Modelling and optimization of sewage sludge composting using biomass ash via deep neural network and genetic algorithm - CAĞCAĞ YOLCU, ÖZGE (My University, 2023)
© 2022 Elsevier LtdIn 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, also GA is a satisfactory tool for determining which outputs the input parameters will produce in an experimental setup.
Keywords: Tarımsal Bilimler; Ziraat; Tarım Makineleri; Tarımda Enerji; Biyoyakıt Teknolojisi; Biyomedikal Mühendisliği; Mühendislik ve Teknoloji; Agricultural Sciences; Agriculture; Farm Machinery; Energy in Agriculture; Biofuels Technology; Biomedical Engineering; Engineering and Technology; Mühendislik, Bilişim ve Teknoloji (ENG); Mühendislik; MÜHENDİSLİK, ÇEVRE; ENERJİ VE YAKITLAR; MÜHENDİSLİK, BİYOMEDİKAL; Engineering, Computing & Technology (ENG); ENGINEERING; ENGINEERING, ENVIRONMENTAL; ENERGY & FUELS; ENGINEERING, BIOMEDICAL; Biyomühendislik; Fizik Bilimleri; Çevre Mühendisliği; Yenilenebilir Enerji, Sürdürülebilirlik ve Çevre; Atık Yönetimi ve Bertarafı; Bioengineering; Physical Sciences; Environmental Engineering; Renewable Energy, Sustainability and the Environment; Waste Management and Disposal; Biomass fly ash; Cascade neural network; Co-composting; Heuristic algorithm; Machine learning; Sewage sludge; Biomass fly ash; Sewage sludge; Co-composting; Machine learning; Cascade neural network; Heuristic algorithm