Average Relative Error Values Research Articles

Drying of yam starch (Discorea ssp.) and glycerol filmogenic solutions at different temperatures

The objective of the present study was to obtain and model drying curves of filmogenic solutions at the temperatures of 25, 30, 35, 40 and 45 °C, consisting of different concentrations of starch and glycerol. The percentage of yam starch influenced the initial moisture content, while the critical moisture content varied as a function of the concentration of starch and temperature applied during drying of the filmogenic solutions. Glycerol concentration did not influence any of the parameters analyzed. Models for the two distinct phases (constant and decreasing drying periods) fit well for all drying temperatures and compositions, with average relative error values less than 10%. The interaction between starch content and temperature influenced the drying rate parameters during the constant period and the diffusion coefficients.

Testing Method of Infrared Emissivity Used in Powders Industry

The test apparatus of infrared emissivity used in powders industry was researched by controlling the temperature accurately and collecting the signals of surface temperature of two standard samples and a test sample. The tourmaline samples from ten mines had been tested by the new test apparatus. Compared with the results from FTIR, it was shown that average value of relative error was 0.378% and repeatability error was less than 0.6% after correcting by the coefficient kc = 0.958.

Dynamic modeling and control of a direct expansion air conditioning system using artificial neural network

An artificial neural network (ANN)-based dynamic model for an experimental variable speed direct expansion (DX) air conditioning (A/C) system has been developed, linking the indoor air temperature and humidity controlled by the DX A/C system with the variations of compressor and supply fan speeds. The values of average relative error ( ARE) and maximum relative error ( MRE) when validating the ANN-based dynamic model developed under three different input patterns were 0.33%, 0.27%, 0.27% and 0.89%, 0.99%, 1.15%, respectively, indicating the high accuracy of the ANN-based dynamic model developed. An ANN-based controller was then developed for controlling the indoor air temperature and humidity simultaneously by varying the compressor speed and supply fan speed in a space served by the experimental DX A/C system. The controllability tests including command following test and disturbance rejection test were carried out using the experimental DX A/C system, and the test results showed that the ANN-based controller developed was able to track the changes in setpoints and to resist the disturbances.

Suspended Sediment Flow Routing by Discrete Linear Models

Discrete Linear sediment models were developed using sediment mobilized and direct sediment flow graphs of Chaukhutia watershed of the Ramganga reservoir catchment, India, and applied for sediment flow routing. The parameters of the models were estimated by Lagrangian method. The sediment graphs computed by these models compared with remarkable accuracy to the measured sediment graphs. The average values of integral square error, coefficient of efficiency and ratio of mean error to the mean observed output for four parameter sediment discharge model are 5.991 %, 97.183% and 0.0022 respectively. For three parameter sediment discharge model, the average value of relative error in estimated peak is 6.976%.