Support vector regression-based model for phenol adsorption in rotating packed bed adsorber

Aftab, Rameez Ahmad; Zaidi, Sadaf; Danish, Mohd; Adnan, Sayed Mohammad; Ansari, Khursheed B.; Danish, Mohammad

doi:10.1007/s11356-021-14953-9

Cited by 17 publications

(2 citation statements)

References 40 publications

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“…Previously, supervised learning approaches, specifically SVM, were mainly utilized for classification purposes. However, contemporary research has also demonstrated successful adaptations of these techniques for regression problems 50 . Furthermore, kernel functions are employed in SVM to transform the training data, thereby mapping it to a space with higher dimensions where the data can be effectively segregated 51 .…”

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%

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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“…In this paper, online QTP system based on machine learning controller such as GPR-QTP, and SVM-OTP are proposed and the input-output streaming data are collected. With the real time e streaming data QTP sensor systems, four machine learning based system models such as Gaussian Process Regression [13] and Support Vector Machine Regression [14][15][16][17] The rest of the paper is organized as follows: Section 2 describes the Quadruple Tank Process. Regression Modeling is explained in section 3 followed by Differential Equation and State-Space Modeling in Section 4.…”

Section: Contributionsmentioning

confidence: 99%

Modeling of Quadruple Tank System using Machine Learning Algorithms

Aravindan¹

2023

Preprint

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Modeling is essential for understanding the input and output behavior of quadruple tank system, analysis and design of controllers. Precise mathematical model for multi loop system-based Quadruple Tank Process (QTP) is a challenging task, due to strong interaction between pump inputs and output liquid level sensor values. Traditional methods such as transfer function and state space model limitations are removed through the proposed Model. Transfer function model can never be applied to multiple input and multiple output QTP system. State space model never address the internal state of QTP system. In this paper, Machine Learning based Quadruple Tank Process model is proposed such as Gaussian Process Regression Quadruple Tank Process (GPR-QTP) model and Support Vector Machine Quadruple Tank Process (SVM-QTP) model for runtime input and output sensor level data from laboratory based QTP station. Regression technique is performed with pump inputs and output liquid level data which can change in experimental environments such as ambient temperature and tube condition change, due to unknown disturbances and due to change in power supply to the pumps. The regression technique is verified with R-square values of proposed models. GPR-QTP and SVM-QTP models are developed using MATLAB software. From experimental results, GPR-QTP and SVM-QTP models perform better than conventional transfer function and state space model and validated through performance analysis such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared (R2) and Mean Squared Error (MSE). GPR-QTP and SVM-QTP models provides an accuracy of about 98%.

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Adsorptive removal of Pb(II) using nanostructured γ-alumina in a packed bed adsorber: Simulation using gPROMS

Danish

Ansari

Danish

2022

Environ Sci Pollut Res

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Support vector regression-based model for phenol adsorption in rotating packed bed adsorber

Cited by 17 publications

References 40 publications

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

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

Modeling of Quadruple Tank System using Machine Learning Algorithms

Adsorptive removal of Pb(II) using nanostructured γ-alumina in a packed bed adsorber: Simulation using gPROMS

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