Artificial neural network modelling of xylose yield from water hyacinth by dilute sulphuric acid hydrolysis for ethanol production. (2016)

• Record Type: Journal Article
• Authors: Das, Subhabrata; Bhattacharya, Anamica; Ganguly, Amit; Dey, Apurba; Chatterjee, Pradip K.
• Is Part Of: International journal of environmental technology and management. Volume 19:Number 2(2016)
• Page Start: 150
• Page End: 166
• Publication Date: 2016
• Subjects: water hyacinth -- ANNs -- artificial neural networks -- modelling -- xylose yield -- dilute sulphuric acid -- hydrolysis -- ethanol production -- enzymatic saccharification -- fermentation -- biomass -- sugar yield -- temperature -- concentration -- treatment time -- residence time -- response surface methodology -- RSM -- biofuels -- experimental design -- P stipitis; Environmental management -- Periodicals; Green technology -- Periodicals; 333.705
• Languages: English
• ISSNs: 1466-2132

Abstract

Studies on dilute sulphuric acid pretreatment of water hyacinth for xylose yield was carried out with four independent parameters namely temperature, concentration, treatment time and residence time. Response surface methodology (RSM) was implemented to develop an experimental design matrix. Artificial neural network modelling was studied to develop and optimise the process based on the results obtained from the RSM design. The xylose yield under optimised condition obtained experimentally was 164.76 mg/g of dry water hyacinth biomass when hydrolysed with 4.89/ sulphuric acid, at 130°C operating temperature, residence time of 59.67 minutes and treatment time of ten minutes. Enzymatic saccharification was followed using a cocktail of cellulase and xylanase enzyme, which resulted in a total reducing sugar yield of 396.34 mg/g which followed fermentation. A substantial yield of 6.84 g/L of ethanol was obtained using P. stipitis the hydrolysate derived from hydrolysis.