No abstract

The diminishing reserves and environmental consequences of the fossil fuel-based petrodiesel necessitate the exploration of an alternative fuel with better quality and minimum environmental impacts. The study explores the optimization of biodiesel production from nonfood and locally available mixed feedstocks as an effective and a sustainable approach to solve the insufficiency and high costs of single oil feedstock. The selection of suitable oil feedstocks and optimization of process variables are the prime issues for cost-effective industrial scale production of biodiesel from mixed feedstocks toward the industrial scale production of biodiesel. The objective of this study was to optimize process variables for the alkaline transesterification of mixed castor seed and microalgae oils to optimize the yield of biodiesel. Oils were extracted from dried microalgae (Chlorella vulgaris) biomass and castor seed kernel using methanol. The oils were purified, characterized, mixed in a 1 : 1 ratio, and converted to biodiesel. The transesterification experiments designed according to the central composite design (CCD) were used to optimize the yield of biodiesel through the response surface methodology (RSM). Experimental results were analyzed by response surface regression to produce a model for predicting biodiesel yield. Model significance, fitness, the effect of significant variables, and interactions between the variables on the yield of biodiesel were studied through the analysis of variance (ANOVA). The optimization of transesterification process variables revealed that the catalyst concentration of 1.23% (w/w), ethanol to mixed oil ratio of 5.94 : 1 (v/v), and reaction temperature of 51.0°C were the optimum conditions to achieve an optimum biodiesel yield of 92.88%. Validation experiments conducted under the optimum conditions resulted in the biodiesel yield of 92.36%, which is very close to the model predicted value. Various standard methods were used to characterize the biodiesel produced under optimum conditions, and it was found compatible with ASTM 751 and EN14214 biodiesel standards.

To mitigate the negative effects of pollution produced by the growing levels of pollutants in the environment, research and development of novel and more effective materials for the treatment of pollutants originating from a variety of industrial sources should be prioritized. In this research, a UV-irradiated nano-graphene oxide (UV/n-GO) was developed and studied for methylene blue (MB) adsorption. Furthermore, the batch adsorption studies were modelled using response surface modelling (RSM) and artificial neural networks (ANNs). Investigations employing FTIR, XRD, and SEM were carried out to characterize the adsorbent. The best MB removal of 95.81% was obtained at a pH of 6, a dose of 0.4 g/L, an MB concentration of 25 mg/L, and a period of 40 min. This was accomplished with a desirability score of 0.853. A three-layer backpropagation network with an ideal structure of 4-4-1 was used to create an ANN model. The R2 and MSE values determined by comparing the modelled data with the experimental data were 0.9572 and 0.00012, respectively. The % MB removal predicted by ANN was 94.76%. The kinetics of adsorption corresponded well with the pseudo-second-order model (R2 > 0.97). According to correlation coefficients, the order of adsorption isotherm models is Redlich–Peterson > Temkin > Langmuir > Freundlich. Thermodynamic investigations show that MB adsorption was both spontaneous and endothermic.

A two-phase separation method called cloud point extraction (CPE) does not use hazardous or flammable organic solvents. The efficient removal of the dye Reactive Black-5 (RB-5) from an aqueous solution using Triton X-114, a nonionic surfactant, is described in this study. Three-level factorial design and response-surface methods were used to quantify the impact of process variables on the CPE process, such as operating temperature and surfactant concentration. Investigations were conducted into how these process variables affected the ratio between the phase volumes, the concentration of dye in the surfactant-rich phase, and the residual amounts of dye in the diluted phase. As a result, ANOVA was used to create and validate mathematical models. The findings demonstrated that the correlation coefficients (R2) exceeded 0.98. The acquired findings showed that the suggested extraction process is efficient, and the proposed CPE approach removes 98% of the RB-5 dye under optimal conditions.