Optimization of CO2 biofixation rate by microalgae in a hybrid microfluidic differential carbonator using response surface methodology and desirability function

2020 ◽  
Vol 42 ◽  
pp. 101291 ◽  
Author(s):  
Hayat Abdulla Yusuf ◽  
S. M. Zakir Hossain ◽  
Ahmed Ali Khamis ◽  
Hassan Tariq Radhi ◽  
Ahmed Salman Jaafar
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Desirability Function ◽  
Co2 Biofixation

scholarly journals Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design

2021 ◽  
Vol 11 (15) ◽  
pp. 6768
Author(s):  
Tuan-Ho Le ◽  
Hyeonae Jang ◽  
Sangmun Shin
Keyword(s):  
Response Surface Methodology ◽  
Transfer Function ◽  
Response Surface ◽  
Robust Design ◽  
Transfer Functions ◽  
Desirability Function ◽  
Likelihood Estimation ◽  
Statistical Simulation ◽  
Screening Procedure ◽  
Function Estimation

Response surface methodology (RSM) has been widely recognized as an essential estimation tool in many robust design studies investigating the second-order polynomial functional relationship between the responses of interest and their associated input variables. However, there is scope for improvement in the flexibility of estimation models and the accuracy of their results. Although many NN-based estimations and optimization approaches have been reported in the literature, a closed functional form is not readily available. To address this limitation, a maximum-likelihood estimation approach for an NN-based response function estimation (NRFE) is used to obtain the functional forms of the process mean and standard deviation. While the estimation results of most existing NN-based approaches depend primarily on their transfer functions, this approach often requires a screening procedure for various transfer functions. In this study, the proposed NRFE identifies a new screening procedure to obtain the best transfer function in an NN structure using a desirability function family while determining its associated weight parameters. A statistical simulation was performed to evaluate the efficiency of the proposed NRFE method. In this particular simulation, the proposed NRFE method provided significantly better results than conventional RSM. Finally, a numerical example is used for validating the proposed method.

Modeling and Optimization of Vibration Amplitude in Turning of Ti-6Al-4V Using Response Surface Methodology

2012 ◽  
Vol 217-219 ◽  
pp. 1567-1570
Author(s):  
A.K.M. Nurul Amin ◽  
Muammer Din Arif ◽  
Syidatul Akma Sulaiman
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Vibration Amplitude ◽  
Desirability Function ◽  
Cutting Speed ◽  
Vibration Sensor ◽  
Experimental Investigations ◽  
Depth Of Cut ◽  
Work Piece ◽  
Inferior Surface

Chatter is detrimental to turning operations and leads to inferior surface topography, reduced productivity, dimensional accuracy, and shortened tool life. Avoidance of chatter has mostly been through reliance on heuristics such as: limiting material removal rates or selecting low spindle speeds and shallow depth of cuts. But, modern industries demand increased output and not steady operational limits. Various research efforts have therefore focused on developing mathematical models for chatter formation. However, as yet there is no existent model that meets all experimental verification. This research employed a novel technique based on the synergy of statistical modeling and experimental investigations in order to develop an effective empirical mathematical model for chatter amplitude and to subsequently find optimal machining conditions. Ti-6Al-4V, Titanium alloy, was used as the work-piece due to its increased popularity in applications related to aerospace, automotive, nuclear, medical, marine etc. A sequence of 15 experimental runs was conducted based on a small Central Composite Design (CCD) model in Response Surface Methodology (RSM). The primary (independent) parameters were: cutting speed, feed, and depth of cut. The tool overhang was kept constant at 70 mm. An engine lathe (Harrison M390) was employed for turning purposes. The data acquisition system comprised a vibration sensor (accelerometer) and a signal conditioning unit. The resultant vibrations were analyzed using the DASYLab 5.6 software. The best model was found to be quadratic which had a confidence level of 95% (ANOVA) and insignificant Lack of Fit (LOF) in Fit and Summary analyses. Desirability Function (DF) approach predicted minimum vibration amplitude of 0.0276 Volts and overlay plots identified two preferred machining regimes for optimal vibration amplitude.

Multi-Performance Optimization in Friction Stir Welding of Aluminum Alloy Using Response Surface Methodology

2018 ◽  
pp. 240-263
Author(s):  
Rajat Gupta ◽  
Kamal Kumar ◽  
Neeraj Sharma
Keyword(s):  
Response Surface Methodology ◽  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Response Surface ◽  
Process Parameters ◽  
Performance Optimization ◽  
Desirability Function ◽  
High Carbon Steel ◽  
Friction Stir ◽  
Tool Shoulder

This chapter presents the friction stir welding (FSW) of aluminum alloy AA-5083-O using vertical milling machine. In present FSW experimentation, effects of different process parameter namely tool rotation speed, welding speed, tool geometry, and tool shoulder diameter have been determined on welding quality of two pieces of AA-5083-O using response surface methodology (RSM). The optimal sets of process parameters have been determined for weld quality characteristics namely tensile strength (UTS) and percentage elongation (%EL). In present experimentations, a specially designed tool made of high carbon steel with different shoulder diameters (15mm, 17.5mm, and 20 mm) having constant pin length (6 mm) were used for FSW of two pieces of aluminum alloy. The ANOVA and pooled ANOVA were used to study the effect of FSW parameters on UTS and %EL. Multi response optimization has been carried out using desirability function in conjunction with RSM to obtain the optimal setting of process parameters for higher UTS and lower %EL.

Optimization of Automatic TIG Welding Parameters of AISI 304L ASS Welds Using Response Surface Methodology

2021 ◽  
Vol 406 ◽  
pp. 319-333
Author(s):  
Tahar Saadi ◽  
Mohamed Farid Benlamnouar ◽  
Nabil Bensaid ◽  
Amar Boutaghane ◽  
Mohamed Amine Soualili ◽  
...  
Keyword(s):  
Tensile Strength ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Gas Flow ◽  
Desirability Function ◽  
Welding Parameters ◽  
Automatic Welding ◽  
Aisi 304L ◽  
Rsm Optimization ◽  
Single Response

The present study, aims to investigate, under welding parameters of current, voltage and gas flow, the effects of welding parameters on tensile strength of AISI 304L ASS welds using response surface methodology (RSM). The RSM and variance analysis (ANOVA) were used to check the validity of quadratic regression model and to determine the significant parameter affecting tensile strength of welds. Hence, ANOVA clearly revealed that the contribution of each factor is 71.40% of voltage, 19.2% of current and 8.30% of gas flow. It was found that combined contributions of welding parameters contributes significantly to the metallurgical changes by varying fractions, morphology and grain size of metallic compounds. Furthermore, the optimum automatic welding conditions lead to produce the best possible weld quality in the range of our experiment using desirability function approach for single response of RSM optimization factors, in which it concluded that tensile strength components are influenced principally by voltage. Finally, the ranges for best welding conditions are proposed for serial industrial production.

Bioremediation Condition Optimization of Hydrocarbon in Soil Using Response Surface Methodology by Microbial Consortium KL9-1

2012 ◽  
Vol 518-523 ◽  
pp. 2073-2078 ◽  
Author(s):  
Qi You Liu ◽  
Yun Bo Zhang ◽  
Dong Feng Zhao ◽  
Chao Cheng Zhao
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Microbial Consortium ◽  
Ph Value ◽  
Removal Rate ◽  
Desirability Function ◽  
Nitrogen And Phosphorus ◽  
Predictive Values ◽  
Independent Variables ◽  
Condition Optimization

A response surface methodology was applied to optimize the bioremediation condition of hydrocarbon in soil by microbial consortium KL9-1. A four-level Box-Behnken factorial design was employed to study the relationship of independent variables and dependent variable by applying pH value, inoculation amount of microbial consortium KL9-1, ratio of nitrogen and phosphorus (N/P ) and surfactant (SDBS) concentration as independent variables (factors) and crude oil removal rate as dependent variable (response). Then the statistically significant model was obtained and numerical optimization based on desirability function was carried out for pH 7.0, inoculation amount 50.0 mL, N/P 2: 1 and SDBS concentration 4.0 g, and the hydrocarbon removal rate reached as high as 52.58%. The predictive values showed good agreement with the experimental values under the optimization conditions, by standard variance <1.3%. It showed that the optimal result was reliable.

Investigating the quality characteristics of Al5052/SiC metal matrix composites machined by CO2 laser curve cutting

2015 ◽  
Vol 232 (1) ◽  
pp. 3-19
Author(s):  
Vikas Sharma ◽  
Vinod Kumar
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Metal Matrix ◽  
Desirability Function ◽  
Cutting Speed ◽  
Optical Microscope ◽  
Quality Characteristics ◽  
X Ray ◽  
Sic Particles ◽  
Quality Aspects

This article explores the quality characteristics of laser curve cutting of metal matrix aluminium 5052 alloy reinforced with SiC particles. These alloys are extensively used in aerospace industries due to their unique mechanical properties. The response surface methodology has been used to design the mathematical models with respect to input and output characteristics parameters. The desirability function approach has been used to optimize the input parameters like cutting speed, laser power, stand-off distance, nozzle diameter, nitrogen gas pressure, percentage of reinforced SiC particles, arc radius by considering multiple-performance characteristics. The various quality aspects of machined specimens were analysed using optical microscope, scanning electron microscopy, X-ray diffraction and energy-dispersive X-ray analysis techniques. The response surface methodology predicted models were validated by performing various confirmatory experiments. The percentage of error for the dross height, kerf taper and kerf width was found to be 4.62%, 6.55% and 5.04 % which signifies that predicted model is adequate.

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