Artificial intelligence for greywater treatment using electrocoagulation process. Issue 1 (2nd January 2016)

• Record Type: Journal Article
• Authors: Nasr, Mahmoud; Ateia, Mohamed; Hassan, Kareem
• Is Part Of: Separation science and technology. Volume 51:Issue 1(2016)
• Page Start: 96
• Page End: 105
• Publication Date: 2016-01-02
• Subjects: Artificial intelligence -- current density -- electric-energy consumption -- electrolysis time -- inter-electrode distance; Separation (Technology) -- Periodicals; 660.284205
• DOI: 10.1080/01496395.2015.1062399
• Languages: English
• ISSNs: 0149-6395

Abstract

Abstract: Treatment of greywater by electrocoagulation using aluminum electrodes was studied. The effects of current-density, electrolysis-time, and inter-electrode-gap on turbidity-removal and electrical-energy consumption were investigated. Under the optimal conditions ( J = 12.5 mA/cm 2, t = 30 min, and l = 0.5 cm), pollutants removal were: CODtotal = 52.8%, CODsoluble = 31.4%, BODtotal = 32.8%, BODsoluble = 27.6%, SS = 64.6%, TN = 30.1%, and TP = 13.6%. The consumed electrical-energy recorded 4.1 kWh/m 3 with an operating cost 0.25 US $/m 3 . Artificial intelligence was developed to simulate the influence of variables on the turbidity-removal. A 3–6–1 neural network achieved R -values: 0.99 (training), 0.84 (validation) and 0.89 (testing). An adaptive neuro-fuzzy inference system indicated that current-density is the most influential input.