scholarly journals Applying the Response Surface Methodology (RSM) Approach to Predict the Tractive Performance of an Agricultural Tractor during Semi-Deep Tillage

Agriculture ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1043
Author(s):  
Mohammad Askari ◽  
Yousef Abbaspour-Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Ahmed Mohamed El El Shal ◽  
Rashad Hegazy ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Vertical Load ◽  
Forward Speed ◽  
Test Results ◽  
Deep Tillage ◽  
Different Types ◽  
Rsm Model ◽  
Agricultural Tractor ◽  
Different Levels

This study aimed to evaluate the ability of the response surface methodology (RSM) approach to predict the tractive performance of an agricultural tractor during semi-deep tillage operations. The studied parameters of tractor performance, including slippage (S), drawbar power (DP) and traction efficiency (TE), were affected by two different types of tillage tool (paraplow and subsoiler), three different levels of operating depth (30, 40 and 50 cm), and four different levels of forward speed (1.8, 2.3, 2.9 and 3.5 km h−1). Tractors drove a vertical load at two levels (225 kg and no weight) in four replications, forming a total of 192 datapoints. Field test results showed that all variables except vertical load, and different combinations of this and other variables, were effective for the S, DP and TE. Increments in speed and depth resulted in an increase and decrease in S and TE, respectively. Additionally, the RSM approach displayed changes in slippage, drawbar power and traction efficiency, resulting from alterations in tine type, depth, speed and vertical load at 3D views, with high accuracy due to the graph’s surfaces, with many small pixels. The RSM model predicted the slippage as 6.75%, drawbar power as 2.23 kW and traction efficiency as 82.91% at the optimal state for the paraplow tine, with an operating depth of 30 cm, forward speed of 2.07 km h−1 and a vertical load of 0.01 kg.

Optimization of the operational parameters of a picking-type pneumatic planter using response surface methodology

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
SK PATEL ◽  
JB BHIMANI ◽  
P GUPTA ◽  
BK YADUVANSHI
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Important Variable ◽  
Vacuum Pressure ◽  
Forward Speed ◽  
Operational Parameters ◽  
Seed Spacing ◽  
Lady’S Finger ◽  
Multiple Regression Technique ◽  
Seed Metering Device

Singulation of seeds has been investigated extensively by researchers all over the world and a large number of precision seeding systems with design variations have been developed for different crops. A picking type metering mechanism was developed at CAET, AAU, Godhra, Gujarat, India. The performance of the picking type seed-metering device of a pneumatic planter was investigated under laboratory conditions to optimize the operating parameters for lady's finger seed. The picking of single seed the three operational parameters i.e. hole diameters for the nozzle: 1.0, 1.5, 2.5 and 3.0 mm; forward speed: 0.37, 0.56, 0.83, 1.11 and 1.30 m/s and vacuum pressure: 19.33, 39.32, 43.98, 58.64 and 68.63 kPa were selected for the study. The metering system of the planter was set to place the seed to seed spacing at 300 mm. The response surface methodology (RSM) technique was used to optimize the operational parameters of a precision planter. For optimizing the forward speed, vacuum pressure and nozzle size for developed machine was evaluated by examining the miss index, multiple index, quality of feed index and precision. The data obtained in the experiments were used to develop functions in polynomial form using multiple regression technique. The optimum value was found to be around 0.96 m/s, 36.25 kPa and 2.0 mm of forward speed, vacuum pressure and the holes diameter of nozzle, respectively. The most important variable that governs planting phenomenon is the combination of hole diameter of nozzle and vacuum pressure accounts 89.18 per cent.

scholarly journals Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques

Membranes ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 70
Author(s):  
Jasir Jawad ◽  
Alaa H. Hawari ◽  
Syed Javaid Zaidi
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Response Surface ◽  
Forward Osmosis ◽  
Feed Solution ◽  
Operating Conditions ◽  
Ann Model ◽  
Membrane Flux ◽  
Input Variables ◽  
Rsm Model

The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.

scholarly journals Efficient Removal of Levofloxacin by Activated Persulfate with Magnetic CuFe2O4/MMT-k10 Nanocomposite: Characterization, Response Surface Methodology, and Degradation Mechanism

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3583
Author(s):  
Junying Yang ◽  
Minye Huang ◽  
Shengsen Wang ◽  
Xiaoyun Mao ◽  
Yueming Hu ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Photoelectron Spectroscopy ◽  
Degradation Mechanism ◽  
Sol Gel ◽  
Combustion Method ◽  
Copper Ferrite ◽  
X Ray ◽  
Rsm Model

In this study, a magnetic copper ferrite/montmorillonite-k10 nanocomposite (CuFe2O4/MMT-k10) was successfully fabricated by a simple sol-gel combustion method and was characterised by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), the Brunner–Emmett–Teller (BET) method, vibrating sample magnetometer (VSM), and X-ray photoelectron spectroscopy (XPS). For levofloxacin (LVF) degradation, CuFe2O4/MMT-k10 was utilized to activate persulfate (PS). Due to the relative high adsorption capacity of CuFe2O4/MMT-k10, the adsorption feature was considered an enhancement of LVF degradation. In addition, the response surface methodology (RSM) model was established with the parameters of pH, temperature, PS dosage, and CuFe2O4/MMT-k10 dosage as the independent variables to obtain the optimal response for LVF degradation. In cycle experiments, we identified the good stability and reusability of CuFe2O4/MMT-k10. We proposed a potential mechanism of CuFe2O4/MMT-k10 activating PS through free radical quenching tests and XPS analysis. These results reveal that CuFe2O4/MMT-k10 nanocomposite could activate the persulfate, which is an efficient technique for LVF degradation in water.

scholarly journals Application of Response Surface Methodology for Optimizing the Production of Bioethanol from Calabash (Crescentia cujete) Substrate Using Saccharomyces cerevisiae

2019 ◽  
pp. 1-12 ◽  
Author(s):  
N. U. Nwogwugwu ◽  
G. O. Abu ◽  
O. Akaranta ◽  
E. C. Chinakwe
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Reducing Sugars ◽  
Surface Model ◽  
Wide Range ◽  
Crescentia Cujete ◽  
Input Variables ◽  
Ph Range ◽  
Rsm Model

Aim: The study employed the Response surface methodology (RSM) model to optimize ethanol production from Calabash (Crescentia cujete) pulp juice using Saccharomyces cerevisiae. Study Design: The Calabash pulp was squeezed with muslin cloth, and vacuum filtered to clear solution before use. The clear juice was tested for reducing sugars using the Dinitrosalicylic acid (DNS) method. Twenty three (23) runs, including 3 controls, of the fermentation was conducted at varying temperatures, pH, and volumes of inoculum.The process parameters (input variables): volumes of inoculum, temperature,and pH were subjected to response surface model, using the Central Composite Design (CCD). Place and Duration of Study: This study was carried out in the Environmental Microbiology Laboratory, University of Port Harcourt for six months. Methodology: Fermentation was done in conical flasks covered with cotton wool and foil in a stationary incubator for four days (96 hours). Active stock culture of Saccharomyces cerevisiae was used, with inoculum developed using Marcfaland’s method. Samples were collected every 24 hours, centrifuged, filtered and analyzed for measurement of the output variables: Reducing sugar, cell density and ethanol concentration. Results: The concentration of reducing sugars from Calabash pulp was 3.2 mg/ml. Results obtained also revealed that the fermentation can take place on a wide range of temperature 25-40°C. The optimal pH range for performance of S. cerevisiae for the fermentation process was pH 5.0-6.5. The optimum volume of inoculum was 5.5%v/v (ie 5.5 ml in 94.5ml juice). The optimized process using the RSM model gave 6.19% v/v bioethanol. Control: The bioethanol yield from Calabash substrate is reasonable considering the concentration of reducing sugars obtained from the juice and the duration of the fermentation.

scholarly journals Optimasi Selulase pada Enzyme Assisted Extraction Lemak dari Caulerpa Lentillifera Menggunakan Response Surface Methodology

2021 ◽  
Vol 13 (1) ◽  
pp. 19-37
Author(s):  
Ishmah Hanifah
Keyword(s):  
Response Surface Methodology ◽  
Central Composite Design ◽  
Response Surface ◽  
Assisted Extraction ◽  
Caulerpa Lentillifera ◽  
Rsm Model ◽  
Central Composite ◽  
Enzyme Assisted Extraction

Penelitian dirancang untuk mengetahui kondisi optimum proses enzyme assisted extraction lemak rumput laut hijau segar Caulerpa lentillifera dengan menggunakan enzim selulase. Proses optimasi dilakukan menggunakan Response Surface Methodology (RSM) model Central Composite Design dengan 15 perlakuan. Perlakuan yang didapatkan untuk mengetahui pengaruh variabel bebas diantaranya konsentrasi enzim, suhu hidrolisis, dan waktu hidrolisis terhadap respon yaitu jumlah ekstrak lemak dan aktivitas antioksidan. Dari hasil penelitian didapatkan model 2FI dan Linier berturut-turut untuk respon jumlah lemak dan aktivitas antioksidan. Kondisi optimum yang diperoleh yaitu konsentrasi enzim sebesar 2%, suhu hidrolisis sebesar 30 °C, dan waktu hidrolisis selama 1 jam. Kondisi optimum tersebut kemudian dapat diverifikasi dengan melakukan perlakuan terpilih sebanyak 2 kali ulangan atau lebih hingga mendekati hasil prediksi. Asam lemak yang diperoleh setelah metilasi dan identifikasi dengan GC-MS yaitu asam palmitat dan asam laurat. 

Optimization of photolysis of diclofenac using a response surface methodology

2013 ◽  
Vol 67 (4) ◽  
pp. 907-914 ◽  
Author(s):  
Jong-Kwon Im ◽  
Moon-Kyung Kim ◽  
Kyung-Duk Zoh
Keyword(s):  
Response Surface Methodology ◽  
Humic Acid ◽  
Response Surface ◽  
Goodness Of Fit ◽  
Environmental Parameters ◽  
Test Results ◽  
Optimal Conditions ◽  
Negative Contribution ◽  
Pareto Analysis ◽  
Determination Coefficient

This study investigates the effects of environmental parameters such as UV intensity (X1, 2.1 ∼ 6.3 mW/cm2), Fe(III) (X2, 0 ∼ 0.94 mg/L), NO3− (X3, 0 ∼ 20 mg/L) and humic acid (X4, 0 ∼ 30 mg/L) on the removal efficiency of diclofenac (DCF, Y), and optimization using a response surface methodology (RSM) based on Box–Behnken design (BBD). According to analysis of variance and t-test results (p < 0.001), the proposed quadratic BBD model based on a total of 29 experimental runs fitted well to the experimental data. Moreover, the determination coefficient (R2 = 0.990) and adjusted determination coefficient (Ra2 = 0.981) indicated that this model is adequate with a high goodness-of-fit. Variables of X1, X2 and X3 had significant positive contributions (p < 0.001), while X4 had significant negative contribution to the DCF removal (p < 0.001). A Pareto analysis showed that X4 was the most important factor (57.18%) in DCF photolytic removal. The predicted and observed DCF removal were 94.98 and 94.2% under optimal conditions (X1 = 6.29 mW/cm2, X2 = 0.75 mg/L, X3 = 15.65 mg/L and X4 = zero), respectively. The RSM not only gives valuable information on the interactions between these photoreactive species (UV intensity, Fe(III), NO3−, and humic acid) that influence DCF removal, but also identifies the optimal conditions for effective DCF removal in water.

Prediction the Influence of Machining Parameters for CNC Turning of Aluminum Alloy Using RSM and ANN

2020 ◽  
Vol 38 (6A) ◽  
pp. 887-895
Author(s):  
Hind H. Abdulridha ◽  
Aseel J. Haleel ◽  
Ahmed A. Al-duroobi
Keyword(s):  
Response Surface Methodology ◽  
Aluminum Alloy ◽  
Prediction Model ◽  
Response Surface ◽  
Feed Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Rsm Model ◽  
Good Agreement

The main objective of this paper is to develop a prediction model using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) for the turning process of Aluminum alloy 6061 round rod. The turning experiments carried out based on the Central Composite Design (CCD) of Response Surface Methodology. The influence of three independent variables such as Cutting speed (150, 175 and 200 mm/ min), depth of cut (0.5, 1 and 1.5 mm) and feed rate (0.1, 0.2 and 0.3 mm/rev) on the Surface Roughness (Ra) were analyzed through analysis of variance (ANOVA). The response graphs from the Analysis of Variance (ANOVA) present that feed-rate has the strongest influence on Ra dependent on cutting speed and depth of cut. Surface response methodology developed between the machining parameters and response and confirmation experiments reveals that the good agreement with the regression models. The coefficient of determination value for RSM model is found to be high (R2 = 0.961). It indicates the goodness of fit for the model and high significance of the model. From the result, the maximum error between the experimental value and ANN model is less than the RSM model significantly. However, if the test patterns number will be increased then this error can be further minimized. The proposed RSM and ANN prediction model sufficiently predict Ra accurately. However, ANN prediction model is found to be better compared to RSM model. The artificial neutral network is applied to experimental results to find prediction results for two response parameters. The predicted results taken from ANN show a good agreement between experimental and predicted values with the mean squared error of training indices equal to (0.000) which produces flexibility to the manufacturing industries to select the best setting based on applications.

Vertical wind speed extrapolation: Modelling using a response surface methodology (RSM) based on unconventional designs

2021 ◽  
pp. 0309524X2110463
Author(s):  
Feriel Adli ◽  
Nawel Cheggaga ◽  
Farouk Hannane ◽  
Leila Ouzeri
Keyword(s):  
Response Surface Methodology ◽  
Wind Speed ◽  
Response Surface ◽  
Vertical Wind ◽  
Industrial Processes ◽  
Wind Speed Profile ◽  
Wind Speeds ◽  
Vertical Wind Speed ◽  
Mathematical Techniques ◽  
Rsm Model

The main objective of this paper is to develop a predictive model of vertical wind speed profile. Response surface methodology (RSM) is used for this purpose. RSM is a set of statistical and mathematical techniques useful for the development, improvement and optimisation of processes. It is mainly used in industrial processes and is successfully applied in this paper to model the wind speed at the hub height of the wind turbine. An unconventional model is adopted due to the nature of the input parameters which cannot be controlled or modified. The model validation indicators, namely correlation coefficient ([Formula: see text]) and root mean square error (RMSE = 1.02), give excellent results when comparing predicted and measured wind speeds. For the same data, the RSM model gives a better RMSE compared to the conventional power law and the artificial neural network.

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