In Bioresource technology
In this study, the use of Deep Cascade Forward Neural Network (DCFNN) was investigated to model both linear and non-linear chaotic relationships in co-composting of dewatered sewage sludge and biomass fly ash (BFA). Model results were evaluated in comparison with RSM, Feed Forward Neural Network (FFNN) and Feed Back Neural Network (FBNN), and Cascade Forward Neural Network (CFNN). DCFNN produced predictive results with MAPE values less than 1% for all datasets in all experimental designs except one with 1.99%. Furthermore, the decision variables were optimized by Genetic Algorithm (GA). The desirability level obtained from the optimization results was found to be 100% in a few designs and above 95% in all other designs. The results showed that DCFNN is a reliable and consistent tool for modeling composting process parameters and, also GA is a satisfactory tool for determining which outputs the input parameters will produce in an experimental setup.
Dogan Hale, Temel Fulya Aydın, Yolcu Ozge Cagcag, Turan Nurdan Gamze
2022-Dec-26
Biomass fly ash, Cascade neural network, Co-composting, Heuristic algorithm, Machine learning, Sewage sludge
