Prediction and optimization of nitrogen losses in co-composting process by using a hybrid cascaded prediction model and genetic algorithm

Kabak E. T., CAĞCAĞ YOLCU Ö., TEMEL F. A., TURAN N. G.

CHEMICAL ENGINEERING JOURNAL, cilt.437, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 437
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.cej.2022.135499
  • Dergi Adı: CHEMICAL ENGINEERING JOURNAL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aqualine, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, Chimica, Compendex, Food Science & Technology Abstracts, INSPEC, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Co-composting, Food Waste, Poultry Waste, Cascade Forward Neural Network, Response Surface Methodology, Genetic Algorithm, PIG-MANURE, PROCESS PARAMETERS, BIOCHAR AMENDMENT, ORGANIC-MATTER, GREENHOUSE-GAS, SOLID-WASTE, FOOD WASTE, SLUDGE, STABILIZATION, EMISSIONS
  • Marmara Üniversitesi Adresli: Evet

Özet

In this study, the effects of co-composting of food waste and poultry waste on nitrogen losses and maturity were investigated. The different mixture ratios were used and the effectiveness of co-composting was compared with mono-composting of each waste. Also, a linear and nonlinear hybrid tool based on a cascaded forward neural network was used to estimate nitrogen losses of all reactors. The proposed hybrid tool produced predictions with mean absolute percentage error (MAPE) values of approximately 1-2% on all data points containing the training, validation, and test datasets. These results can be considered outstanding, especially when compared to Response Surface Methodology (RSM), which produces predictions with MAPE values of approximately 15% on all data points. The optimal values from the genetic algorithm (GA) were for poultry waste of 17.20%, for a duration of 97.64 days. These findings are invaluable, especially when it is costly and difficult to renew the composting process by creating a new experimental setup.