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• W3040819961 abstract "Optimizing the backwashing procedure of ultrafiltration membranes poses novel challenges in regards to the modeling and simulation of the fouling process. Traditional modeling approaches the problem through a physical and chemical understanding of the complicated fouling phenomena. In this study, a large amount of data has been collected from a pilot-scale ultrafiltration membrane system treating spent filter backwash water at a water treatment plant. Environmental variables and operational parameters including temperature, hydraulic pressure, water turbidity, etc. are monitored continuously. This work focuses on revealing the hidden nonlinear relationships of these variables via data driven methodologies without building a first principles process model. Machine learning tools are used to establish a connection between environmental variables, dynamic parameters, the efficiency of foulant removal through backwashing, and the increase rate of foulants. The prediction performance is compared to regression models including linear regression, artificial neural networks, and random forests. Our data driven model of the fouling dynamics is then used for optimization purposes, in particular, to optimize the backwashing sequence timing by applying tools from stochastic dynamic programming. Optimized backwash performance is compared with experimental data acquired via a fixed interval sequence and shows a more efficient schedule at less cost and with lower membrane resistance. This work establishes a full pipeline of data processing, model building, and operational optimization. The only requirement for employing our methodology is the collection of operational data, in order to identify the membrane dynamics. The reported methodology offers a great potential for implementation on large-scale ultrafiltration applications, which can improve energy consumption and elongate membrane life." @default.
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• W3040819961 date "2020-10-01" @default.
• W3040819961 modified "2023-10-14" @default.
• W3040819961 title "Backwash sequence optimization of a pilot-scale ultrafiltration membrane system using data-driven modeling for parameter forecasting" @default.
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• W3040819961 doi "https://doi.org/10.1016/j.memsci.2020.118464" @default.
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