Formulation and Evaluation of O/W Body Cream Containing Patchouli Oil (Pogostemon cablin Benth.) and Drumstick Oil (Moringa oleifera) as Potential Moisturizing Agent

Various compounds and extracts obtained from a wide variety of plants can be employed as natural antioxidants in cosmetics applications. Patchouli oil (Pogostemon cablin Benth.) and drumstick oil (Moringa oleifera) have the ability to fight free radicals and keep skin hydrated, making them excellent candidates for usage as an active component in cosmetic goods like body cream. This research aims to determine the physical quality of a body cream formulation by subjecting it to an accelerated stability test technique for seven cycles. The preparation was evaluated in each cycle, and the results were analyzed using student's t-test. The evaluation results of body cream preparation showed that there was no significant difference (p>0.05) in the preparation during each cycle. As a result, the formulation of the body cream remained stable throughout the storage term of at least six months, which indicates that it can be utilized for an entire year when kept at room temperature. After two weeks of application on dry skin, it was demonstrated that the formulation had the capacity to increase the amount of moisture contained in the skin (from 30% to 60%). In conclusion, the formulation is potentially produced as commercial cosmetic product in the future

RSM and Artificial Neural Networking based production optimization of sustainable Cotton bio-lubricant and evaluation of its lubricity & tribological properties

Depletion of mineral reservoirs along with health and environmental concerns have led to a greater focus on bio-lubricants. The purpose of this study was to analyze and optimize the reaction conditions of the transesterification process for cotton biolubricant synthesis by using Response Surface Methodology (RSM). In RSM, Rotatable central composite design was selected to examine the effect of reaction input factors on the yield of cotton bio-lubricant during the transesterification process. ANOVA analysis showed that temperature was the most significant factor followed by time, pressure and catalyst-concentration. Optimum reaction conditions obtained by RSM for maximum TMP tri-ester (cotton bio-lubricant) yield of about 37.52% were 144 °C temperature, 10 h time, 25 mbar pressure, and 0.8% catalyst-concentration. RSM predicted results were successfully validated experimentally and by artificial neural networking. About 90%–94% cotton seed oil bio-lubricant was obtained after purification and its physiochemical, lubricity and tribological properties were evaluated and found comparable with ISO VG-46 and SAE-40 mineral lubricant. Hence, cottonseed oil is a potential source for the bio-lubricant industry

Modelo de predicción de la concentración de cloroformo durante el proceso de destilación de una mezcla metanol-cloroformo

El objetivo de este trabajo consiste en modelar el sistema de destilación por oscilación de presión para separar la mezcla metanol-cloroformo y predecir por inteligencia artificial la concentración de cloroformo. La mezcla de metanol-cloroformo genera un azeótropo de ebullición mínima con aproximadamente 64% en moles de cloroformo a 327 K bajo presión atmosférica. Las simulaciones del sistema de destilación se llevan a cabo con el software DWSIM. Los datos experimentales relacionados de la literatura se han utilizado para construir el modelo. El modelo de predicción utiliza una capa oculta y 100 neuronas en la capa oculta. La temperatura y la fracción molar de cloroformo en la alimentación, la relación de reflujo y temperatura de reboiler en la columna de baja y alta presión se han seleccionado como variables de entrada y la fracción molar de cloroformo y velocidad de flujo en el destilado y residuo de las columnas como variables de salida. El coeficiente de correlación de Pearson de 0,99919 y error cuadrático medio de 1,52 E-14 para un conjunto con 100 datos de entrenamiento y prueba de la red; y un valor p estadístico mayor que 0,05 en la validación de la red con un nuevo conjunto de 25 datos, confirman que existe una conformidad razonable entre los valores predichos y los datos reales. Los resultados indican que el modelo de red neuronal artificial demostró ser eficiente para predecir la concentración de cloroformo obtenida al destilar una mezcla de metanol-cloroformo con una alimentación constante de 100 kmol/h en un sistema de destilación por oscilación de presión que opera con dos columnas mantenidas a 1 y 10 atm. Se recomienda usar el modelo de predicción para calcular la composición de los productos obtenidos al destilar por oscilación de presión otras mezclas binarias o multicomponentes que exhiban azeótropos de ebullición mínima.The goal of this work is to model the pressure swing distillation system to separate the methanol-chloroform mixture and predict the concentration of chloroform by artificial intelligence. The methanol-chloroform mixture generates a minimum boiling azeotrope with approximately 64 mol% chloroform at 327 K under atmospheric pressure. The simulations of the distillation system are carried out with the DWSIM software. Related experimental data from the literature have been used to construct the model. The prediction model uses one hidden layer and 100 neurons in the hidden layer. The temperature and the molar fraction of chloroform in the feed, the reflux ratio, and reboiler temperature in the low and high-pressure column have been selected as input variables and the molar fraction of chloroform and flow rate in the distillate and residue of the columns as output variables. Pearson's correlation coefficient of 0.99999 and mean squared error of 1.52 E-14 for a set with 100 training and test data from the network; and a statistical p-value greater than 0.05 in the network validation with a new set of 25 data, confirm that there is reasonable compliance between the predicted values and the actual data. The results indicate that the artificial neural network model proved to be efficient in predicting the chloroform concentration obtained by distilling a methanol-chloroform mixture with a constant feed of 100 kmol / h in a pressure swing distillation system that operates with two columns maintained at 1 and 10 atm. It is recommended to use the prediction model to calculate the composition of the products obtained distilling by pressure oscillation other binary or multi-component mixtures that exhibit minimum boiling azeotropes