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Related Experiment Video

Updated: Jun 15, 2025

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Explainable machine learning-driven predictive performance and process parameter optimization for caproic acid

Hongzhi Ma1, Yichan Liu2, Jihua Zhao2

  • 1Department of Environmental Science and Engineering, University of Science and Technology Beijing, Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, 100083, China; Xinjiang Key Laboratory of Clean Conversion and High Value Utilization of Biomass Resources, School of Resource and Environmental Science, Yili Normal University, Yining 835000, China.

Bioresource Technology
|August 21, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning models optimized caproic acid production. XGBoost excelled, identifying hydraulic retention time and butyric acid as key factors for enhanced bioproduction.

Keywords:
Anaerobic fermentationCaproic acid productionMachine learningOrganic wasteParameter optimizationPrediction

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Area of Science:

  • Biotechnology
  • Chemical Engineering
  • Data Science

Background:

  • Caproic acid is a valuable chemical with diverse applications.
  • Optimizing its production is crucial for sustainable and economic bioprocesses.
  • Current methods require advanced predictive modeling for efficiency.

Purpose of the Study:

  • To develop and compare machine learning (ML) models for predicting caproic acid production performance.
  • To identify key process parameters influencing caproic acid yield.
  • To optimize bioprocess conditions for maximum caproic acid output.

Main Methods:

  • Development and evaluation of four ML prediction models, including XGBoost.
  • Application of SHAP (SHapley Additive exPlanations) for feature importance and interaction analysis.
  • Integration with Adaptive Variation Particle Swarm Optimization (AVPSO) for process optimization.

Main Results:

  • XGBoost demonstrated superior performance with R² values of 0.998 (training) and 0.885 (test).
  • Hydraulic retention time (HRT) and butyric acid concentration were identified as critical factors.
  • Synergistic effects of butyric acid and lactic acid were revealed, with glucose addition benefiting co-fermentation.

Conclusions:

  • The study presents a versatile ML-driven strategy for optimizing bioprocesses, specifically caproic acid production.
  • Optimal conditions determined via AVPSO achieved a maximum caproic acid yield of 8.64 g/L.
  • This approach offers a transformative potential for sustainable and economically viable bioproduction.