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Improving kitchen waste composting maturity by optimizing the processing parameters based on machine learning model.

Shang Ding1, Wuji Huang1, Weijian Xu1

  • 1Department of Environmental Engineering, Zhejiang University, Hangzhou 310029, PR China.

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Summary

Machine learning accurately predicts composting maturity and identifies key parameters. Optimal conditions involve specific temperature, moisture, pH, and nutrient levels for enhanced compost quality.

Keywords:
Aerobic compostingInterpretive analysisKitchen wasteMachine learning

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Area of Science:

  • Environmental Science
  • Biotechnology
  • Data Science

Background:

  • Composting is a vital process for waste management and nutrient recycling.
  • Predicting composting maturity and optimizing parameters are crucial for efficient decomposition.
  • Machine learning offers novel analytical capabilities for complex biological processes.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting composting maturity.
  • To identify key parameters influencing composting and determine their optimal ranges.
  • To provide data-driven insights for enhancing the composting process.

Main Methods:

  • Utilized a Stacking machine learning model for prediction.
  • Employed SHapley Additive exPlanations (SHAP) and Partial Dependence Analysis (PDA) for parameter importance.
  • Analyzed composting data to determine optimal conditions for different stages.

Main Results:

  • The Stacking model demonstrated excellent prediction accuracy for composting maturity.
  • Identified optimal mesophilic conditions (30-45°C, 55-65% moisture, pH 6.3-8.0).
  • Determined optimal thermophilic nutrient adjustments (total nitrogen > 2.3%, total organic carbon > 35%).

Conclusions:

  • Model-based optimization strategies significantly improve composting maturity.
  • Optimized conditions enhance compost microbial communities and carbon cycling functions.
  • This study offers novel insights into advanced composting process enhancement.