Peng Liu , Hangbin Xu , Pengrui Jin , Xuewu Zhu , Junfeng Zheng , Yanling Liu , Jiaxuan Yang , Daliang Xu , Heng Liang
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引用次数: 0
Abstract
Resource recovery from textile wastewater has attracted increasing interest because it simultaneously addresses wastewater treatment and maximizes the utilization of the residual dyes. Although polyester membranes have demonstrated great potential for textile wastewater recovery, tailoring high-performance polyester membranes remains a multidimensional challenge because of the complex nonlinear relationships between the membrane materials and their performance. Here we developed density functional theory (DFT)-assisted machine learning models that integrates DFT descriptors with fabrication and operation parameters to facilitate the generative design of polyester membranes. The developed machine learning model demonstrated the ability to accurately predict permeance and separation performance. The contribution analysis revealed that the fabrication parameters emerged as the critical factors influencing permeance, whereas the DFT descriptors played important roles in determining the dye and salt rejection. Additionally, optimal combinations of monomer, fabrication, and operation conditions were identified from a chemical space of 8,000 candidates using the developed model combined with Bayesian optimization, targeting dye/salt and dye/dye selectivity. Five polyester membranes were then fabricated under these identified combinations. These membranes surpassed the current performance upper bound and achieved efficient recovery of the dyes from textile wastewater. Overall, a feasible and universal machine learning model aimed at driving a paradigm shift in the inverse design of polyester membranes was developed.
期刊介绍:
Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include:
•Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management;
•Urban hydrology including sewer systems, stormwater management, and green infrastructure;
•Drinking water treatment and distribution;
•Potable and non-potable water reuse;
•Sanitation, public health, and risk assessment;
•Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions;
•Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment;
•Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution;
•Environmental restoration, linked to surface water, groundwater and groundwater remediation;
•Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts;
•Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle;
•Socio-economic, policy, and regulations studies.