scholarly journals Optimization of a divided wall column for the separation of C4-C6 normal paraffin mixture using Box-Behnken design

2013 ◽  
Vol 19 (1) ◽  
pp. 107-119 ◽  
Author(s):  
Vikas Sangal ◽  
Vineet Kumar ◽  
Mani Mishra
Keyword(s):  
Energy Efficiency ◽  
Response Surface Methodology ◽  
Product Quality ◽  
Response Surface ◽  
Process Parameters ◽  
Ternary Mixture ◽  
Normal Paraffin ◽  
Box Behnken Design ◽  
Optimization Of Parameters ◽  
Divided Wall Column

In the present study, simulation of a divided wall column (DWC) was carried out to study the product quality and energy efficiency as a function of reflux rate, liquid spilt and vapour split for the separation of C4-C6 normal paraffin ternary mixture. Rigorous simulation of the DWC was carried out using Multifrac model of ASPEN Plus software. Box-Behnken design (BBD) was used for the optimization of parameters and to evaluate the effects and interaction of the process parameters such as reflux rate (r), liquid split (l) and vapour split (v). It was found that the number of simulation runs reduced significantly for the optimization of DWC by BBD. Optimization by BBD under response surface methodology (RSM) vividly underscores interactions between variables and their effects. The predictions agree well with the results of the rigorous simulation.

Application of Response Surface Methodology to Predict Ovality of AA6082 Flow Formed Tubes

2013 ◽  
Vol 3 (1) ◽  
pp. 52-65 ◽  
Author(s):  
M. Srinivasulu ◽  
M. Komaraiah ◽  
C.S. Krishna Prasada Rao
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Response Surface Model ◽  
Surface Model ◽  
Flow Forming ◽  
Box Behnken Design ◽  
Standard Response ◽  
Tubular Components ◽  
Roller Radius

Flow-forming is eco-friendly, chipless manufacturing process employed in the manufacture of thin walled seamless tubes. Ovality, the out of roundness is one of basic form of errors encountered in the tubular components. In the present research, a response surface model has been developed to predict ovality of AA6082 alloy pre-forms using Design of Experiments. The experiments are performed on a flow forming machine with a single roller. The process parameters selected for the present investigation are axial feed of the roller, the speed of the mandrel, and roller radius. Box-Behnken Design, a standard response surface methodology has been used to conduct the experimental runs. The developed response surface model successfully predicts the ovality of AA6082 flow formed tube within the range of selected process parameters. It has been found that, roller feed is the most important process parameter influencing the ovality of AA6082 flow formed tube.

scholarly journals OPTIMIZATION OF OIL EXTRACTION FROM GARCINIA KOLA USING ARTIFICIAL NEURAL NETWORK AND RESPONSE SURFACE METHODOLOGY

2020 ◽  
Vol 26 (2) ◽  
pp. 77-84
Author(s):  
SYLVESTER UWADIAE ◽  
FAITH OVIESU ◽  
BAMIDELE AYODELE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Box Behnken Design ◽  
Garcinia Kola ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Solvent Volume

The target of this investigation was to model and optimize selected process parameters when extracting oil from Garcinia kola. Artificial neural network (ANN) and Box-Behnken design (BBD) in response surface methodology (RSM) were used for the modelling and optimization of the process parameters. The optimized process values were 397.86 mL and 399.99 mL for solvent volume; 109.32 min and 107.55 min for extraction time; 72.64 g and 70 g for sample mass and maximum yields of 20.839 wt% and 20.488 wt% for RSM and ANN respectively. The highly positively correlated experimental and anticipated values validated the models.

Optimisation of the Oil Extraction from Nigella sativa Seeds Using Response Surface Methodology

2017 ◽  
Vol 68 (2) ◽  
pp. 331-336
Author(s):  
Gabriela Isopencu ◽  
Mirela Marfa ◽  
Iuliana Jipa ◽  
Marta Stroescu ◽  
Anicuta Stoica Guzun ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Nigella Sativa ◽  
Polynomial Equation ◽  
Herbaceous Plant ◽  
Box Behnken Design ◽  
Pareto Analysis ◽  
Black Cumin ◽  
Mediterranean Countries ◽  
Experimental Values

Nigella sativa, also known as black cumin, an annual herbaceous plant growing especially in Mediterranean countries, has recently gained considerable interest not only for its use as spice and condiment but also for its healthy properties of the fixed and essential oil and its potential as a biofuel. Nigella sativa seeds fixed oil, due to its high content in linoleic acid followed by oleic and palmitic acid, could be beneficial to human health. The objective of this study is to determine the optimum conditions for the solvent extraction of Nigella sativa seeds fixed oil using a three-level, three-factor Box-Behnken design (BBD) under response surface methodology (RSM). The obtained experimental data, fitted by a second-order polynomial equation were analysed by Pareto analysis of variance (ANOVA). From a total of 10 coefficients of the statistical model only 5 are important. The obtained experimental values agreed with the predicted ones.

scholarly journals Optimization of injection molding process for car fender in consideration of energy efficiency and product quality

2014 ◽  
Vol 1 (4) ◽  
pp. 256-265 ◽  
Author(s):  
Hong Seok Park ◽  
Trung Thanh Nguyen
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Injection Molding ◽  
Product Quality ◽  
Process Parameters ◽  
Optimization Problems ◽  
Computational Cost ◽  
Injection Molding Process ◽  
Molding Process ◽  
Nsga Ii

Abstract Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using nondominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.

scholarly journals Optimization of Crude Oil and PAHs Degradation by Stenotrophomonas rhizophila KX082814 Strain through Response Surface Methodology Using Box-Behnken Design

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Praveen Kumar Siddalingappa Virupakshappa ◽  
Manjunatha Bukkambudhi Krishnaswamy ◽  
Gaurav Mishra ◽  
Mohammed Ameenuddin Mehkri
Keyword(s):  
Response Surface Methodology ◽  
Crude Oil ◽  
Response Surface ◽  
Oxygen Demand ◽  
Inoculum Size ◽  
Coefficient Of Determination ◽  
Oil Removal ◽  
Box Behnken Design ◽  
Stenotrophomonas Rhizophila ◽  
Crude Oil Removal

The present paper describes the process optimization study for crude oil degradation which is a continuation of our earlier work on hydrocarbon degradation study of the isolate Stenotrophomonas rhizophila (PM-1) with GenBank accession number KX082814. Response Surface Methodology with Box-Behnken Design was used to optimize the process wherein temperature, pH, salinity, and inoculum size (at three levels) were used as independent variables and Total Petroleum Hydrocarbon, Biological Oxygen Demand, and Chemical Oxygen Demand of crude oil and PAHs as dependent variables (response). The statistical analysis, via ANOVA, showed coefficient of determination R2 as 0.7678 with statistically significant P value 0.0163 fitting in second-order quadratic regression model for crude oil removal. The predicted optimum parameters, namely, temperature, pH, salinity, and inoculum size, were found to be 32.5°C, 9, 12.5, and 12.5 mL, respectively. At this optimum condition, the observed and predicted PAHs and crude oil removal were found to be 71.82% and 79.53% in validation experiments, respectively. The % TPH results correlate with GC/MS studies, BOD, COD, and TPC. The validation of numerical optimization was done through GC/MS studies and   % removal of crude oil.

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