Cleaner Energy Systems最新文献

This study explores the economic, energetic, exergy efficiency, and environmental benefits of energy integration in pressure-swing distillation, focusing on the separation of tetrahydrofuran/water and acetone/chloroform azeotropes. Heat integration and heat pump techniques are applied to reduce energy consumption. Three energy-efficient configurations are examined, comparing total annual cost (TAC), total energy consumption (TEC), CO₂ emissions, and second-law efficiency. In the tetrahydrofuran/water system, heat integration and heat pump technologies outperform conventional processes, achieving up to 50.2% TAC reduction, 59.6% TEC reduction, 82.8% CO₂ emission reduction, and thermodynamic efficiencies up to 23.5%. In the acetone/chloroform system, similar improvements are observed, with up to 70.9% TAC reduction, 87.2% CO₂ emission reduction, and thermodynamic efficiencies up to 17.6%. These findings demonstrate the effectiveness of energy-saving strategies, endorsing process intensification for environmentally sustainable azeotropic mixture separations.

Global municipal solid waste production is rising, causing significant environmental, health, and economic issues. Developed countries have advanced recycling technologies, but cities like Dhaka, Bangladesh—among the most densely populated-struggle with inadequate waste management. This feasibility study aims to improve environmental protection and create new energy sources by proposing a waste management system across Dhaka, focusing on waste valorization for bioenergy with optimized efficiency and minimal impact. The study includes design and optimization of a biomass-based power plant to meet the energy needs of EV charging stations and the national grid, evaluating its economic performance through discounted cash flow and payback period analyses. The paper explores the integration of an EV charging station powered by biogas, addressing the growing need for EV infrastructure in Dhaka. By evaluating biomass generators as a greener alternative to fossil fuels, the study analyzes the technical, economic, and environmental feasibility, including CO2 emissions, using HOMER Pro.

This research employs a comprehensive Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis to investigate the advancement of photovoltaic (PV) energy in Northern Nigeria. The study delves into the intricacies of introducing PV systems within the context of economic challenges, including issues such as currency volatility and inflation, which amplify costs and impede capital investments. Environmental factors, such as dust and sandstorms, are identified as obstacles diminishing the efficiency of solar panels. Additionally, security concerns in remote areas elevate operational costs and influence investment decisions. This paper proposes effective mitigation strategies, encompassing widespread public awareness campaigns to augment market engagement, the establishment of mini-grid systems for enhanced energy distribution, customised on-the-job training programs to foster local expertise in PV technology, and the utilisation of micro-grid systems as experimental grounds for regulatory and policy testing. By synthesising these components, the study offers a comprehensive overview of the prerequisites essential for the successful proliferation of PV energy in Northern Nigeria. Emphasis is placed on the potential for solar energy to significantly contribute to the region's sustainable development and achieve energy independence when the identified strength, and opportunities are exploited. The key strength identified are the average Global horizontal irradiance (GHI) of 5.436 kWh/m², Direct Normal Irradiance (DNI) of 1534–1680 kWh/m², Levelised Cost of Electricity (LCoE) of $ 0.1, and an opportunity to fully utilise the over $ 7.88 million grant authorised by the African Development Bank (AfDB) from the Sustainable Energy Fund for Africa.

This paper conducts a comprehensive assessment of the potential of water, solar, and wind resources for sustainable energy generation. The study is situated in a Moroccan region within eastern Saharan Africa. It presents a detailed comparative analysis between a photovoltaic system (PV) integrated with a pumped hydro storage (PHS), a wind turbine, and a conventional grid, considering both energy production and economic analysis using HOMER software. Moreover, the paper provides an initial social impact assessment of hybrid energy systems integrating locally available water resources, especially during the winter season, alongside photovoltaic and wind technologies. This evaluation delves into aspects of rural electrification and community development. The findings underscore the potential of sustainable energy solutions to drive economic and social progress in the studied area by harnessing the region’s water resources. We proposed this technology because the owners of the area do not greatly benefit from the seasonal groundwater that passes through the valley, despite the presence of a dam. Accordingly, we will exploit this water to generate energy and achieve energy self-sufficiency. By harnessing this underutilized resource, we aim to provide sustainable energy solutions and drive economic and social progress in the region. The results given by HOMER identify the most cost-effective system capable of serving the load at the lowest cost of energy (COE) of about $0.03831 and net present cost (NPC) of about $262,596 under the modeled conditions, and the most satisfactory system chosen by the HOMER optimizer is a PV/Wind/PHS-based hybrid energy system.

Use of fossil fuel in industries causes Carbon emission, which is mostly responsible for global warming. Another aspect is that environment friendly energy production and sustainable development goal is highly dependent on the production of clean energy. According to the IEA solar energy has a huge potential and will contribute up to 16 % of the global electricity by 2050. Hence, prediction of solar energy production has a great deal of demand in renewable energy sector. This paper compares machine-learning algorithms to evaluate the impact of PV module back surface temperature (degC) on the generated power. Support Vector Machine for Regression (SMOreg), Multilayer Perceptron (ANN), Linear Regression, M5 Rules, k-Nearest-Neighbor (Ibk) and Random Forest methods are employed to test their performance in different ratio of training and testing data. The dataset comprises five independent parameters such as PV module back surface temperature (degC), Dry bulb temperature (degC), Relative humidity (%RH), Atmospheric pressure (mb), and Precipitation (mm). The dependent parameter is Maximum power of PV module (W). The correlation coefficient was determined by varying the percentage of training data from 60 % to 85 %. The numerical tests were done for two data sets, one dataset includes all the independent variables and another one excluded the PV module back surface temperature. Except for M5 Rules, other models exhibit consistent correlation coefficients with several of training data. All models demonstrate a dependency on the PV module back surface temperature, with Random Forest surpassing others in overall performance with a correlation coefficient of 0.9713 at 75 % of training set.

Floating photovoltaic (FPV) power generation requires accurate short-term forecasting to optimize operational efficiency and enhance grid integration. This study investigates the application of machine learning models for predicting FPV power generation using data from the Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) solar installation, which has a capacity of 157.20 kWp. Data were collected at 15-minute intervals from January 15 to January 21, 2024, encompassing nine input features such as ambient temperature, transient horizontal irradiation, daily horizontal irradiation, AC voltages, and AC currents for phases A, B, and C, with the total active power in kW as the target variable. The dataset was divided into a training set (first five days) and a testing set (remaining two days), and five machine learning models—Neural Networks (NN), Random Forest (RF), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM)—were employed. The results indicate that the Neural Networks model consistently outperforms the other machine learning algorithms in terms of predictive accuracy. These findings underscore the efficacy of machine learning techniques in forecasting FPV power generation, which has significant implications for enhancing the operational efficiency and grid integration of floating solar installations.

In rural areas lacking an electricity grid, cell phone operators use generators to power their facilities. At the same time, however, the local population is finding it difficult to use the cell phones and other electronic devices for which these operators are deploying their efforts. This situation, due to the problem of access to energy, hinders universal access to telecommunications. The present study aims to solve this problem using microgrid techniques. A microgrid consisting of photovoltaic panels, a genset and storage batteries has been designed to meet the needs of cell phone operators' sites in Bapure, a rural locality in Togo. The focus is on managing energy flows between the various sources of the microgrid, and between the needs of the cell phone operators' site and those of the local population. To resolve the lack of solar irradiation data at Bapure, hourly solar irradiation was predicted using the Adaptive Neuro Fuzzy Inference System (ANFIS) algorithm to obtain a realistic result. Optimization studies were then carried out using the Particle Swarm Optimization (PSO) algorithm to determine the optimum system configuration to ensure continuity of service at the operator's site. The simulation results show that the proposed system has a surplus of energy production at all times, which can be used to supply electricity to the population at a cost equal to 0.0185 USD, with a solar energy utilization rate of 98,95 % and a generator that only needs to operate at 0.15 % throughout the year. The results obtained indicate that a renewable energy system can provide a more efficient solution for electrifying the rural mobile operator's sites and the local population, and can improve the quality of service for the telecommunications industries.