Machine Learning Shows Promise for Unlocking Aerobic Composting Optimization
• Aerobic composting plays a pivotal role in circular economies, but introduces complex nonlinear relationships between parameters, hampering process understanding and optimization.
• Mathematical models have limitations in depicting the complex nonlinear biochemical reactions in composting; machine learning offers advantages in comprehending these reactions.
• Machine learning has found extensive recent application in predicting, optimizing, and monitoring organic waste composting processes.
• Reviewed studies show machine learning exhibits higher predictive accuracy in simulating composting than traditional models, with additional benefits like speed and lower computational costs.
• Machine learning application in aerobic composting is still in early stages overall, presenting opportunities for further exploration of its potential.