Artificial neural network and cost estimation for Cr(VI) removal using polycationic composite adsorbent

Mithil Kumar Nayunigari, Sanjay Kumar Gupta, Mahmoud Nasr, Gangadhar Andaluri, Rominder P.S. Suri, Arjun Maity

Research output: Contribution to journalArticle

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 (HCrO4-and Cr2O72-) 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 (R2 = 0.919). The total cost of scaling up the adsorption system was $3.53 per m3 of wastewater containing 200 mg-Cr(VI)/L.

Original languageEnglish
JournalWater and Environment Journal
DOIs
Publication statusPublished - 1 Jan 2019

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Keywords

  • adsorption
  • artificial intelligence
  • hexavalent chromium
  • polycationic adsorbent
  • water treatment

ASJC Scopus subject areas

  • Environmental Engineering
  • Water Science and Technology
  • Pollution
  • Management, Monitoring, Policy and Law

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