Search

This paper focuses on a CCHP (Combined Cooling, Heating and Power) system based on co-firing in an Internal Combustion Engine (ICE) of biogas from anaerobic digestion and syngas produced by biomass gasification. From an energy perspective, in order for the mixture to make sense, a relationship setting the threshold percentage of methane in the biogas has been established. Gasification and Organic Rankine Cycle (ORC) models developed in Aspen Plus software and thermodynamic modeling of the Internal Combustion Engine (ICE) have been validated by comparison with experimental work conducted by other authors. The results show a decrease in energy efficiency with an increase in the percentage of methane in biogas and the mass ratio of the mixture. For extraction rates of 80 % and 90 %, respectively, exergy efficiency increases with an increase in the percentage of methane in biogas and the mass ratio of the mixture. Additionally, an increase in gasification temperature improves the efficiencies, while an increase in biogas temperature reduces them. The ICE is a significant source of exergy destruction.

The application of machine learning techniques for monitoring and diagnosing faults in photovoltaic (PV) systems has been shown to enhance the reliability of PV power generation. This research introduced a novel machine learning classifier for fault diagnosis in PV systems, utilizing an ensemble algorithm known as extra trees (ETC). The study initially proposed a system with two PV modules and developed a low-cost Arduino-based data logger to gather data from the PV system in free-fault and faulty conditions. Subsequently, the study evaluated six other advanced classifiers for fault diagnosis in PV systems, namely logistic regression (LR), k-nearest neighbor (kNN), support vector machine (SVM), decision tree (DT), AdaBoost, and random forest (RF) models using the collected data from the proposed PV system. The assessment of the various models' performance indicated that the extra trees model exhibits superior classification capabilities for partial shading (PS), open circuit (OCF), partial shading with bypass diode disconnected (PSBD), and combined partial shading with bypass diode disconnected plus open circuit (PSBDOC) faults. The results demonstrated that the new ETC classifier achieves an accuracy of 92%, surpassing the 91%, 87%, 7%, and 59% accuracy of the RF, DT, kNN, and LR classifiers, respectively. This highlights the effectiveness of the extra trees model in enhancing fault detection and classification by distinguishing between open circuits and twin faults. Consequently, these results can be utilized to develop advanced diagnostic tools for photovoltaic systems, thereby improving the reliability of solar technology and accelerating the rate of installation. [ABSTRACT FROM AUTHOR]

Sub Saharan Africa has a great renewable energy potential. Rural areas are suffering from poor energy access. Some systems designed to address this issue are still faced with some difficulties. Appropriate approaches and energy plant development will help remote areas to address the issue of electricity access. The current development of some micro hydro and micro solar energy plants is of poor quality and maintenance, sometimes resulting in failures. There are also some common mistakes made when promoting (designing) an energy system in an African environment. Identifying issues from local constraints and lessons learned will contribute to determining the appropriate sizing, technology and tools to correctly develop micro hydro and micro solar energy plants.

Copyright of African Journal of Ecology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)