首页|Recent Findings in Machine Learning Described by Researchers from Saveetha Schoo l of Engineering (Model Forecasting of Hydrogen Yield and Lower Heating Value In Waste Mahua Wood Gasification With Machine Learning)
Recent Findings in Machine Learning Described by Researchers from Saveetha Schoo l of Engineering (Model Forecasting of Hydrogen Yield and Lower Heating Value In Waste Mahua Wood Gasification With Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting from Tamil Nadu, India, by NewsRx jo urnalists, research stated, “Biomass is an excellent source of green energy with numerous benefits such as abundant availability, net carbon zero, and renewable nature. However, the conventional methods of biomass combustion are polluting a nd poor efficiency processes.” Financial support for this research came from Deanship of Scientific Research at Shaqra University. The news correspondents obtained a quote from the research from the Saveetha Sch ool of Engineering, “Biomass gasification overcomes these challenges and provide s a sustainable method for the supply of greener fuel in the form of producer ga s. The producer gas can be employed as a gaseous fuel in compression ignition en gines in dual-fuel systems. The biomass gasification process is a complex as wel l as a nonlinear process that is highly dependent on the ambient environment, ty pe of biomass, and biomass composition as well as the gasification medium. This makes the modeling of such systems quite difficult and time-consuming. Modern ma chine learning (ML) techniques offer the use of experimental data as a convenien t approach to modeling and forecasting such systems. In the present study, two m odern and highly efficient ML techniques, random forest (RF) and AdaBoost, were employed for this purpose. The outcomes were employed with results of a baseline method, i.e., linear regression. The RF could forecast the hydrogen yield with R2 as 0.978 during model training and 0.998 during the model test phase. AdaBoos t ML was close behind with R2 at 0.948 during model training and 0.842 during th e model test phase. The mean squared error was as low as 0.17 and 0.181 during m odel training and testing, respectively. In the case of the low heating value mo del, during model testing, the R2 was 0.971 and RF and AdaBoost, respectively, d uring model training and 0.842 during the model test phase.”
Tamil NaduIndiaAsiaCyborgsElemen tsEmerging TechnologiesGasesHydrogenInorganic ChemicalsMachine Learnin gSaveetha School of Engineering
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