Application of artificial neural network and multiple linear regression in modeling nutrient recovery in vermicompost under different conditions. (May 2020)

• Record Type: Journal Article
• Authors: Hosseinzadeh, Ahmad; Baziar, Mansour; Alidadi, Hossein; Zhou, John L.; Altaee, Ali; Najafpoor, Ali Asghar; Jafarpour, Salman
• Is Part Of: Bioresource technology. Volume 303(2020)
• Publication Date: 2020-05
• Subjects: Nutrient recovery -- Vermicompost -- Nitrogen -- Phosphorus -- Municipal solid waste -- Modeling; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2020.122926
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: Vermicomposting increased TN and TP in final products by 1.5 and 16 times. ANN and MLR models were developed to predict TN and TP in vermicompost. ANN models demonstrated better performance than MLR. TN was the most important factor for prediction of TP. Carbon nitrogen ratio was the most important factor for prediction of TN. Abstract: Vermicomposting is one of the best technologies for nutrient recovery from solid waste. This study aims to assess the efficiency of Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models in predicting nutrient recovery from solid waste under different vermicompost treatments. Seven chemical and biological indices were studied as input variables to predict total nitrogen (TN) and total phosphorus (TP) recovery. The developed ANN and MLR models were compared by statistical analysis including R-squared (R 2 ), Adjusted-R 2, Root Mean Square Error and Absolute Average Deviation. The results showed that vermicomposting increased TN and TP proportions in final products by 1.5 and 16 times. The ANN models provided better prediction for TN and TP with R 2 of 0.9983 and 0.9991 respectively, compared with MLR models with R 2 of 0.834 and 0.729. TN and C/N ratio were key factors for TP and TN prediction by ANN with percentages of 17.76 and 18.33.