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

Yilmaz E. C., TEMEL F. A., CAĞCAĞ YOLCU Ö., Turan N. G.

BIORESOURCE TECHNOLOGY, vol.363, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 363
  • Publication Date: 2022
  • Doi Number: 10.1016/j.biortech.2022.127910
  • Journal Name: BIORESOURCE TECHNOLOGY
  • Journal Indexes: 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
  • Keywords: 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
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

In 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 con-taining 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.