Arrhenius-Type Relationship of Viscosity as a Function of Temperature for Mustard and Cotton Seed Oils (Published)
The knowledge and evaluation of transport behaviors of fluids are very important in heat and mass flow. In this study, we adopted a statistical technique for regression analysis and statistical correlation tests. An equation modeling the relationship between the two parameters of viscosity Arrhenius-type equation, such as the Arrhenius energy (Ea) or the pre-exponential factor (A) was used. In addition, we introduce two other parameters; the Arrhenius temperature (T) and Arrhenius activation temperature (T*) to enrich the discussion. The viscosity data from two vegetable oils at different temperature ranges gives excellent statistical results. In addition, the model in this case is very useful for engineering data and permits the estimation of one non-available parameter when the other is available. The Activation energy Ea, Entropic (pre-exponential) factor A, Arrhenius temperature TA and the Arrhenius activation temperature for the mustard oil were observed to be 374.37381 J/mole, 12.39260595 cP, -17.89797783 oC, 45.051 oC respectively while Activation energy Ea, Entropic (pre-exponential) factor A, Arrhenius temperature TA and the Arrhenius activation temperature for the cotton seed oil are respectively 451.90611 J/mole, 8.210386507 cP, -25.8292961 oC, 54.381 oC . The coefficients of regressions (R2) for the graph of the natural log of viscosity versus reciprocal of temperature (Figures 2 and 4) for the mustard oil and cotton seed oil are 0.9996 and 0.9996 respectively. Since the correlation coefficient is the measure of how well a collection of data points can be modeled by a line, we can hence conclude that the natural log of the viscosity of both seed oil samples versus the inverse of their respective temperatures have a very good fit.
Keywords: Correlation, Model, Temperature, arrhenius parameters, statistics, vegetable oil, viscosity
Investigating the Optical Study of Electrodeaposited GaSe thin film at Different Temperatures (Published)
The GaSe thin films have been obtained by cathodic electrodeposition technique onto the fluorine tin oxide (FTO) glass substrates from aqueous acidic solutions at various temperatures of 3230K, 3330K, 3430K, and 3530K. UV-VIS spectrophotometer was used to measure the optical absorption of GaSe thin film at their temperature variations. The optical study illustrated that the direct energy band gap of 3.0 eV, 3.0 eV, 2.8 eV, and 4.0 eV were appraised for electrodeposited GaSe thin film layers at different temperatures of 323 0K, 333 0K, 343 0K and 353 0K respectively. The thickness of the thin film layers increases along with the increase of electrodeposited temperature.
Citation: Olusola O.O., Awodun A. O., and Aladejana A. L. (2021) Investigating the Optical Study of Electrodeaposited GaSe thin film at Different Temperatures, International Research Journal of Pure and Applied Physics, Vol.8, No.2, pp.36-43
Keywords: GaSe, Temperature, electrodeposition technique, optical study
Design of a Renewable Energy Output Prediction System for 1000mw Solar-Wind Hybrid Power Plant (Published)
Problems associated with non-renewable energy sources such as fossil fuels make it necessary to move to cleaner renewable energy sources such as wind and solar. But the wind and sun are both intermittent sources of energy therefore accurate forecasts of wind and solar power are necessary to ensure the safety, stability and economy of utilizing these resources in large scale power generation. In this study, five meteorological parameters namely Temperature, Rainfall, Dew Point, Relative Humidity and Cloud Cover were collected for the year 2012 and used to predict wind and solar power output in Jos, Nigeria. The study used prediction algorithms such as Regression techniques and Artificial Neural Networks to predict the output of a 1000mW Solar-Wind Hybrid Power Plant over a period of one year. Individual prediction techniques were compared and Isotonic Regression was found to have the highest accuracy with errors of 40.5% in predicting solar power generation and 35.4% in predicting wind power generation. The relatively high levels of error are attributed to several limitations of the research work.
Keywords: Cloud Cover, Dew Point, Output Prediction, Power, Rainfall, Relative Humidity, Renewable Energy, Solar, Temperature, Weather, Wind