首页|Findings in the Area of Machine Learning Reported from National Technological Institute of Mexico (Multi-objective and Machine Learning Strategies for Addressing the Water-energy-waste Nexus In the Design of Energy Systems)
Findings in the Area of Machine Learning Reported from National Technological Institute of Mexico (Multi-objective and Machine Learning Strategies for Addressing the Water-energy-waste Nexus In the Design of Energy Systems)
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NETL
NSTL
Elsevier
Current study results on Machine Learning have been published. According to news originating from Guanajuato, Mexico, by NewsRx correspondents, research stated, “This paper presents a multi-objective strategy coupled with fuzzy C-means to address synergies and conflicts around the waterenergy- waste nexus. The proposal deals with an optimal design and operation scheme in a multi-objective framework where the objective functions are linked to the nexus.” Funders for this research include Chemical Engineering Department of the TecNM-Instituto Tecnologico de Celaya, Consejo Nacional de Humanidades, Ciencias y Tecnologias (CONAHCyT). Our news journalists obtained a quote from the research from the National Technological Institute of Mexico, “The objective functions are normalized to obtain subsets of functions that are used to assess the performance of the nexus and compute trade-off optimal solutions. The soft clustering algorithm is used to determine levels of synergy among Pareto optimal solutions. The proposed strategy has the potential to address problems with many-objective functions, in which the Pareto front has representation limitations, allowing for the identification of conflicts. The soft clustering algorithm indicates different levels of synergy among the Pareto optimal solutions based on the level of membership. The focus of the analysis is on distributed generation systems. As a demonstration, the coupling of a combined heat and power unit with a biodigester and a thermal storage system, interconnected to the local grid.”
GuanajuatoMexicoNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningNational Technological Institute of Mexico
NETL
NSTL
Elsevier
