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Machine learning regression algorithms to predict emissions from steam boilers.

Bárbara D Ross-Veitía1, Dayana Palma-Ramírez1, Ramón Arias-Gilart1

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Machine learning accurately predicts industrial boiler emissions like CO, CO2, and NOx, along with exhaust gas temperature. This approach enhances operational efficiency and reduces environmental pollution.

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Area of Science:

  • * Industrial process optimization
  • * Environmental engineering
  • * Applied artificial intelligence

Background:

  • * Modeling complex chemical-physical processes is crucial for industrial advancement.
  • * Machine learning (ML) offers a powerful approach to analyze and optimize industrial boiler operations.
  • * Predicting emissions and temperature is vital for efficiency and environmental compliance.

Purpose of the Study:

  • * To predict carbon monoxide (CO), carbon dioxide (CO2), nitrogen oxides (NOx) emissions, and exhaust gas temperature in industrial boilers.
  • * To evaluate and compare various ML regression algorithms for predictive modeling.
  • * To identify the most effective ML model for boiler emission and temperature prediction.

Main Methods:

  • * Utilized real-world data from approximately 20 industrial boilers.
  • * Input variables included ambient temperature, working pressure, steam production, and fuel type.
  • * Employed and compared multiple ML regression algorithms: Gradient Boosting Regression (GBR), Deep Neural Networks (DNN), Multiple Linear Regression (MLR), and Random Forest Regression (RFR).
  • * Emission data collected using a TESTO 350 Combustion Gas Analyzer.

Main Results:

  • * Gradient Boosting Regression (GBR) demonstrated superior performance in predicting emissions and temperature.
  • * The GBR model achieved a mean absolute error of 0.51 and a coefficient of determination of 99.80% on test data.
  • * Deep Neural Networks (DNN) also showed better predictive performance compared to traditional Linear Regression models.

Conclusions:

  • * The Gradient Boosting Regression model offers a highly accurate method for predicting industrial boiler emissions (CO, CO2, NOx) and exhaust gas temperature.
  • * This ML-driven approach provides a novel tool for enhancing boiler efficiency and mitigating environmental impact.
  • * The study highlights the effectiveness of ML in optimizing complex industrial processes.