Artificial neural network and cost estimation for Cr(VI) removal using polycationic composite adsorbent. (14th June 2019)

• Record Type: Journal Article
• Authors: Nayunigari, Mithil Kumar; Gupta, Sanjay Kumar; Nasr, Mahmoud; Andaluri, Gangadhar; Suri, Rominder P. S.; Maity, Arjun
• Is Part Of: Water and environment journal. Volume 34(2020)Supplement 1
• Page Start: 29
• Page End: 40
• Publication Date: 2019-06-14
• Subjects: adsorption -- artificial intelligence -- hexavalent chromium -- polycationic adsorbent -- water treatment; Sewage -- Periodicals; Water-supply -- Periodicals; Environmental management -- Periodicals; Water-supply engineering -- Periodicals; Pollution -- Periodicals; 628.1
• DOI: 10.1111/wej.12501
• Languages: English
• ISSNs: 1747-6585

Abstract

Abstract: A cationic adsorbent, namely polyamine/folic acid composite (PFC) was used to remove Cr(VI) from aqueous solutions. The optimum pH was 2.0, achieving Cr(VI) removal of 99.4% at an initial concentration 200 mg/L, PFC dosage 0.5 g/L and temperature 298 K within 90 min. Under the acidic condition, electrostatic attractions between anionic species of chromium ( HCrO 4 - and Cr 2 O 7 2 - ) and cations on PFC (NR4 + and H + ) enhanced Cr(VI) uptake. Additional removal mechanisms including anion exchange with Cl − (because of the use of epichlorohydrin for PFC preparation), reduction of Cr(VI) to Cr(III) and physical interaction could be involved in the adsorption process. Artificial neural network containing a first layer with five inputs, a hidden layer with eight neurons and the last layer with one output was able to predict the Cr(VI) removal efficiency ( R 2 = 0.919). The total cost of scaling up the adsorption system was $3.53 per m 3 of wastewater containing 200 mg‐Cr(VI)/L.