机器学习驱动的辉光放电等离子体降解碱性紫16性能研究

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (1164 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要为了量化研究辉光放电等离子体(GDEP)降解碱性紫16(BV16)的效率影响因素以进一步提升其降解性能,共收集462条降解实验数据建立数据集,训练并评价了9种回归模型.结果表明,基于梯度提升树(GBDT)的集成学习模型预测性能优异,且以类别型特征提升(CatBoost)算法训练的模型性能最佳(R² = 0.988, MAE = 2.050%).此外,沙普利加和解释方法(SHAP)对最佳模型的参数影响程度定量解析结果显示,反应时间(43.74%)、初始污染物浓度(23.00%)、氯化钾浓度(15.65%)和平均电流(12.63%)是影响BV16降解效率的关键因素.同时,基于部分依赖图(PDP)提出了参数交互影响优化方案.所建立的CatBoost-SHAP-PDP模型不仅能实现GDEP对BV16降解效果的模拟预测,而且是优化GDEP降解过程变量的有效方法,为GDEP降解染料废水复杂体系领域的建模与应用提供科学依据和技术支持.

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	方野
	王玉如
	曾静懿
	王亚欣
	郑伟
	李敏睿

关键词 ：辉光放电等离子体, 染料废水, SHAP解释方法, 机器学习

Abstract：This study aimed to quantitatively investigate the factors influencing the degradation efficiency of Basic Violet 16 (BV16) by Glow Discharge Electrolysis Plasma (GDEP) and to enhance its degradation performance. A dataset was constructed from 462 experimental data points, and 9 regression models were trained and evaluated. The integrated learning models based on the Gradient Boosting Decision Tree (GBDT) demonstrated superior predictive performance, with the model trained using the Categorical Boosting (CatBoost) algorithm exhibiting the highest performance (R² = 0.988, MAE = 2.050%). The SHapley Additive exPlanation (SHAP) interpretation method was employed to quantitatively analyze the impact of parameters in the optimal model. The quantitative weight ranking results indicated that reaction time (43.74%), initial pollutant concentration (23.00%), KCl concentration (15.65%), and average current (12.63%) were the most significant factors influencing BV16 degradation. Furthermore, Partial Dependence Plot (PDP) analysis was utilized to propose an optimization scheme for parameter interactions. The CatBoost- SHAP-PDP model facilitated the simulation and prediction of BV16 degradation by GDEP and provided an effective method for optimizing the variables in the GDEP process. This research offers a scientific foundation and technical support for modeling and application in the field of complex dye wastewater treatment by GDEP.

Key words： glow discharge electrolysis plasma dye wastewater SHAP interpretation method machine learning

收稿日期: 2023-11-13

PACS:

X703

基金资助:陕西省自然科学基础研究计划-资助项目(2021JM-192)

通讯作者: 李敏睿,副教授,liminrui@snnu.edu.cn E-mail: liminrui@snnu.edu.cn

作者简介: 方野(1998-),男,陕西西安人,陕西师范大学硕士研究生,主要从事等离子体水处理技术及机器学习应用研究.fang_ye@outlook.com.

引用本文:

方野, 王玉如, 曾静懿, 王亚欣, 郑伟, 李敏睿. 机器学习驱动的辉光放电等离子体降解碱性紫16性能研究[J]. 中国环境科学, 2024, 44(6): 3206-3216. FANG Ye, WANG Yu-ru, ZENG Jing-yi, WANG Ya-xin, ZHENG Wei, LI Min-rui. Performance study of basic violet 16 degradation by glow discharge electrolysis plasma based on machine learning. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(6): 3206-3216.

链接本文:

http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2024/V44/I6/3206

[1] Haji A, Naebe M. Cleaner dyeing of textiles using plasma treatment and natural dyes:A review[J]. Journal of Cleaner Production, 2020, 265:121866.
[2] Ismail G A, Sakai H. Review on effect of different type of dyes on advanced oxidation processes (AOPs) for textile color removal[J]. Chemosphere, 2022,291:132906.
[3] Wang X, Zhou M, Jin X. Application of glow discharge plasma for wastewater treatment[J]. Electrochimica Acta, 2012,83:501-512.
[4] Zhou R, Zhou R, Wang P, et al. Plasma-activated water:generation, origin of reactive species and biological applications[J]. Journal of Physics D-Applied Physics, 2020,53(30):303001.
[5] Foster J E. Plasma-based water purification:Challenges and prospects for the future[J]. Physics of Plasmas, 2017,24(5):055501.
[6] Jin X L, Wang X Y, Wang Q F, et al. Plasma degradation of cationic blue dye with contact glow discharge electrolysis[J]. Water Science and Technology, 2010,62(7):1457-1463.
[7] Budikania T S, Irawan C, Afriani K, et al. Degradation of linear alkylbenzene sulfonate (LAS) by using multi-contact glow discharge electrolysis (m-CGDE) and Fe²⁺ ion as catalyst[J]. Journal of Environmental Chemical Engineering, 2017,5(3):2346-2349.
[8] Rao N R H, Chu X, Hadinoto K, et al. Algal cell inactivation and damage via cold plasma-activated bubbles:Mechanistic insights and process benefits[J]. Chemical Engineering Journal, 2023,454:140304.
[9] Yang Y, Wan K, Yang Z, et al. Inactivation of antibiotic resistant Escherichia coli and degradation of its resistance genes by glow discharge plasma in an aqueous solution[J]. Chemosphere, 2020,252:126476.
[10] Jordan M I, Mitchell T M. Machine learning:Trends, perspectives, and prospects[J]. Science, 2015,349(6245):255-260.
[11] Huang R, Ma C, Ma J, et al. Machine learning in natural and engineered water systems[J]. Water Research, 2021,205:117666.
[12] Khan H, Wahab F, Hussain S, et al. Multi-object optimization of Navy-blue anodic oxidation via response surface models assisted with statistical and machine learning techniques[J]. Chemosphere, 2022, 291:132818.
[13] 杜尚海,古成科,张文静.随机森林理论及其在水文地质领域的研究进展[J].中国环境科学, 2022,42(9):4285-4295. Du S H, Gu C K, Zhang W J. A review on the progresses in random forests theory and its applications in hydrogeology[J]. China Environmental Science, 2022,42(9):4285-4295.
[14] 陈治池,何强,蔡然,等.碳中和趋势下数学模拟在污水处理系统中的发展与综合应用[J].中国环境科学, 2022,42(6):2587-2602. Chen Z C, He Q, Cai R, et al. Development and comprehensive application of mathematical simulation in sewage treatment system under the trend of carbon neutralization[J]. China Environmental Science, 2022,42(6):2587-2602.
[15] Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead[J]. Nature Machine Intelligence, 2019,1(5):206-215.
[16] 董佳奇,胡冬梅,闫雨龙,等.基于可解释性机器学习的城市O₃驱动因素挖掘[J].环境科学, 2023,44(7):3660-3668. Dong J Q, Hu D M, Yan Y L, et al. Revealing driving factors of urban O₃ based on explainable machine learning[J]. Environmental Science, 2023,44(7):3660-3668.
[17] Wang F, Wang W, Wang H, et al. Experiments and machine learning-based modeling for haloacetic acids rejection by nanofiltration:Influence of solute properties and operating conditions[J]. Science of the Total Environment, 2023,883:163610.
[18] Zhang S Z, Chen S, Jiang H. A new tool to predict the advanced oxidation process efficiency:Using machine learning methods to predict the degradation of organic pollutants with Fe-carbon catalyst as a sample[J]. Chemical Engineering Science, 2023,280:119069.
[19] Wang Z, Feng J, Liang M, et al. Prediction model and application of machine learning for supersaturated total dissolved gas generation in high dam discharge[J]. Water Research, 2022,220:118682.
[20] Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn:Machine learning in python[J]. The Journal of Machine Learning Research, 2011,12:2825-2830.
[21] Sundhararajan S, Pahwa A, Krishnaswami P. A comparative analysis of genetic algorithms and directed grid search for parametric optimization[J]. Engineering with Computers, 1998,14(3):197-205.
[22] Liu Q, Pan K, Lu Y, et al. Data-driven for accelerated design strategy of photocatalytic degradation activity prediction of doped TiO₂ photocatalyst[J]. Journal of Water Process Engineering, 2022,49:103126.
[23] Li M, Zeng J, Zhao P, et al. The roles of oxygen and chloride in the degradation efficiency and mechanism of basic violet 16 by liquid glow discharge plasma[J]. Separation and Purification Technology, 2022,280:119886.
[24] 马井会,瞿元昊,余钟奇,等.上海市PM_2.5浓度延伸期预测模型的构建及评估[J].中国环境科学, 2023,43(7):3290-3298. Ma J H, Qu Y H, Yu Z Q, et al. Establishment and evaluation of prediction model for PM_2.5 concentration extension period in Shanghai[J]. China Environmental Science, 2023,43(7):3290-3298.
[25] 李航.机器学习方法[M].北京:清华大学出版社, 2022. Li H. Machine Learning Methods[M]. Beijing:Tsinghua University Press, 2022.
[26] Balogun A L, Tella A. Modelling and investigating the impacts of climatic variables on ozone concentration in Malaysia using correlation analysis with random forest, decision tree regression, linear regression, and support vector regression[J]. Chemosphere, 2022, 299:134250.
[27] 周志华.机器学习[M].北京:清华大学出版社, 2016. Zhou Z H. Machine Learning[M]. Beijing:Tsinghua University Press, 2016.
[28] 左飞,补彬.机器学习原理与实践PYTHON版[M].北京:清华大学出版社, 2021. Zuo F, Bu B. Machine learning principles and practice PYTHON edition[M]. Beijing:Tsinghua University Press, 2021.
[29] 姜明星,王斯坦,许端平.基于机器学习的金属有机框架吸附水中重金属性能预测[J].中国环境科学, 2023,43(5):2319-2327. Jiang M X, Wang S T, Xu D P. Prediction of adsorption performance of MOFs for heavy metals in water based on machine learning[J]. China Environmental Science, 2023,43(5):2319-2327.
[30] Chen T, Guestrin C. XGBoost:A scalable tree boosting system[C]. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016:785-794.
[31] Prokhorenkova L, Gusev G, Vorobev A, et al. CatBoost:unbiased boosting with categorical features[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems, 2018:6639-6649.
[32] Ke G, Meng Q, Finley T, et al. LightGBM:a highly efficient gradient boosting decision tree[C]. Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017:3149-3157.
[33] Lundberg S M, Lee S I. A unified approach to interpreting model predictions[C]. Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017:4768-4777.
[34] Gao J Z, Wang X Y, Hu Z G, et al. Plasma degradation of dyes in water with contact glow discharge electrolysis[J]. Water Research, 2003, 37(2):267-272.
[35] Jiang B, Zheng J, Qiu S, et al. Review on electrical discharge plasma technology for wastewater remediation[J]. Chemical Engineering Journal, 2014,236:348-368.
[36] 郑继东,陆泉芳,俞洁,等.辉光放电电解等离子体降解水体中的罗丹明B[J].环境科学学报, 2017,37(6):2164-2170. Zheng J D, Lu Q F, Yu J, et al. Degradation of rhodamine B in aqueous solution by glow discharge electrolysis plasma[J]. Acta Scientiae Circumstantiae, 2017,37(6):2164-2170.
[37] Wang L. 4-Chlorophenol degradation and hydrogen peroxide formation induced by DC diaphragm glow discharge in an aqueous solution[J]. Plasma Chemistry and Plasma Processing, 2009,29(3):241-250.
[38] Xin Y Y, Zhou L, Ma K K, et al. Removal of bromoamine acid in dye wastewater by gas-liquid plasma:The role of ozone and hydroxyl radical[J]. Journal of Water Process Engineering, 2020,37:101457.
[39] Gao J, Yu J, Li Y, et al. Decoloration of aqueous brilliant green by using glow discharge electrolysis[J]. Journal of Hazardous Materials 2006,137(1):431-436.
[40] Wang X, Jin X, Zhou M, et al. Decolorization of acid orange 7 with DC diaphragm glow discharge[J]. Electrochimica Acta, 2013,103:237-242.
[41] Ramjaun S N, Yuan R, Wang Z, et al. Degradation of reactive dyes by contact glow discharge electrolysis in the presence of Cl^- ions:Kinetics and AOX formation[J]. Electrochimica Acta, 2011,58:364-371.
[42] Moradi H, Kim D S, Kim S H, et al. Experimental and numerical study on diazinon removal using plasma bubble column reactor:Modeling, kinetics, mechanisms, and degradation products[J]. Journal of Environmental Chemical Engineering, 2022,10(5):108291.
[43] Sukreni T, Bismo S, Saksono N. Effect of low flow rate of air injection on remazol red degradation in contact glow discharge electrolysis reactor[J]. Journal of Environmental Science and Technology, 2019, 12(5):197-204.
[44] Wang X, Mei J, Luo J, et al. Process intensification of Tetrabromobisphenol S removal with a bubble-film hybrid plasma reactor[J]. Chemical Engineering Journal, 2022,427:131736.
[45] Zhou R W, Zhang T Q, Zhou R S, et al. Underwater microplasma bubbles for efficient and simultaneous degradation of mixed dye pollutants[J]. Science of the Total Environment, 2021,750:142295.
[46] Zhang S, Shen X, Li J, et al. Study on degradation of alizarine reds in simulated dye wastewater by gas-liquid two-phase discharge plasma[J]. Chemical Engineering and Processing-Process Intensification, 2022,181:109114.
[47] Liu J, Xu Z, Zhang W. Unraveling the role of Fe in As (III&V) removal by biochar via machine learning exploration[J]. Separation and Purification Technology, 2023,311:123245.
[48] Zheng S, Guo W, Li C, et al. Application of machine learning and deep learning methods for hydrated electron rate constant prediction[J]. Environmental Research, 2023,231:115996.
[49] Gao W, Zheng W, Yan K, et al. Accelerating the discovery of acid gas-selective MOFs for natural gas purification:A combination of machine learning and molecular fingerprint[J]. Fuel, 2023,350:128757.
[50] Shen Y, Zhao E, Zhang W, et al. Predicting pesticide dissipation half-life intervals in plants with machine learning models[J]. Journal of Hazardous Materials, 2022,436:129177.
[51] Zhang Z C, Wang F X, Wang F, et al. Efficient atrazine degradation via photoactivated SR-AOP over S-BUC-21(Fe):The formation and contribution of different reactive oxygen species[J]. Separation and Purification Technology, 2023,307:122864.
[52] Ta M, Wang T, Guo J, et al. Enhanced norfloxacin degradation by three-dimensional (3D) electrochemical activation of peroxymonosulfate using Mn/Cu co-doped activated carbon particle electrode[J]. Separation and Purification Technology, 2023,310:123067.
[53] Shen W, Yang L, Zhou Z, et al. Activation of peracetic acid by thermally modified carbon nanotubes:Organic radicals contribution and active sites identification[J]. Chemical Engineering Journal, 2023, 474:145521.
[54] Hu R, Yang S Q, Li J Y, et al. Insight into micropollutant abatement during ultraviolet light-emitting diode combined electrochemical process:Reaction mechanism, contributions of reactive species and degradation routes[J]. Science of the Total Environment, 2023,876:162798.