Search Results (31)

Direct methanol fuel cells (DMFCs) are promising form of energy conversion technology that have the potential to take the role of lithium-ion batteries in portable electronics and electric cars. To increase the efficiency of DMFCs, many operating conditions ought to be optimized. Developing a reliable fuzzy model to simulate DMFCs is a major objective. To increase the power output of a DMFC, three process variables are considered: temperature, methanol concentration, and oxygen flow rate. First, a fuzzy model of the DMFC was developed using experimental data. The best operational circumstances to increase power density were then determined using the beetle antennae search (BAS) method. The RMSE values for the fuzzy DMFC model are 0.1982 and 1.5460 for the training and testing data. For training and testing, the coefficient of determination (R²) values were 0.9977 and 0.89, respectively. Thanks to fuzzy logic, the RMSE was reduced by 88% compared to ANOVA. It decreased from 7.29 (using ANOVA) to 0.8628 (using fuzzy). The fuzzy model’s low RMSE and high R² values show that the modeling phase was successful. In comparison with the measured data and RSM, the combination of fuzzy modeling and the BAS algorithm increased the power density of the DMFC by 8.88% and 7.5%, respectively, and 75 °C, 1.2 M, and 400 mL/min were the ideal values for temperature, methanol concentration, and oxygen flow rate, respectively. Full article

Fuel cell hybrid electric vehicles (FCEVs) have gained significant attention due to their environmentally friendly nature and competitive performance. These vehicles utilize a fuel cell system as the primary power source, with a secondary power source such as a battery pack or supercapacitor. An energy management strategy (EMS) for FCEVs is critical in optimizing power distribution among different energy sources, considering factors such as hydrogen consumption and efficiency. The proposed EMS presents an optimized external energy maximization strategy using the Harris Hawks Optimization to reduce hydrogen consumption and enhance the system’s efficiency. Through a comparative simulation using the Federal Test Procedure (FTP-75) for the city driving cycle, the performance of the proposed EMS was evaluated and compared to existing algorithms. The simulation results indicate that the proposed EMS outperforms other existing solutions in terms of fuel consumption reduction, with a potential reduction of 19.81%. Furthermore, the proposed energy management strategy also exhibited an increase in system efficiency of 0.09%. This improvement can contribute to reducing the reliance on fossil fuels and mitigating the negative environmental impacts associated with vehicle emissions. Full article

Redox flow batteries represent a captivating class of electrochemical energy systems that are gaining prominence in large-scale storage applications. These batteries offer remarkable scalability, flexible operation, extended cycling life, and moderate maintenance costs. The fundamental operation and structure of these batteries revolve around the flow of an electrolyte, which facilitates energy conversion and storage. Notably, the power and energy capacities can be independently designed, allowing for the conversion of chemical energy from input fuel into electricity at working electrodes, resembling the functioning of fuel cells. This work provides a comprehensive overview of the components, advantages, disadvantages, and challenges of redox flow batteries (RFBs). Moreover, it explores various diagnostic techniques employed in analyzing flow batteries. The discussion encompasses the utilization of RFBs for large-scale energy storage applications and summarizes the engineering design aspects related to these batteries. Additionally, this study delves into emerging technologies, applications, and challenges in the realm of redox flow batteries. Full article

The parameter identification of a PEMFC is the process of using optimization algorithms to determine the ideal unknown variables suitable for the development of an accurate fuel-cell-performance prediction model. These parameters are not always available from the manufacturer’s datasheet, so they need to be determined to accurately model and predict the fuel cell’s performance. Five optimization methods—bald eagle search (BES) algorithm, equilibrium optimizer (EO), coot (COOT) algorithm, antlion optimizer (ALO), and heap-based optimizer (HBO)—are used to compute seven unknown parameters of a PEMFC. During optimization, these seven parameters are used as decision variables, and the fitness function to be minimized is the sum square error (SSE) between the estimated cell voltage and the actual measured cell voltage. The SSE obtained for the BES algorithm was noted to be 0.035102. The COOT algorithm recorded an SSE of 0.04155, followed by ALO with an SSE of 0.04022 and HBO with an SSE of 0.056021. BES predicted the performance of the fuel cell accurately; hence, it is suitable for the development of a digital twin for fuel-cell applications and control systems for the automotive industry. Furthermore, it was deduced that the convergence speed for BES was faster compared to the other algorithms investigated. This study aims to use metaheuristic algorithms to predict fuel-cell performance for the development and commercialization of digital twins in the automotive industry. Full article

The main target is the maximization of the output power of PEM “proton exchange membrane” fuel cell via fuzzy modelling and optimization. In the beginning, using the experimental data, a robust fuzzy model is designed for simulating the PEM fuel cell using the relative humidity (%) and stoichiometric ratio at the anode and cathode. Then, the artificial ecosystem optimiser (AEO) is applied to determine the best values of the controlling input parameters. During the optimization process, the four controlling input parameters of the PEMFC are used as the decision variables, whereas as the cost function is required to be at the maximum of the output power density of the PEMFC. For the fuzzy model of the power, the RMSE values are 1.5588 and 3.1906, respectively, for training and testing data. The coefficient of determination values are 0.9826 and 0.8743 for training and testing, respectively. This confirms a successful modelling phase. Finally, the integration between fuzzy and AEO boosted the power of the PEMFC from 57.95 W to 78.44 W (by around 35%). Under this optimal condition, the controlling input parameters values are 26.65%, 56.77%, 1.14, and 1.68, respectively, for anode relative humidity, cathode relative humidity, anode stoichiometric ratio and cathode stoichiometric ratio. The present study, however, intends to highlight the importance of fuzzy modelling and metaheuristic algorithms in the development of digital twins to accelerate the commercialization of fuel cells as well as its applicability in diverse global economic sectors where a higher power requirement is needed. It is also aimed at informing the fuel cell research community and policy makers on strategies that could be adopted in boosting fuel cell performance and therefore could be a good reference source in decision-making for fuel cell commercialization and its practical implementation. Full article

For many electrical systems, such as renewable energy sources, their internal parameters are exposed to degradation due to the operating conditions. Since the model’s accuracy is required for establishing proper control and management plans, identifying their parameters is a critical and prominent task. Various techniques have been developed to identify these parameters. However, metaheuristic algorithms have received much attention for their use in tackling a wide range of optimization issues relating to parameter extraction. This work provides an exhaustive literature review on solving parameter extraction utilizing recently developed metaheuristic algorithms. This paper includes newly published articles in each studied context and its discussion. It aims to approve the applicability of these algorithms and make understanding their deployment easier. However, there are not any exact optimization algorithms that can offer a satisfactory performance to all optimization issues, especially for problems that have large search space dimensions. As a result, metaheuristic algorithms capable of searching very large spaces of possible solutions have been thoroughly investigated in the literature review. Furthermore, depending on their behavior, metaheuristic algorithms have been divided into four types. These types and their details are included in this paper. Then, the basics of the identification process are presented and discussed. Fuel cells, electrochemical batteries, and photovoltaic panel parameters identification are investigated and analyzed. Full article

The use of green hydrogen as a fuel source for marine applications has the potential to significantly reduce the carbon footprint of the industry. The development of a sustainable and cost-effective method for producing green hydrogen has gained a lot of attention. Water electrolysis is the best and most environmentally friendly method for producing green hydrogen-based renewable energy. Therefore, identifying the ideal operating parameters of the water electrolysis process is critical to hydrogen production. Three controlling factors must be appropriately identified to boost hydrogen generation, namely electrolysis time (min), electric voltage (V), and catalyst amount (μg). The proposed methodology contains the following two phases: modeling and optimization. Initially, a robust model of the water electrolysis process in terms of controlling factors was established using an adaptive neuro-fuzzy inference system (ANFIS) based on the experimental dataset. After that, a modern pelican optimization algorithm (POA) was employed to identify the ideal parameters of electrolysis duration, electric voltage, and catalyst amount to enhance hydrogen production. Compared to the measured datasets and response surface methodology (RSM), the integration of ANFIS and POA improved the generated hydrogen by around 1.3% and 1.7%, respectively. Overall, this study highlights the potential of ANFIS modeling and optimal parameter identification in optimizing the performance of solar-powered water electrocatalysis systems for green hydrogen production in marine applications. This research could pave the way for the more widespread adoption of this technology in the marine industry, which would help to reduce the industry’s carbon footprint and promote sustainability. Full article

A thermoelectric generator (TEG) is used for converting temperature difference and into DC directly to electric energy based on the Seebeck effect. This new technology has attracted researchers of sustainable energy. The energy obtained from the TEG depends on the temperature difference between the two sides of the TEG. A reliable MPP “maximum power point” tracker (MPPT) is mandatory to guarantee that the TEG is working close to the MPP under different operational conditions. There are two common methods that have been widely used to track the MPP: hill climbing (HC) and incremental conductance (INR). The HC method is very fast in tracking the MPP; however, oscillation can occur under a high steady state. On the contrary, the INR method needs more time to track the MPP but does not oscillate around the MPP. To overcome these issues, fractional control is adopted. Furthermore, the proposed MPPT requires only a single current sensor, as opposed to conventional MPPTs, which require at least two sensors: current and voltage sensors. The cost of the control system is reduced when the number of sensors is reduced. Hunger games search optimization is used to estimate the parameters of a single sensor optimized fractional MPPT (OFMPPT). During the optimization process, three parameters were assigned as decision variables: proportional gain, integral gain, and order, with the objective function being the TEG’s energy. The results demonstrated the superiority of OFMPPT in both transient and steady state compared to HC and INR. Full article

The increased interest in renewable-based microgrids imposes several challenges, such as source integration, power quality, and operating cost. Dealing with these problems requires solving nonlinear optimization problems that include multiple linear or nonlinear constraints and continuous variables or discrete ones that require large dimensionality search space to find the optimal or sub-optimal solution. These problems may include the optimal power flow in the microgrid, the best possible configurations, and the accuracy of the models within the microgrid. Metaheuristic optimization algorithms are getting more suggested in the literature contributions for microgrid applications to solve these optimization problems. This paper intends to thoroughly review some significant issues surrounding microgrid operation and solve them using metaheuristic optimization algorithms. This study provides a collection of fundamental principles and concepts that describe metaheuristic optimization algorithms. Then, the most significant metaheuristic optimization algorithms that have been published in the last years in the context of microgrid applications are investigated and analyzed. Finally, the employment of metaheuristic optimization algorithms to specific microgrid issue applications is reviewed, including examples of some used algorithms. These issues include unit commitment, economic dispatch, optimal power flow, distribution system reconfiguration, transmission network expansion and distribution system planning, load and generation forecasting, maintenance schedules, and renewable sources max power tracking. Full article

Clean and safe energy sources are essential for the long-term growth of society. Wind energy is rapidly expanding and contributes to many countries’ efforts to decrease greenhouse gas emissions. In terms of sustainable development goals (SDGs), renewable energy development promotes energy security while also facilitating community development and environmental conservation on a global scale. In this context, the current article aims to investigate wind energy’s role within the SDGs. Furthermore, the present study highlights the role of the London Array wind farm in achieving the SDGs. Indeed, deploying clean and economical energy sources in place of conventional fossil fuel power plants provides vital insights into environmental impacts. The London Array operation is saving approximately 1 million tons of carbon dioxide (CO₂) equivalent. Furthermore, the London Array contributes to the achievement of multiple SDGs, including SDG 8: decent employment and economic growth; SDG 9: industry, innovation, and infrastructure; SDG 11: sustainable cities and communities; and SDG 15: life on land. To enhance the London Array’s contribution to the SDGs, a total of 77 indicators (key performance indicators) were proposed and compared to the current measurements that have been carried out. The results showed that the London Array used most of the suggested indicators without classifying them from the SDGs’ perspective. The proposed indicators will help cut operation costs, mitigate climate change and environmental damage, improve employee engagement and morale, reduce learning gaps, set goals and plans, and use resources efficiently. Full article

The rapid growth in fossil fuels has resulted in climate change that needs to be controlled in the near future. Several methods have been proposed to control climate change, including the development of efficient energy conversion devices. Fuel cells are environmentally friendly energy conversion devices that can be fuelled by green hydrogen, with only water as a by-product, or by using different biofuels such as biomass in wastewater, urea in wastewater, biogas from municipal and agricultural wastes, syngas from agriculture wastes, and waste carbon. This editorial discusses the fundamentals of the operation of the fuel cell, and their application in various sectors such as residential, transportation, and power generation. Full article

Chemical oxygen demand (COD) and total organic carbon (TOC) removal efficiencies of saline wastewater treatment indicate the efficiency of the electrochemical oxidation process. Therefore, the main target of this paper is to simultaneously increase COD and TOC removal efficiencies using artificial intelligence and modern optimization. Firstly, an accurate model based on ANFIS was established to simulate the electrochemical oxidation process in terms of reaction time, pH, salt concentration, and DC applied voltage. Compared with ANOVA, thanks to ANFIS modelling, the RMSE values are decreased by 84% and 86%, respectively, for COD and TOC models. Additionally, the coefficient of determination values increased by 3.26% and 7.87% for COD and TOC models, respectively. Secondly, the optimal reaction time values, pH, salt concentration, and applied voltage were determined using the hunger games search algorithm (HGSA). To prove the effectiveness of the HGSA, a comparison with a slime mold algorithm, sine cosine algorithm, and Harris’s hawks optimization was conducted. The optimal values were found at a pH of 8, a reaction time of 36.6 min, a salt concentration of 29.7 g/L, and a DC applied voltage of 9 V. Under this condition, the maximum COD and TOC removal values were 97.6% and 69.4%, respectively. The overall efficiency increased from 76.75% to 83.5% (increased by 6.75%). Full article

The use of fossil fuels has contributed to climate change and global warming, which has led to a growing need for renewable and ecologically friendly alternatives to these. It is accepted that renewable energy sources are the ideal option to substitute fossil fuels in the near future. Significant progress has been made to produce renewable energy sources with acceptable prices at a commercial scale, such as solar, wind, and biomass energies. This success has been due to technological advances that can use renewable energy sources effectively at lower prices. More work is needed to maximize the capacity of renewable energy sources with a focus on their dispatchability, where the function of storage is considered crucial. Furthermore, hybrid renewable energy systems are needed with good energy management to balance the various renewable energy sources’ production/consumption/storage. This work covers the progress done in the main renewable energy sources at a commercial scale, including solar, wind, biomass, and hybrid renewable energy sources. Moreover, energy management between the various renewable energy sources and storage systems is discussed. Finally, this work discusses the recent progress in green hydrogen production and fuel cells that could pave the way for commercial usage of renewable energy in a wide range of applications. Full article

Microbial fuel cells convert the chemical energy conserved in organic matter in wastewater directly to electrical energy through living microorganisms. These devices are environmentally friendly thanks to their ability to simultaneously produce electrical energy and wastewater treatment. The output power of the yeast microbial fuel cell (YMFC) depends mainly on glucose concentration and glucose/yeast ratio. Thus, the paper aims to boost the power of YMFC by identifying the best values of glucose concentration and glucose/yeast ratio. The suggested approach comprises fuzzy modelling and optimization. Fuzzy is used to build the model based on the measured data. In the optimization stage, the marine predators’ algorithm (MPA) is applied to identify the best glucose concentration values and glucose/yeast ratio corresponding to the maximum output power of YMFC. The results revealed the superiority of the combination of fuzzy and MPA compared with the response surface methodology (RSM) approach. Regarding the modelling accuracy, the coefficient of determination increased by 13.32% and 8.37%, respectively, for without methylene blue and with methylene blue compared with RSM. The integration between fuzzy and MPA succeeded in maximizing the output power from YMFC. Without MB, the power density increased by 25% and 29.3%, respectively, compared with measured data and RSM. In addition, with MB, the power density increased by 22.4% and 26%, compared with measured data and RSM. Full article

Hydrogen is a new promising energy source. Three operating parameters, including inlet gas flow rate, pH and impeller speed, mainly determine the biohydrogen production from membrane bioreactor. The work aims to boost biohydrogen production by determining the optimal values of the control parameters. The proposed methodology contains two parts: modeling and parameter estimation. A robust ANIFS model to simulate a membrane bioreactor has been constructed for the modeling stage. Compared with RMS, thanks to ANFIS, the RMSE decreased from 2.89 using ANOVA to 0.0183 using ANFIS. Capturing the proper correlation between the inputs and output of the membrane bioreactor process system encourages the constructed ANFIS model to predict the output performance exactly. Then, the optimal operating parameters were identified using the honey badger algorithm. During the optimization process, inlet gas flow rate, pH and impeller speed are used as decision variables, whereas the biohydrogen production is the objective function required to be maximum. The integration between ANFIS and HBA boosted the hydrogen production yield from 23.8 L to 25.52 L, increasing by 7.22%. Full article