The data includes a series of datasets for both short-term and long-term UF operational periods. Performance data includes the following variables: - Microfilter pressure driving force - UF transmembrane pressure during filtration - UF transmembrane pressure during backwash - Initial UF membrane resistance - UF Coagulant dose  - UF feedwater turbidity - Filtrate temperature - UF backwash duration - Filtrate pH - Chlorophyll a (RFU) of feedwater - UF Backwash flux - Operation time ------------------------------------------------------------------------------------------------------------------------------------------------------------ This DATASET Readme.txt file was generated on 2021-03-06 by Yoram Cohen <help text is included in angle brackets, and can be deleted before saving> GENERAL INFORMATION 1. Title of Dataset: Ultrafiltration Performance Data for Treatment of Seawater Reverse Osmosis Feedwater 2. Author Information A. Principal Investigator Contact Information Name: Yoram Cohen Institution: University of California, Los Angeles Address: 420 Westwood Plaza, Los Angeles, CA 90095 Email: yoram@ucla.edu B. Associate or Co-investigator Contact Information Name: Yang Zhou Institution: East China University of Science and Technology Address: Meilong Road 130, Shanghai, China Email: zhouyangucla@gmail.com C. Alternate Contact Information Name: Han Gu Institution: Orange County Water District Address: 18700 Ward Street, Fountain Valley, CA 92708 Email: hgu@ocwd.com 3. Date of data collection (single date, range, approximate date): Dates of data collection are provided which each of the dataset files 4. Geographic location of data collection <latitude, longitude, or city/region, State, Country, as appropriate>: Port Hueneme, California, U.S.A 5. Information about funding sources that supported the collection of the data:  The United States Office of Naval Research (N00014-11-1-0950 ONR and ONR N00014-09-1-1132),  California Department of Water Resources(46-4120 and RD-2006-09),  U.S. Bureau of Reclamation (R13AC80025),  Naval Facilities Engineering Command (N62583-11-C-0630),  and UCLA Water Technology Research (WaTeR) Center. SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data: No restrictions, but acknowledgement of the data generators/authors is requested 2. Links to publications that cite or use the data:  The Modeling UF Fouling and Backwash of Seawater RO Feedwater Using Neural Network with Evolutionary Algorithm and Bayesian Binary Classification(paper number: DES-S-21-00364); Gu H. et al., Fouling indicators for field monitoring the effectiveness of operational strategies of ultrafiltration as pretreatment for seawater desalination(doi: 10.1016/j.desal.2017.11.038) Gao, L. et al., Self-adaptive cycle-to-cycle control of in-line coagulant dosing in ultrafiltration for pre-treatment of reverse osmosis feed water(doi: 10.1016/j.desal.2016.09.024) 3. Links to other publicly accessible locations of the data: This repository Link: 4. Links/relationships to ancillary data sets: None 5. Was data derived from another source? no A. If yes, list source(s): 6. Recommended citation for this dataset: Yang Zhou, Bilal Khan, Han Gu, Panagiotis Christofides and Yoram Cohen, "Performance Data for Ultrafiltration Treatment of Seawater Reverse Osmosis Feedwater", doi: 10.5068/D1310B DATA & FILE OVERVIEW 1. File List: 150 training datasets in training folder, and 30 test datasets in test folder, the datasets in the folder are named as the date of the collection. All datasets are included in the uploaded zipped file. 2. Relationship between files, if important: Files are independent with all file descriptors and operating conditions provided in each dataset 3. Additional related data collected that was not included in the current data package: There is no additional uncollected data 4. Are there multiple versions of the dataset? No A. If yes, name of file(s) that was updated:  i. Why was the file updated?  ii. When was the file updated? METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data:  The pertinent publications containing experimental design/protocols used in data collection are referenced below: Gu, H. et al., Fouling indicators for field monitoring the effectiveness of operational strategies of ultrafiltration as pretreatment for seawater desalination  doi: 10.1016/j.desal.2017.11.038 Gao, L. et al., Self-adaptive cycle-to-cycle control of in-line coagulant dosing in ultrafiltration for pre-treatment of reverse osmosis feed water doi: 10.1016/j.desal.2016.09.024 2. Methods for processing the data:  Data collection is detailed in the manuscript referenced below: Gu, H. et al., Fouling indicators for field monitoring the effectiveness of operational strategies of ultrafiltration as pretreatment for seawater desalination doi: 10.1016/j.desal.2017.11.038 Gao, L. et al., Self-adaptive cycle-to-cycle control of in-line coagulant dosing in ultrafiltration for pre-treatment of reverse osmosis feed water doi: 10.1016/j.desal.2016.09.024 3. Instrument- or software-specific information needed to interpret the data:  Data files are in Excel format. A total of 180 datasets are included in the uploaded Zipped file 4. Standards and calibration information, if appropriate: No 5. Environmental/experimental conditions:  All experimental conditions are in the dataset. Information regarding meteorological conditions (if needed)  can be obtained from local Meteorological stations given the specified dates of data collections. 6. Describe any quality-assurance procedures performed on the data:  Gu, H., Fouling indicators for field monitoring the effectiveness of operational strategies of ultrafiltration as pretreatment for seawater desalination doi: 10.1016/j.desal.2017.11.038 Gao., L. et al., Self-adaptive cycle-to-cycle control of in-line coagulant dosing in ultrafiltration for pre-treatment of reverse osmosis feed water doi: 10.1016/j.desal.2016.09.024 7. People involved with sample collection, processing, analysis and/or submission:  Yang Zhou, Bilal Khan, Han Gu, Panagiotis Christofides, Yoram Cohen and Gao, Larry. DATA-SPECIFIC INFORMATION FOR Files  The files are number as given below: #TR1-#TR85 (TR -training dataset followed by the dataset number) #TR98-#TR150 (TR -training dataset followed by the dataset number) ##TE1-#TE20 (TE- test dataset followed by the dataset number) #TE26-#TE30 (TE- test dataset followed by the dataset number) <repeat this section for each dataset, folder or file, as appropriate> 1. Number of variables: 23 2. Number of datasets: 105; 3. Dates of collection indicator in each dataset 4. Total number of variables:23 Time and Date UF Inflow Rate UF Element 1 (E1) Inflow rate UF Element 2 (E2) Inflow rate UF Element 3 (E3) Inflow rate UF Backwash Flow Rate Filtrate pH MF Inlet Pressure MF Trans-filter Pressure UF Inlet Filtration Pressure UF Feed-Side Backwash Pressure UF filtrate side backwash Pressure UF Filtrate-side Pressure UF Filtrate Turbidity UF Feedwater Turbidity UF Feed Pump RPM Filtrate Temperature Coagulant Dose Initial UF Resistance Filtration Duration Backwash Flux Backwash Duration (2) Datasets #TR86-#TR97 and #TE21-#TE25 (Dataset #TE23 is for UF oepration during a storm event) 1. Number of variables: 24 2. Number of datasets: 17 3. Dates of collection indicater in each dataset 4. Total nummber of variables:24 Time and Date UF Inflow Rate UF Element 1 (E1) Inflow rate UF Element 2 (E2) Inflow rate UF Element 3 (E3) Inflow rate UF Backwash Flow Rate Filtrate pH MF Inlet Pressure MF Trans-filter Pressure UF Inlet Filtration Pressure UF Feed-Side Backwash Pressure UF filtrate side backwash Pressure UF Filtrate-side Pressure UF Filtrate Turbidity UF Feedwater Turbidity UF Feed Pump RPM Filtrate Temperature Coagulant Dose Chlorophyll RFU Initial UF Resistance Filtration Duration Backwash Flux Backwash Duration

The datasets represent ultrafiltration (UF) operation, for pre-treatment of seawater RO feedwater, over a total period of 422 days. The operational data for UF filtration and backwash were obtained in a field study at Port Hueneme (CA) over a wide range of water quality conditions and coagulant dose. The data were utilized to develop a machine learning model for UF membrane resistance and backwash efficiency.

Data of ultrafiltration UF performance (during both filtration and backwash) were collected over a 4-year field study of seawater desalination with UF feedwater pretreatment.