Optimization of in-situ soil thermal desorption technology based on machine learning and heat transfer process model

Abstract

Introduction: In-situ soil thermal desorption (ISTD) has been recognized as an effective and promising technology for remediating organic contamination in soil and groundwater. However, high energy consumption poses a major constraint on its remediation costs. Methods: In this study, an optimization method for in-situ soil thermal desorption was proposed that combines machine learning with a heat transfer process model. This method optimized the heat flow by effectively predicting the temperature distribution during ISTD, thereby enhancing energy utilization and reducing technical costs. Results: The results show that total energy consumption can be significantly reduced under variable heat flow conditions compared to constant heat flow, with energy savings of 35.93–48.86%. The practical technical implementation requires careful consideration of factors such as heating time, fluctuations at the cold spot temperature, and the intensity of the heat flow. Discussion: This study provides essential technical support for the advancement of ISTD technology in practical engineering applications and the strategic optimization of soil remediation methods. The proposed optimization method addresses the core issue of high energy consumption in ISTD, offering a feasible solution to enhance the economic viability and sustainability of organic-contaminated soil remediation.