In recent decades,the generation of Municipal Solid Waste(MSW)is steadily increasing due to urbanization and technological advancement.The col-lection and disposal of municipal solid waste cause considerable environme...In recent decades,the generation of Municipal Solid Waste(MSW)is steadily increasing due to urbanization and technological advancement.The col-lection and disposal of municipal solid waste cause considerable environmental degradation,making MSW management a global priority.Waste-to-energy(WTE)using thermochemical process has been identified as the key solution in this area.After evaluating many automated Higher Heating Value(HHV)predic-tion approaches,an Optimal Deep Learning-based HHV Prediction(ODL-HHVP)model for MSW management has been developed.The objective of the ODL-HHVP model is to forecast the HHV of municipal solid waste,based on its oxy-gen,water,hydrogen,carbon,nitrogen,sulphur and ash constituents.In addition,the ODL-HHVP model contains a Deep Support Vector Machine(DSVM)regres-sion component that can accurately predict the HHV.In addition,the Beetle Swarm Optimization(BSO)method is utilised as a hyperparameter optimizer in conjunction with the DSVM model,resulting in the highest HHV prediction accu-racy.A comprehensive simulation study is conducted to validate the performance of the ODL-HHVP method.The Multiple Linear Regression(MLR),Genetic Pro-gramming(GP),Resilient backpropagation(RP),Levenberg Marquardt(LM)and DSVM approaches have attained an ineffective result with RMSEs of 4.360,2.870,3.590,3.100 and 3.050,respectively.The experimentalfindings demon-strate that the ODL-HHVP technique outperforms existing state-of-art technolo-gies in a variety of respects.展开更多
文摘In recent decades,the generation of Municipal Solid Waste(MSW)is steadily increasing due to urbanization and technological advancement.The col-lection and disposal of municipal solid waste cause considerable environmental degradation,making MSW management a global priority.Waste-to-energy(WTE)using thermochemical process has been identified as the key solution in this area.After evaluating many automated Higher Heating Value(HHV)predic-tion approaches,an Optimal Deep Learning-based HHV Prediction(ODL-HHVP)model for MSW management has been developed.The objective of the ODL-HHVP model is to forecast the HHV of municipal solid waste,based on its oxy-gen,water,hydrogen,carbon,nitrogen,sulphur and ash constituents.In addition,the ODL-HHVP model contains a Deep Support Vector Machine(DSVM)regres-sion component that can accurately predict the HHV.In addition,the Beetle Swarm Optimization(BSO)method is utilised as a hyperparameter optimizer in conjunction with the DSVM model,resulting in the highest HHV prediction accu-racy.A comprehensive simulation study is conducted to validate the performance of the ODL-HHVP method.The Multiple Linear Regression(MLR),Genetic Pro-gramming(GP),Resilient backpropagation(RP),Levenberg Marquardt(LM)and DSVM approaches have attained an ineffective result with RMSEs of 4.360,2.870,3.590,3.100 and 3.050,respectively.The experimentalfindings demon-strate that the ODL-HHVP technique outperforms existing state-of-art technolo-gies in a variety of respects.
