Soil permeability enhancement using pneumatic fracturing coupled by vacuum extraction for in-situ remediation: Pilot-scale tests with an artificial neural network model. Issue 1 (February 2022)

• Record Type: Journal Article
• Authors: Choong, Choe Earn; Wong, Kien Tiek; Jang, Seok Byum; Song, Jae-Yong; An, Sang-Gon; Kang, Cha-Won; Yoon, Yeomin; Jang, Min
• Is Part Of: Journal of environmental chemical engineering. Volume 10:Issue 1(2022)
• Publication Date: 2022-02
• Subjects: Artificial neural network -- Pneumatic fracturing -- Multilayer perceptron -- Vacuum extraction; Chemical engineering -- Environmental aspects -- Periodicals; Environmental engineering -- Periodicals; Chemical engineering -- Environmental aspects; Environmental engineering; Periodicals; 660.0286
• DOI: 10.1016/j.jece.2021.107075
• Languages: English
• ISSNs: 2213-2929

Abstract

Abstract: Enhancing soil permeability is of huge practical significance for the in-situ chemical oxidation in soil and groundwater remediation. Hence, we conducted pilot-scale (2 m 3 of soil) pneumatic fracturing (PF) and pneumatic fracturing-vacuum extraction (PFV) experiments for improving the fluid infiltration rate in low permeable soil zone in this study. Moreover, the correlation interpretation for soil permeability enhancement suffers from limited databases and complexities (i.e., geological properties) due to many factors that affect the fluid injection performance toward low permeability soil. Further, this is the first paper to introduce a novel approach using a soft computational model, a feedforward backpropagation artificial neural network (FFBP-ANN), to predict the infiltration coefficient for PF or PFV treated soils. As a result, the PV and PFV methods significantly enhanced the infiltration coefficients. Notably, the established FFBP-ANN model with the configuration of eight neurons associated with one hidden layer connected by tangent sigmoid transfer function and trained by Levenberg-Marquart backpropagation algorithm achieved the 0.999 of regression with 0.001 of mean square error accuracy performance. Therefore, this study shows that PFV can significantly enhance the infiltration coefficient, and the computational FFBP-ANN models can help extend the infiltration coefficient estimation for low permeable soil. Graphical Abstract: ga1 Highlights: Infiltration coefficients for low permeable soil were significantly enhanced by the PFV. FFBP-ANN models for infiltration coefficient prediction have been established. The developed model exhibited prediction accuracy with MSE (0.001) and R 2 (0.999).