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

TEMEL F. A., CAĞCAĞ YOLCU Ö., Turan N. G.

BIORESOURCE TECHNOLOGY, vol.370, 2023 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Review
  • Volume: 370
  • Publication Date: 2023
  • Doi Number: 10.1016/j.biortech.2022.128539
  • 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, Maturity, Process stability, Modeling, Machine learning, CHICKEN MANURE, AMMONIA EMISSION, WASTE, PREDICTION, BIOCHAR, MITIGATION, REGRESSION, STABILITY, TRENDS, GENES
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

Studies 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.