AIMC Topic: Solid Waste

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Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor.

Bioresource technology Mar 12, 2016
The performance of a laboratory-scale anaerobic bioreactor was investigated in the present study to determine methane (CH4) content in biogas yield from digestion of organic fraction of municipal solid waste (OFMSW). OFMSW consists of food waste, veg...
Neural Networks, Computer Cities Organic Chemicals Principal Component Analysis Anaerobiosis Methane Fatty Acids, Volatile Bioreactors Hydrogen-Ion Concentration Laboratories Biofuels Food Waste Products Solid Waste
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Impact of pH Management Interval on Biohydrogen Production from Organic Fraction of Municipal Solid Wastes by Mesophilic Thermophilic Anaerobic Codigestion.

BioMed research international Dec 27, 2015
The biohydrogen productions from the organic fraction of municipal solid wastes (OFMSW) were studied under pH management intervals of 12 h (PM12) and 24 h (PM24) for temperature of 37 ± 0.1°C and 55 ± 0.1°C. The OFMSW or food waste (FW) along with it...
Anaerobiosis Hydrogen-Ion Concentration Hot Temperature Solid Waste Waste Management Hydrogen
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Prediction of municipal solid waste generation using nonlinear autoregressive network.

Environmental monitoring and assessment Nov 17, 2015
Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generati...
Neural Networks, Computer Algorithms Forecasting Models, Theoretical Environmental Monitoring Malaysia Developing Countries Solid Waste Waste Management Population Growth Gross Domestic Product
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Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran.

Waste management (New York, N.Y.) Oct 9, 2015
Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verifi...
Neural Networks, Computer Models, Theoretical Regression Analysis Seasons Iran Multivariate Analysis Solid Waste Waste Management
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Machine learning predicting sintering temperature for ceramsite production from multiple solid wastes.

Waste management (New York, N.Y.) Jul 15, 2025
An efficient machine learning model was developed to accurately predict the sintering temperature of ceramsite synthesized from various solid waste materials. Based on experimental data from 236 samples, eight key chemical components were defined as ...
Machine Learning Temperature Hot Temperature Solid Waste Ceramics
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