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

Bayindir Y., CAĞCAĞ YOLCU Ö., TEMEL F. A., Turan N. G.

JOURNAL OF ENVIRONMENTAL MANAGEMENT, vol.318, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 318
  • Publication Date: 2022
  • Doi Number: 10.1016/j.jenvman.2022.115496
  • Journal Name: JOURNAL OF ENVIRONMENTAL MANAGEMENT
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, International Bibliography of Social Sciences, PASCAL, Aerospace Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Communication Abstracts, EMBASE, Environment Index, Geobase, Greenfile, Index Islamicus, MEDLINE, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Keywords: 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
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

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.