Saybolt color is a standard measurement scale used to determine the quality of petroleum products and the appropriate refinement process. However, the current color measurement methods are mostly laboratory-based, thereby consuming much time and being costly. Hence, we designed an automated model based on an artificial neural network to predict Saybolt color. The network has been built with five input variables, density, kinematic viscosity, sulfur content, cetane index, and total acid number; and one output, i.e., Saybolt color. Two backpropagation algorithms with different transfer functions and neurons number were tested. Mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) were used to assess the performance of the developed model. Additionally, the results of the ANN model are compared with the multiple linear regression (MLR). The results demonstrate that the ANN with the Levenberg–Marquart algorithm, tangent sigmoid transfer function, and three neurons achieved the highest performance (R2 = 0.995, MAE = 1.000, and RMSE = 1.658) in predicting the Saybolt color. The ANN model appeared to be superior to MLR (R2 = 0.830). Hence, this shows the potential of the ANN model as an effective method with which to predict Saybolt color in real time.
Diallyl disulfide (DADS) is an organosulfur compound that is expected to exhibit inhibitory property against urease similar to allicin, affirmed through preliminary study. The research aims to optimize DADS’s concentration and duration of inhibition and observe the effect of soil moisture, temperature and pH on the inhibitory action of DADS. The calorimetric method was applied to optimize DADS’s concentration significant for inhibition. High-performance liquid chromatography was used to quantify DADS present under different parameters relevant to selected soil conditions. The results obtained suggested that 5% of DADS/urea-N (w/w) treatment exhibited the highest urea hydrolysis reduction by 27.91% compared to the control sample at the end of 30 days. ANOVA results observed urea hydrolysis is significantly slower by applying 5% DADS/urea-N (w/w) treatment compared to the other DADS treatments. DADS also retained its original form longer in soil when the soil conditions were altered to 15% moisture content, 20 °C and pH 4. The findings exhibit the potential of DADS as a natural based inhibitor that is effective at low concentrations, compatible with urea and chemically stable.
The application of urea fertilizer on soil surfaces causes hydrolysis reactions to occur rapidly, thus releasing ammonia gas to the atmosphere and, in turn, reducing the availability of nitrogen for plant uptake. The urease inhibitor application with urea is considered a mitigation method to reduce the loss of nitrogen. Chemical-based urease inhibitors effectively reduce nitrogen loss, but they also demonstrate adverse side effects on plants. Thus, there is a need to discover potential candidates for urease inhibitors from natural sources. This study evaluates the potential of organosulfur compounds from garlic, namely, allicin, diallyl sulfide (DAS), and diallyl disulfide (DADS), as inhibitors to urea hydrolysis. The effect of allicin, DAS, and DADS on kinetic parameters of soil urease and urea hydrolysis rate was evaluated using the Lineweaver-Burk plot and incubation study. All tested organosulfur compounds exhibited mixed-type inhibition with a high Michaelis constant (K m ) and low maximum velocity of reaction (V max ), compared with control (urea). The calculated inhibitory (dissociation) constant (K i ) for allicin, DAS, and DADS were 0.53, 0.92, and 0.40 mM, respectively. This indicates that DADS is the most potent soil urease inhibitor, followed by allicin and DAS. After DADS treatment, about 46% of the urea-nitrogen (N) content remained after 21 days. Meanwhile, allicin and DAS treatments retained urea-N in the soil for 7 and 11 days, respectively. The results demonstrate that DADS is more effective in delaying enzymatic urea hydrolysis compared with allicin and DAS and showed a high potential as a bio-based urease inhibitor in agriculture.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.