Optimization of Synthesis of Polyurethane Foam Prepared from Liquefied Corn Stover by Response Surface Methodology

2010 ◽  
Author(s):  
Tipeng Wang ◽  
Zongming Zheng ◽  
Zhihuai Mao
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Corn Stover ◽  
Polyurethane Foam

scholarly journals Synthesis of flexible polyurethane foam for applications in planar orthosis using the response surface methodology

2020 ◽  
pp. 1-4
Author(s):  
Jorge Alejandro TORRES-OCHOA ◽  
Nadia Renata OSORNIO-RUBIO ◽  
Orlando CORTAZAR-MARTINEZ ◽  
Victor Alfonso MORALES-NIETO
Keyword(s):  
Response Surface Methodology ◽  
Design Of Experiments ◽  
Response Surface ◽  
Polyurethane Foam ◽  
Reaction Temperature ◽  
Foam Formulation ◽  
Flexible Polyurethane Foam ◽  
Flexible Polyurethane ◽  
Response Variables ◽  
Foaming Time

In this work, the process for the formulation of flexible polyurethane foam is presented following a design of experiments for mixtures. The proportion of polyol, diisocyanate, and crosslinker was considered as factors. The response variables considered were foaming time and reaction temperature. The result of the experiments showed that there is an area where the foam formulation is better. This zone is closed with 5% crosslinker, 50% polyol, and 45% diisocyanate, in this formulation denser foams with more uniform bubbles were obtained

scholarly journals Modeling the Bending Strength of MDF Faced, Polyurethane Foam-Cored Sandwich Panels Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1514
Author(s):  
Morteza Nazerian ◽  
Fateme Naderi ◽  
Ali Partovinia ◽  
Antonios N. Papadopoulos ◽  
Hamed Younesi-Kordkheili
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Polyurethane Foam ◽  
Sandwich Panel ◽  
Bending Strength ◽  
Validation Data ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The present study evaluates and compares predictions on the performance and the approaches of the response surface methodology (RSM) and the artificial neural network (ANN) so to model the bending strength of the polyurethane foam-cored sandwich panel. The effect of the independent variables (formaldehyde to urea molar ratio (MR), sandwich panel thickness (PT) and the oxidized protein to melamine-urea-formaldehyde synthesized resin weight ratio (WR)) was examined based on the bending strength by the central composite design of the RSM and the multilayer perceptron of the ANN. The models were statistically compared based on the training and validation data sets via the determination coefficient (R2), the root mean squares error (RMSE), the absolute average deviation (AAD) and the mean absolute percentage error (MAPE). The R2 calculated for the ANN and the RSM models was 0.9969 and 0.9960, respectively. The models offered good predictions; however, the ANN model was more precise than the RSM model, thus proving that the ANN and the RSM models are valuable instruments to model and optimize the bending properties of the sandwich panel.

Application of Response Surface Methodology for Optimization of Cellulose Solution by ZnCl2

2011 ◽  
Vol 391-392 ◽  
pp. 1008-1011
Author(s):  
Guo Ming Zeng ◽  
Yuan Liang Wang ◽  
Xin Qiang Ning ◽  
Mao Lan Zhang
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Corn Stover ◽  
Chemical Industry ◽  
Steam Explosion ◽  
Renewable Resource ◽  
Production Costs ◽  
Attractive Alternative ◽  
Cellulose Solution ◽  
Commercial Value

Corn stover is a largely feasible and cheap renewable resource with low commercial value. An attractive alternative is utilization of corn stover for chemical industry，medicine，biochemistry etc. However, the production costs are still too high to apply on commercialization. The purpose of this study was to use the response surface methodology (RSM) to optimize of cellulose salvation by ZnCl2 after the steam explosion .The solution of cellulose that had been pretreated with 87% ZnCl2 at 139 °C for 49 min resulted in an optimum solubility of 76.2%.

Optimization of CO2 laser-based pretreatment of corn stover using response surface methodology

2011 ◽  
Vol 102 (22) ◽  
pp. 10493-10497 ◽  
Author(s):  
Shuang-Qi Tian ◽  
Zhen-Yu Wang ◽  
Zi-Luan Fan ◽  
Li-Li Zuo
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Corn Stover ◽  
Co2 Laser

Optimization of Dilute Acid Hydrolysis of Corn Stover for Separate Production of Xylose and Glucose by Response Surface Methodology

2012 ◽  
Vol 608-609 ◽  
pp. 298-301
Author(s):  
Su Li Zhi ◽  
Jing Yang ◽  
Yan Yao ◽  
Shu Ting Zhang ◽  
Xue Bin Lu
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Corn Stover ◽  
High Concentration ◽  
Dilute Acid Hydrolysis ◽  
Acid Pretreatment ◽  
Glucose Yield ◽  
H2so4 Concentration ◽  
Glucose Ratio ◽  
Dilute Sulfuric Acid Pretreatment

Dilute sulfuric acid pretreatment of corn stover was used to obtain a solution of high concentration of xylose from the hemicellulosic fraction and a relatively low concentration of glucose, which not only saved the hemicellulase but also made a full use of corn stover. Then the study considered the selectivity (xylose-glucose ratio) as an important parameter to optimize the hydrolysis conditions. The results optimized by response surface methodology (RSM) showed that the optimum conditions for pretreatment were found to be H2SO4 concentration of 2.4% and reaction time of 100min at 100°C. Under these conditions, 78.8% of xylose yield was achieved and the glucose yield was lower than 11.6%. To confirm these results, the optimum condition was performed and the actual results of xylose yield and glucose yield were 78% and 11.3%.

scholarly journals Response surface methodology (RSM) to evaluate moisture effects on corn stover in recovering xylose by DEO hydrolysis

2012 ◽  
Vol 108 ◽  
pp. 134-139 ◽  
Author(s):  
Rita C.L.B. Rodrigues ◽  
William R. Kenealy ◽  
Diane Dietrich ◽  
Thomas W. Jeffries
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Corn Stover ◽  
Moisture Effects
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