CISET - 2nd International Symposium on Engineering and Technology 10-12 October, 2019, /

DETERMINATION OF SPRAY ANGLE IN SPRAYER NOZZLES USING COMPUTER VISION TECHNIQUE

Ahmet Nusret Toprak1, Bahadır Sayıncı**2, Bünyamin Demir3, Fehim Köylü4, Necati Çetin5

1 Computer Engineering, Faculty of Engineering, 38039 Talas-Kayseri/Turkey, [email protected]

2 Mechanical Engineering, Faculty of Engineering, 33150, Yenişehir-Mersin/Turkey, [email protected]

3 Vocational School of Technical Sciences, Department of Mechanical and Metal Technologies, Mersin University, 33150 Yenişehir-Mersin/Turkey, [email protected]

4 Computer Engineering, Faculty of Engineering, Erciyes University 38039 Talas, Kayseri/Turkey, [email protected]

5 Biosystem Engineering, Faculty of Agriculture, Erciyes University 38039 Talas, Kayseri/Turkey, [email protected]

ABSTRACT Before the pesticide applications with sprayers, the flow rates of the spray nozzles should be controlled, and the flow rate uniformity of the spraying should be checked in the calibration processes. Irregular flow in the sprayer nozzles causes deterioration of the spray pattern and the volumetric distribution uniformity. Production errors in the orifice geometry of the hydraulic nozzle used in sprayers, wear due to long-term use, tearing or cracking of the nozzle body over time, faulty nozzle connections and sealing problems can be listed as structural factors disrupting spray angle and pattern. The aim of this research is to develop a measurement system and software that controls nozzle spray angle in real time in addition to the measurement and control systems currently used to perform flow controls in sprayer nozzles. The measurement system consists of light apparatus and camera. In the software creation, it is aimed to determine the spray angle online through the real-time flow images. The angle values determined on a standard basis can be viewed on the control screen and various statistics can be obtained from the images taken online with the camera. According to preliminary results, the spray angle measurement system and software can be used efficiently and practically in research, production and development studies, measurement and control laboratories and hydraulic nozzle flow tests. Keywords: Sprayer, Spray pattern, Hydraulic nozzle, Image processing

PÜLVERIZATÖR MEMELERINDE PÜSKÜRTME AÇISININ BILGISAYARLA GÖRME TEKNIĞİ KULLANILARAK BELIRLENMESI

ÖZET Pülverizatörlerde pestisit uygulamaları öncesinde yapılan kalibrasyon işlemlerinde meme debisi ölçümleriyle birlikte püskürtmede akış düzgünlüğünün de kontrol edilmesi gerekmektedir. Pülverizatör memelerinde düzgün olmayan akış püskürtme paterninin bozulmasına ve hacimsel dağılım düzgünlüğünün bozulmasına neden olmaktadır. Pülverizatörlerde hidrolik memenin orifis geometrisinde oluşan üretim hataları, uzun süreli kullanımdan dolayı oluşan aşınmalar, meme gövdesinde zamanla oluşan yırtılma veya çatlamalar, hatalı meme bağlantıları ve sızdırmazlık problemleri püskürtme açısı ve paternini bozan yapısal faktörler olarak sıralanabilir. Bu araştırmanın amacı, pülverizatör memelerinde akış kontrollerini gerçekleştirmek için mevcutta kullanılan ölçüm ve kontrol sistemlerine ek olarak meme püskürtme açısını gerçek zamanlı kontrol eden bir ölçüm sistemi ve yazılımı geliştirmektir. Ölçüm sistemi ışık apareyleri ve kameradan oluşmaktadır. Yazılım oluşturmada gerçek zamanlı alınan akış görüntüleri üzerinden püskürtme açısının çevrimiçi belirlenmesi hedeflenmiştir. Kamerayla çevrimiçi alınan görüntüler üzerinden standart bir esasa göre belirlenen açı değerleri kontrol ekranından izlenebilmekte ve çeşitli istatistikler elde edilebilmektedir. Ön deneme sonuçlarına göre püskürtme açısı ölçüm sistemi ve yazılımının araştırma, üretim ve geliştirme çalışmalarında, ölçme ve kontrol laboratuvarlarında ve hidrolik meme akış testlerinde verimli ve pratik bir şekilde kullanılabileceği kanaatine varılmıştır. Anahtar kelimeler: Pülverizatör, Püskürtme paterni, Hidrolik meme, Görüntü işleme

* Corresponding Author

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25 2nd Cilicia International Symposium on Engineering and Technology 10-12 October, 2019, Mersin / TURKEY

1. INTRODUCTION These factors, which disrupt the flow uniformity, often cause deviations in the spray angle of the hydraulic The fastest and most effective method against nozzle. This appears to be detectable with control diseases and pests that harm the culture plants is the equipment which measures the spray angle in a practical chemical method, which makes it necessary to use way. powder or liquid chemicals called pesticides. Most of the There is no standard method used in practice for applications are carried out with equipment called determining the spray angle. In the present case, sprayers equipped with hydraulic pump. The chemicals protractors are used in practice or can be measured by prepared by diluted in the liquid form in the tank are means of image processing software via spray images. sprayed under the influence of hydraulic pressure and However, this process takes time as well as the contours transmitted to the target in drops. The preparation of a of the angle on the image are determined by the operator's particular concentration prepared in a homogeneous personal preference. Determination of the spray angle on manner during spraying ensures that the effective a standard basis is of great importance for the accuracy substance reaches the same dosage to all surfaces and thus and practicality of the measurements. The aim of this increases the expected success of the chemical control. study is to develop a new measurement system and The uniformly transport of the chemical to the target, software that can calculate, record and calculate spray which is broken into drops, varies depending on the type angle values online through flow images of sprayer of hydraulic nozzle, operating pressure, spray height, nozzles in laboratory conditions. application speed and meteorological factors (Hoffmann and Salyani, 1996; Panneton et al., 2000; Zhu et al., 2002; 2. MATERIAL AND METHOD Zhu et al., 2004; Bayat and Bozdoğan, 2005). In order to uniformly transport of the drops set Computer vision is an interdisciplinary field that leaving the orifice in a given distribution pattern, it is enables us to evaluate and make useful decisions about necessary to provide sufficient coating on the surface by the environment through digital images of the overlapping at a certain height. The coating rate required environment of interest (Aslantaş and Toprak, 2017). for this is dependent on the spray height, which is Calculation of the spray angle on the spray images by determined by the spray angle of the hydraulic nozzle. For computer vision techniques will make this process conventional spray applications, the minimum spray objective by removing the operator's initiative. This heights recommended for 65º, 80º, 110º and 120º nozzles section provides details of the system and software were reported to be 75, 60, 40 and 40 cm, respectively developed for calculating the nozzle spray angle in the (Teejet®, 2016). Narrow angled spray nozzles are laboratory using computer vision techniques. In addition, recommended to be used in banded applications for plants the spray angle calculation method used in the developed planted in rows, but are preferred to increase drop software is introduced. penetration in tall plants (Matthews and Thornhill, 1994; Matthews, 2004). However, increasing the spray height 2.1. Image Acquisition System as the spray angle decreases can cause the droplets to evaporate by the effect of temperature and humidity, In order to determine the spray angle variation during turbulence with wind speed and reverse air currents, spray application, it is aimed to develop an application in leading to increased pesticide consumption and losses which spray angle is calculated instantly in spray nozzles (Dursun et al., 2000). As the spray angle varies depending displayed in the laboratory condition. In the system, the on the design of the outlet orifice in the hydraulic nozzles, images obtained through a camera in the laboratory at the same spray pressure, the liquid strip leaving the condition are transferred to the developed software. With orifice becomes thinner and the resistance to the application developed, both the current spray image disintegration decreases and finer droplets occurs. As the and the spray angle are presented to the user. The height of such nozzles increases, the energy of operation of the system designed for calculating the spray transporting the drug drops to the target decreases and angle of the sprayer nozzle is shown in Figure 1. thus drug losses increase due to evaporation and drift. Therefore, in order to reduce drag on nozzles with a wide spray angle, the spray distance must be reduced. For these reasons, there is a general tendency in the choice of nozzle to use sprayer nozzles with a larger spray angle (Matthews, 2004). Spray pattern and flow uniformity tests related to hydraulic nozzles used in sprayers are carried out on measuring tables called patternators. It was reported that the volumetric distribution was acceptable for the 15% variation and the distribution for the 10% variation was quite homogeneous (Bode et al., 1983; Azimi et al., 1985; Krishnan et al., 1988). The measurements and tests performed on hydraulic nozzles reveal the operating Fig. 1. System diagram for measuring the spray angle of characteristics (height, pressure, mounting range, a spray nozzle in a laboratory position angle, etc.) required for uniform pesticide applications. In addition, flow tests reveal production The Nikon D300 DSLR camera is used for spraying errors in the nozzle orifice geometry, wear caused by images. The camera was placed on a tripod to stabilize the long-term use, tearing or cracking of the nozzle body over images. To achieve high contrast images, a black time, faulty nozzle connections and sealing problems.

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background was placed on the background of the spray the lines on which the analysis is to be performed before boom, and two paraflashes are again positioned behind starting the analysis. the spray boom. To improve the quality of the spray Spray images to calculate the spray angle can be image, the floor-mounted lighting device is arranged to refreshed at any time using the take image command. The illuminate the output of the flow beam. user should start the process of calculating the angle again with the start analysis command for each image taken 2.2. Application of Spray Angle Measurement from the camera.

The spray angle measurement application is intended 2.3. Spray Angle Calculation Method to enable the user to instantly display the spray image and calculated spray angle value of a sprayer nozzle in the Three key points, right, left and top, are used to laboratory with an easy to use and interactive interface. In calculate the spray angle on the spray image from the the development of the software, Python programming camera. The first step of the spray angle calculation language has been preferred in order to be able to design process is to determine the right and left key points on the the interface easily and to perform visual operations with user-specified lines. The points where the sprayed liquid the computer. A spray image and an angle value starts and ends on the user-specified line should be representation on the interface is shown in Figure 2. determined as left and right key points. Since the user does not have sufficient information about the constantly changing images, a manual angle calculation will not give the desired result in all cases. Therefore, in the developed application, a method has been proposed which allows the automatic finding of the right and left key points. The proposed method requires user-defined lines to calculate points. The right and left key points are located on the boundary between the area covered by the sprayed liquid and the background area in the user-specified line. This boundary line corresponds to the points at which the intensity change occurs in the respective lines. From this perspective, if images are divided into spray and background zones, the right and left switch points can be specified as the transition between the background and Fig. 2. Interface of spray angle measurement application spray zone. Automatic thresholding methods can be used

to determine this transition. The spray angle calculation starts with the acquisition One of the most common and successful automatic of the spray image from the camera. To do this, the user thresholding functions is the Otsu method (Otsu, 1979). must first select the start option from the camera drop- The herbaceous automatic thresholding method works down list in the top menu. When the user clicks the start with the assumption that the image consists of pixels that tab, a connection is established between the camera and fall into two classes, foreground and background. the software defined in the system and the system is ready Iteratively investigates the minimized threshold value of to receive the image. The camera drop-down list also the in-class variances of the obtained classes and the includes the pause and stop options for pausing and value is found as the optimal threshold value. In-class turning off the camera. variance is found by Eq. (1) as the weighted sum of the The display tab in the top menu of the main screen variance values of the two classes: also includes the take-up and start analysis tabs, in which the user can perform image acquisition from the camera 2 ( ) 2( ) ( ) 2( ) (1) and calculate the angle value, respectively. When the user 휎푠푖 = 휔0 푡 휎0 푡 + 휔1 푡 휎1 푡 clicks on the take image command, the image of the scene here ω0 and ω1 are the probability values of the two displayed by the camera defined in the system is captured 2 2 and shown to the user on the interface. When the user classes separated by the threshold value t, and σ0 and σ1 clicks the start analysis command again under the view are the variance values of these classes. Minimizing in- menu, the recorded image is analysed in the following class variance is equal to maximizing inter-class variance. section with the method detailed and the spray angle is Since the calculation of variance between classes brings calculated and presented to the user on the interface. less processing load, Eq. (2) preferred to find the If the positions of the points used by the operator to optimum threshold value: calculate the angle in the spray angle calculation process 2 2 2 ( ) 2 2 are not selected appropriately, the spray angle may be 휎푠푎 = 휎 − 휎푠푖 푡 = 휔0(휇0 − 휇푇) + 휔1(휇1 − 휇푇) (2) calculated incorrectly. In order to accurately calculate the spraying angles of sprayer nozzles of different types and where µ0 and µ1 are the means of the two classes, and pressures, it is expected that the lines where the right and µT is the mean of the entire grey-level image. As given by left edge points will be searched on the image will be Eq. (3), the Otsu method determines the threshold value determined interactively by the user. For this purpose, the that will maximize the differences between the classes as user can determine the lines through which the right and the optimum threshold value: left switch points are searched by means of the slider bars 2 on both sides of the spray image received from the 푡표푝푡 = 푎푟푔 푚푎푥{휎푠푎(푡)} (3) camera. The user must specify the process of determining

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After applying the Otsu threshold value method to the 3. RESULTS user-selected lines, the pixel at which the transition from the spray zone to the background on the black and white In order to investigate the performance of the image of that line is determined as the key point. proposed method, the spray angle values calculated on the After the right and left points obtained by the Otsu instant spray images were compared with the angle values threshold determination method, the end point required found by the expert using the image processing methods for the calculation of the spray angle should be on the same images. The spray angle at the nozzle orifice determined. This third point is the peak, the starting point outlet was determined by the operator using the angle of the sprayed liquid. This point cannot be detected module of the ImageJ v.1.38x (Wayne Rasband, National directly since it remains essentially within the sprayer Institutes of Health, US) image processing software on nozzle and cannot be imaged. However, it can be the obtained high contrast fixed images (Abràmoff et al., estimated approximately using the spray zone. In order to 2004). Images were taken at three different spray estimate the peak, the curves forming the boundaries of pressures (200, 400 and 600 kPa) on disc-core type the spray zone need to be known. hollow cone nozzles made of stainless steel (Cr-Ni) with In order to find the peak, the spraying image is first orifice diameter 1.0 mm and 1.5 mm. In repetitive studies, replaced by pixels 1 higher than a certain threshold value five different nozzle discs taken from the same diameter and pixels with low intensity 0, resulting in a binary group by chance were used. (black and white) image. The above mentioned Otsu Table 1 shows the results from the orifice diameter method is used to find the threshold value. However, the 1.0 mm nozzle disc. When the results were examined, the resulting dual image may include the spraying zone as average of the angles measured by the operator by image well as the spray nozzle and different objects. Since only processing technique were measured as 41.77º, 53.77º the spray zone is needed to locate the peak, only the spray and 59.83º for 200, 400 and 600 kPa, respectively. The zone must be removed first on the binary image. angle values measured by the spray angle calculation Therefore, the linked regions are determined by applying software were 40.80º, 57.77º and 62.04º. Accordingly, the component analysis based on binary image. The linked spray angle provided very close results in both methods. component analysis operator scans the binary image pixel by pixel to identify adjacent pixel regions that share the Table 1. Spray angle (º) values in hollow cone nozzle with same intensity value. The largest of the zones obtained as orifice diameter 1.0 mm a result of this process is determined as the spray zone. Image processing technique Spray angle software The peak may be defined as the intersection of two Nozzle Pressure (kPa) Pressure (kPa) curves forming the edges of the spray zone. The pixels discs 200 400 600 200 400 600 forming the right and left edges of the spray area are 1 43.09 56.17 62.26 42.90 58.49 63.65 found and their horizontal and vertical coordinate values 2 40.27 55.74 61.67 40.65 60.51 63.36 are recorded. The polynomial is best defined by the curve 3 44.70 53.09 56.84 43.50 58.00 59.53 fitting method using the coordinates of the points that 4 40.59 51.20 58.22 40.53 55.67 60.20 make up the left and right sides. Curve fitting is defined 5 40.20 52.67 60.16 36.43 56.17 63.44 as finding an appropriate curve or the right equation to Mean 41.77 53.77 59.83 40.80 57.77 62.04 reflect the pattern of the relationship between the two SD* 2.03 2.12 2.29 2.78 1.94 2.00 variables (Guest, 2012). Within the scope of the paper, *: standard deviation there are two functions of the 3rd order using points expressing the right and left edges. If the polynomials The spray angle values determined by image expressing the left and right edges are considered f(x) and processing technique and spray angle calculation p(x) respectively, the intersection point of these software in hollow cone nozzle disc with orifice diameter polynomials is found by the exit point of the sprayed 1.5 mm were compared in Table 2. The average spray liquid, in other words, the peak point (T) by Eq. (4): angle values varied from 69.85º to 84.15º in image processing at 400 kPa spray pressure, and between 69.88º 푇 = 푓(푥) ∩ 푝(푥). (4) and 78.34º in the software. When the image processing technique and the spray angle calculation software are The spray angle can then be calculated using these compared, it is generally found that all spray angle values three points found after this step. The vertex, left margin overlap with each other. and right margin are defined as T, A and B respectively. The vectors between the vertex and edge points are Table 2. Spray angle (º) values in hollow cone nozzle disc defined in Eq. 5. with orifice diameter 1.5 mm Nozzle Image processing technique Spray angle software 푇퐴⃗⃗⃗⃗⃗ = 퐴 − 푇, discs Pressure (kPa) Pressure (kPa) (5) 푇퐵⃗⃗⃗⃗⃗ = 퐵 − 푇. 200 400 600 200 400 600 1 67.97 79.95 76.49 67.71 78.51 78.17 In this case, since the points T, A and B are known, 2 71.65 84.15 88.84 75.06 78.34 83.45 the angle (θ) between the two vectors can be calculated 3 66.15 69.85 68.36 72.16 69.88 74.57 4 65.40 76.06 77.54 64.18 75.32 78.02 by Eq. (6). 5 66.74 72.09 71.52 67.72 74.24 75.39 Mean 67.58 76.42 76.55 69.37 75.26 77.92 푇퐴⃗⃗⃗⃗⃗ ∙ 푇퐵⃗⃗⃗⃗⃗ SD* 2.46 5.79 7.82 4.26 3.54 3.47 휃 = 푎푟푐푐표푠 ( ), (6) ‖푇퐴⃗⃗⃗⃗⃗ ‖‖푇퐵⃗⃗⃗⃗⃗ ‖ *: standard deviation

here || || refers to the length of the vector.

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The proposed method has 0.1379 seconds processing Bilimleri Dergisi-Journal of Agricultural Sciences, time for 1072×712 source images on a computer with Vol. 6, No. 3, pp. 135-140. (in Turkish) AMD FX-8350 4Ghz processor and 16GB main memory. Guest, P. G., (2012). Numerical methods of curve fitting. This result is much shorter than the time required for an Cambridge University Press, p. 438. expert to analyse the image, place the dots on the images, Hoffmann, W. C., Salyani, M. (1996). “Spray deposition and determine the angle. on citrus canopies under different meteorological conditions.” Transactions of the ASAE, Vol. 39, No. 4. CONCLUSION 1, pp. 17-32. Krishnan, P., Williams, T. H., Kemble, L. J. (1988). Hollow cone nozzle discs with different orifice “Technical Note: Spray pattern displacement diameters were used in the study and spray angle changes measurement technique for agricultural nozzles using were compared with two different methods at three spray table.” Transactions of the ASAE, Vol. 31, No. different operating pressures. When the measurement 2, pp. 386-389. method is evaluated, it is observed that the flow contours Matthews, G. A. (2004). “How was the pesticide that allow the determination of the spray angle on the applied?” Crop Protection, Vol. 23, pp. 651-653. images can vary from user to user, and the time taken in Matthews, G. A., Thornhill, E. W. (1994). “Pesticide the measurements is higher than the newly developed application equipment for use in Agriculture.” Vol. 1. application. With the developed spray angle detection Manually carried equipment. FAO Agricultural method, no operator experience is required, it is observed Services Bulletin, 112/1, ISSN: 1010-1365, Rome- that the spray angle can be determined on a standard basis Italy, p. 163. thanks to the easy-to-use and interactive interface, and the Otsu, N. (1979). “A threshold selection method from spray angle values can be observed instantly on the gray-level histograms.” IEEE Transactions on control screen depending on time. As a result of the Systems, Man, and Cybernetics, Vol. 9, No. 1, pp. 62– research, it was determined that the spray angle values 66. determined by two different measurement methods Panneton, B., Philion, H., Thériault, R., Khelifi, M. matched at a high rate and the developed software showed (2000). “Spray chamber evaluation of air-assisted successful results. spraying on potato plants.” Transactions of the ASAE, The spray angle calculation method proposed within Vol. 43, No. 3, pp. 529-534. the scope of this study is able to calculate on single Teejet®, 2006. “Sprayer Nozzles.” images taken in the laboratory. In future studies, it is http://www.teejet.com [Accessed 06 Dec 2006]. planned to introduce a more practical and modular new Zhu, H., Dorner, J. W., Rowland, D. L., Derksen, R. C., software that can detect spray angle in real time on Ozkan, H. E. (2004). “Spray penetration into peanut continuous images. canopies with hydraulic nozzle tips.” Biosystems Engineering, Vol. 87, No. 3, pp. 275-273. ACKNOWLEDGMENT Zhu, H., Rowland, D. L., Dorner, J. W., Derksen, R. C., Sorensen, R. B. (2002). “Influence of plant structure, This study was supported by Mersin University orifice size, and nozzle inclination on spray Scientific Research Projects (BAP) Unit with project penetration into peanut canopy.” Transactions of the number 2017-2-AP4-2565. ASAE, Vol. 45, No. 5, pp. 1295-1301.

REFERENCES

Abràmoff, M. D., Magalhães, P. J., Ram, S. J. (2004). “Image processing with ImageJ,” Biophotonics International, Vol. 11, No. 7, pp. 36-42. Aslantas V., Toprak, A. N., (2017). “Multi-focus image fusion based on optimal defocus estimation.” Computers and Electrical Engineering, Vol. 62., pp. 302-318. Azimi, A. H., Carpenter, T. G., Reichard, D. L. (1985). “Nozzle spray distribution for pesticide application.” Transactions of the ASAE, Vol. 28, No. 5, pp. 1410- 1414. Bayat, A., Bozdogan, N. Y. (2005). “An air-assisted spinning disc nozzle and its performance on spray deposition and reduction of drift potential.” Crop Protection, Vol. 24, pp. 951-960. Bode, L. E., Butler, B. J., Pearson, S. L., Bouse, L. F. (1983). “Characteristics of the micromax rotary atomizer.” Transactions of the ASAE, Vol. 24, No. 4, pp. 999-1004. Dursun, E., Karahan, Y., Çilingir, İ. (2000). “Türkiye’de üretilen konik hüzmeli bazı meme plakalarında delik çapı ve düzgünlüğünün belirlenmesi.” Tarım

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29 2nd CILICIA INTERNATIONAL SYMPOSIUM ON ENGINEERING AND TECHNOLOGY (CISET 2019)

Proceedings Book

October 10-12, 2019

Mersin University Engineering Faculty

Editor Prof.Dr. Murat YAKAR

Assistant Editor Assoc.Prof.Dr. Erdinç AVAROĞLU Assist.Prof.Dr. Çiğdem ACI

Honor Board

Ali İhsan SU

Governor of Mersin

Vahap SEÇER

Mersin Metropolitan Municipality Mayor

Prof. Dr. Ahmet ÇAMSARI

Mersin University Rector

Organization Committee

Prof. Dr. Murat YAKAR

Symposium Chairman

Executive Committee

Prof. Dr. İlker Fatih KARA (Department of Civil Engineering) Dean

Prof.Dr. Sedat SAYAR (Department of Food Engineering)

Prof.Dr. Cahit BİLİM (Department of Civil Engineering)

Prof.Dr. Bahadır Kürşad KÖRBAHTİ (Department of Chemical Engineering)

Prof.Dr. Şükrü Dursun (Department of Environmental Engineering)

Assoc.Prof.Dr. Erdinç AVAROĞLU (Department of Computer Engineering)

Assoc.Prof.Dr. Hüseyin ERİŞTİ (Department of Electrical and Electronics Engineering)

Assoc.Prof.Dr. Bahadır SAYINCI (Department of Mechanical Engineering)

Assoc.Prof.Dr. Memduh KARA (Department of Mechanical Engineering)

Assoc.Prof.Dr. Ömer GÜLER (Department of Metallurgical and Materials Engineering)

Assist.Prof.Dr. Çiğdem ACI (Department of Computer Engineering)

Assist.Prof.Dr. Hüdaverdi ASLAN (Department of Environmental Engineering)

Assist.Prof.Dr. Fatma BÜNYAN ÜNEL (Department of Geomatic Engineering)

Assist.Prof.Dr. Lütfiye KUŞAK (Department of Geomatic Engineering) Assist.Prof.Dr. Hidayet TAĞA (Department of Geological Engineering)

Res.Assist.Dr. Cemile SOLAK (Department of Geological Engineering)

Res.Assist. Yasin ÖZAY (Department of Environmental Engineering)

Res.Assist. Hüseyin YANIK (Department of Electrical and Electronics Engineering)

Res.Assist. Fırat ÇINAR (Department of Food Engineering)

Res.Assist. Mehmet Özgür ÇELİK (Department of Geomatic Engineering)

Res.Assist. Furkan İNAL (Department of Civil Engineering)

Res.Assist. Didem DEMİR KARAKUŞ (Department of Chemical Engineering)

Res.Assist. Alper GÜNÖZ (Department of Mechanical Engineering)

Res.Assist. Öyküm BAŞGÖZ (Department of Metallurgical and Materials Engineering)

Res.Assist. Ayşegül Yaman (Department of Computer Engineering)

Symposium Secretary

Hüseyin KARA

Secretary of Engineering Faculty

Symposium Website Admin

Veli YILDIZ

Engineer Scientific Committee

Prof.Dr. Reha Metin Alkan Istanbul Technical University Prof.Dr. Ahmet Bedri ÖZER Fırat University Prof.Dr. Hamza EROL Mersin University Prof.Dr. Zeki YETGİN Mersin University Prof.Dr. Ali Karcı İnönü University Prof.Dr. Caner ÖZDEMİR Mersin University Prof.Dr. Ali AKDAĞLI Mersin University Prof.Dr. Cemil Cengiz ARCASOY Prof.Dr. Mehmet TÜMAY Çukurova University Prof.Dr. Hamit SERBEST Çukurova University Prof.Dr. Turgut İKİZ Çukurova University Prof.Dr. İlyas EKER Çukurova University Prof.Dr. Mustafa GÖK Çukurova University Prof.Dr. Ulus ÇEVİK Çukurova University Prof.Dr. Erol Özer Mersin University Prof.Dr. Fevzi ÖNER Mersin University Prof.Dr. Kemal TASLI Mersin University Prof.Dr. Cüneyt GÜLER Mersin University Prof.Dr. Tolga ÇAN Çukurova University Prof.Dr. Arzu Başman Prof.Dr. Haşim Kelebek Alparslan Türkeş Science and Technology University Prof.Dr. Hüseyin Erten Çukurova University Prof.Dr. İhsan Karabulut İnönü University Prof.Dr. Hacı İbrahim Ekiz Mersin University Prof.Dr. Tunç Koray Palazoğlu Mersin University Prof.Dr. Mahir Turhan Mersin University Prof.Dr. Nüzhet İkbal Türker Mersin University Prof. Dr. Sedat Sayar Mersin University Prof.Dr. Ayla ÖZER Mersin University Prof.Dr. Ayten ATEŞ Cumhuriyet University Prof.Dr. Bahadır Kürşad KÖRBAHTİ Mersin University Prof.Dr. Dilhan M. KALYON Stevens Institute of Technology Prof.Dr. Hüseyin KARACA İnönü University Prof.Dr. Mahmut BAYRAMOĞLU Gebze Technical University Prof.Dr. Mehmet YÜCEER İnönü University Prof.Dr. Nahit AKTAŞ Kyrgyz-Turkish Manas University Prof.Dr. Satılmış BASAN Hitit University Prof.Dr. Tonguç ÖZDEMİR Mersin University Prof.Dr. Uğur SALGIN Cumhuriyet University Prof. Dr. A. Alper ÖNER Erciyes University Prof. Dr. Mustafa ŞAHMARAN Hacettepe University Prof.Dr. Emel ORAL Çukurova University Prof.Dr. Ertan EVİN Fırat University Prof.Dr. Onuralp YÜCEL İstanbul Technical University Prof.Dr. Nuran AY Prof.Dr. Ali KALKANLI Orta Doğu Technical University Prof.Dr. Yoke Khin YAP Michigan Technological University Prof.Dr. Adem KURT Prof.Dr. Şaduman ŞEN Prof.Dr. Murat YAKAR Mersin University Prof.Dr. Hacı Murat YILMAZ Prof.Dr. Bülent BAYRAM Yıldız Technical University Prof.Dr. Tayfun ÇAY Konya Technical University Prof.Dr. Şükrü Dursun Konya Technical University Prof.Dr. Hanlar Reşidoğlu Mersin University Prof.Dr. Hamza Menken Mersin University Prof.Dr. C. Cengiz Arcasoy Toros University Prof.Dr. Selma Erat Mersin University Assoc.Prof. Dr Hüseyin ERİŞTİ Mersin University Assoc.Prof.Dr. Taner Tuncer Fırat University Assoc.Prof. Dr. Kadir ABACI Mersin University Assoc.Prof. Dr. Alkan ALKAYA Mersin University Assoc.Prof. Dr. Sami ARICA Çukurova University Assoc.Prof. Dr. Turgay İBRİKCİ Çukurova University Assoc.Prof. Dr. Ahmet TEKE Çukurova University Assoc.Prof. Dr. Murat AKSOY Çukurova University Assoc.Prof. Dr. Kerem ÜN Çukurova University Assoc.Prof. Dr. Mehmet Uğraş CUMA Çukurova University Assoc.Prof.Dr. İlkay Şensoy Middle East Technical University Assoc.Prof.Dr. Serpil Öztürk Sakarya University Assoc.Prof.Dr. Hakan Karaca Assoc.Prof.Dr. Ayşe AYTAÇ Assoc.Prof.Dr. Ayşe KARAKEÇİLİ Assoc.Prof.Dr. Bora GARİPCAN Boğaziçi University Assoc.Prof.Dr. Dilek KILIÇ APAR Yıldız Technical University Assoc.Prof.Dr. Elif ÖDEŞ AKBAY Eskişehir Technical University Assoc.Prof.Dr. Ferda GÖNEN Mersin University Assoc.Prof.Dr. Feridun DEMİR Osmaniye Korkut Ata University Assoc.Prof.Dr. Hakan KAYI Ankara University Assoc.Prof.Dr. Hilal DEMİR KIVRAK Van Yüzüncü Yıl University Assoc.Prof.Dr. M. Oğuzhan ÇAĞLAYAN Bilecik Şeyh Edebali University Assoc.Prof.Dr. Nimet KARAGÜLLE Mersin University Assoc.Prof.Dr. Rükan GENÇ ALTÜRK Mersin University Assoc.Prof.Dr. Ercan ERDİŞ İskenderun Teknik University Assoc.Prof.Dr. Erdal UNCUOĞLU Erciyes University Assoc.Prof.Dr. Özgür Lütfi ERTUĞRUL Mersin University Assoc.Prof.Dr. Kubilay AKÇAÖZOĞLU Niğde Ömer Halisdemir University Assoc.Prof.Dr. Bahadır SAYINCI Mersin University Assoc.Prof.Dr. İskender ÖZKUL Mersin University Assoc.Prof.Dr. Memduh KARAKARA Mersin University Assoc.Prof.Dr. Osman ÇULHAÇULHA Celal Bayar University Assoc.Prof.Dr. Serdar ALTIN İnönü University Assoc.Prof.Dr. Uğur ÇALIGÜLÜ Fırat University Assoc.Prof.Dr. Aykut ÇANAKÇIÇANAKÇI Karadeniz Technical University Assoc.Prof.Dr. Mehmet Deniz Turan Fırat University Assc.Prof.Dr. Serkan ISLAKISLAK Kastamonu Ünv. Assoc.Prof.Dr. Ömer GülerGüler Mersin University Assoc. Prof. Dr. İbrahim YILMAZ Afyon Kocatepe University Assoc. Prof. Dr. Şinasi KAYA İstanbul Teknik University Assoc. Prof. Dr. Serkan DOĞANALP Konya Teknik University Assoc. Prof. Dr. Mevlüt YETKİN İzmir Katip Çelebi University Assoc. Prof. Dr. Mustafa ZEYBEKZEYBEK Artvin Çoruh University Assoc. Prof. Dr. Mehmet Küçükaslan Mersin University Assoc. Prof. Dr. Yahya NuralNural Mersin University Assoc. Prof. Dr. Bünyamin DemirDemir Mersin University Assist. Prof. Dr. Mehmet ERTAŞ Konya Technical University Assist. Prof. Dr. Sertaç Göktaş Mersin University Dr. Ali YILDIZYILDIZ Mersin University Dr. Ahmet Naci METEMETE Mersin University Dr. Evren DEĞİRMENCİ Mersin University Dr. Şevket DEMİRCİ Mersin University Dr. Filiz KARAÖMERLİOĞLU Mersin University Dr. Betül YILMAZYILMAZ Mersin University Dr. Cevher AKAK Toros University Dr. Adnan TANTAN Çukurova University Dr. Ercan AVŞAR Çukurova University Dr. Osman ORHANORHAN Konya Teknik University Assist.Prof.Dr. Kevser Kahraman Abdullah Gül University Assist.Prof.Dr. Esma EserEser Çanakkale Onsekiz Mart University Assist. Prof. Serpil Yalım Kaya Mersin University Assist.Prof.Dr. Ebru ERÜNALERÜNAL Çukurova University Assist.Prof.Dr. Onur DÖKERDÖKER Mersin University Assist.Prof.Dr. Yeliz GÜRDAL DURĞUN Adana Alparslan Türkeş Science and Technology University Assist.Prof. Dr. Murat ÖZENÖZEN Mersin University AssAssist.Prof.Dr. Erhan Akkaya İnönü University AssAssist.Prof.Dr. Kemal AdemAdem Aksaray University Assist.Prof.Dr. İsmail Koyuncu Afyon Kocatepe University Assist.Prof.Dr. Akın Tatoğlu University of Hartford Assist.Prof.Dr. İpek ABASIKELEŞ TURGUT Iskenderun Technical University Assist.Prof.Dr. Esra SARAÇ EŞSİZ Adana Alparslan Türkeş Science and Technology University Assist.Prof.Dr. Abdullah Elewi Mersin University Assist.Prof.Dr. Mehmet Acı Mersin University Assist.Prof. Dr. Hasan GÜZEL İskenderun Teknik University Assist.Prof. Dr. Gökhan ARSLAN Mersin University Assist.Prof. Dr. Bengi ŞANLI Mersin University Assist.Prof. Dr. İlker SUGÖZÜ Mersin University Assist.Prof.Dr. Yakup SAY Assist.Prof.Dr. Ayşe KALEMTAŞ Bursa Technical University Assist.Prof. Dr. Fatih TAKTAK Uşak University Assist.Prof. Dr. Osman Sami KIRTILOĞLU İzmir Katip Çelebi University Assist.Prof. Dr. Lütfiye KUŞAK Mersin University Assist.Prof. Dr. Fatma BÜNYAN ÜNEL Mersin University Lecturer Dr. Ali ULVİ Selçuk University Paper ID Title Paper No.

2 Application of Discrete Controllers to a Pilot Scale Packed Distillation Column 1-5

3 Trend Analysis And Mapping Of The Black Sea Region 6-9

Long-Term Month Temperature Forecast With Inverse Distances Weighted, Kriging And 10-16 6 Artificial Neural Networks

13 Ecosystems As Reservoirs Of Antibiotic-Resistant Bacteria 17-24

14 Determination Of Spray Angle In Sprayer Nozzles Using Computer Vision Technique 25-29

Comparison Of GPS-TEC With IRI-2007, IRI-2012 And IRI-2016 TEC Predictions At 30-34 15 ISTA Station, Turkey

Dynamic Performance Comparison Of PI And Interval Type-2 Takagi-Sugeno-Kang Fuzzy 35-39 16 Controller On Positive Output Luo Converter

17 Renewable Bio-Based Filler For EPDM Rubber: Indian Laura (Ficus Nitida) 40-43

18 The Application Of Waste Blue Crab Shell As A Bio-Based Filler In EPDM Rubber 44-48

Analysis Of Worth Assessment Of Information Sources Of Some Socio-Economic 49-53 19 Characteristics Of Artisanal Fishers In Niger Delta

Low Velocity Impact Behavior Of Basalt/Epoxy Fiber Reinforced Composite Laminates 54-57 20 With Different Fiber Orientation

21 The Implementation Of Real Time Window Functions On Field Programmable Gate Array 58-63

23 Ambient Particle Matter Pollution Near University Region Of Konya City 64-69

Physical, Chemical And Microbiological Qualities Of Potable Water Used In Different 70-73 24 Poultry Farms A Review

Optimization Of Production Times Of Power Transformers Using Developed Artificial 74-77 26 Bee/Ant Hybrid Heuristic Algorithm

27 Risk Assessment Analysis In Power Transformer Center In District Of Mersin 78-81

Waste Management And Cost Analysis Of Construction Sites: A Comparative Study On 82-91 29 Ankara (Bilkent) And Mersin City Hospitals In Turkey

The Production Of Novel Magnesium Alloys And Investigation Of Their Mechanical 92-94 32 Properties

33 XAFS, A Powerful Technique For Electronic And Crystal Structure Analysis 95-97

34 Living And Working With Radiation 98-101 Effects Of Weathering On Petrographic Properties Of The Basalts Employed In Diyarbakir 318-322 80 City Walls

Design Analysis Of A Compound Fresnel Solar Concentrator (CFC) Using Ray Tracing 323-326 81 Method

Comparison Of DC-DC Converters For Maximum Power Point Tracking In Photovoltaic 327-331 82 Systems

83 Artificial Neural Network Implementation For DC-DC Converters In Solar Power Systems 332-338

The GUI Application For Calculating The Drag Torque In A Disengaged Multi-Disc Wet 339-344 84 Clutch Using Multiple Models

Parallelization Of Dragonfly Optimization Algorithm On Distributed And Shared Memory 345-348 85 Architectures

Investigation Of Mechanical Properties Of Geofoam Materials Under Dynamic Loads 349-354 87 Caused By Rock Fall

88 Numerical Modeling Of The Rockfall Induced Impact On Simply Supported RC Beams 355-358

Influence Of Coagulant Type In Removal Of Telon Red A2FR Textile Dye By Chemical 359-363 89 Coagulation

90 Cistern In Mersin-Erdemli: Static Analysis And Risk Assessment 364-371

92 Application Of Supercritical Drying For Food Products 372-377

Bioclimatic Evaluations In The Mountainous Ecosystem Of Dajt: Case Study Tirana, 378-382 95 Albania

Synthesis Of Co O /Fe O Bimetallic Nanoparticles For Effective Adsorption Of 383-390 96 3 4 3 4 Tetracycline

97 Robust Control Of Boost Converter Using Interval Type-2 Tsk Fuzzy Logic Controller 391-395

98 Production Of CoNiMnFe High Entropy Alloys With Mechanical Alloying 396-399

99 The Importance Of Historical Turkish Work Of Arts In Terms Of Engineering 400-403

100 About The Work And Map Of Mahmud Al-Kashgari 404-406

101 Some Applications Of Fibonacci Numbers In Apartments Modelling 407-411

102 The Classical Aes-Like Cryptology Via The Fibonacci Polynomial Matrix 412-416

103 Waste Mineral Oils Re-Refining With Physicochemical Methods 417-424

104 Radar Cross Section Analysis Of Unmanned Aerial Vehicle Using Predics 425-429