Comparison of extraction procedures for the determination of mesosulfuron methyl and iodosulfuron methyl sodium from soil and wheat using Response surface modelling

2021 ◽  
pp. 106456
Author(s):  
Harshdeep Kaur ◽  
Pervinder Kaur
Keyword(s):  
Response Surface ◽  
Surface Modelling ◽  
Extraction Procedures ◽  
Response Surface Modelling

scholarly journals Experimental Investigation of Thermal Fatigue Die Casting Dies by Using Response Surface Modelling

Metals ◽  
2017 ◽  
Vol 7 (6) ◽  
pp. 191 ◽  
Author(s):  
Hassan Abdulhadi ◽  
Syarifah Ahmad ◽  
Izwan Ismail ◽  
Mahadzir Ishak ◽  
Ghusoon Mohammed
Keyword(s):  
Experimental Investigation ◽  
Response Surface ◽  
Thermal Fatigue ◽  
Die Casting ◽  
Surface Modelling ◽  
Response Surface Modelling

Response Surface Modelling through Box-Behnken approach to optimize bacterial quorum sensing inhibitory action of Tribulus terrestris root extract

Rhizosphere ◽  
2018 ◽  
Vol 6 ◽  
pp. 134-140
Author(s):  
Kayeen Vadakkan ◽  
Ramya Gunasekaran ◽  
Abbas Alam Choudhury ◽  
Ashwini Ravi ◽  
Shyamala Arumugham ◽  
...  
Keyword(s):  
Quorum Sensing ◽  
Response Surface ◽  
Inhibitory Action ◽  
Root Extract ◽  
Tribulus Terrestris ◽  
Surface Modelling ◽  
Bacterial Quorum Sensing ◽  
Response Surface Modelling ◽  
Bacterial Quorum

scholarly journals A green membrane distillation system for seawater desalination: Response surface modelling and optimization

2018 ◽  
Vol 361 ◽  
pp. 012011 ◽  
Author(s):  
Siti Khadijah Hubadillah ◽  
Mohd Hafiz Dzarfan Othman ◽  
Paran Gani ◽  
Ahmad Fauzi Ismail ◽  
Mukhlis A. Rahman ◽  
...  
Keyword(s):  
Response Surface ◽  
Membrane Distillation ◽  
Seawater Desalination ◽  
Surface Modelling ◽  
Distillation System ◽  
Response Surface Modelling ◽  
Modelling And Optimization

scholarly journals Predictive modelling to support sensitivity analysis for robust design in aerospace engineering

2020 ◽  
Vol 61 (5) ◽  
pp. 2177-2192 ◽  
Author(s):  
Siva Krishna Dasari ◽  
Abbas Cheddad ◽  
Petter Andersson
Keyword(s):  
Sensitivity Analysis ◽  
Response Surface ◽  
Robust Design ◽  
Aerospace Engineering ◽  
Multivariate Adaptive Regression Splines ◽  
Linear Functions ◽  
Computational Time ◽  
Surface Modelling ◽  
Non Linear ◽  
Response Surface Modelling

AbstractThe design of aircraft engines involves computationally expensive engineering simulations. One way to solve this problem is the use of response surface models to approximate the high-fidelity time-consuming simulations while reducing computational time. For a robust design, sensitivity analysis based on these models allows for the efficient study of uncertain variables’ effect on system performance. The aim of this study is to support sensitivity analysis for a robust design in aerospace engineering. For this, an approach is presented in which random forests (RF) and multivariate adaptive regression splines (MARS) are explored to handle linear and non-linear response types for response surface modelling. Quantitative experiments are conducted to evaluate the predictive performance of these methods with Turbine Rear Structure (a component of aircraft) case study datasets for response surface modelling. Furthermore, to test these models’ applicability to perform sensitivity analysis, experiments are conducted using mathematical test problems (linear and non-linear functions) and their results are presented. From the experimental investigations, it appears that RF fits better on non-linear functions compared with MARS, whereas MARS fits well on linear functions.

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