In this study, machine learning techniques were employed to estimate and predict the system efficiency of a pumping plant at various hydraulic head levels. The measured parameters, including flow rate, outlet pressure, drawdown, and power, were used for estimating the system efficiency. Two approaches, Approach-I and Approach-II, were utilized. Approach-I incorporated additional parameters such as hydraulic head, drawdown, flow, power, and outlet pressure, while Approach-II focused solely on hydraulic head, outlet pressure, and power. Seven machine learning algorithms were employed to model and predict the efficiency of the pumping plant.

Enerji ihtiyacının maliyetini minimum indirmek tarımsal işletmelerin kar payının arttırmasına katkı sağlayacaktır. Çiftliklerimiz enerji tüketimi fazla olan tarımsal üretim işlemlerini yerine getirebilmek için yenilenebilir enerji kaynakları olan biyoyakıt, güneş ve rüzgar enerjisinden yararlanmaktadır. Tarımsal üretimde özellikle de hayvan yetiştiriciliğinde daha kaliteli ve yüksek verime ulaşabilmek makineleşme ile orantılıdır. Makineleşmeye bağlı olarak enerji tüketimi de artış göstermektedir. Günümüzde enerji maliyetinin yükselmesi, çiftçilerimizin gelirlerinin önemli bir bölümünü elektrik faturalarına ayırmasına sebep olmaktadır. Bu çalışmada, artan bu elektrik ihtiyacımızı karşılamak için Konya’da, bir hayvan çiftliğinde güneş enerjisinin uygulanabilirliği ve işletmeye ekonomik getirisinin hesaplanması amaçlanmıştır. İşletmenin çatısına kurulan güneş enerji sisteminin üretileceği elektrik hesaplamaları iki farklı yönteme göre yapılmıştır. Güneş panellerinden anlık ölçümler ve aylık güneşlenme süreleri dikkate alınarak yapılan hesaplama ile photovoltaic geographical information system hesaplamaları karşılaştırılmıştır. Yöntemler arasında %3,7’lik bir fark bulunmuştur. İşletmenin çatısına kurulan güneş enerji sisteminin toplam maliyet 1.884.600 TL hesaplanmıştır. İşletmenin aylık elektriksel yükü 35.746 kWh olarak belirlenmiştir. İşletmenin aylık ortalama olarak elektrik tüketimi 27.881 TL, yıllık ise 334.582 TL hesaplanmıştır. Güneş enerji sisteminin aylık elektrik üretim miktarı 21.280 TL, yıllık ise 255.368 TL’dir. Sistemin kendini amorti etme süresi yaklaşık 7,2 yıl hesaplanmıştır. Güneş enerji sisteminin yaklaşık 25 yıl ömrü olduğunu hesaba katarsak geriye kalan 17,8 yıl sonunda işletmenin maliyetlerinde yaklaşık olarak 4,5 milyon TL azalmanın olacağı tahmin edilmektedir. Ayrıca işletmenin güneş enerji sistemi ile temiz enerji üretimi sayesinde, yıllık elektrik tüketim değerine eşdeğer 670.340 kg CO2 salınımı engellenmiş olacaktır. Bu işletme için güneş enerji sisteminin ekonomik bir yatırım olmasının yanı sıra elektrik üretimi bakımından CO2 salınımına sebep olmaması büyük bir avantajdır.

This study explores the impact of tractor drawbar height (520, 530 and 540 mm from the ground) on key performance indicators, including pulling force, fuel consumption, pulling power and overall tractor efficiency. Present experiments were conducted on a special pulling power test track under controlled conditions at the Directorate of Agricultural Equipment and Machinery Test Centre of the Ministry of Agriculture and Forestry. Data were collected at different tractor forward speeds and performance parameters were modeled and optimized using Response Surface Methodology (RSM), a statistical approach widely used for multi-factor optimization. Present findings revealed that a drawbar height of 520 mm yielded the highest pulling force (47.4 kN) at a speed of 5.48 km h⁻¹, while the same height combined with a speed of 8.33 km h⁻¹ produced the maximum pulling power (78.75 kW) and the lowest specific fuel consumption (267.03 g kWh⁻¹). Statistical analysis confirmed that both drawbar height and tractor speed had significant effects on pulling force and fuel consumption (P < 0.05). However, for overall tractor efficiency, only tractor speed showed a significance. The developed RSM models demonstrated high predictive accuracy, with an R² value of 0.98 for pulling force and error metrics such as RMSE of 0.42% for efficiency, highlighting the reliability of the approach. The study concludes that optimizing the drawbar height and tractor speed not only improves pulling performance but also enhances fuel efficiency, offering a practical framework for achieving sustainable agricultural practices. Present findings emphasize the critical role of drawbar height adjustments in minimizing energy losses and maximizing operational efficiency during field operations.

This research was carried out with the help of a deep well simulator, which is commonly utilized for irrigation studies. In this study, flow types based on Reynolds number, well drawdown, head losses, and cost changes of these head losses were analyzed for four different sieve types used in wells. In addition, the flow types and head losses for the types of screen used were examined as per the theoretical calculations. Theoretically, the turbulent head loss among types of screens was calculated at the highest (0.37 m) in the bridge slot screen (ST4) and at least (0.028 m) in the round slot (ST3). The drawdown is the sum of the head losses for deep wells. Among the well-types, the maximum drawdown (113.46 cm) was measured within the bridge slot well type (WT4), and the minimum drawdown (50.37 cm) was measured in the horizontal slot oblong well type (WT2). The least head loss per unit flow rate in the wells was measured in the well, which was formed with a horizontal oblong slot (WT2) screen. Here, gravel and screen hole position affected the percentage of clogging in the screens. Overclogging of the screens caused the narrowing of the opening area, increased flow velocity and turbulent head loss, and increased drawdown. It has been revealed that the use of a horizontal oblong slot screen minimizes the head loss in the wells, depending on the physical properties of the gravel used in this study. One of the most important factors to be considered in well designs is the compatibility of the screen type depending on the physical properties of the gravel. Even the change of the geometric shape among the screen types having similar apertures changed the blockage of the gravel, causing the head loss to change. The well-designers should pay attention to the selection of gravel depending on the screen type or the selection of the screen depending on the type of gravel used.

This study explores the interrelationship between pump performance, system efficiency, and noise/vibration levels by analyzing the influence of pump frequency and outlet pressure. System efficiency predictions were conducted utilizing both the Response Surface Method (RSM) and advanced machine learning algorithms, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), and XGBoost. The comparative analysis revealed that ANN provided the highest prediction accuracy with an R ² value of 0.946, Root Mean Square Error (RMSE) of 1.2% and Mean Absolute Percentage Error (MAPE) of 2.32%. However, when predicting system efficiency using external data inputs, RSM outperformed other models, achieving an R ² value of 0.96 and a mean error rate of 3.84%. Optimization via RSM was performed for target flow rates of 35, 40, and 45 m ³ h ⁻¹ , with the optimal flow rate determined at 35 m ³ h ⁻¹ , corresponding to a system efficiency of 42%. To validate these optimization results, experimental tests were conducted, revealing a flow rate of 35.4 m ³ h ⁻¹ and system efficiency of 42.95%, with error margins of 1.12% and 2.21%, respectively. The study demonstrates that RSM is a robust and effective tool for optimizing pump system performance, offering practical applications in improving energy efficiency and operational stability in pumping facilities.