The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 26, 2024

Filed:

Sep. 06, 2023
Applicants:

Dileep Agnihotri, Austin, TX (US);

Masoud Aghajani, Boulder, CO (US);

Sriram Sambasivam, Schaumberg, IL (US);

Joseph John Barelli, Rockford, IL (US);

Ian Tonner, Sun Prairie, WI (US);

Inventors:

Dileep Agnihotri, Austin, TX (US);

Masoud Aghajani, Boulder, CO (US);

Sriram Sambasivam, Schaumberg, IL (US);

Joseph John Barelli, Rockford, IL (US);

Ian Tonner, Sun Prairie, WI (US);

Assignee:

Surplus Management, Inc, Loves Park, IL (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B01D 63/10 (2006.01); B01D 61/02 (2006.01); B01D 61/10 (2006.01); B01D 61/12 (2006.01); B01D 65/08 (2006.01); B01D 65/10 (2006.01); C02F 1/44 (2023.01); C02F 103/08 (2006.01);
U.S. Cl.
CPC ...
B01D 65/109 (2022.08); B01D 61/026 (2022.08); B01D 61/0271 (2022.08); B01D 61/10 (2013.01); B01D 61/12 (2013.01); B01D 63/10 (2013.01); B01D 65/08 (2013.01); C02F 1/441 (2013.01); C02F 1/442 (2013.01); B01D 2317/02 (2013.01); C02F 2103/08 (2013.01); C02F 2209/02 (2013.01); C02F 2209/03 (2013.01); C02F 2209/05 (2013.01); C02F 2209/40 (2013.01); C02F 2301/08 (2013.01); C02F 2303/14 (2013.01); C02F 2303/22 (2013.01);
Abstract

A novel reverse osmosis or nanofiltration system (RO/NF) capable of detecting and responding to onset of fouling within the system utilizing uniquely configured membrane permeate flow path within the system which generates a time-sensitive data. Membrane performance data in real-time operating conditions is then utilized for rapid detection of membrane fouling, fouling rate, and cause of fouling, followed by controller-based system generated actions to stop, and recover from fouling or slow-down fouling, and, if required, to predict, plan, and schedule operator intervention steps to recover optimum system operating conditions. The end-result is a novel energy-efficient and fouling-managed advanced (machine learning) reverse osmosis system for brackish water desalination.


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