Chemical kinetics of a two-step transesterification of dyacrodes edulis seed oil using acid-alkali catalyst

Integrating artificial neural networks and response surface methodology for predictive modeling and mechanistic insights into the detoxification of hazardous MB and CV dyes using Saccharum officinarum L. biomass

The presence of dye pollutants in industrial wastewater poses significant environmental and health risks, necessitating effective treatment methods. The optimal adsorption treatment of methylene blue (MB) and crystal violet (CV) dye-simulated wastewater utilising Saccharum officinarum L presents a key challenge in the selection of appropriate modelling approaches. While RSM and ANN models are frequently used, there is a noticeable knowledge gap when it comes to evaluating their relative strengths and weaknesses in this context. The study compared the predictive abilities of response surface methodology (RSM) and artificial neural network (ANN) for the adsorption treatment of MB and CV dye-simulated wastewater using Saccharum officinarum L. The process experimental variables were modelled and predicted using a three-layer artificial neural network trained using the Levenberg-Marquard backpropagation algorithm and 30 central composite designs (CCD). The adsorption study used a specific mechanism, which led to noteworthy maximum removals of 98.3% and 98.2% for dyes (MB and CV), respectively. The RSM model achieved an impressive R2 of 0.9417, while the ANN model achieved 0.9236 in MB. Adsorption is commonly used to remove colour from many different materials. Saccharum officinarum L., a byproduct of sugarcane processing, has shown potential as an efficient and ecological adsorbent in this environment. The purpose of this study is to evaluate sugarcane bagasse's potential as an adsorbent for the removal of dyes MB and CV from industrial wastewater, providing a long-term strategy for reducing dye pollution. Due to its beneficial economic and environmental characteristics, the Saccharum officinarum L. adsorbent has prompted research into sustainable resources with low pollutant indices.

Preparation of Ca- and Na-Modified Activated Clay as a Promising Heterogeneous Catalyst for Biodiesel Production via Transesterification

For efficient biodiesel production, an acid-activated clay (AC) modified by calcium hydroxide and sodium hydroxide (CaNa/AC) was prepared as a catalyst. CaNa/AC and Na/AC were characterized by Hammett indicators, CO2-TPD, FT-IR, XRD, and N2 adsorption techniques. The influence of catalyst dose, reaction temperature, methanol/oil molar ratio, and reaction time on the transesterification of Jatropha oil was studied. Due to the introduction of calcium, CaNa/AC displayed a higher activity and stability, thereby achieving an oil conversion of 97% under the optimal reaction conditions and maintaining over 80% activity after five successive reuses. The reaction was accelerated as the temperature rose, and the apparent activation energy of CaNa/AC was 75.6 kJ·mol−1. The enhanced biodiesel production by CaNa/AC was ascribed to the increase in active sites and higher basic strength. This study presents a facile and practical method for producing biodiesel on large-scale operation.

Use of heterogeneous catalysis in sustainable biofuel production

Biofuel is a sustainable energy source that may use to replace fossil-based carbon dioxide and mitigate the adverse effects of exhaust emissions. Nowadays, we need to replace petroleum fuels with alternatives from environmentally sustainable sources of increasing importance. Biofuels derived from biomass have gained considerable attention, and thus most of the traditional methods that harm the environment and humans have retreated. Developing an active and stable heterogeneous catalyst is a step of utmost importance in the renewable liquid fuel technology. Thus, there is a great interest in developing methods for producing liquid fuels from non-edible sources. It may also be from dry plant tissues such as agricultural waste. Lignocellulosic biomass can be a sustainable source for producing renewable fuels and chemicals, as well as the replacement of petroleum products. Hence, the researchers aspired to synthesize new catalysts using a cheap technology developed to hydrolyze cellulose and then produce bioethanol without needing expensive enzymes, which may ultimately lead to a lower fuel price. In this paper, we will focus on the recent technologies used to produce sustainable biofuels through inexpensive incentives and innocuous to the environment.

Application of soft-computing techniques for statistical modeling and optimization of Dyacrodes edulis seed oil extraction using polar and non-polar solvents

This research presents optimal factor evaluation for maximum Dyacrodes edulis seed oil (DESO) extraction by applying central composite design (CCD) based on Box-Behnken (BB) experimental design of response surface methodology (RSM) and Artificial neural network (ANN) on feed forward-back propagation (FFBP) of Levenberg Marquardt (LM) training algorithm. Polar solvents (ethanol and combination of methanol and chloroform (M/C)) and non-polar solvents (n-hexane) were used for the extraction. The RSM optimal predicted oil yields were 45.21%, 38.61% and 30.87% while experimental values were 46.01%, 40.71% and 32.45% for n-hexane, ethanol and M/C respectively. The RSM optimum conditions were particle size of 450.67, 451.19 and 450.22μm, extraction time of 55.57, 55.16 and 56.11min and solute/solvent ratio of 0.19, 0.16 and 0.18 g/ml for n-hexane, ethanol and M/C respectively. The ANN-GA optimized conditions showed 5.14, 5.81 and 2.12 % higher DESO yields at 1.10, 0.26 and 0.65% smaller particle sizes, 5.47, 0.30 and 0.62 % faster extraction rate, and 24, 11.11 and 10% more solute requirement, for n-hexane, ethanol and M/C solvents respectively. The particle size was found to be the most significant factor. ANN and RSM established good correlations with the experimental data but ANN showed higher predictive supremacy than RSM based on its higher values of R² and lower error indices. Also, ANN-GA provided more economical optimal DESO extraction route. The physico-chemical characteristics, functional groups and fatty acid compositions of the seed oil compared with literature values and suggest high commercial values for DESO. Therefore, the obtained results present a viable method to harness the useful and highly potential seed oil from dyacrodes edulis for many industrial applications.

Effects on Biodiesel Production Caused by Feed Oil Changes in a Continuous Stirred-Tank Reactor

Continuous production processes and an adequate supply of raw materials are necessary to satisfy the growing demand for biodiesel. The use of different feed oils could be necessary to ensure sufficient supply for biodiesel production in certain circumstances; however, changing feed oil during the operation of a continuous reactor causes process disturbances. The present study analyses the effect of feed oil changes on the continuous operation of a industrial continuous stirred-tank reactor (CSTR) using a model which takes the dynamics of oil changes into account. The models previously reported only consider the operation of reactor with only one vegetable oil. The model in this work was developed by mass and energy balances. A methodology to model oil changes is presented. Glycerides and esters were characterized using adequate approaches. Moreover, accurate methods for predicting essential properties in the biodiesel manufacturing were used for the estimation of their thermo-physical properties. The kinetic parameters of the transesterifications were calculated from consistent studies selected from an exhaustive literature revision. The results show that temperature is practically not affected after oil changes; however, the ester concentration varies considerably. The ester mass fraction varying by up to 22.07% after an disturbance.