Anzeige
Erschienen in:
2018 | OriginalPaper | Buchkapitel
Predicting the Calorific Value of Municipal Solid Waste of Ghaziabad City, Uttar Pradesh, India, Using Artificial Neural Network Approach
verfasst von : Dipti Singh, Ajay Satija, Athar Hussain
Erschienen in: Soft Computing: Theories and Applications
Verlag: Springer Singapore
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
The municipal solid waste (MSW) can be used as renewable energy resource. The amount of energy generated depends upon calorific value of the MSW. The aim of present study is to discuss about the chemical composition and corresponding calorific value of 39 samples of municipal solid waste of Ghaziabad City, Uttar Pradesh (India). In this study, artificial neural network (ANN) technique has been applied to predict the calorific value of MSW of the city. The elements such as carbon, hydrogen, oxygen, nitrogen, sulfur, phosphorus, potassium, and ash obtained from chemical analysis have been used as input data points to predict the calorific value of MSW in ANN model. The developed ANN model has been validated with the help of minimum value of mean squared error [0.003703 (training), 0.03760 (validation), and 0.2269 (testing operations)] and optimized value of coefficient of correlation (0.9088) between observed and predicted calorific values of MSW samples. The proposed ANN model has shown better predictive results.