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

Fulya Aydın Temel1, Ozge Cagcag Yolcu2, Nurdan Gamze Turan3

  • 1Department of Environmental Engineering, Faculty of Engineering, Giresun University, Giresun 28200, Turkey.

Bioresource Technology
|January 7, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) aids composting process optimization and prediction. However, AI algorithms like Genetic Algorithms (GA) are underutilized for model parameter optimization, focusing instead on ML parameter tuning.

Keywords:
CompostingMachine learningMaturityModelingProcess stability

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Area of Science:

  • Environmental Science
  • Computer Science
  • Biotechnology

Background:

  • Composting process stability and performance prediction strategies are gaining research interest.
  • Machine learning (ML) is increasingly applied for optimizing composting, predicting data, detecting anomalies, and managing complex variables.

Approach:

  • This review examines the application, perspectives, and challenges of ML algorithms in composting.
  • Key ML algorithms discussed include Artificial Neural Networks (ANNs), Random Forest (RF), Adaptive-network-based fuzzy inference systems (ANFIS), Support Vector Machines (SVMs), and Deep Neural Networks (DNNs).

Key Points:

  • The review highlights limitations in current error and performance metrics used in composting studies.
  • Artificial Intelligence (AI) algorithms, such as Genetic Algorithm (GA), Differential Evaluation Algorithm (DEA), and Particle Swarm Optimization (PSO), are predominantly used for tuning ML algorithm parameters, not core model parameters.

Conclusions:

  • ML offers significant potential for enhancing composting process management and prediction.
  • Further research should explore broader applications of AI algorithms for comprehensive composting process optimization.