Modeling based on machine learning to investigate flue gas desulfurization performance by calcium silicate absorbent in a sand bed reactor

Kamyar Naderi; Mohammad Sadegh Kalami Yazdi; Hanieh Jafarabadi; Fatemeh Bahmanzadegan; Ahad Ghaemi; Mohammad Reza Mosavi

doi:10.1038/s41598-024-51586-7

Modeling based on machine learning to investigate flue gas desulfurization performance by calcium silicate absorbent in a sand bed reactor

Sci Rep. 2024 Jan 10;14(1):954. doi: 10.1038/s41598-024-51586-7.

Authors

Kamyar Naderi¹, Mohammad Sadegh Kalami Yazdi², Hanieh Jafarabadi¹, Fatemeh Bahmanzadegan¹, Ahad Ghaemi³, Mohammad Reza Mosavi²

Affiliations

¹ School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846, Iran.
² Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846-13114, Iran.
³ School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846, Iran. aghaemi@iust.ac.ir.

Abstract

Flue gas desulfurization (FGD) is a critical process for reducing sulfur dioxide (SO₂) emissions from industrial sources, particularly power plants. This research uses calcium silicate absorbent in combination with machine learning (ML) to predict SO₂ concentration within an FGD process. The collected dataset encompasses four input parameters, specifically relative humidity, absorbent weight, temperature, and time, and incorporates one output parameter, which pertains to the concentration of SO₂. Six ML models were developed to estimate the output parameters. Statistical metrics such as the coefficient of determination (R²) and mean squared error (MSE) were employed to identify the most suitable model and assess its fitting effectiveness. The random forest (RF) model emerged as the top-performing model, boasting an R² of 0.9902 and an MSE of 0.0008. The model's predictions aligned closely with experimental results, confirming its high accuracy. The most suitable hyperparameter values for RF model were found to be 74 for n_estimators, 41 for max_depth, false for bootstrap, sqrt for max_features, 1 for min_samples_leaf, absolute_error for criterion, and 3 for min_samples_split. Three-dimensional surface plots were generated to explore the impact of input variables on SO₂ concentration. Global sensitivity analysis (GSA) revealed absorbent weight and time significantly influence SO₂ concentration. The integration of ML into FGD modeling offers a novel approach to optimizing the efficiency and effectiveness of this environmentally crucial process.