| 引用本文: | 刘海波,谢博,肖嘉鑫,朱静怡,谢坤廷,伍洋涛,周石庆.基于机器学习逆向设计的纳米气泡制备方法优化[J].哈尔滨工业大学学报,2026,58(6):48.DOI:10.11918/202507022 |
| LIU Haibo,XIE Bo,XIAO Jiaxin,ZHU Jingyi,XIE Kunting,WU Yangtao,ZHOU Shiqing.Optimization of nanobubble preparation method based on machine learning and inverse design[J].Journal of Harbin Institute of Technology,2026,58(6):48.DOI:10.11918/202507022 |
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| 基于机器学习逆向设计的纳米气泡制备方法优化 |
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刘海波1,2,谢博2,肖嘉鑫1,朱静怡1,谢坤廷1,伍洋涛1,周石庆1
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(1．湖南大学 水安全保障技术及应用湖南省工程研究中心,长沙 410082; 2．中国电建集团中南勘测设计研究院有限公司,长沙 410082)
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| 摘要: |
| 纳米气泡因比表面积大、Zeta电位负值高和稳定性强而在水处理中展现出应用潜力,但其稳定性受气体种类、压力、流速、介质、pH与温度等多因素耦合影响。传统依赖经验试错的方法效率低、可重复性差,缺乏普适性。为此,构建数据驱动的逆向设计框架,结合可解释机器学习与实验验证,揭示纳米气泡稳定性的关键规律并提出可迁移的制备准则。优化后的随机森林模型在测试集上的精确率、召回率和F1值分别为0.816、0.814和0.810。模型特征分析表明:臭氧、碳酸盐或偏碱性介质、适宜压力和温度条件是提升稳定性的主要因素；依据逆向处方制备的纳米气泡在贮存21 d后仍保持高稳定性,在与过硫酸盐耦合的体系中对亚甲基蓝的去除率达(96.8±2.3)%,显著优于对照组。长期性能测试表明,NBs/PMS体系在模拟废水（NOM）中30 d内实现了约51%的TOC矿化,展现出在复杂基质下的持续氧化能力。能耗评估显示,在去除90%亚甲基蓝时,NBs/PMS体系的单次处理能耗约为常规UV/PMS工艺的35.7%,相当于降低约64.3%,体现出在能效与成本控制方面的优势。本研究不仅提供了稳定性调控的设计准则,还在理论上揭示了多因素协同作用规律,并从长期稳定性与工程能效的角度验证了其应用潜力,对推动纳米气泡技术的规模化应用和绿色水处理产业化具有意义。 |
| 关键词: 纳米气泡 Zeta电位 机器学习 模型解释 逆向设计 高级氧化 |
| DOI：10.11918/202507022 |
| 分类号:TU991 |
| 文献标识码:A |
| 基金项目:湖南省自然科学基金(2026JJ60469) |
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| Optimization of nanobubble preparation method based on machine learning and inverse design |
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LIU Haibo1,2,XIE Bo2,XIAO Jiaxin1,ZHU Jingyi1,XIE Kunting1,WU Yangtao1,ZHOU Shiqing1
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(1.Hunan Engineering Research Center of Water Security Technology and Application, Hunan University, Changsha 410082, China; 2.PowerChina Zhongnan Engineering Corporation Limited, Changsha 410082, China)
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| Abstract: |
| Nanobubbles (NBs) exhibit great application potential in water treatment owing to their large specific surface area, highly negative Zeta potential, and extended stability. However, the stability of the NBs is affected by the coupled interaction of multiple factors such as gas type, pressure, flow rate, medium, pH, and temperature. Conventional empirical trial-and-error approaches are inefficient, difficult to reproduce, and non-generalizable. To overcome these limitations, this paper established a data-driven inverse design framework combining interpretable machine learning and experimental validation to reveal key laws of NBs′ stability and propose transferable preparation strategies. A random forest model was optimized, achieving a precision of 0.816, a recall of 0.814, and an F1 score of 0.810 on the test set. Model feature analysis indicated that ozone, carbonate or mildly alkaline media, and suitable pressure and temperature were the main factors improving the stability. Guided by inverse prescriptions, NBs retained strong stability after 21 days of storage, and in the system coupled with persulfate, the removal rate of methylene blue by NBs reached (96.8±2.3)%, outperforming the control group. Long-term performance tests further showed that the NBs/PMS system achieved about 51% TOC mineralization of natural organic matter (NOM) within 30 days, demonstrating sustained oxidative capacity under complex matrices. Energy consumption evaluation indicated that, for 90% methylene blue removal, the single-treatment energy consumption of the NBs/PMS system was approximately 35.7% of that required by the conventional UV/PMS process, corresponding to a reduction of about 64.3%. This result highlights its advantages in energy efficiency and cost control. This study not only provides design principles for regulating the stability and theoretically reveals the laws of multi-factor synergistic effect but also verifies its application potentials in terms of long-term stability and engineering energy efficiency. It is of great significance for promoting the large-scale application of NB technology and the industrialization of green water treatment. |
| Key words: nanobubble Zeta potential machine learning model interpretation inverse design advanced oxidation |
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