Browsing by Author "Orhan, Nuri"

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Citation - WoS: 1
Citation - Scopus: 1
Detection of Vortex Cavitation With the Method Adaptive Neural Fuzzy Networks in the Deep Well Pumps
(Univ Namik Kemal, 2021) Durdu, Akif; Çeltek, Seyit Alperen; Orhan, Nuri
Nowadays submersible deep well pumps are the most used irrigation systems in agriculture field. Efficient operation and economical life of pumps is an important issue. One of the most important parameters affecting pump efficiency and life is cavitation The cavitation is one of the problems frequently faced in the pump systems that widely used in the agriculture field. The cavitation could cause more undesired effects such as loss of hydraulic performance, erosion, vibration and noise. This paper presents a novel model for the detection of vortex cavitation in the deep well pump used in the agriculture system using adaptive neural fuzzy networks. The data submergence, flow rate, power consumption, pressure values, and noise values used for training the ANFIS (Adaptive-Network Based Fuzzy Inference Systems) network are acquired from an experimental pump. In this study, we use to the sixty-seven data for training process, while the fifteen data have used for testing of our model. The average percentage error (APE) has obtained as 0.08 % and as 0.34 % respectively for 67 training data and for 15 test data. The performance of the implemented model shows the advantages of ANFIS. The result of this study shows that ANFIS can be successfully used to detect vortex cavitation. This paper has two novel contributions which are the usage of noise value on cavitation detection and find out cavitation by using adaptive neural fuzzy networks. During the cavitation, the pump parameters must change by controller for prevent unwanted pump errors. The strategy proposed could be preliminary study of automatic pump control. Also proposed novel control strategy can be used for cavitation control in agriculture irrigation pumps, because of easy set up and no need extra cost. The ANFIS based model has real-time applicable thanks to rapid and easy control. It is possible to set safe boundaries in submergence in this model. Thus, users by adjusting controllable parameters can prevent cavitation and increase pump efficiency.
Submersible Pump Vortex Detection Using Image Processing Technique and Neuro-Fuzzy
(2020) Durdu, Akif; Orhan, Nuri; Çeltek, Seyit Alperen; Aslan, Muhammet Fatih; Sabancı, Kadir
The vortex means the mass of air or water that spins around very fast that often faced in the agriculture irrigation systems used the pump. The undesired effects like loss of hydraulic performance, erosion, vibration and noise may occur because of the vortex in pump systems. It is important to detect and prevent vortex for the economic life and efficiency of the agriculture pump. The image processing and neuro-fuzzy based novel model is proposed for the detection of a vortex in the deep well pump used in the agriculture system with this paper. The used images and data - submergence, flow rate, the diameter of the pipe, power consumption, pressure values and noise values- is acquired from an experimental pump. The proposed approach consists of three steps; Neuro-Fuzzy Learning, Image Processing and Neuro-Fuzzy Testing. In the first step, the eightytwo data have employed for the training process of the Neuro-Fuzzy. Then, the images derived from a camera placed near the experimental pump are used to detect vortex in the image processing step. Finally, the relevant data to vortex cases have employed for the testing process of the NeuroFuzzy. The result of this study demonstrates that image processing and neuro-fuzzy based design can be successfully used to detect vortex formation. This paper has provided novel contributions in the vortex detection issue such as find out vortex cases by using image processing and NeuroFuzzy. The image processing method has shed light on the studies to be done in the classification of vortexes and the measurement of their strength.