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Updated: Sep 21, 2025

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

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Published on: September 7, 2019

Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions.

Javier Rubio-Loyola¹, Wolph Ronald Shwagger Paul-Fils¹

¹Centre for Research and Advanced Studies (Cinvestav), Ciudad Victoria 87130, Mexico.

Sensors (Basel, Switzerland)

|May 28, 2022

Summary

This study introduces a machine learning approach to predict black carbon emissions from industrial furnaces. The developed model accurately forecasts undesirable emissions in advance, aiding industrial process optimization.

Keywords:

black carbon industrial furnaces machine learning predictive models

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Last Updated: Sep 21, 2025

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Area of Science:

Industrial Engineering
Environmental Science
Data Science

Background:

Industrial furnaces (IFs) are critical in manufacturing, requiring precise heat treatment.
Emission of black carbon (EoBC) from IFs is a significant operational challenge.
EoBC is influenced by fuel quality, furnace efficiency, operational practices, and process conditions.

Purpose of the Study:

To present a methodological approach for predicting EoBC in IFs using machine learning (ML).
To identify the most suitable ML model for EoBC prediction based on real-world data and implementation constraints.

Main Methods:

Utilized a real-world dataset of historical IF operation data.
Trained and evaluated various ML models for EoBC prediction.
Selected the optimal ML model through rigorous evaluation against operational data.

Main Results:

Confirmed the feasibility of accurately predicting undesirable EoBC well in advance.
Identified a specific ML approach that best fits the dataset and industrial constraints.
Demonstrated the potential for proactive management of black carbon emissions.

Conclusions:

Machine learning offers a viable solution for predicting EoBC in industrial furnace operations.
This research pioneers the application of ML for EoBC prediction in the IF industry.
Predictive modeling can enhance environmental compliance and operational efficiency in industrial settings.