Using Neural Networks to Process a Large Database to Quantify Causes of Reverse Osmosis Fouling
نویسنده
چکیده
The Orange County Water District (OCWD) in California has been operating a Groundwater Replenishment System (GWRS, Figure 1) since 2008. The GWRS treats secondary effluent from a wastewater treatment plant (WWTP) for distribution to reinjection wells, spreading basins, and industrial users. Upstream of the reverse osmosis system (RO), the secondary effluent goes through a microfiltration process, and is chemically treated with chlorine, sulfuric acid, and an anti-scalant. The RO is followed by an advanced disinfection process using a combination of ultraviolet irradiation and hydrogen peroxide, followed by pH adjustment and de-chlorination, and then distribution. OCWD wants to improve the RO’s performance and sought to mine the large database it has compiled since the GWRS’ startup to obtain new process knowledge.
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