Lately, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models have been recognized as potential and good tools for mathematical modeling of complex and nonlinear behavior of specific wear rate (SWR) of composite materials. In this study, modeling and prediction of specific wear rate of polytetraflouroethylene (PTFE) composites using FFNN and ANFIS models were examined. The performances of the models were compared with conventional multilinear regression (MLR) model. To establish the proper choice of input variables, a sensitivity analysis was performed to determine the most influential parameter on the SWR. The modeling and prediction performance results showed that FFNN and ANFIS models outperformed that of the MLR model by 45.36% and 45.80%, respectively. The sensitivity analysis findings revealed that the volume fraction of reinforcement and density of the composites and sliding distance were the most and more influential parameters, respectively. The goodness of fit of the ANN and ANFIS models was further checked using t-test at 5% level of significance and the results proved that ANN and ANFIS models are powerful and efficient tools in dealing with complex and nonlinear behavior of SWR of the PTFE composites.
Soil’s infiltration characteristics remain important variables of consideration in irrigation systems designs as well as in soil and water management. This work was conducted to evaluate the performances of three different infiltration models (Kostiakov, Horton and Philip) in order to select a suitable model for use in determination of infiltration characteristics of soils neighbouring Bayero University, Kano-Nigeria. Experiments were conducted in three different locations within the study area and the performance of each model was analysed and compared against a direct measurement using double ring infiltrometer. The results showed no significant difference (Least significant difference = 0.29 @ 0.05 level of significance) between the direct measurement on field and the result obtained using Kostiakov and Horton’s model having mean infiltration rate of 1.05 and 1.04cm per minutes respectively, while the Philip’s model showed a significant difference in means (1.76cm/min) indicating higher sensitivity of its measurement. There was no significance difference (p=0.19 @ 0.05 level of significance) on replicating the experiment for the 3 different locations. Thus, the Kostiakov’s and Horton’s models were recommended for the infiltration characteristics measurement in Kano and elsewhere with similar environmental conditions.
1. Background: Urea is the main product of the nitrogenous breakdown of protein metabolism in mammals. In this study, process intensification for enzymatic hydrolysis of urea by urease enzyme (jack bean urease) was examined in a membrane reactor. 2. Methods: Batch and continuous enzymatic hydrolysis reactions were performed at different substrate concentrations to determine the digestibility and affinity of the substrate with that of the enzyme. The hydrolysate samples were obtained by an optimized continuous enzyme membrane reactor (EMR) coupled with an ultra-filtration membrane (250 kDa). Feed concentration varied from 100 to 500 mg/L. Laboratory experiments were conducted at room temperature (20 ± 1 °C), with a flow rate of 20 mL/min, urease concentration of 0.067 g/L, ionic strength (I = 0, 0.01, 0.05), and ammonium nitrogen addition of (0, 100 mg/L, 200 mg/L, 500 mg/L). Moreover, the effect of ionic strength, ammonium nitrogen concentration, feed concentration, and enzyme concentration on urea hydrolysis was examined. Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDAX) analysis were used to identify the physicochemical properties as well as the elemental composition of the Ultra-Filtration membrane used in this study. 3. Results: The study revealed that higher ionic strength and higher concentrations of NH4SO2 and ammonium nitrogen (NH3-N) inhibithydrolysis of urea by reducing the urease enzyme activity in the system over time. 4. Conclusions: Herein, a sustainable alternative for the conversion of urea to ammonia by utilizing urease in an EMR was demonstrated.
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