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|Title:||Enzymatic degradation of pyridine raffinate using response surface and artificial neural network simulation|
|Authors:||Rajput, Manish Singh|
|Keywords:||Backpropagation;Biodegradation;Central composite design (CCD);Industrial pollution;Laccase production;Organic pollutant;RSM|
|Abstract:||Pyridine is a heterocyclic aromatic compound present in pyridine raffinate, an organic discharge of the pyridine manufacturing industry. Besides pyridine, raffinate also contains formaldehyde, picolines and phenolics. Earlier, we isolated Gamma proteobacterium from timber soil for laccase production and optimized the involved process parameters. Here, we studied the optimization of process parameters for biodegradation of pyridine raffinate with the help of mathematical modeling [central composite design with response surface methodology (CCD-RSM) and artificial neural network (ANN)]. The results predicted ANN to be a better tool for optimization of pyridine raffinate degradation. CCD was used to develop the best fit second-order polynomial quadratic regression equation. Prediction of degradation percentage for pyridine raffinate was done using the equation which was found to be 71.60% at temperature 36.76°C, pH 7.45 and inoculum concentration 1.96 mL/10mL. The predicted response was experimentally validated in the wet lab to verify the degradation efficiency. The outcome was 65.76±2%, further confirmed by Gas Chromatography-Flame Ionization Detector (GC-FID). The result of GC-FID () data showed no trace of pyridine (Area 0%) which was reduced from initial area of 1.38% pyridine in raffinate sample.|
|ISSN:||0975-1009 (Online); 0019-5189 (Print)|
|Appears in Collections:||IJEB Vol.58(08) [August 2020]|
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