matlab program

In the present study modeling and formulation of thermal gradient effect on reverse osmosis process for evaluation treated water penetration and water production are studied. modeling is done by MATLAB using Solution-Diffusion model. At present work the effect of two different parameters (temperature difference gradient and salt concentration) on different parameters is studied. Studying on important parameters for temperature driven reverse osmosis, shows the direct effect of temperature difference between permeate-water part and saline water part on different parameters. On temperature driven reverse osmosis, temperature difference between permeate-water part and saline-water part acts as a driving force. penetration rate at each salt concentration rises by increasing temperature difference between permeate-water part and saline-water part. The variations of different parameters versus temperature difference for two different saline water part concentrations (1 gr/lit and 0.35 gr/lit) are presented. For 1 gr/lit salt concentration and 1.5, 2.5 and 10.8 OC temperature difference between permeate-water part and saline-water part, treated water penetrations are obtained 0.9, 0.9545 and 1.3118 l/m2.h.bar respectively. Also, for 0.35 gr/lit salt concentration and 1.5 and 9.3 OC temperature difference between permeate-water part and saline-water part, treated water penetrations are obtained 0.917 and 1.167 l/m2.h.bar respectively.

In this research, submitting a method for evaluation of detection cavitation specifications and also automation of cavitation threshold has been investigated. The case study was based on Kaplan hydro turbine data located on Tarik hydropower plant at Sefidroud dam. The foundation of method was employment MATLAB program, sensor classification sensor locations and cavitation sensitivity. For training in MATLAB program, 12 individual data sets and 4095 unique combinations were created and 408 data selected for examinations. The training data combined with sensor types and cavitation sensitivity features were employed to predict the cavitation threshold and the best training data set with more than 90 % accuracy. The results showed that the use of a fully automated process for sensitivity determination and cavitation classification was more suitable than the use of a process based on manually selected methods. The proposed research is useful for automation cavitation detection to hydro power turbine operators to predict the remaining useful life in futures.

In this research، the evaluation of cavitation threshold detection and the automation of the detection process with regard to the Remaining Useful Life (RUL) of the Sefidrood power plant turbine، has been studied. The input of generated model by MATLAB program includes data driven from kaplan hydro turbine located on Tarik hydro power plant. The proposed model is based on 61 features resulting from 6 cavitation sensitivity parameters and 17 operational conditions. For training in MATLAB program, 12 individual data sets and 4095 unique combinations were created and 408 data were selected for examination. The training data combined with sensor rating and cavitation sensitivity feature were employed to predict the cavitation and the best training data set with 98% accuracy. The results showed that the use of a fully automated process for sensitivity determination and cavitation classification was more suitable than the use of a process based on manually selected thresholds. Furthermore, considering the operational conditions and RUL, the automation of determination of cavitation threshold without human intervention was much more accurate.