Evaluation of a cascade artificial neural network for modeling and optimization of process parameters in co-composting of cattle manure and municipal solid waste - (:unkn) unknown (My University, 2022)
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.
Keywords: Tarımsal Bilimler; Çevre Mühendisliği; Mühendislik ve Teknoloji; Agricultural Sciences; Environmental Engineering; Engineering and Technology; ÇEVRE BİLİMLERİ; Çevre / Ekoloji; Tarım ve Çevre Bilimleri (AGE); ENVIRONMENTAL SCIENCES; ENVIRONMENT/ECOLOGY; Agriculture & Environment Sciences (AGE); Doğa ve Peyzaj Koruma; Çevre Bilimi (çeşitli); Su Bilimi; Fizik Bilimleri; Yaşam Bilimleri; Nature and Landscape Conservation; Environmental Science (miscellaneous); Aquatic Science; Physical Sciences; Life Sciences; Cattle manure; Municipal solid waste; Co-composting; Cascade forward neural network; Feed-forward neural network; Genetic algorithm; PIG MANURE; ORGANIC-MATTER; GREEN WASTE; PHYSICOCHEMICAL PARAMETERS; STRUVITE CRYSTALLIZATION; CHEMICAL-PROPERTIES; SLUDGE; FRACTION; SOIL; PHYTOTOXICITY