Higher heating value(HHV)is the key parameter for replacing Refuse-Derived Fuel(RDF)with fossil fuels in the cement industry.HHV can be measured with a bomb calorimeter or predicted from direct elemental data by using...Higher heating value(HHV)is the key parameter for replacing Refuse-Derived Fuel(RDF)with fossil fuels in the cement industry.HHV can be measured with a bomb calorimeter or predicted from direct elemental data by using regression models.Both methods require the continuous use of special laboratory equipment and are time consuming.To overcome these limitations,this study aims to predict the HHV value of RDF from predicted elemental data by using regression models.Therefore,once the predicted elemental data are generated,there will be no need to have continuous elemental data to predict HHV.Predicted elemental data were generated from direct elemental data and Near Infrared(NIR)camera-based spectrometric data by using a deep learning model.A convolutional neural networks(CNN)model was used for deep learning and was trained with 10,500 NIR image samples,each of which was 28×28×1.Different regression models(Linear,Tree,Support-Vector Machine,Ensemble and Gaussian process)were applied for HHV prediction.According to these results,higher R2 values(>0.85)were obtained with Gaussian process models(except for the Rational Quadratic model)for the predicted elemental data.Among the Gaussian models,the highest R2(0.95)but the lowest Root Mean Square Error(RMSE)(0.0563),Mean Squared Error(MSE)(0.0317)and Mean Absolute Error(MAE)(0.0431)were obtained with the Mattern 5/2 model.The results of predictions from predicted elemental data were compared to predictions from direct elemental data.The results show that the regression from predicted elemental data has an adequate prediction(R2=0.95)compared to the prediction from the direct elemental data(R^(2)=0.99).展开更多
Solid waste is a promising renewable fuel that can substitute conventional fuel. According to the researchers, thermoconversion of solid waste such as municipal solid waste or residual household waste (RHW) is benefic...Solid waste is a promising renewable fuel that can substitute conventional fuel. According to the researchers, thermoconversion of solid waste such as municipal solid waste or residual household waste (RHW) is beneficial to society. However, due to its heterogeneity, the gasification of RHW is more complex. This review article discusses the steps that RHW must undergo before its thermoconversion and the state of the art of solid waste gasification. First, characterisation methods of RHW are surveyed. Second, the properties of RHW, the production lines of refuse derived fuel (RDF) from RHW, the influence of RDF composition and operating parameters such as equivalence ratio and temperature are reviewed. Moreover, RDF gasification products, scientific barriers and proposed solutions are evaluated. In conclusion, concerning emissions, costs and technical aspects related to each thermochemical process, it can be said that gasification is a promising technique for the recovery of RHW. However, studies on cogasification of waste and biomass on a pilot-industrial scale are still scarce and synergistic effects of this cogasification need to be clarified.展开更多
Solid waste management is a severe challenge in India due to massive and rapid growth in waste generation rates,environmental difficulties,and financial constraints for proper treatment.Poorly managed municipal solid ...Solid waste management is a severe challenge in India due to massive and rapid growth in waste generation rates,environmental difficulties,and financial constraints for proper treatment.Poorly managed municipal solid waste(MSW)has substantial negative consequences for society,including financial and aesthetic harm,contamination of natural resources,environmental pollution,and severe health danger.Both qualitative and quantitative factors are required to select the appropriate solid waste treatment and disposal technologies.Multi-Criteria decision-making tools helped in analyzing solid waste in terms of qualitative and quantitative factors.In this paper,seven criteria and their sub-criteria are selected for ranking solid waste treatment and disposal technology using fuzzy-analytic hierarchy process.The results showed that composting is the most suitable option for solid waste treatment and disposal technology,followed by refuse-derived fuel.The incineration and sanitary landfills are the least preferred MSW management alternatives.The sensitivity analysis reveals a high consistency,robustness,and stability level.展开更多
基金supported by the Turkish Scientific and Technological Research Council(TUBITAK)(Project No.118Y135).
文摘Higher heating value(HHV)is the key parameter for replacing Refuse-Derived Fuel(RDF)with fossil fuels in the cement industry.HHV can be measured with a bomb calorimeter or predicted from direct elemental data by using regression models.Both methods require the continuous use of special laboratory equipment and are time consuming.To overcome these limitations,this study aims to predict the HHV value of RDF from predicted elemental data by using regression models.Therefore,once the predicted elemental data are generated,there will be no need to have continuous elemental data to predict HHV.Predicted elemental data were generated from direct elemental data and Near Infrared(NIR)camera-based spectrometric data by using a deep learning model.A convolutional neural networks(CNN)model was used for deep learning and was trained with 10,500 NIR image samples,each of which was 28×28×1.Different regression models(Linear,Tree,Support-Vector Machine,Ensemble and Gaussian process)were applied for HHV prediction.According to these results,higher R2 values(>0.85)were obtained with Gaussian process models(except for the Rational Quadratic model)for the predicted elemental data.Among the Gaussian models,the highest R2(0.95)but the lowest Root Mean Square Error(RMSE)(0.0563),Mean Squared Error(MSE)(0.0317)and Mean Absolute Error(MAE)(0.0431)were obtained with the Mattern 5/2 model.The results of predictions from predicted elemental data were compared to predictions from direct elemental data.The results show that the regression from predicted elemental data has an adequate prediction(R2=0.95)compared to the prediction from the direct elemental data(R^(2)=0.99).
文摘Solid waste is a promising renewable fuel that can substitute conventional fuel. According to the researchers, thermoconversion of solid waste such as municipal solid waste or residual household waste (RHW) is beneficial to society. However, due to its heterogeneity, the gasification of RHW is more complex. This review article discusses the steps that RHW must undergo before its thermoconversion and the state of the art of solid waste gasification. First, characterisation methods of RHW are surveyed. Second, the properties of RHW, the production lines of refuse derived fuel (RDF) from RHW, the influence of RDF composition and operating parameters such as equivalence ratio and temperature are reviewed. Moreover, RDF gasification products, scientific barriers and proposed solutions are evaluated. In conclusion, concerning emissions, costs and technical aspects related to each thermochemical process, it can be said that gasification is a promising technique for the recovery of RHW. However, studies on cogasification of waste and biomass on a pilot-industrial scale are still scarce and synergistic effects of this cogasification need to be clarified.
文摘Solid waste management is a severe challenge in India due to massive and rapid growth in waste generation rates,environmental difficulties,and financial constraints for proper treatment.Poorly managed municipal solid waste(MSW)has substantial negative consequences for society,including financial and aesthetic harm,contamination of natural resources,environmental pollution,and severe health danger.Both qualitative and quantitative factors are required to select the appropriate solid waste treatment and disposal technologies.Multi-Criteria decision-making tools helped in analyzing solid waste in terms of qualitative and quantitative factors.In this paper,seven criteria and their sub-criteria are selected for ranking solid waste treatment and disposal technology using fuzzy-analytic hierarchy process.The results showed that composting is the most suitable option for solid waste treatment and disposal technology,followed by refuse-derived fuel.The incineration and sanitary landfills are the least preferred MSW management alternatives.The sensitivity analysis reveals a high consistency,robustness,and stability level.
