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Journal of Environmental Biology

pISSN: 0254-8704 ; eISSN: 2394-0379 ; CODEN: JEBIDP

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    Abstract - Issue Nov 2021, 42 (6)                                     Back

nstantaneous and historical temperature effects on a-pinene

Adsorptive removal of dairy industrial pollutants by chitosan zinc –oxide nanoadsorbent-  A comparitive study of artificial neural network and quadratic Box-Behnken design


B.L. Dinesha1*, S. Hiregoudar1, U. Nidoni2, K.T. Ramappa2, A.T. Dandekar3, M.V. Ravi4 and K.B. Sankalpa5 

1Centre for Nanotechnology, College of Agricultural Engineering, University of Agricultural Sciences, Raichur- 584 101, India

2Department of Processing and Food Engineering, College of Agricultural Engineering, University of Agricultural Sciences, Raichur- 584 101, India

3Department of Agricultural Engineering, College of Agriculture, University of Agricultural Sciences, Mandya- 571 405, India

4Department of Soil Science and Agricultural Chemistry, College of Agriculture University of Agricultural Sciences, Raichur- 584 101, India

5Department of Agricultural Engineering, College of Horticulture, Kerala Agricultural University, Thrissur- 680 656, India

*Corresponding Author Email :


Received: 13.10.2020                                                                           Revised: 24.03.2021                                                        Accepted: 08.06.2021




Aim: To investigate the effect of operational parameters on the adsorption of biological oxygen demand (BOD) and chemical oxygen demand (COD)  on to Chitosan zinc oxide (CZnO) nanoadsorbent using cost-effective and eco-friendly nanoadsorbent based  effluent treatment processes.

Methodology: CZnO nanoadsorbent particle was synthesized using chemical precipitation method. The nano size <100 nm was achieved using high-speed cryo all mill, followed by the characterization using high-end instruments such as scanning electron microscope with elemental detection sensor (SEM-EDS), atomic force microscope (AFM), X-ray diffractometer (XRD) and Fourier transform inform infrared spectroscopy (FT-IR). Modeling and optimization of operational parameters were done with the artificial neural network (ANN) and Box-BehnkenDesign (BBD) statistical tools.

Results: Optimized treatment combination for adsorption of BOD and COD were found at initial BOD and COD concentration of 100 and 200 mg l−1, pH of 7.0 and 2.0, adsorbent dosage of 1.25 mg l−1, contact time of 100 and 60 min. In these conditionsthe desirability values of 0.988 and 0.950 were found for BOD and COD adsorption. The maximum per cent reduction of BOD and COD by using CZnO nanoadsorbent was found to be 96.71 and 87.56. Two models such as Quadratic Box-Behnken and ANN were compared in term of sum of square errors (SSE), root mean square error (RMSE) and correlation coefficient (R2) values.

Interpretation: The results obtained revel the well trained ANN model found to be more accurate in prediction of BOD and COD adsorption process parameters compared to BBD model.       

Key words: Biological oxygen demand, Chemical oxygen demand, Dairy industry, Nanotechnology, Wastewater treatment




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