Tool path optimization of a 3D printer via an enhanced electromagnetism-like mechanism algorithm for solar panel brackets fabrication

electromagnetism-like mechanism (em) algorithm, a meta-heuristic algorithm for global optimisation, utilises an attraction-repulsion mechanism to move the sample points towards the optimality. the original em has a strong ability for diversification. and a simple random line search algorithm for local search has been added to improve its intensification. however, it plays a quite limited role and the performance of the original em is not satisfactory. therefore, in this paper, a modified solis and wets local search is proposed to improve the performance of em. two self-adapt parameters are introduced into solis and wets local search. in addition, an accelerated force formula is adopted for high-dimensional function optimisation. some benchmark test problems have been used to evaluate the proposed-algorithm. results obtained are compared with those from other algorithms including original em algorithm and all kinds of particle swarm optimisation methods. the comparisons show that the novel em has achieved significant improvement.

An improved electromagnetism-like mechanism algorithm for constrained optimization

This paper presents an electromagnetism-like mechanism (EM) algorithm in order to solve cell formation problems. Based on the attraction-repulsion principle in electromagnetic theory, each combination of the part and machine groups was regarded as a charged particle. The total force calculation and the move along the total force calculation were then determined out to either maximize the cell group effectiveness or minimize the exceptional elements (EE) and the number of vacancies (NV). A comparison with other algorithms in the related literature found that the final result of the EM algorithm met the expected quality in terms of the exceptional elements and number of vacancies, regardless of whether or not new cells were added. Key words: Cellular manufacturing system, group technology, electromagnetic-like mechanism.

The estimation of the power values obtained from photovoltaic (PV) systems is of critical importance for the reliable and economical use of solar energy panels. This estimation affects many processes, starting from the installation phase of solar panels to guiding electricity companies, energy management, and distribution. At the same time, it is necessary to detect the adaptations of solar panels in a timely manner and reach the optimal production capacity to provide the most efficient energy production. In this context, Artificial Neural Networks (ANN) were used to estimate the power values obtained from PV panels. In this study, heuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Clonal Selection Algorithm (CSA), Ant Colony Optimization, and Artificial Bee Colony (ABC) were used to estimate the power values obtained from monocrystalline and polycrystalline photovoltaic panels. In the verification of the estimation results, the most common statistical evaluation criteria, Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Variance (R2) equations were used. The estimation values made with the PSO algorithm were the closest to the real values. 98.95% estimation was achieved in monocrystalline photovoltaic solar panels and 93.94% in polycrystalline photovoltaic solar panels.

Solar collectors and photovoltaic (PV) solar panels can convert solar radiation into heat and electrical energy. A hybrid PV/thermal (PV/T) solar panel was tested in this study. The hybrid PV/T solar panel was tested using a spiral absorber and water flow levels range from 0.01 kg/s to 0.03 kg/s. As a result, with variable water flow rates and irradiation intensity of 700 and 900 W/m2, the efficiency of hybrid PV/T solar panels varied. The highest electrical efficiency attained was 4.06% in hybrid PV/T solar panels at 900 W/m2 with 0.025 kg/s of mass flow while 3.65% by PV solar panels. In addition, plotted current-voltage and power-voltage curves were used to show the electrical properties of the solar PV panels and hybrid PV/T solar panels.

Although Egypt falls within the solar belt, solar energy use on the household level remains very sparse. This research examines the factors driving and hindering households’ willingness to shift from fossil fuels to solar energy. The study evaluates the socioeconomic determinants of households’ willingness to adopt photovoltaic (PV) solar panels, based on a random distribution of 301 questionnaires in Cairo and Giza – two of Egypt’s most densely populated governorates. Two models were used? a Logistic Model and a Structural Equations Model (SEM) that regresses the willingness to install PV panels on household characteristics. For a deeper analysis of how socioeconomic differences shape renewable energy adoption, we separated the SEM model into behavioural and economic characteristics to be able to identify whether willingness to adopt solar PV systems, (or the lack of), stem from either set of factors. Accordingly, these insights can inform policy and support the implementation of targeted actions in both directions. Behavioural factors include household heads’ willingness to take a credit facility to finance solar PV panels, household heads’ level of education, living in rural versus urban areas, household size, whether any of the household members needed medical attention owing to heat waves during past year, household heads’ age and gender. Economic variables include household heads’ monthly income, whether the household owns a farm, whether the household owns gas at home, access to internet connection at home, access to credit facility, the number of times on average the household was disconnected from electricity per week during the past year, and education reflecting the socioeconomic level of the household head. Behavioural variables that positively contribute to adopting solar panels are revealed to be willingness to take a credit facility, age, education and household members needing medical attention owing to heat waves during past year; behavioural variables that contribute negatively to solar panel adoption include gender of the household head (in favour of females) and the geographical dimension (in favour of rural areas). The economic model’s results reveal a positive relationship for the average monthly income of the household head, ownership of farmland, and the years of education of the household head. Households with access to internet at home is negatively associated with the willingness to install solar panels. Suggested policy implications include packages that target programs supporting low-income households. Keywords: PV Solar Panels, Behavioural & Socioeconomic determinants, Structural Equation Model, Logistic Regression Model

Due to the complex nature of training neural network (NN), this problem has gained popularity in the nonlinear optimization field. In order to avoid falling into local minimum because of inappropriate initial weights, a number of global search techniques are developed. This paper applies a novel global algorithm, which is electromagnetism-like mechanism (EM) algorithm, to train NN and the EM based algorithm for neural network training is presented. The performance of the proposed algorithm is evaluated in classification problems and the comparison with BP and GA algorithms shows its effectiveness.

Simultaneous adjustment of all cutter locations in a tool path using meta-heuristic algorithms provides a systematic approach to reducing machining errors in 5-axis flank machining of ruled surfaces. However, these algorithms experienced unsatisfactory quality of optimal solutions and lengthy search time in high-dimensional search space. To solve these problems, we propose an iterative optimization scheme that progressively simplifies solution space by sampling techniques. Akaike information criterion (AIC) is used to screen significant factors from sampling data. An electromagnetism-like mechanism (EM) algorithm searches through a simplified solution space constructed only using these factors. An iteration process consisting of such sampling, screening, and searching steps repeats several times until final optimal solutions are obtained. Test results of representative surfaces validate the effectiveness of the proposed scheme. Both solution quality and search efficiency are improved comparing to those produced by previous studies. This work enhances the practicality of optimization-driven tool path planning in 5-axis flank machining.

Nonlinear systems design by a novel fuzzy neural system via hybridization of electromagnetism-like mechanism and particle swarm optimisation algorithms

A NEW HEURISTIC-EM FOR PERMUTATION FLOWSHOP SCHEDULING

Solar photovoltaic (PV) panels are projected to become the largest contributor of clean electricity generation worldwide. Maintenance and cleaning strategies are crucial for optimizing solar PV operations, ensuring a satisfactory economic return of investment. Nanocoating may have potential for optimizing PV operations; however, there is insufficient scientific evidence that supports this idea. Therefore, this study aims to investigate the effectiveness of nanocoating on the performance of solar photovoltaic (PV) panels installed in Al Seeb, Oman. A further study was also carried out to observe the influence of coating layers on the performance of PV panels. One SiO2 nanocoated solar panel, another regularly cleaned PV panel, and a reference uncleaned panel were used to carry out the study. The site of the study was treeless and sandy, with a hot and dry climate. A data logger was connected to the solar PV panel and glass panel to record the resulting voltage, current, temperature, and solar radiation. It was observed that nanocoated PV panels outperformed both regular PV panels and uncleaned PV panels. Nanocoated PV panels demonstrated an average efficiency of 21.6%, showing a 31.7% improvement over uncleaned panels and a 9.6% improvement over regularly cleaned panels. Although nanocoating displayed high efficiency, regular cleaning also contributes positively. Furthermore, even though nanocoated PV panels outperformed the other two panels, it is important to note that the performance difference between the regular cleaned PV panels and the nanocoated PV panels was small. This indicates that regular cleaning strategies and nanocoating can further contribute to maintaining a more efficient solar PV system. Coating in many layers was also observed to influence the performance of PV panels insignificantly, mainly the fourth layer coating appeared to have formed sufficient mass to retain heat.

Ultra-low energy reverse osmosis with thermal energy recovery from photovoltaic panel cooling and TFC RO membrane modification

In this paper, a combined photovoltaic (PV) solar panels (PVSP) and Super capacitor (SC) renewable power generating unit feeding 3-phase, 380V/ 50 Hz loads is studied. The renewable power generation unit is operated as an off-grid setup. SC is used to maintain the required load power demand during lower power generation stages by PV system due to changes in environmental conditions. It is aimed to have clean power at stable frequency and voltage on the loads. The voltage on loads in the system is controlled by both Proportional-Integral (PI) and Fuzzy Tuned PI (FTPI). A comparison is made for the performance of these controllers to select the one that has better applicable properties. The complete setup contains PVSP, SC, filters, inverter, controllers, measurement devices and loads. All of the subsystem models are developed and combined using operational blocks of MATLAB/Simulink to obtain the overall system model.

Technical potential of materials recovered from end-of-life solar PV panels could exceed $15 billion by 2050. The global solar photovoltaic (PV) boom currently underway will represent a significant untapped business opportunity as decommissioned solar panels enter the waste stream in the years ahead, according to a report released today by the International Renewable Energy Agency (IRENA) and the International Energy Agency's Photovoltaic Power Systems Programme (IEA-PVPS). The report, End-of-Life Management: Solar Photovoltaic Panels, is the first-ever projection of PV panel waste volumes to 2050 and highlights that recycling or repurposing solar PV panels at the end of their roughly 30-year lifetime can unlock a large stock of raw materials and other valuable components. It estimates that PV panel waste, comprised mostly of glass, could total 78 million tonnes globally by 2050. If fully injected back into the economy, the value of the recovered material could exceed USD 15 billion by 2050. This potential material influx could produce 2 billion new panels or be sold into global commodity markets, thus increasing the security of future PV supply or other raw material-dependent products. The report suggests that addressing growing solar PV waste, and spurring the establishment of an industry to handle it, would require: the adoption of effective, PV-specific waste regulation; the expansion of existing waste management infrastructure to include end-of-life treatment of PV panels, and; the promotion of ongoing innovation in panel waste management. In most countries, PV panels fall under the classification of 'general waste' but the European Union (EU) was the first to adopt PV-specific waste regulations, which include PV-specific collection, recovery, and recycling targets. EU's directive requires all panel producers that supply PV panels to the EU market (wherever they may be based) to finance the costs of collecting and recycling end-of-life PV panels put on the market in Europe. End-of-Life Management: Solar Photovoltaic Panels, is the second of several solar-focused publications IRENA is releasing this summer. Last week, IRENA released The Power to Change, which predicts average costs for electricity generated by solar and wind technologies could decrease by between 26 and 59 per cent by 2025. Later this week, IRENA will release Letting in the Light: How Solar Photovoltaics Will Revolutionize the Electricity System - which provides a comprehensive overview of solar PV across the globe and its prospects for the future.

The increasing demand for sustainable and reliable power sources in portable and implantable medical devices has led to growing interest in photovoltaic (PV) energy harvesting. Traditional power sources, such as batteries, are limited by finite energy capacity and frequent replacement or recharging needs, particularly in implantable devices where surgical intervention is required for battery replacement. Photovoltaic energy harvesting, which converts light into electrical energy, offers a promising alternative, especially in environments with consistent light exposure. This review provides an in-depth analysis of the advancements in PV technologies for powering medical devices. It covers various types of PV materials, design innovations, and the integration of energy storage systems. Additionally, the review highlights the application of PV systems in both external and implantable medical devices, while addressing critical challenges such as ensuring biocompatibility, optimizing performance in low-light conditions, and miniaturizing PV systems for implantation. The potential of PV energy harvesting to improve device longevity and reduce the need for invasive procedures is emphasized. This review concludes by outlining the current challenges and future directions needed to achieve widespread clinical adoption, aiming to contribute to the development of sustainable power solutions in healthcare.