Treatment of Chromium from Tannery Wastewater Using Nano-Bio Polymer Complex

Abstract

Treatment of rich chromium effluent is a matter of concern in many leather industries. Nano zero valent iron (NZI) is considered as one of the promising adsorbent materials due to its large surface area. Also, biomaterials have great remediation efficiency because of their surface-active groups such as hydroxyl and carboxyl groups. In this paper, we study the use of nano-bio polymer complex (NBPC) in chromium removal. Batch experiments were carried out in terms of operating medium pH, contact times, initial chromium concentrations, and speed rates. The maximum chromium removal of 88% was observed at pH values 7.0, contact time 30 min, and speed rate 150 rpm for chromium concentration 100 mg/L. The adsorption linear isotherms fitted well to Freundlich’s model compared to Langmuir’s model. Treatment of tannery wastewater through NBPC beads showed a decrease in heavy metal concentrations at equilibrium with removal orders of Mn > Cu > Ni > Cr > Pb > Fe > Cd. The removal efficiency of chromium ions is 52% with specific uptake of 25.6 mg(Cr)/g(NBPC). Results showed a decrease in the organic loads represented in COD, BOD, ammonia nitrogen, nitrate, and phosphorous with removal efficiencies 51.8, 48.6, 66.1, 63, and 43.3%, respectively. The reusability of NBPC beads was studied for three subsequent cycles. Results revealed that the availability of NBPC beads for reuse but with removal efficiencies is less than that of the first cycle. X-ray diffraction, SEM micrograph with EDAX analysis, and FT-IR spectroscopy for NBPC beads were studied for surface characterizations.

Heptachlor Epoxide -Pesticide- Removal from Aqueous Solutions Using Nano Zero Valent Iron: Operating Conditions, Response Surface Methodology, and Artificial Intelligence Neural Networks

“…where q is the quantity of heptachlor epoxide adsorbed by nZVI per unit mass (mg/g), initial concentration (C0) (µg/L), Ct is the concentration t time (µg/L), W is the mass of nZVI (g), and volume (V) by (L) [35][36][37].…”

Predicting Cu(II) Adsorption from Aqueous Solutions onto Nano Zero-Valent Aluminum (nZVAl) by Machine Learning and Artificial Intelligence Techniques

“…The LR methods are appropriately employed to predict the concentrations of multiple water quality parameters, either single output or multiple outputs [30,31]. The artificial neural network (ANN) is a promising mathematical model alternative that has been recently applied to capture the nonlinear correlations in the environmental engineering sector [32][33][34][35]. For instance, Fan et al [36] used ANN for forecasting copper elimination from aqueous media using reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) magnetic nanocomposites.…”