Prediction of the Dynamic Viscosity of N-Alcohol by Three Intelligent Models (ANN, LSSVM, and ANFIS) in Operation Conditions

Viscosity is an essential property in chemical engineering for different applications. If predicted accurately, the viscosity has a significant effect on chemical applications. In this paper, the capability of the three intelligence models, artificial neural network (ANN), least squares support vector machine (LSSVM), and adaptive neuro-fuzzy inference system (ANFIS), were evaluated to model the dynamic viscosity of n-alcohol at the different operational conditions. The models were improved based on a 237 data set collected from reliable articles. The used databank contains temperature (T), pressure (P), and carbon number of n-alcohols (n-C) were chosen as the input of models. The result of these models was studied by Statistical parameters such as Mean of the squares errors (MSE), Root mean of the squares errors (RMSE), Maximum absolute error (MAAE %), Mean absolute error (MEAE %), correlation coefficient and graphical technique like Taylor diagram and William plot. The proposed models are known to appropriately estimate the viscosity of n-alcohol at the different operational conditions. It was found that the ANN with R2= 0.999, MSE=0.000017, MAAE %= 1.6, MEAE %=0.32 for Test, and R2= 0.999, MSE=0.0000094, MAAE %= 0.83, MEAE %=0.23 for Train exhibited a high performance than LSSVM and ANFIS for predicting dynamic viscosity of n-alcohol at the operational conditions

Experimental investigation of ultrasonic cycle/magnetic stirrer (UC/MS) effect on water/α -Al2O3 nanofluid stability and thermal conductivity and its ANFIS/PSO modeling

Nanoparticles suspended in a fluid can improve heat transfer and lubrication performance. In this study, a new method was selected to investigate water/α-Al2O3 nanofluid stability, so a combined approach (Ultrasonic Cycle/Magnetic Stirrer (UC/MS)) was used to this aim. Also, an adaptive neuro-fuzzy system (ANFIS) joined with particle swarm optimization (PSO) to predict the stability of α-Al2O3 nanofluid. The ultrasonic cycle showed that it can be considered a vital parameter in creating the stability of nanofluids. Nanofluid was prepared by UC/MS technique at different pHs, ultrasonic cycles, and concentrations of nanoparticles, which resulted in reliable stability. In this method, with 10 min of sonication operation with cycle 7, PH 11, and volume concentration of 0.25 vol%, the value of zeta potential reached −32.8 mV, while in other studies, to achieve zeta potential above ±30 mV, a sonication operation was performed for up to 120 min. Also, the effect of ultrasonic cycles includes 1, 3, and 7, pH and volume concentration of nanoparticles specifically investigated and showed that with the increases of the ultrasonic cycles, pH, and concentration, the value of zeta potential increased