Modelling and optimization of sewage sludge composting using biomass ash via deep neural network and genetic algorithm

Dogan H., Aydın Temel F., CAĞCAĞ YOLCU Ö., Turan N. G.

Bioresource Technology, cilt.370, 2023 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 370
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.biortech.2022.128541
  • 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: Biomass fly ash, Sewage sludge, Co-composting, Machine learning, Cascade neural network, Heuristic algorithm, REDUCING NITROGEN LOSS, GASEOUS EMISSIONS, AERATION RATE, FOOD WASTE, MATURITY, STABILITY, PARAMETERS, QUALITY
  • Marmara Üniversitesi Adresli: Evet

Özet

© 2022 Elsevier LtdIn this study, the use of Deep Cascade Forward Neural Network (DCFNN) was investigated to model both linear and non-linear chaotic relationships in co-composting of dewatered sewage sludge and biomass fly ash (BFA). Model results were evaluated in comparison with RSM, Feed Forward Neural Network (FFNN) and Feed Back Neural Network (FBNN), and Cascade Forward Neural Network (CFNN). DCFNN produced predictive results with MAPE values less than 1% for all datasets in all experimental designs except one with 1.99%. Furthermore, the decision variables were optimized by Genetic Algorithm (GA). The desirability level obtained from the optimization results was found to be 100% in a few designs and above 95% in all other designs. The results showed that DCFNN is a reliable and consistent tool for modeling composting process parameters, also GA is a satisfactory tool for determining which outputs the input parameters will produce in an experimental setup.