| 引用本文: | 刘鹏,徐杭镔,刘超,徐达梁,李圭白,梁恒.基于机器学习识别面向重金属分离的纳滤膜物化特性[J].哈尔滨工业大学学报,2026,58(6):40.DOI:10.11918/202508022 |
| LIU Peng,XU Hangbin,LIU Chao,XU Daliang,LI Guibai,LIANG Heng.Physicochemical property identification of nanofiltration membranes for heavy metal separation based on machine learning[J].Journal of Harbin Institute of Technology,2026,58(6):40.DOI:10.11918/202508022 |
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| 摘要: |
| 纳滤技术为工业废水中重金属分离与资源化回收提供了解决方案。然而,适配重金属分离的纳滤膜物化特性仍然不明确,难以制备面向重金属分离的高性能纳滤膜。为此,构建融合膜属性、操作参数和溶液特性的机器学习预测模型,10种模型对比中,XGBoost模型对膜通量和重金属截留率的R2分别为0.98和0.92。SHAP分析结果表明,水接触角、截留分子质量和膜表面电位是影响性能的关键特征,通过适当提高亲水性和截留分子质量以及增加膜表面正电荷有利于膜通量的提高,而重金属截留率受到尺寸排阻效应与静电排斥效应的共同影响。基于高精度预测模型构建NGSA-Ⅱ双目标优化算法,结果表明,水接触角为60°~66°、截留分子质量为200~412 u、膜表面电位为47~73 mV时,纳滤膜可实现对10种重金属离子的高效处理,重金属截留率超过99%,膜通量为25~30 L/(m2·h)。本研究构建的纳滤膜优化设计框架为高性能纳滤膜材料的开发与应用提供了理论与技术支持。 |
| 关键词: 纳滤 重金属回收 机器学习 沙普利加性解释 双目标优化 |
| DOI：10.11918/202508022 |
| 分类号:TU991 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(52341001)；黑龙江省自然科学基金(TD2023E003) |
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| Physicochemical property identification of nanofiltration membranes for heavy metal separation based on machine learning |
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LIU Peng,XU Hangbin,LIU Chao,XU Daliang,LI Guibai,LIANG Heng
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(State Key Laboratory of Urban-rural Water Resource and Environment (Harbin Institute of Technology), Harbin 150090, China)
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| Abstract: |
| Nanofiltration technology offers an effective solution for the separation and resource recovery of heavy metals in industrial wastewater. However, the physicochemical properties of nanofiltration membranes suitable for heavy metal separation remain unclear, making it difficult to prepare high-performance nanofiltration membranes for heavy metal separation. This paper developed a machine learning prediction model incorporating membrane characteristics, operating parameters, and solution properties. Through a comparison of ten models, the XGBoost model yielded R2 values of 0.98 and 0.92 for membrane flux and heavy metal rejection rate, respectively. The SHAP analysis results revealed that water contact angle, molecular weight cut-off, and membrane surface potential were the key determinants of performance. Enhancing hydrophilicity, moderately increasing the molecular weight cut-off, and imparting more positive charges on the membrane surface collectively enhanced membrane flux. In contrast, heavy metal rejection rate was governed by the combined effects of size exclusion and electrostatic repulsion. Based on the high-accuracy prediction model, an NSGA-Ⅱ bi-objective optimization algorithm was constructed. The results show that within the ranges of water contact angle from 60° to 66°, molecular weight cut-off from 200 to 412 u, and membrane surface potential from 47 to 73 mV, nanofiltration membranes can achieve efficient treatment of 10 kinds of heavy metal ions, with rejection rate exceeding 99% and membrane flux ranging from 25 to 30 L/(m2·h). The nanofiltration membrane optimization framework developed in this paper provides both theoretical and technical support for the development and application of high-performance nanofiltration materials. |
| Key words: nanofiltration heavy metal recovery machine learning SHapley Additive exPlanation bi-objective optimization |
