Journal
JOURNAL OF ENVIRONMENTAL MANAGEMENT
Volume 318, Issue -, Pages -Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jenvman.2022.115496
Keywords
Cattle manure; Municipal solid waste; Co-composting; Cascade forward neural network; Feed-forward neural network; Genetic algorithm
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The present study aimed to improve, test, and validate the Cascade Forward Neural Network (CFNN) for co-composting of municipal solid waste and cattle manure. The results showed that CFNN exhibited superior performance in compost modeling and prediction.
The present study was carried out to improve, test, and validate the Cascade Forward Neural Network (CFNN) for co-composting of municipal solid waste (MSW) and cattle manure (CM). Composting was performed in vessel pilot-scale reactors with different CM rates for 105 days. The CFNN used 5 input variables containing CM and MSW mixture combinations, and 1 output for each of the compost quality parameters. The CFNN results were compared with Response Surface Methodology (RSM) and Feed Forward Neural Network (FFNN) results. Multi-objective optimization process using Genetic Algorithm (GA), the total desirability, which has a much better value than the RSM, was obtained as 0.4455 and the CM ratio and processing time were determined as approximately 23.39% and 104.86 days, respectively. It is concluded that CFNN is a unique modeling tool, exhibiting superior modeling and prediction performance in MSW and compost modeling for CM.
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