Modeling the impact of some independent parameters on the syngas characteristics during plasma gasification of municipal solid waste using artificial neural network and stepwise linear regression methods

2022 ◽  
Vol 157 ◽  
pp. 112052
Author(s):  
C. Chu ◽  
A. Boré ◽  
X.W. Liu ◽  
J.C. Cui ◽  
P. Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Plasma Gasification ◽  
Stepwise Linear Regression ◽  
Regression Methods ◽  
The Impact ◽  
Independent Parameters

scholarly journals Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor

2016 ◽  
Vol 58 ◽  
pp. 202-213 ◽  
Author(s):  
Daya Shankar Pandey ◽  
Saptarshi Das ◽  
Indranil Pan ◽  
James J. Leahy ◽  
Witold Kwapinski
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Fluidized Bed ◽  
Fluidized Bed Reactor ◽  
Artificial Neural ◽  
Waste Gasification ◽  
Modelling Approach

scholarly journals ARTIFICIAL NEURAL NETWORK APPLIED IN FORECASTING THE COMPOSITION OF MUNICIPAL SOLID WASTE IN IASI, ROMANIA

2021 ◽  
Vol 29 (3) ◽  
pp. 368-380
Author(s):  
Cristina Ghinea ◽  
Petronela Cozma ◽  
Maria Gavrilescu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Root Mean Square Error ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Mean Square Error ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Ann Model

Neural network time series (NNTS) tool was used to predict municipal solid waste composition in Iasi, Romania. The nonlinear input output (NIO) time series model and nonlinear autoregressive model with external (exogenous) input (NARX) included in this tool were selected. The coefficient of determination (R2) and root mean square error (RMSE) were chosen for evaluation. By applying NIO, the optimum model is 4-11-6 artificial neural network (ANN, R2 = 0.929) in the case of testing as for the validation, with all 0.849 and 0.885, respectively. Applying NARX, the suitable model became 4-13-6 ANN model, with R2 = 0.999 for training, 0.879 for testing, and 0.931, respectively 0.944 for validation and all. The resulted RMSE is zero for training and 0.0109 for validation in the case of this model which had 4 inputs, 13 neurons and 6 outputs. The four input variables were: number of residents, population aged 15–59 years, urban life expectancy, total municipal solid waste (ton/year). The suitable ANN model revealed the lowest root mean square error and the highest coefficient of determination. Results indicate that NNTS tool is a complex instrument, NARX is more accurate than NIO model, and can be used and applied easily.

scholarly journals Multilayer perceptron artificial neural network for the prediction of heating value of municipal solid waste

AIMS Energy ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 944-956 ◽  
Author(s):  
Obafemi O. Olatunji ◽  
◽  
Stephen Akinlabi ◽  
Nkosinathi Madushele ◽  
Paul A. Adedeji ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Multilayer Perceptron ◽  
Heating Value ◽  
Artificial Neural
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