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.

Allicin is an organosulfur compound from garlic that has shown potential as a soil urease inhibitor. However, little information was available on the degradation of allicin in different soil conditions and its effect toward kinetic properties of urease enzyme. Degradation of allicin was quantified by measuring the remaining concentration of allicin using high performance liquid chromatography (HPLC). The outcomes of the study showed allicin is able to retain longer than 24 h when moisture of soil was at 30% compared to 50 and 100% soil moisture levels. Meanwhile, when the temperature increased from 25 to 35ºC, the percentage of allicin decreased significantly. Contrary to this, as pH of soil increased to 6.5, the rate degradation of allicin was slower compared to pH 4.5 and 5.5. The Michaelis constant (K_m) is increased while the maximum enzyme reaction velocity (V_max) decreases after 24 h incubation compared to control showing inhibition of urease enzymes by allicin. Overall results revealed that the effectiveness of allicin as urease inhibitor was significantly related to its degradation rate in soil.

Gas turbines are essential devices used in nearly all fields, especially power generation. However, they are monitored without a man on duty in most cases. Gas turbines are essential devices used in nearly all fields, especially power generation. However, they are monitored without a man on duty in most cases.