnetic resonance imaging (MRI), maintaining a highly uniform main magnetic field (B0) is essential for producing detailed images of human anatomy. Passive shimming (PS) is a technique used to enhance B0 uniformity by strategically arranging shimming iron pieces inside the magnet bore. Traditionally, PS optimization has been implemented using linear programming (LP), posing challenges in balancing field quality with the quantity of iron used for shimming. PURPOSE: In this work, we aimed to improve the efficacy of passive shimming that has the advantages of balancing field quality, iron usage, and harmonics in an optimal manner and leads to a smoother field profile. METHODS: This study introduces a hybrid algorithm that combines particle swarm optimization with sequential quadratic programming (PSO-SQP) to enhance shimming performance. Additionally, a regularization method is employed to reduce the iron pieces' weight effectively. RESULTS: The simulation study demonstrated that the magnetic field was improved from 462  to 3.6 ppm, utilizing merely 1.2 kg of iron in a 40 cm diameter spherical volume (DSV) of a 7T MRI magnet. Compared to traditional optimization techniques, this method notably enhanced magnetic field uniformity by 96.7% and reduced the iron weight requirement by 81.8%. CONCLUSION: The results indicated that the proposed method is expected to be effective for passive shimming.

Evaluating the influence of Nano-GO concrete pavement mechanical properties on road performance and traffic safety using ANN-GA and PSO techniques.

The burgeoning demand for durable and eco-friendly road infrastructure necessitates the exploration of innovative materials and methodologies. This study investigates the potential of Graphene Oxide (GO), a nano-material known for its exceptional dispersibility and mechanical reinforcement capabilities, to enhance the sustainability and durability of concrete pavements. Leveraging the synergy between advanced artificial intelligence techniques-Artificial Neural Networks (ANN), Genetic Algorithms (GA), and Particle Swarm Optimization (PSO)-it is aimed to delve into the intricate effects of Nano-GO on concrete's mechanical properties. The empirical analysis, underpinned by a comparative evaluation of ANN-GA and ANN-PSO models, reveals that the ANN-GA model excels with a minimal forecast error of 2.73%, underscoring its efficacy in capturing the nuanced interactions between GO and cementitious materials. An optimal concentration is identified through meticulous experimentation across varied Nano-GO dosages that amplify concrete's compressive, flexural, and tensile strengths without compromising workability. This optimal dosage enhances the initial strength significantly, and positions GO as a cornerstone for next-generation premium-grade pavement concretes. The findings advocate for the further exploration and eventual integration of GO in road construction projects, aiming to bolster ecological sustainability and propel the adoption of a circular economy in infrastructure development.

An improve nonlinear robust control approach for robotic manipulators with PSO-based global optimization strategy.

During the trajectory tracking of robotic manipulators, many factors including dead zones, saturation, and uncertain dynamics, greatly increase the modeling and control difficulty. Aiming for this issue, a nonlinear active disturbance rejection control (NADRC)-based control strategy is proposed for robotic manipulators. In this controller, an extended state observer is introduced on basis of the dynamic model, to observe the extend state of model uncertainties and external disturbances. Then, in combination with the nonlinear feedback control structure, the robust trajectory tracking of robotic manipulators is achieved. Furthermore, to optimize the key parameters of the controller, an improved particle swarm optimization algorithm (IPSO) is designed using chaos theory, which improves the tracking accuracy of the proposed NDRC strategy effectively. Finally, using comparative studies, the effectiveness of the proposed control strategy is demonstrated by comparing with several commonly used controllers.

Mapping climate suitability index for rainfed cultivation of medicinal plants by developing an AI-based probabilistic framework.

The Climate Suitability Index (CSI) can increase agricultural efficiency by identifying the high-potential areas for cultivation from the climate perspective. The present study develops a probabilistic framework to calculate CSI for rainfed cultivation of 12 medicinal plants from the climate perspective of precipitation and temperature. Unlike the ongoing frameworks based on expert judgments, this formulation decreases the inherent subjectivity by using two components: frequency analysis and Particle Swarm Optimization (PSO). In the first component, the precipitation and temperature layers were prepared by calculating the occurrence probability for each plant, and the obtained probabilities were spatially interpolated using geographical information system processes. In the second component, PSO quantifies CSI by classifying a study area into clusters using an unsupervised clustering technique. The formulation was implemented in the Lake Urmia basin, which was distressed by unsustainable water resources management. By identifying clusters with higher CSI values for each plant, the results provide deeper insights to optimize cultivation patterns in the basin. These insights can help managers and farmers increase yields, reduce costs, and improve profitability.

Performance analysis of deep learning-based electric load forecasting model with particle swarm optimization.

With the widespread application of deep learning technology in various fields, power load forecasting, as an important link in power system operation and planning, has also ushered in new opportunities and challenges. Traditional forecasting methods perform poorly when faced with the high uncertainty and complexity of power loads. In view of this, this paper proposes a power load forecasting model PSO-BiTC based on deep learning and particle swarm optimization. This model combines a temporal convolutional network (TCN) and a bidirectional long short-term memory network (BiLSTM), using TCN to process long sequence data and capture features and patterns in time series, while using BiLSTM to capture long-term and short-term dependencies. In addition, the particle swarm optimization algorithm (PSO) is used to optimize model parameters to improve the model's predictive performance and generalization ability. Experimental results show that the PSO-BiTC model performs well in power load forecasting. Compared with traditional methods, this model reduces the MAE (Mean Absolute Error) to 20.18, 17.57, 18.61 and 16.7 on four extensive data sets, respectively. It has been proven that it achieves the best performance in various indicators, with a low number of parameters and training time. This research is of great significance for improving the operating efficiency of the power system, optimizing resource allocation, and promoting carbon emission reduction goals in the urban building sector.

Rate-Dependent Tensile Properties of Aluminum-Hydroxide-Enhanced Ethylene Propylene Diene Monomer Coatings for Solid Rocket Motors.

Quasi-static and dynamic tensile tests on aluminum-hydroxide-enhanced ethylene propylene diene monomer (EPDM) coatings were conducted using a universal testing machine and a Split Hopkinson Tension Bar (SHTB) over a strain rate range of 10-3 to 103 s-1. This comprehensive study explored the tensile performance of enhanced EPDM coatings in solid rocket motors. The results demonstrated a significant impact of strain rate on the mechanical properties of EPDM coatings. To capture the hyperelastic and viscoelastic characteristics of EPDM coatings at large strains, the Ogden hyperelastic model was used to replace the standard elastic component to develop an enhanced Zhu-Wang-Tang (ZWT) nonlinear viscoelastic constitutive model. The model parameters were fitted using a particle swarm optimization (PSO) algorithm. The improved constitutive model's predictions closely matched the experimental data, accurately capturing stress-strain responses and inflection points. It effectively predicts the tensile behavior of aluminum-hydroxide-enhanced EPDM coatings within a 20% strain range and a wide strain rate range.

Advanced temperature control in ethanol fermentation using a PSO-PID controller with split-range control strategy.

Global energy demand is experiencing a notable surge due to growing energy security. Renewable energy sources, like ethanol, are becoming more viable. In the present study, the application of a PSO-PID (Particle Swarm Optimization - Proportional Integral Derivative) controller with a split-range control strategy was suggested for the regulation of temperature within the fermentation system. To optimize performance, a POS-PID controller with a split-range arrangement utilizing two control valves for hot and cold utilities was constructed. The study began by examining the open-loop dynamic response of the system to inlet temperature and concentration disturbances during ethanol production fermentation. Subsequently, a transfer function model was developed through linearization at the steady-state operating point. The split-range controller structure, implemented by optimizing the PSO-PID controller parameters using PSO, effectively demonstrated temperature control in simulations of a nonlinear model. In this investigation, the ethanol fermentation system was modeled as a CSTR using a modified Monod equation for microbial growth kinetics. Various dynamic behavioral disturbances were explored and verified in the model with plant data in this study. The simulation model results were validated through plant data. The proposed method showed superior closed-loop performance with respect to errors, with the actuators proving to be effective than other reported methods for temperature control.

Dual-loop control and state prediction analysis of QUAV trajectory tracking based on biological swarm intelligent optimization algorithm.

Quadrotor unmanned aerial vehicles (QUAVs) have attracted significant research focus due to their outstanding Vertical Take-Off and Landing (VTOL) capabilities. This research addresses the challenge of maintaining precise trajectory tracking in QUAV systems when faced with external disturbances by introducing a robust, two-tier control system based on sliding mode technology. For position control, this approach utilizes a virtual sliding mode control signal to enhance tracking precision and includes adaptive mechanisms to adjust for changes in mass and external disruptions. In controlling the attitude subsystem, the method employs a sliding mode control framework that secures system stability and compliance with intermediate commands, eliminating the reliance on precise models of the inertia matrix. Furthermore, this study incorporates a deep learning approach that combines Particle Swarm Optimization (PSO) with the Long Short-Term Memory (LSTM) network to foresee and mitigate trajectory tracking errors, thereby significantly enhancing the reliability and safety of mission operations. The robustness and effectiveness of this innovative control strategy are validated through comprehensive numerical simulations.

Energy efficient clustering and routing protocol based on quantum particle swarm optimization and fuzzy logic for wireless sensor networks.

Clustering and routing protocols play a pivotal role in reducing energy consumption and extending the lifespan of wireless sensor networks. However, optimizing energy efficiency to maximize network longevity remains a primary challenge for these protocols. This paper introduces QPSOFL, a clustering and routing protocol that integrates quantum particle swarm optimization and a fuzzy logic system to enhance energy efficiency and prolong network lifespan. QPSOFL employs an enhanced quantum particle swarm optimization algorithm to select optimal cluster heads, utilizing Sobol sequences for population diversification during initialization. Additionally, it incorporates Lévy flight and Gaussian perturbation-based position updates to prevent trapping in local optima. Benchmark experiments validate QPSOFL's efficacy compared to Harris Hawks Optimization (HHO), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Quantum Particle Swarm Optimization (QPSO), focusing on accuracy, search capability, and convergence speed. Within QPSOFL, a fuzzy logic system determines the best next-hop cluster head based on descriptors such as residual energy, energy deviation, and relay distance. Extensive simulations compare QPSOFL's performance in terms of network lifetime, throughput, energy consumption, and scalability against existing protocols E-FUCA, IHHO-F, F-GWO, and FLPSOC, demonstrating its superior performance over these counterparts.

Fine-tuning inflow prediction models: integrating optimization algorithms and TRMM data for enhanced accuracy.

This research explores machine learning algorithms for reservoir inflow prediction, including long short-term memory (LSTM), random forest (RF), and metaheuristic-optimized models. The impact of feature engineering techniques such as discrete wavelet transform (DWT) and XGBoost feature selection is investigated. LSTM shows promise, with LSTM-XGBoost exhibiting strong generalization from 179.81 m3/s RMSE (root mean square error) in training to 49.42 m3/s in testing. The RF-XGBoost and models incorporating DWT, like LSTM-DWT and RF-DWT, also perform well, underscoring the significance of feature engineering. Comparisons illustrate enhancements with DWT: LSTM and RF reduce training and testing RMSE substantially when using DWT. Metaheuristic models like MLP-ABC and LSSVR-PSO benefit from DWT as well, with the LSSVR-PSO-DWT model demonstrating excellent predictive accuracy, showing 133.97 m3/s RMSE in training and 47.08 m3/s RMSE in testing. This model synergistically combines LSSVR, PSO, and DWT, emerging as the top performers by effectively capturing intricate reservoir inflow patterns.