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Simulation and optimization of cheese whey additive for value-added compost production: Hyperparameter tuning

Cem Şahin1, Fulya Aydın Temel2, Ozge Cagcag Yolcu3

  • 1Department of Environmental Engineering, Faculty of Engineering, Ondokuz Mayıs University, Samsun, 55200, Turkiye.

Journal of Environmental Management
|October 3, 2024
PubMed
Summary

Adding 3% cheese whey to sewage sludge and poultry waste composting significantly enhances compost quality and process efficiency. Machine learning models, particularly Gaussian Process Regression, accurately simulated and optimized this process for effective waste management.

Keywords:
CompostingGaussian process regressionNeural network regressionPoultry wasteSewage sludgeSupport vector regression

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Area of Science:

  • Environmental Science
  • Waste Management
  • Agricultural Science

Background:

  • Cheese whey presents a significant wastewater treatment challenge due to high organic and mineral content.
  • Current whey management strategies are costly, and its application in composting remains limited.
  • Effective valorization of cheese whey is crucial for sustainable waste management.

Purpose of the Study:

  • To investigate the impact of cheese whey addition on the composting of sewage sludge and poultry waste.
  • To evaluate compost quality and process efficiency with varying cheese whey ratios.
  • To develop and validate machine learning models for simulating and optimizing the composting process.

Main Methods:

  • Composting experiments were conducted with sewage sludge and poultry waste, incorporating cheese whey at different percentages.
  • Physicochemical parameters of the compost were analyzed to assess quality and process efficiency.
  • Machine learning algorithms, including Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Neural Network Regression (NNR), were employed for simulation.
  • Hyperparameter tuning and a genetic algorithm were used for model optimization.

Main Results:

  • Addition of 3% cheese whey demonstrably improved composting efficiency and final compost quality for both feedstocks.
  • Gaussian Process Regression (GPR) proved to be the most effective algorithm for realistic and reliable process simulations.
  • Optimization studies using a genetic algorithm indicated optimal cheese whey ratios of 3.27% for sewage sludge and 3.15% for poultry waste.
  • Experimental and simulation results showed strong compatibility, validating the predictive models.

Conclusions:

  • Cheese whey can be effectively utilized as a composting amendment to enhance waste treatment and produce higher quality compost.
  • Machine learning, especially GPR, offers a powerful tool for modeling, simulating, and optimizing biological waste treatment processes.
  • This study presents a novel strategy for cheese whey recovery and provides insights into its effects on composting dynamics and predictive modeling.