Optimisation and modelling of draft and rupture width using response surface methodology and artificial neural network for tillage tools

Gautam, Prem Veer; Tiwari, Prem Shanker; Agrawal, Kamal Nayan; Roul, Ajay Kumar; Kumar, M.; Singh, Karan P.

doi:10.1071/sr21271

Cited by 4 publications

(7 citation statements)

References 48 publications

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“…The developed algorithm for rupture width measurement was explained in the study of Gautam et al. (2022). Three images were taken in one pass of the tillage tool, which gives 15 readings of rupture width.…”

Section: Methodsmentioning

confidence: 99%

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Predictive modelling of sweep's specific draft using machine learning regression approaches

Gautam,

Agrawal,

Roul

et al. 2023

Soil Use and Management

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Modelling and optimizing soil‐tool interaction parameters for tillage operations is crucial for developing efficient and precise tools. This study focused on a specific draft of sweep tools in the soil bin filled with vertisol, considering factors such as tool geometry, cone index, working depth and operational speed. Data analysis showed that the range of specific draft values, from 9.51 to 38.95 kN/m2. Machine learning models, including artificial neural network (ANN), support vector machine (SVM), bagged trees (BT) and boosted trees (BoT), were developed using experimental data to predict the specific draft of sweep tools with hyperparameter configuration. The developed machine learning models have also been compared with the predictive multiple linear regression (MLR) model, and it was found that the predictive performance of the machine learning models was better than the MLR model during training and testing. The fine‐tuned ANN model achieved impressive statistical performance with the lowest mean absolute error (MAE) of 0.489 kN/m2, root mean square error (RMSE) of 0.619 kN/m2, standard error of prediction (SEP) of 3.462% and highest coefficient of determination (R2) of .99 during testing. R2 values for BT, BoT, SVM and MLR models were .97, .96, .94 and .83, respectively, for specific draft predictions. The findings from this study have practical implications for optimizing sweep tool design and improving tillage operation. Manufacturers and farmers can benefit from predictive modelling using machine learning to design and select appropriate tillage tools for specific soil conditions. This approach can lead to improved soil health, increased yields and reduced costs.

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Section: Methodsmentioning

confidence: 99%

“…The camera was fixed at an angle of 45°, a height of 750 mm and a distance of 850 mm during image capturing (Figure 4). The developed algorithm for rupture width measurement was explained in the study of Gautam et al (2022). Three images were taken in one pass of the tillage tool, which gives 15 readings of rupture width.…”

Section: Tests Proceduresmentioning

confidence: 99%

Predictive modelling of sweep's specific draft using machine learning regression approaches

Gautam,

Agrawal,

Roul

et al. 2023

Soil Use and Management

Self Cite

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“…The main advantages of RSM optimization involve statistically designed experiments, statistical analysis, optimization, response profile analysis, and generating an empirical and mathematical model in one step. There are various RSM methods in DE software such as Box-Behnken (BBD), central composite, factorial, optimal (custom), user-defined, and historical data design can be selected for multi-response surface analysis (Gautam et al, 2022). Among these designs, the BBD is a more efficient and frequently used technique for optimizing variables, where a set of three levels with equal intervals between these levels for each factor is needed (Nishad et al, 2022).…”

Section: Rsm Modelmentioning

confidence: 99%

“…Furthermore, the performance of cooling system is often affected by many input parameters, including the thickness of the cooling pad, water flow rate, air velocity, and so forth. Therefore, optimizing input parameters is an important engineering job to achieve desired results and a fascinating approach to designing and developing energy‐efficient cooling systems (Gautam et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Modeling and optimization of integrated earth air heat exchanger and direct evaporative cooling system using ANN and RSM approaches for preserving fruits and vegetables

Nishad,

Mangaraj

2023

J Food Process Engineering

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Conventional air‐conditioned storage systems are more energy intensive which requires a consistent supply of electricity. Apart from this, it is costly and not suitable in remote areas where electricity is almost inaccessible. Therefore, the present study aimed to test the performance and feasibility of an integrated earth air heat exchanger and direct evaporative cooling system for storage of fruits and vegetables. The effects of air velocity (4–6 m/s), water flow rate (0.032–0.096 kg/s), and thickness of the cooling pad (50–150 mm) with corresponding responses were investigated in order to develop response surface methodology (RSM) and artificial neural network (ANN) models and compared them to predict temperature drop and humidity rise. Using RSM model, the optimum values for maximum temperature drop (16.80°C) and humidity rise (70.85%) within a range of input variables were found to be 4 m/s air velocity, 0.065 kg/s water flow rate, and 150 mm thickness of the cooling pad. For predicting the temperature drop and humidity rise, the higher (0.992 and 0.998) lower MAE (0.045 and 0.064), RMSE (0.187 and 0.266), and values (0.028 and 0.001) were observed in the ANN model. The developed ANN model was better than RSM models, and the model prediction was in good agreement with experiment values within the range of ±5% uncertainty. The results of this study indicate that the proposed cooling system can be successfully applied to store horticultural commodities.Practical ApplicationIntroduced hybrid cooling technique is low‐cost and eco‐friendly, and can be commercially used for on‐farm storage of fruits and vegetables to enhance their shelf life. The system is helpful for smallholder farmers/retailers to maintain the quality of the commodity during storage, minimize post‐harvest losses, and fetch good market value for the commodity.

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“…In 2003, Hamed et al used the ANN model to predict the draught demand and energy demand of a disc plow by using 10 input parameters (the plow's depth and forward speed, sand content, silt content, clay content, soil water content, the disk's diameter and angle, tilt angle and soil density) and the predicted results were reliable, with R 2 values of 0.934 and 0.915 [32]. Gautam et al compared the performances of the RSM and ANN models in predicting resistance, where the R 2 value of RMS was 0.997 and that of ANN was 0.987, indicating that the ANN model is suitable for predicting resistance [33]. RF is a supervised ML technique based on an ensemble method, which usually combines multiple models of the random forest algorithm to improve the accuracy of predictions.…”

Section: Introductionmentioning

confidence: 99%

A Study of a Model for Predicting Pneumatic Subsoiling Resistance Based on Machine Learning Techniques

et al. 2023

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In order to explore the drag reduction mechanism of pneumatic subsoiling and study the influence of pneumatic subsoiling on the soil, this study used machine learning models to predict the working resistance of a pneumatic subsoiler and adopted random forest (RF), error back-propagation (BP), eXtreme gradient boosting (XGBoost) and support vector regression (SVR) to analyze and compare the predictions of these four models. Field experiments were carried out in two fields with different bulk densities and moisture content. The effects of these parameters on the resistance of pneumatic subsoiling were studied by changing the working air pressure, depth and forward speed. In the RF, SVR, XGBoost and BP models, five parameters (working air pressure, working depth, forward speed, bulk density and moisture content) were inputted as independent variables, and the operating resistance of pneumatic subsoiling was used as the predicted value. After training the four models, the results showed that the R2 value of the RF model was the highest and the error was the smallest, which made it better than the SVR, XGBoost and BP models. The values of MAPE, R2 and RMSE for the RF model’s test set were 0.01, 0.99, and 3.61 N, respectively, indicating that the RF model could predict the resistance value of subsoiling well. When the RF model was used to analyze the five input parameters, the experimental results showed that the contribution of working air pressure to reducing the resistance of subsoiling reached 29%, indicating that pneumatic subsoiling can reduce the resistance, drag and consumption.

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Optimisation and modelling of draft and rupture width using response surface methodology and artificial neural network for tillage tools

Cited by 4 publications

References 48 publications

Predictive modelling of sweep's specific draft using machine learning regression approaches

Predictive modelling of sweep's specific draft using machine learning regression approaches

Modeling and optimization of integrated earth air heat exchanger and direct evaporative cooling system using ANN and RSM approaches for preserving fruits and vegetables

A Study of a Model for Predicting Pneumatic Subsoiling Resistance Based on Machine Learning Techniques

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