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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #350898
Research Project: Design and Implementation of Monitoring and Modeling Methods to Evaluate Microbial Quality of Surface Water Sources Used for Irrigation

Location: Environmental Microbial & Food Safety Laboratory

Title: Deep learning model for membrane biofouling prediction using optical coherence tomography imaging

Author
item PARK, SANGHUN - University Of Ulsan College Of Medicine
item BAEK, SANGSOO - University Of Ulsan College Of Medicine
item CHUN, JONG AHN - University Of Ulsan College Of Medicine
item PARK, YOUNGEUN - University Of Ulsan College Of Medicine
item Pachepsky, Yakov
item PARK, JONKWAN - University Of Ulsan College Of Medicine
item CHO, KYUNGHWA - University Of Ulsan College Of Medicine

Submitted to: Journal Membrane Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/3/2019
Publication Date: 6/8/2019
Citation: Park, S., Baek, S., Chun, J., Park, Y., Pachepsky, Y.A., Park, J., Cho, K. 2019. Deep learning model for membrane biofouling prediction using optical coherence tomography imaging. Journal Membrane Science. 587:117164. https://doi.org/10.1016/j.memsci.2019.06.004.
DOI: https://doi.org/10.1016/j.memsci.2019.06.004

Interpretive Summary: Biofouling is common in water distributions and filtration systems where biofilms provide habitats for various microorganisms, including pathogens. The optical coherence tomography provides an excellent source of data on biofilm development. The volume of his data is very large. The objective of this work was to research the applicability of the artificial intelligence algorithms to package these data into a predictive model to relate growth of biofilms to the dissolved organic matter in water. The deep learning artificial intelligence algorithms appeared to be very efficient and reproduced observed growth with high accuracy. Results of this work will be of interest for researchers and practitioners working with imaging for characterization and evaluation of microorganism habitats.

Technical Abstract: A new approach to modeling of biofouling on nanofiltration membrane surfaces was developed using an artificial intelligence (AI) algorithm with in situ fouling image data obtained through optical coherence tomography (OCT). Fouling experiments were conducted on different bioavailable organic matter concentrations. The OCT device produced a total of 8,000 fouling layer images. A fluorescence regional integration (FRI) method was used to analyze three-dimensional (3D) excitation-emission matrices (EEMs) quantitatively and determine organic matter composition. The AI-based fouling growth model used high-resolution fouling layer images and dissolved organic matter composition as input data to train a network. The developed model considers two types of biofouling growth depending on the bioavailability of organic matter based on FRI analysis. Based on two-dimensional (2D) and 3D spatiotemporal results of continuous simulation, the performance of the model was satisfactory with R2 values ranging from 0.97 to 0.98 and RMSE values ranging from 2.35 to 2.30 for correlation of observed and simulated biofouling areas in the fouling images. These results demonstrated the development of a new tool for reliably and rapidly simulating biofouling processes using high-resolution fouling images and a deep learning technique. It is expected that a deep learning model can be readily constructed and queried by engineers and decision-makers working with nanofiltration membranes.