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

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 ran

SUBMITTER: Naderi K 

PROVIDER: S-EPMC10781758 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature