首页|Study Data from Yunnan Normal University Update Understanding of Machine Learning (Optimization of an Eco-friendly Municipal Solid Waste-to-multi-generation Energy Scheme Integrated By Msw Gasification and Hsofc: Regression Analysis and Machine ...)
Study Data from Yunnan Normal University Update Understanding of Machine Learning (Optimization of an Eco-friendly Municipal Solid Waste-to-multi-generation Energy Scheme Integrated By Msw Gasification and Hsofc: Regression Analysis and Machine ...)
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Elsevier
A new study on Machine Learning is now available. According to news reporting originating from Yunnan, People’s Republic of China, by NewsRx correspondents, research stated, “The utilization of regression models and machine learning algorithms in waste-to-energy systems has the potential to enhance the efficiency and effectiveness of these systems. The predictive capacity to ascertain energy production and optimize operational methodologies can contribute to increased efficacy and proficiency of waste-to-energy systems, thereby diminishing waste generation and fostering the generation of sustainable energy.” Our news editors obtained a quote from the research from Yunnan Normal University, “The gasification technology for Municipal Solid Waste (MSW) provides a multitude of environmental advantages and holds significant importance in safeguarding the environment. This approach entails the use of sustainable and environmentally friendly practices in waste management, which effectively mitigates the release of greenhouse gases while also preserving finite natural resources. Considering the plethora of benefits it offers, it is anticipated that the utilization of MSW gasification as a waste management technique will experience a noticeable surge in popularity in the near future. This study presents a modeling approach for a multigeneration energy system that utilizes municipal solid waste (MSW) gasification technology integrated with a proton conducting solid oxide fuel cell (SOFC). Machine learning algorithms have been devised for the purposes of predicting and optimizing the performance of systems. The heating generation, power generation, electrical efficiency, exergy efficiency, and emission levels of the system have been accurately predicted, exhibiting a substantial level of precision as indicated by R2 values predominantly exceeding 94%.”
YunnanPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningTechnologyYunnan Normal University
NETL
NSTL
Elsevier
