Modeling of CO2 solubility in piperazine (PZ) and diethanolamine (DEA) solution via machine learning approach and response surface methodology

Khoshraftar, Zohreh; Ghaemi, Ahad

doi:10.1016/j.cscee.2023.100457

Cited by 10 publications

(1 citation statement)

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“…SVM does not rely on a predefined structure since it assesses the significance of training samples to determine their contributions. "Support vectors" are only established for models based on specific data samples 53 . In this research, SVM regression was conducted using the support vector regression (SVR) class available in the scikit-learn API's SVM module.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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

Naderi,

Kalami Yazdi,

Jafarabadi

et al. 2024

Sci Rep

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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.

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Section: Theoretical Backgroundmentioning

confidence: 99%