Modeling and optimization of process parameters in co-composting of tea waste and food waste: Radial basis function neural networks and genetic algorithm

Yılmaz E. C., Aydın Temel F., CAĞCAĞ YOLCU Ö., Turan N. G.

Bioresource Technology, cilt.363, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 363
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.biortech.2022.127910
  • Dergi Adı: Bioresource Technology
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, Compendex, EMBASE, Environment Index, Food Science & Technology Abstracts, Geobase, INSPEC, MEDLINE, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Composting, Organic Waste, Stability, Maturity, Artificial Neural Networks, GREENHOUSE-GAS EMISSION, REDUCING NITROGEN LOSS, PIG MANURE, ORGANIC FRACTION, BULKING AGENTS, BIOCHAR, AMMONIA, ADDITIVES, EVOLUTION, RESIDUES
  • Marmara Üniversitesi Adresli: Evet

Özet

© 2022 Elsevier LtdIn this study, the effects of co-composting of food waste (FW) and tea waste (TW) on the losses of total nitrogen (TN), total organic carbon (TOC), and moisture content (MC) were investigated. TW and FW were composted separately and compared with the co-composting of FW and TW at different ratios. While the MC losses were close to each other in all processes, the lowest TN and TOC losses were found in the composting process containing 25% TW as 26.80% and 40.11%, respectively. Moreover, Radial Basis Function Neural Networks (RBFNNs) were used to predict the losses of TN, TOC, and MC. The outputs of RBFNN were compared with Response Surface Methodology (RSM), Support Vector Regression (SVR), and Feed Forward Neural Network (FF-NN). In addition, the optimal parameter values were determined by Genetic algorithm (GA). As a result, it will be possible to simulate and improve different co-composting processes with obtained data.