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使用人工智能模型优化电解超声和过硫酸盐混合工艺修复石油污染土壤

Optimizing a hybrid process of electrolysis ultrasound and persulfate for remediation of petroleum contaminated soils using AI models.

作者信息

Feizi Rozhan, Parseh Iman, Zafarzadeh Ali, Jorfi Sahand, Sheikhmohammadi Amir

机构信息

Behbahan Faculty of Medical Sciences, Behbahan, Iran.

Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.

出版信息

Sci Rep. 2025 Jul 2;15(1):22555. doi: 10.1038/s41598-025-06819-8.

DOI:10.1038/s41598-025-06819-8
PMID:40596205
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12216880/
Abstract

Research uses both RANSAC Regressor and Monte Carlo Optimization to improve the performance of electrolysis/ultrasound/persulfate system which detoxifies petroleum-contaminated soils. The Artificial Intelligence (AI) models used to optimize six process parameters showed X2 (humidity) and X3 (voltage) and X5 (surfactant) enhanced removal efficiency the most but X1 (pH) presented a robust negative impact. The selected optimal conditions for pollutant removal resulted from Monte Carlo simulations which specified X1 at 8.50 and X2 at 188.67 with X3 set to 2.45 and X4 at 0.64 and X5 at 0.07 and X6 at 198.02. The study supports AI-based models as strong tools which enable optimization of complex environmental remediation methods and enhance pollutant remediation procedures. The study hypothesis demonstrates that artificial intelligence models (RANSAC Regressor and Monte Carlo Optimization) precisely find crucial process parameters which enhance the efficiency of hybrid electrolysis/ultrasound/persulfate treatment in removing petroleum hydrocarbons from contaminated soil. Hybrid remediation technologies receive improved performance efficiency through the application of AI optimization with RANSAC and Monte Carlo models combined. These discoveries lead to worldwide applications that use affordable flexible methods for treating petroleum-contaminated soils to be deployed extensively in global contaminated sites.

摘要

研究采用随机抽样一致性回归器（RANSAC Regressor）和蒙特卡洛优化方法来提高电解/超声/过硫酸盐系统处理石油污染土壤的性能。用于优化六个工艺参数的人工智能（AI）模型显示，X2（湿度）、X3（电压）和X5（表面活性剂）对去除效率的提升最为显著，但X1（pH值）呈现出强烈的负面影响。通过蒙特卡洛模拟得出了污染物去除的选定最佳条件，其中X1设定为8.50，X2设定为188.67，X3设定为2.45，X4设定为0.64，X5设定为0.07，X6设定为198.02。该研究支持基于人工智能的模型作为强大工具，能够优化复杂的环境修复方法并加强污染物修复程序。研究假设表明，人工智能模型（随机抽样一致性回归器和蒙特卡洛优化）能够精确找到关键工艺参数，从而提高混合电解/超声/过硫酸盐处理从污染土壤中去除石油烃的效率。通过结合随机抽样一致性回归器和蒙特卡洛模型应用人工智能优化，混合修复技术的性能效率得到提高。这些发现促使全球范围内广泛应用经济实惠且灵活的方法来处理石油污染土壤，以便在全球受污染场地中大规模部署。

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