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- Title
Prediction Modelling to Enhance Anaerobic Co-digestion Process of OFMSW and Bio-flocculated Sludge Using ANN.
- Authors
Shroff, Kinjal Chintan; Shah, Nirav G.
- Abstract
Artificial neural networks (ANNs) simulate an anaerobic co-digestion process of Organic Fraction of Municipal Solid Waste (OFMSW) and bio-flocculated sludge for a mesophilic lab-scale semi-continuous feed reactor. The operational, substrate quality and process control parameters such as Organic Loading Rate, Hydraulic Retention Time, pH, VFA/Alkalinity ratio and Total Solids are input variables and methane yield and Volatile Solids removal are outputs for ANN modelling. The lab-scale experimental results are used to develop a prediction model using fitting application for ANN. The network architecture was optimized to achieve accurate predictions, resulting in a 5-19-2 architecture for methane yield and a 5-17-2 architecture for %VSremoval. The training was performed using the Bayesian Regularization (trainbr) algorithm, leading to high coefficients of determination (R2) of 0.953 and 0.978 for methane yield and %VSremoval, respectively. The results demonstrate the effectiveness of neural network-based modelling in capturing complex relationships within the methane yield process, facilitating accurate prediction of crucial output parameters.
- Subjects
PREDICTION models; ANAEROBIC digestion; ARTIFICIAL neural networks; SEWAGE sludge; BIOREACTORS
- Publication
Pollution (2383451X), 2024, Vol 10, Issue 1, p481
- ISSN
2383-451X
- Publication type
Article
- DOI
10.22059/POLL.2023.365129.2065