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Öğe Artificial Neural Network Model for Predicting Specific Draft Force and Fuel Consumption Requirement of a Mouldboard Plough(Selçuk Üniversitesi, 2019) Çarman, Kazım; Çıtıl, Ergün; Taner, AlperA 2-(5-8)-2 artificial neural network (ANN) model, with a back propagation learning algorithm, was developed to predict specific draft force and fuel consumption requirements of mouldboard plough in a clay loam soil under varying operating conditions. The input parameters of the network were tillage depth and forward speed of operation. The output from the network were the specific draft force and fuel consumption requirement of the mouldboard plough. The developed model predicted the specific draft force and fuel consumption requirement of mouldboard plough with an error <1 % when compared to the measured draft force and fuel consumption values. Such encouraging results indicate that the developed ANN model for specific draft force and fuel consumption requirement prediction could be considered as an alternative and practical tool for predicting draft force and fuel consumption requirement of tillage implements under the selected experimental conditions in clay loam soils. Further work is required to demonstrate the generalised value of this ANN in other soil conditions.Öğe Measurements and modelling of wind erosion rate in different tillage practices using a portable wind erosion tunnel(LITHUANIAN RESEARCH CENTRE AGRICULTURE & FORESTRY, 2016) Carman, Kazim; Marakoglu, Tamer; Taner, Alper; Mikailsoy, FarizArtificial intelligence systems are widely accepted as a technology providing an alternative method to solve complex and ill-defined problems. Artificial neural network (ANN) is a technique with a flexible mathematical structure, which is capable of identifying a complex nonlinear relationship between the input and output data. The objective of this study was to investigate the relationship between dust concentration and wind erosion rate, and to illustrate how ANN might play an important role in the prediction of wind erosion rate. Data were recorded via field experiments by using a portable field wind tunnel. The experiments were carried out for eight different tillage applications that include the conventional, six different reduced tillage and the direct seeding practices. Particulate matter (PM) concentration generally decreased with a decrease in number or intensity of tillage operations. Direct seeding resulted in the lowest PM,, concentration. After tillage applications, wind erosion rate varied between 113 and 1365 g m(-2) h(-1). Results showed that wind erosion rate was lower in direct seeding than in conventional and reduced tillage applications. In this paper, a sophisticated intelligent model, based on a 1-(8-5)-1 ANN model with a back-propagation learning algorithm, was developed to predict the changes in the wind erosion rate due to dust concentration occurring during tillage. In addition, the prediction of the model was made according to traditional methods of wind erosion rate by using the programme Statistica, version 5. The verification of the proposed model was carried out by applying various numerical error criteria. The ANN model consistently provided better predictions compared with the nonlinear regression-based model. The relative error of the predicted values was found to be less than the acceptable limits (10%). Based on the results of this study, ANN appears to be a promising technique for predicting wind erosion rate.Öğe Prediction of Draft Force and Disturbed Soil Area of a Chisel Tine in Soil Bin Conditions Using Draft Force and Its Comparison with Regression Model(Selçuk Üniversitesi, 2021) Çarman, Kazım; Marakoğlu, Tamer; Taner, Alper; Çıtıl, ErgünOne of our most valuable natural resources is soil. Sustainable agricultural production is achieved with proper soil management. Tillage is considered to be one of the largest operations, as the most energy need in agricultural production occurs in tillage. The main purpose of this study is to investigate the effects of chisel tine on draft force and disturbed soil area and estimate them using artificial neural networks (ANN) and multiple linear regression equations (MLR). The experiments were carried out in a closed soil bin filled with clay loam soil at an average moisture content of 13.2% (on dry basis). The draft force and disturbed soil area were evaluated as affected by the share width at two levels (60 and 120 mm), forward speed at four levels (0.7, 1, 1.25 and 1.5 ms-1 ) and working depth at four levels (160, 200, 240 and 280 mm) at three replications. The draft force varied from 0.5 to 1.42 kN, depending on the controlled variables, while the disturbed soil area varied from 260 to 865 cm2 . Test results show that share width, forward speed and working depth were significant on the draft force and disturbed soil area. Input variables of the ANN models were considered share width, forward speed and working depth. In prediction of required draft force and disturbed soil area respectively, on account of statistical performance criteria, the best ANN model with coefficient of determination of 0.999 and 0.998, root mean square error of 0.010 and 0.016 and mean relative percentage error of 0.960 and 1.673 was better performed than the MLR model.Öğe Prediction of Kinematic Viscosities of Biodiesels Derived from Edible and Non-edible Vegetable Oils by Using Artificial Neural Networks(SPRINGER HEIDELBERG, 2015) Eryilmaz, Tanzer; Yesilyurt, Murat Kadir; Taner, Alper; Celik, Sadiye AyseIn the present study, the seeds named as wild mustard (Sinapis arvensis L.) and safflower (Carthamus tinctorius L.) were used as feedstocks for production of biodiesels. In order to obtain wild mustard seed oil (WMO) and safflower seed oil (SO), screw press apparatus was used. wild mustard seed oil biodiesel (WMOB) and safflower seed oil biodiesel (SOB) were produced using methanol and NaOH by transesterification process. Various properties of these biodiesels such as density (883.62-886.35 ), specific gravity (0.88442-0.88709), kinematic viscosity (5.75-4.11 ), calorific value (40.63-38.97 ), flash point (171-), water content (328.19-412.15 ), color (2.0-1.8), cloud point [5.8-, pour point [(-3.1)-(-13.1), cold filter plugging point [(-2.0)-], copper strip corrosion (1a-1a) and pH (7.831-7.037) were determined. Furthermore, kinematic viscosities of biodiesels and euro-diesel (ED) were measured at 298.15-373.15 K intervals with 1 K increments. Four different equations were used to predict the viscosities of fuels. Regression analyses were done in MATLAB program, and , correlation constants and root-mean-square error were determined. 1-7-7-3 artificial neural network (ANN) model with a back propagation learning algorithm was developed to predict the viscosities of fuels. The performance of neural network-based model was compared with the performance of viscosity prediction models using same observed data. It was found that ANN model consistently gave better predictions (0.9999 values for all fuels) compared to these models. ANN model was showed 0.34 % maximum errors. Based on the results of this study, ANNs appear to be a promising technique for predicting viscosities of biodiesels.Öğe Radyal santrifüj pompaların yapay sinir ağları ile tasarımı(Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2007-10-17) Taner, Alper; Çarman, KazımBu çalışmada santrifüj pompaların YSA teknikleri ile tasarımı yapılmıştır. Bu amaçla pompanın devir sayısı, debi ve manometrik yükseklik parametreleri giriş verisi, çark giriş çapı, çark çıkış çapı, çark kanat sayısı, pompa giriş borusu çapı ve pompa çıkış borusu çapı parametreleri de çıkış verisi olarak kullanılmıştır. Araştırmada YSA modeli iki yönlü çalıştırılmıştır. Bu çalışmada 3 yöntem için 5 model geliştirilmiştir. Geliştirilen Model1'de, 3 giriş ve 5 çıkış parametresi kullanılarak pompa tasarımı yapılmıştır. Geliştirilen Model2'de, 5 giriş ve 3 çıkış parametresi ve Model3, Model4 ve Model5'de ise 5 giriş ve tek çıkış parametresi kullanılarak deneysel performans sonuçları hesaplanmıştır. Ölçüm sonuçları ile YSA modelleri ve klasik metodlardan (regresyon denklemleri ve teorik hesaplamalar) elde edilmiş sonuçlar karşılaştırılmıştır. YSA ile pompa tasarımında, farklı algoritmalar kullanılmış ancak en iyi sonuçlar, LM algoritması ile elde edilmiştir. Pompa tasarım parametrelerinden çark giriş çapı, çark çıkış çapı, çark kanat sayısı, pompa giriş borusu çapı ve pompa çıkış borusu çapına ait, ölçüm değerlerine yaklaşımda ortalama hata, YSA modelinde (Model1) sırasıyla %2.43, %3.37, %2.11, %3.29 ve %5.11 olarak bulunurken uyuşma derecesi sırasıyla 0.0009, 0.0008, 0.0007, 0.0012 ve 0.0019 elde edilmiştir. Performans değerlerinden devir sayısı, debi ve manometrik yüksekliğe ait ölçüm değerlerine yaklaşımda ortalama hata, üç çıkışlı YSA modelinde (Model2), sırasıyla %1.24, %9.01 ve %6.33 olarak bulunurken uyuşma derecesi sırasıyla 0.0001, 0.0022 ve 0.0037 elde edilmiştir. Ayrıca performans değerlerinden devir sayısı, debi ve manometrik yüksekliğe ait ölçüm değerlerine yaklaşımda ortalama hata, tek çıkışlı YSA modellerinde (Model3, Model4 ve Model5) sırasıyla %0.94, %2.39 ve %1.18 olarak bulunurken uyuşma derecesi sırasıyla 0.0001, 0.0002 ve 0.0001 elde edilmiştir. Bu çalışmadaki nöral modeller ile gerek bir pompaya ait performans değerlerinin ve gerekse de performans değerleri verilen bir pompaya ait tasarım parametrelerinin saptanması hızlı ve güvenli bir şekilde yapılabilecektir.
