Optimization of glucose formation in karanja biomass hydrolysis using Taguchi robust method

Printed Circuit Board-Derived Glass Fiber-Epoxy Resin-Supported Mo-Cu Bimetallic Catalyst for Glucose Synthesis

A glass fiber-epoxy resin (GFER) framework derived from mixed waste printed circuit boards (MWPCBs) was utilized to prepare a cost-effective, reusable Mo-Cu bimetallic Bronsted-Lewis solid acid catalyst through wet-impregnation under near-infrared radiation (NIRR) activation. The efficacy of the novel Mo-Cu catalyst was assessed in the synthesis of glucose through hydrolysis of jute (Corchorus olitorius) fiber, and the process parameters were optimized (Mo precursor loading: 1.0 wt %, catalyst concentration: 5 wt %, hydrolysis temperature: 80 °C, and hydrolysis time: 10 min) through Taguchi orthogonal design. The GFER support and the prepared catalysts were characterized through thermogravimetric, X-ray diffraction (XRD), Fourier-transform infrared (FTIR), Brunauer-Emmett-Teller (BET)-density functional theory, and TPD analyses. The optimal Mo-Cu catalyst and the GFER support possessed 45.377 and 7.049 m2/g BET area, 0.04408 and 0.02317 cc/g pore volume, 1.9334 and 0.7482 nm modal pore size, and surface acidity of 0.48 and 0.40 mmol NH3/g catalyst, respectively. X-ray photoelectron spectroscopy bands confirmed the coexistence of Mo6+ and Cu2+ species; XRD and FTIR analyses indicated the presence of MoO3 and CuO crystalline phases in all prepared catalysts. The optimal catalyst prepared through NIRR (wavelength 0.75-1.4 μm)-activated hydrothermal treatment resulted in a significantly greater glucose yield (75.84 mol %) than that achieved (53.64 mol %) using a conventionally prepared catalyst. Thus, an energy-efficient application of NIRR (100 W) could significantly improve catalytic properties over conventional hydrothermal treatment (500 W). The present investigation provides an innovative application of MWPCB-derived GFER as a promising cost-effective support for the preparation of highly efficient inexpensive solid catalysts for sustainable synthesis of glucose from low-cost waste jute fiber.

Comparison of response surface methodology and artificial neural network to enhance the release of reducing sugars from non-edible seed cake by autoclave assisted HCl hydrolysis

In the current investigation, statistical approaches were adopted to hydrolyse non-edible seed cake (NESC) of Pongamia and optimize the hydrolysis process by response surface methodology (RSM). Through the RSM approach, the optimized conditions were found to be 1.17%v/v of HCl concentration at 54.12 min for hydrolysis. Under optimized conditions, the release of reducing sugars was found to be 53.03 g/L. The RSM data were used to train the artificial neural network (ANN) and the predictive ability of both models was compared by calculating various statistical parameters. A three-layered ANN model consisting of 2:12:1 topology was developed; the response of the ANN model indicates that it is precise when compared with the RSM model. The fit of the models was expressed with the regression coefficient R², which was found to be 0.975 and 0.888, respectively, for the ANN and RSM models. This further demonstrated that the performance of ANN was better than that of RSM.

Liquid hot water pretreatment of multi feedstocks and enzymatic hydrolysis of solids obtained thereof

Agricultural feedstocks (brewers' spent grain - BSG, corncob - CC, corn husk - CH, wheat straw - WS and Luffa sponge - LS) were pretreated by liquid hot water (LHW) in order to increase cellulose recovery and enzymatic saccharification. LHW-pretreatment resulted in hemicellulose solubilization, and solids enriched in cellulose. Chemical analysis showed different susceptibilities of the feedstocks to LHW-pretreatment and enzymatic hydrolysis. Pretreated feedstocks presented higher crystallinity (determined through X-ray diffraction) and thermal stability (determined through thermogravimetric analysis) than untreated feedstocks. SEM images confirmed the effect of LHW-pretreatment on structural changes. Moreover, enzymatic hydrolysis and cellulose conversion to glucose (CCG) were improved for pretreated feedstocks, with exception of LS. CCG (in relation to glucose potential on solids) followed the order: BSG>CH>WS>CC>LS. LHW-pretreatment showed to be a good technology to pretreat multi feedstocks and for improving the enzymatic hydrolysis of recalcitrant agricultural feedstocks to sugars, which can be further converted to ethanol-fuel and other value-added chemicals.