Evaluation of a cascade artificial neural network for modeling and optimization of process parameters in co-composting of cattle manure and municipal solid waste

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. Multiobjective 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.

Automated summary

The study focused on improving, testing, and validating the Cascade Forward Neural Network (CFNN) for the co-composting of municipal solid waste (MSW) and cattle manure (CM). The CFNN model outperformed other models in predicting compost quality parameters, with Genetic Algorithm (GA) optimization leading to a total desirability value of 0.4455. It concluded that CFNN is a unique and effective tool for modeling and predicting MSW and CM composting processes.