Artificial intelligence and machine learning approaches in composting process: A review

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.2022.128539
  • 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, Maturity, Process stability, Modeling, Machine learning, CHICKEN MANURE, AMMONIA EMISSION, WASTE, PREDICTION, BIOCHAR, MITIGATION, REGRESSION, STABILITY, TRENDS, GENES
  • Marmara Üniversitesi Adresli: Evet

Özet

© 2022 Elsevier LtdStudies on developing strategies to predict the stability and performance of the composting process have increased in recent years. Machine learning (ML) has focused on process optimization, prediction of missing data, detection of non-conformities, and managing complex variables. This review investigates the perspectives and challenges of ML and its important algorithms such as Artificial Neural Networks (ANNs), Random Forest (RF), Adaptive-network-based fuzzy inference systems (ANFIS), Support Vector Machines (SVMs), and Deep Neural Networks (DNNs) used in the composting process. In addition, the individual shortcomings and inadequacies of the metrics, which were used as error or performance criteria in the studies, were emphasized. Except for a few studies, it was concluded that Artificial Intelligence (AI) algorithms such as Genetic algorithm (GA), Differential Evaluation Algorithm (DEA), and Particle Swarm Optimization (PSO) were not used in the optimization of the model parameters, but in the optimization of the parameters of the ML algorithms.