Modeling of Alkali Pretreatment of Rice Husk Using Response Surface Methodology and Artificial Neural Network

Application of artificial neural network for prediction of fluoride removal efficiency using neutralized activated red mud from aqueous medium in a continuous fixed bed column

The present research work approaches the removal of fluoride from aqueous medium using neutralized activated red mud (NARM) in a continuous fixed bed column. Artificial neural network (ANN) technique was applied effectively for optimization of the model for the practicability of the removal process. The consequences of various experimental variables, like bed length, adsorbate concentration, experimental time, and adsorbate solution flow rate are studied to know the breakthrough point and saturation times. The highest removal potentiality of NARM was considered to be 3.815 mg g^-1 of F^- in the bed height of 15 cm, starting concentration 1 ppm, susceptible time 120 min, adsorbate solution flow rate 0.5 mL min^-1, and constant room temperature, respectively. Bohart-Adams and Thomas models were considered to describe the fixed bed column effect to the bed height and adsorbate concentrations. The experimental data were applied to a back propagation (BP) learning algorithm programme with a four-seven-one architecture model. The artificial neural network model was considered to be functioning correctly as absolute relative percentage error throughout the learning period. Differentiation between the predicted outcomes from ANN model and actual results from experimental analysis affords a high degree of correlation (R² = 0.998) stipulating that the model was able to predict the adsorption efficiency. Experimented adsorbent materials were characterized using different instrumental analysis that is scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD).

Alkaline Fractionation and Subsequent Production of Nano-Structured Silica and Cellulose Nano-Fibrils for the Comprehensive Utilization of Rice Husk

The parameters of the alkaline fractionation process were investigated and optimized using a statistical analysis method to simultaneously remove hemicellulose and ash from rice husk (RH) concomitantly. After the alkaline fractionation process, the residual solid contained high cellulose, and the recovery yield of hemicellulose was enhanced in the fractionated liquid hydrolyzate. The hemicellulosic sugar recovery yield (71.6%), de-ashing yield (>99%), and lignin removal (>80%) were obtained at the reaction conditions of 150 °C of temperature, 40 min of reaction time, and 6% (w/v) of NaOH concentration. Subsequently, nano-structured silica was synthesized using black liquor obtained as a by-product of this fractionation process. For the production of nano-structured silica, it was observed that the pH of a black liquor and the heat treatment temperature significantly influenced the textural properties of silica product. In addition, the two-stage bleaching of solid residue followed by colloid milling for the production of high value-added CNF with was attempted. As a result, in addition to 119 g of fermentable sugar, 143 g of high-purity (>98%) silica with a surface area of 328 m2g−1 and 273.1 g of high-functional CNF with cellulose content of 80.1% were simultaneously obtained from 1000 g of RH.

Pretreatment of Leucaena leucocephala wood by acidified glycerol: optimization, severity index and correlation analysis

In this study, Leucaena leucocephala wood was pretreated with aqueous glycerol having H₂SO₄ as the catalyst. Response surface methodology (RSM) and artificial neural network (ANN) were used to optimize the process parameters, catalyst concentration (1-3%), duration (120-300 min) and temperature (100-150 °C). ANN gave more accurate predictions for total reducing sugar yield than RSM. ANN also had lower values for error functions. Severity index (SI) was calculated based on the temperature, duration and catalyst concentration. Increase in SI from 0.21 * 10³ to 2.06 * 10³ increased total reducing sugar (TRS) production from 39.97 g/kg to 321.8 g/kg. Further increase in SI reduced the TRS and this change positively correlates with the loss of cellulose content. Correlation analysis showed that severity index can also be used to describe pretreatment process.

Thermal assisted alkaline pretreatment of rice husk for enhanced biomass deconstruction and enzymatic saccharification: Physico-chemical and structural characterization

Thermal assisted alkaline pretreatment (TAAP) of rice husk (RH) was investigated to facilitate enzymatic saccharification by enhancing the enzyme accessibility to cellulosic components. Statistically guided experiments based on the Box-Behnken design involving four factors viz. biomass loading, particle size, NaOH loading and reaction time was considered for optimization. The maximum sugar yield of 371 mg g^-1 biomass was obtained at optimized pretreatment condition [biomass loading (10% w/w), particle size (0.25-0.625 mm), NaOH loading (2% w/w), and reaction time (40 min)]. The TAAP of RH resulted in the efficient removal of lignin (14.9-54% (w/w)) with low hemicellulose solubilization [10.7-33.1% (w/w)] and with a simultaneous increase in cellulose concentration [32.65-51.65% (w/w)]. The SEM analysis indicated increased porosity and biomass disruption during TAAP. The FTIR analysis showed progressive removal of noncellulosic constituents, and XRD analysis revealed an increase in cellulose crystallinity post-TAAP indicating the effectiveness of pretreatment.