Modelling of fermentative bioethanol production from indigenous Ulva prolifera biomass by Saccharomyces cerevisiae NFCCI1248 using an integrated ANN-GA approach

One Step Catalytic Conversion of Polysaccharides in Ulva prolifera to Lactic Acid and Value-Added Chemicals

The production of lactic acid and value-added chemicals (such as hydroxypropanone, glycolic acid, and formic acid) directly from Ulva prolifera via one-step catalytic process was studied. The effect of different amounts of YCl3-derived catalysts on the hydrothermal conversion of carbohydrates in Ulva prolifera was explored, and the reaction conditions were optimized. In this catalytic system, rhamnose could be extracted from Ulva prolifera and converted in situ into lactic acid and hydroxypropanone at 160 °C, while all the glucose, xylose, and rhamnose were fractionated and completely converted to lactic acid at 220 °C or at a higher temperature, via several consecutive and/or parallel catalytic processes. The highest yield of lactic acid obtained was 31.4 wt% under the optimized conditions. The hydrothermal conversion of Ulva prolifera occurred rapidly (within 10 min) and showed promise to valorize Ulva prolifera.

As (III) removal using superparamagnetic magnetite nanoparticles synthesized using Ulva prolifera - optimization, isotherm, kinetic and equilibrium studies

In this study, magnetite nanoparticles (MNPs) were synthesized using the seaweed - Ulva prolifera, an amply found marine source in the Western coastal regions of India. The surface and other properties of MNPs were characterized by many sophisticated methods. Spherical nanoclusters were observed in the FESEM image and iron and oxygen elements were seen in EDS results. XRD peaks were consistent with magnetite standards and MNPs had good crystallinity. FTIR portrayed the specific signals for MNPs and TGA profile ascertained the thermal stability. Magnetic saturation of 41.84 emu/g with negligible hysteresis loop substantiated the superparamagnetism. XPS pointed out the presence of Fe and O with oxidation states specific for MNPs, and the results were consistent with EDS. BET revealed a high specific surface area (144.98 m²/g) of MNPs with mesopores. The synthesized MNPs were used as nanoadsorbent for the removal of As (III) from aqueous solution. The central composite design was used for optimizing As (III) adsorption on MNPs. The optimum conditions were found out as 97.5% at pH: 9, rotation speed: 150 rpm, time: 90 min, and MNPs dosage: 1.15 g/L. The adsorption process fitted in a better way with the Langmuir isotherm and pseudo-second-order model. The highest adsorption capacity was 12.45 mg/g, which is substantially larger than the documenter reports. The spontaneous and endothermic nature of adsorption were ascertained from thermodynamic studies. The results suggested that the synthesized MNPs using the extract of U. prolifera could be alternative nanoadsorbents for eliminating toxic heavy metals from waste streams.

Artificial neural network-genetic algorithm-based optimization of aerobic composting process parameters of Ganoderma lucidum residue

The rapid development of traditional Chinese medicine enterprises has put forward higher requirements for the resource utilization of traditional Chinese medicine residues (TCMR). Aerobic composting of TCMR to prepare bio-organic fertilizer is an effective resource utilization method. In this study, a back-propagation artificial neural network (BPNN) model using composting factors as inputs (C/N, initial moisture content, type of inoculant, composting days) and the humic acid content as the output was constructed based on the orthogonal test data. BPNN-GA (a genetic algorithm) was used for extreme value optimization, and the optimal composting process parameter combination was obtained and verified. The results show that the combination of orthogonal testing and BPNN can effectively establish the relationship between the composting process parameters and humic acid content. The R² value was 0. 9064. The optimized parameter combination is as follows: C/N,37.42; moisture content,69.76%; bacteria,no; and composting time,50 d.

Artificial neural network and statistical modelling of biosorptive removal of hexavalent chromium using macroalgal spent biomass

This study focused on the sustainable removal of chromium in its hexavalent form by adsorption using sugar-extracted spent marine macroalgal biomass - Ulva prolifera. The adsorption of Cr (VI) from aqueous solutions utilizing macroalgal biomass was studied under varying conditions of pH, adsorbent amount, agitation speed, and time to assess and optimize the process variables by using a statistical method - response surface methodology (RSM) to enhance the adsorption efficiency. The maximum adsorption efficiency of 99.11 ± 0.23% was obtained using U. prolifera under the optimal conditions: pH: 5.4, adsorbent dosage: 200 mg, agitation speed: 160 rpm, and time: 75 min. Also, a prediction tool - artificial neural network (ANN) model was developed using the RSM experimental data. Eight neurons in the hidden layer yielded the best network topology (4-8-1) with a high correlation coefficient (R_ANN: 0.99219) and low mean squared error (MSE_ANN: 0.99219). Various performance parameters were compared between RSM and ANN models, which confirmed that the ANN model was better in predicting the response with a high coefficient of determination value (R²_ANN: 0.9844, R²_RSM: 0.9721) and low MSE value (MSE_ANN: 3.7002, MSE_RSM: 6.2179). The adsorption data were analyzed by fitting to various equilibrium isotherms. The maximum adsorption capacity was estimated as 6.41 mg/g. Adsorption data was in line with Freundlich isotherm (R² = 0.97) that confirmed the multilayer adsorption process. Therefore, the spent U. prolifera biomass can credibly be applied as a low-cost adsorbent for Cr (VI) removal, and the adsorption process can be modelled and predicted efficiently using ANN.

Recent advances and sustainable development of biofuels production from lignocellulosic biomass

Many countries in the world are facing the demand for non-renewable fossil fuels because of overpopulation and economic boom. To reduce environmental pollution and zero carbon emission, the conversion of biomass into biofuels has paid better attention and is considered to be an innovative approach. A diverse raw material has been utilized as feedstock for the production of biofuel, depending on the availability of biomass, cost-effectiveness, and their geographic location. Among the different raw materials, lignocellulosic biomass has fascinated many researchers around the world. The current review discovers the potential application of lignocellulosic biomass for the production of biofuels. Various pretreatment methods have been widely used to increase the hydrolysis rate and accessibility of biomass. This review highlights recent advances in pretreatment methodologies for the enhanced production of biofuels. Detailed descriptions of the mechanism of biomass processing pathway, optimization, and modeling study have been discussed.