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Methodology Article
Wind Energy Potential Assessment Using the Fuzzy Cognitive Mapping (FCM) Method
Issue:
Volume 13, Issue 3, September 2025
Pages:
86-106
Received:
25 January 2025
Accepted:
13 June 2025
Published:
4 July 2025
Abstract: This study focuses on wind energy potential assessment using the Fuzzy Cognitive Mapping (FCM) method. Given the limitations of fossil resources and their negative environmental impacts, the use of renewable energy sources, especially wind energy, has become an essential necessity. This research emphasizes the assessment of wind energy potential through an analytical fuzzy model that can effectively manage the complexities of energy systems. In this study, 13 key concepts were identified, including economic growth, energy prices, return on investment, investment, demand management, and other important economic, social, and environmental factors. Data were collected through structured interviews with industry experts and the analysis of standard questionnaires, and were subsequently analyzed using the FCM model. The results of this analysis show that factors such as investment levels in wind projects, economic sustainability, and increased energy supply security have significant impacts on the development of wind energy. Additionally, the results highlight the importance of using various scenarios to predict future developments and evaluate strategic decisions in the field of renewable energy. Finally, two scenarios for wind energy development in the country were presented, specifically focusing on investment and supply security. This research not only provides comprehensive analysis of wind energy potential but also offers a conceptual framework for planning and decision-making in the development of renewable energy.
Abstract: This study focuses on wind energy potential assessment using the Fuzzy Cognitive Mapping (FCM) method. Given the limitations of fossil resources and their negative environmental impacts, the use of renewable energy sources, especially wind energy, has become an essential necessity. This research emphasizes the assessment of wind energy potential th...
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Research Article
Ground Monitoring of Microseismic Based on Low Signal-to-Noise Ratio
Liang Beiyuan*
,
Wei Jiang,
Li Yanchao
Issue:
Volume 13, Issue 3, September 2025
Pages:
107-117
Received:
23 March 2025
Accepted:
7 July 2025
Published:
23 July 2025
Abstract: At present, the principle, data acquisition, data processing, and/or interpretation of many microseismic monitoring methods around the world are far from the requirements of microseismic monitoring characteristics, and impossible to analyze the microseismicity. The main technical reason for the situation is still the lack of understanding the characteristics of microseismic and corresponding monitoring for it, so that the monitoring R&D and application are not based on strict seismology, geology, rock mechanics, a large number of reliable experiments and mathematical statistics. We first summarize the characteristics of microseismic and monitoring for it. Based on this, as well as the basic requirements of seismometry, various monitoring methods are discussed, including their applicable conditions, limitations and development prospects. The summary and discussion show that the development and application of microseismic monitoring, even avoiding strong noise sources as much as possible during data acquisition, and effectively denoising during processing, have to face the reality of low signal-to-noise ratio (S/N): in most cases, whether the microseismic signal is implied in the background noise recording, the number of microseismics, and the initial motion form of any microseismic arrival are not known. We then report that in the past 2-3 years, our Vector Scanning (VS) for microseismic ground monitoring has been greatly improved, including: an in-depth understanding of the available principles, the refinement of the conditions necessary for the success of the application with a high probability, and the quantitative integration of automated data processing and interpretation; Among them, the most important is an in-depth understanding of the existing principles: VS uses the focal mechanism (i.e., the relationship between the strain and the stress fields) to implement large-scale migration and stacking, carry out various possible combinations of positive and negative initial movements for all seismic stations, and select the spatiotemporal distribution with high probability of the greater microseismic released energy (i.e., the correlation coefficient recorded of stations, also the minimum S/N). A large number of cases are available for mathematical statistics, which provide a basis for analyzing the details of microseismicity. Finally, we describe the specific morphology of the stimulated rock volume in stimulation, the equivalent microseismic focal mechanism, and the effect of production measures such as in-situ pump shutdown. The necessary conditions, monitoring output patterns and analyses described in the paper also provide a basis for the test of the microseismic methods.
Abstract: At present, the principle, data acquisition, data processing, and/or interpretation of many microseismic monitoring methods around the world are far from the requirements of microseismic monitoring characteristics, and impossible to analyze the microseismicity. The main technical reason for the situation is still the lack of understanding the chara...
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Research Article
Adaptive ECMS for Hybrid Electric Vehicles Based on SOC Feedback
Issue:
Volume 13, Issue 3, September 2025
Pages:
118-132
Received:
23 June 2025
Accepted:
7 July 2025
Published:
30 July 2025
Abstract: The negative impact of atmospheric pollutants emitted by mobile vehicles on human health and environment have been increasingly attracting the attention of public and private policy makers. Those entities and many other have been working together to ensure that emissions related to the consumption of fossil fuels are considerably minimize. One of the main authors of this problem seems to be the means of displacement we are using every day, thermal cars. It is therefore necessary to explore and develop more economical approaches and modern alternatives for vehicle energy consumption. It is within this framework that automobile manufacturers, in collaboration with researchers, are committed to developing new forms of transport, the most ideal of which are electric vehicles and hybrid electric vehicles. This paper discusses the modeling and optimization of energy management of hybrid electric vehicles. The article develops an energy management system to minimize the energy consumption of a hybrid electric vehicle. Hybrid electric vehicle control is managed by the Adaptive Equivalent Consumption Minimization Strategy (A_ECMS). This strategy performs an update of the equivalence factor through the battery state of charge feedback method. The simulation results shown that the A_ECMS approach achieved an average fuel saving of nearly 40% for FTP-75 driving cycle and 13% for the class cycle.
Abstract: The negative impact of atmospheric pollutants emitted by mobile vehicles on human health and environment have been increasingly attracting the attention of public and private policy makers. Those entities and many other have been working together to ensure that emissions related to the consumption of fossil fuels are considerably minimize. One of t...
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Research Article
Improved 2RC-PNGV Modeling and Adaptive Sage-Husa H Infinity Filtering for Battery Power State Estimation Based on Multi-Parameter Constraints
Yan Xinyu,
Wang Shunli*,
Xu Tao,
Cheng Liangwei,
Carlos Fernandez,
Frede Blaabjerg
Issue:
Volume 13, Issue 3, September 2025
Pages:
133-141
Received:
14 July 2025
Accepted:
11 August 2025
Published:
16 August 2025
Abstract: With the transformation of the global energy landscape, lithium-ion batteries have become an important component in the field of new energy storage. Accurate assessment of battery status plays a crucial role in efficiently utilizing electrical energy and extending the battery's service life. The key parameters of battery status include charging state (SOC) and power state (SOP). This paper constructs an improved 2RC-PNGV battery equivalent circuit model and introduces an innovative method to enhance the dynamics of particle swarm optimization. At the same time, an adaptive H infinity (∞) filtering algorithm based on Sage-Husa and a temperature-constrained SOP estimation method for lithium-ion batteries is designed. Among them, the real-time dynamic particle swarm optimization algorithm adjusts the forgetting factor in each iteration; the adaptive H∞ filtering algorithm based on Sage-Husa improves the accuracy of SOC estimation by adapting the noise covariance matrix. Moreover, the multi-parameter constrained state estimation method for lithium-ion batteries can effectively track the changes in state quantities with different durations and instantaneous values. The improved forgetting factor least squares method has an error of fewer than 0.02 volts in the voltage simulation test, with high accuracy. The adaptive H∞ filtering algorithm based on Sage-Husa achieves higher estimation accuracy in three complex operating scenarios, ensuring that the state quantity estimation error remains below 2%. The maximum estimation error of the multi-parameter constrained state quantity estimation method is less than 84.00 watts. These research results provide a solid theoretical foundation for ensuring the safety and efficient operation of batteries.
Abstract: With the transformation of the global energy landscape, lithium-ion batteries have become an important component in the field of new energy storage. Accurate assessment of battery status plays a crucial role in efficiently utilizing electrical energy and extending the battery's service life. The key parameters of battery status include charging sta...
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Research Article
Valorization of Kitchen Waste from University Restaurants for Biogas Production - Case of Joseph KI-ZERBO University
Issue:
Volume 13, Issue 3, September 2025
Pages:
142-149
Received:
18 July 2025
Accepted:
31 July 2025
Published:
25 August 2025
DOI:
10.11648/j.ajee.20251303.15
Downloads:
Views:
Abstract: The management of food waste in university restaurants represents a significant environmental challenge, particularly in developing countries where waste treatment infrastructure is limited. This study investigates the energy valorization of kitchen waste generated by the central restaurant and its annexes (RU SIAO, RU Chinoise) at Joseph KI-ZERBO University in Ouagadougou, Burkina Faso, through anaerobic digestion. The objective is to propose a sustainable solution for managing food waste, reducing its environmental impact, and producing renewable energy. A quantitative and qualitative characterization of the waste was conducted over several weeks, assessing its volume, composition, and methanogenic potential. The results reveal an annual production of approximately 188.78 tons of waste, of which 72.2% consists of fermentable organic matter (rice, pasta, sauces, vegetables, peels), ideal for methanization. Laboratory tests, conducted with inoculum from the ONEA biogas plant in Kossodo, demonstrated a biogas yield of 320 to 350 L/kg of dry matter, with a methane content of 57 to 62%, suitable for local applications such as cooking, heating, or electricity production. The nutrient-rich digestate can be used as organic fertilizer for campus green spaces, reinforcing circular economy principles. Physicochemical analyses indicate a dry matter content of 22.4%, a volatile organic matter content of 86.5%, and an optimal C/N ratio of 25.3, promoting efficient anaerobic digestion under mesophilic conditions (35 ± 2°C). Challenges include seasonal variability in waste composition, the need for rigorous sorting to eliminate contaminants (plastics, packaging), and investment in infrastructure such as industrial digesters. To ensure success, the study recommends a strengthened selective sorting system, medium-scale pilot projects to validate technical and economic feasibility, and awareness-raising among students and staff. By adopting this strategy, Joseph KI-ZERBO University could reduce greenhouse gas emissions, minimize waste management costs, and become a model of sustainability for African academic institutions, contributing to sustainable development goals.
Abstract: The management of food waste in university restaurants represents a significant environmental challenge, particularly in developing countries where waste treatment infrastructure is limited. This study investigates the energy valorization of kitchen waste generated by the central restaurant and its annexes (RU SIAO, RU Chinoise) at Joseph KI-ZERBO ...
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