Experimental and prediction method for membrane separation efficiency of oily waste water based on LSTM

YU Hengyang, ZHANG Bin

doi:10.16411/j.cnki.issn1006-7736.2025.02.017

Journal of Dalian Maritime University >

2025 , Vol. 51 >Issue 2: 154 - 164

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2025.02.017

Experimental and prediction method for membrane separation efficiency of oily waste water based on LSTM

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(Marine Engineering College, Dalian Maritime University, Dalian 116026, China)

Online published: 2025-04-29

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Abstract

To explore the separation mechanism and prediction method of ceramic membranes in marine oilwater separators, based on a selfbuilt experimental platform, the effects of oil concentration, membrane pore size and transmembrane pressure difference on separation efficiency and flux were systematically investigated. The results show that regardless of how the concentration, pore size or pressure difference changes, the retention rate is all greater than 99%. However, the flux attenuation is significantly restricted by above three factors, and the order of influence is: oil concentration > transmembrane pressure difference > membrane pore size. Taking a pore size of 1 μm and a pressure difference of 0.10 MPa as an example, when the concentration increases from 50 mg/L to 200 mg/L, the flux attenuation rate increases from 2% to 12%. Take 100 mg/L as an example, when the pressure difference increases from 0.05 MPa to 0.20 MPa, the initial flux can reach 583 L/(m2·h), but the average attenuation rate is 8%. Under the condition of 0.10 MPa, when the pore size increases from 0.5 μm to 2 μm, the flux at high concentration decreases from 368 L/(m2·h) to 312 L/(m2·h), and the attenuation rate reaches 16%. Based on this, a membrane separation effect prediction network FlowLSTM combined with the influence mechanism is proposed. This model effectively alleviates the problems of insufficient screening and loss of historical information by traditional LSTM through spatiotemporal feature extraction, attention fusion and residual connection, and takes the ranking results of key variables as the input to control the model. Compared with the baselines of MLP, RNN, LSTM and GRU, FlowLSTM outperforms the baselines in terms of R2, MSE and MAE indicators. Compared with the original LSTM model, R2 increases by 5%, and MSE and MAE decreases by 21% and 22% respectively.

Key words： ceramic membrane; oil-water separation; separation efficiency; membrane flux; long short-term memory network (LSTM)

Cite this article

YU Hengyang, ZHANG Bin . Experimental and prediction method for membrane separation efficiency of oily waste water based on LSTM[J]. Journal of Dalian Maritime University, 2025 , 51(2) : 154 -164 . DOI: 10.16411/j.cnki.issn1006-7736.2025.02.017

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