Multiple Response Optimization Research Articles

Wheat Bran Pretreatment by Room Temperature Ionic Liquid-Water Mixture: Optimization of Process Conditions by PLS-Surface Response Design.

Room Temperature Ionic Liquids (RTILs) pretreatment are well-recognized to improve the enzymatic production of platform molecules such as sugar monomers from lignocellulosic biomass (LCB). The conditions for implementing this key step requires henceforth optimization to reach a satisfactory compromise between energy saving, required RTIL amount and hydrolysis yields. Wheat bran (WB) and destarched wheat bran (DWB), which constitute relevant sugar-rich feedstocks were selected for this present study. Pretreatments of these two distinct biomasses with various 1-ethyl-3-methylimidazolium acetate ([C2mim][OAc])-water mixtures prior to hydrolysis catalyzed by hemicellulolytic cocktail (Cellic CTec2) were finely investigated. The main operating conditions such as pretreatment temperature (25–150°C), time (40–180 min), WB and DWB loading (2–5% w/v) and concentration of [C2mim][OAc] in water [10–100% (v/v)] were screened through glucose and xylose yields and then optimized through a Partial Least Square (PLS)—Second Order Design. In an innovative way, the PLS results showed that the four factors and their interactions could be well-fitted by a second-order model (p < 0.05). The quadratic PLS models were used to predict optimal pretreatment conditions. Thus, maximum glucose (83%) and xylose (95%) yields were obtained from enzymatic hydrolysis of WB pretreated at 150°C for 40 min with 10% of [C2mim][OAc] in water and 5% of WB loading. For DWB, maximum glucose (100%) and xylose (57%) yields were achieved for pretreatment temperatures of 150°C and 25°C, respectively. The required duration was still 40 min, with 20% of [C2mim][OAc] in water and a 5% DWB loading. Then, Multiple Response Optimization (MRO) performed by Nelder-Mead Simplex Method displayed sugar yields similar to those obtained by individual PLS optimization. This complete statistical study confirmed that the established models were appropriate to predict the sugar yields achieved after different pretreatment conditions from WB and DWB biomasses. Finally, Scanning Electron microscopy (SEM) studies allowed us to establish clearer link between structural changes induced by pretreatment and the best enzymatic performances obtained.

Optimizing the valorization of industrial by-products for the induction healing of asphalt mixtures

Self-healing within asphalt pavements is the process whereby road cracks can be repaired automatically when thermal and mechanical conditions are met. To accelerate and improve this healing process, metal particles are added to asphalt mixtures. However, this approach is costly both in economic and environmental terms due to the use of virgin metallic particles. So, even though the self-healing of asphalt mixtures has been widely addressed in experimental terms over the years, there is a lack of research aimed at modelling this phenomenon, especially with the purpose of optimizing the use of metal particles through the valorization of industrial by-products. As such, the goal of this study was to develop a statistical methodology to model the healing capacity of asphalt concrete mixtures (AC-16) from the characteristics of the metal particles added and the time and intensity used for magnetic induction. Five metal particles were used as heating inductors, including four types of industrial by-products aimed at transforming waste products into material for use in the road sector. The proposed approach consisted of a combination of cluster algorithms, multiple regression analysis and response optimization, which were applied to model laboratory data obtained after testing asphalt concrete mixtures containing these inductors. The results proved the accuracy of the statistical methods used to reproduce the experimental behaviour of the asphalt mixtures, which enabled the authors to determine the optimal amount of industrial by-products and time needed to make the self-healing process as efficient as possible.

Viability of Lactobacillus casei ATCC 393 and properties in Andean blackberry suspensions with probiotic and prebiotic characteristics

Food industry has grown in recent years mainly due to the supply of functional foods with probiotics and prebiotics. The viability of Lactobacillus casei and the properties of blackberry suspensions added with inulin were evaluated using a central design composed of the following factors: mass fraction of total solid blackberry concentrate (XTSBC) (0.103-0.120), [McFarland] (6-10), inulin (2.78-3.68%), and mass blackberry concentrate (BC)/mass inoculum (R) (5-20); and the dependent variables: moisture (M), pH, acidity, °Brix, total solids suspension (TSS), viscosity (μ), zeta potential (z), and Log CFU/g. Results of the experimental optimization of multiple responses were: XTSBC (0.103), [McFarland] (10), inulin (3.12%w/w), and R (18.5); being the dependent variables: M=86.7±0.0%, pH=2.74±0.01, acidity=3.1±0.0%, °Brix=11.0±0.1%, TSS=13.3±0.0%, µ=1288.0±20.1 cP, z=-13.3±0.3 mV and Log CFU/g=7.8±0.5. The probiotic microorganism Lb. casei ATCC 393 presents an adaptability in suspensions based on BC and inulin, which guarantee its viability and a possible use for obtaining an innovative spray-dried product.

Multiple response optimisation of the aqueous extraction of high quality ulvan from Ulva ohnoi

Response surface methodology was used to determine the effects of the solvent pH, the temperature of extraction, and the duration of extraction on the yield, purity, molecular weight, viscosity, and total metal content of ulvan extracted from U. ohnoi. Quadratic models identified the optimised responses for yield (72.6%) purity (68.2% w/w), molecular weight (92.9 kDa), viscosity (491.1 s), and total metal content (~0 mg/kg). These responses occurred between a solvent pH of 2.2–4.0, an extraction temperature of 61.3–90.0 °C, and an extraction duration of 55.0–90.0 min. The overall desirability of the ulvan product was determined using a Derringer's desirability function, which identified a solvent pH of 2.92, an extraction temperature of 90 °C, and an extraction duration of 90 min. These extraction conditions minimise the requirement for downstream purification and are suitable for upscaling the extraction of a high quality ulvan product.

Multiple response optimization: Analysis of genetic programming for symbolic regression and assessment of desirability functions

Multiple responses optimization (MRO) consists in the search for the best settings in an problem with conflicting responses. MRO is performed following the steps: experimental design; experimental data gathering; mathematical models building; statistical validation of models; agglutination of the models responses in only one function to be optimized; optimization of agglutinated function; experimental validation of the best conditions. This work selected two MRO cases from literature aiming to compare two methods of mathematical models building and two agglutinating functions to assess the best one among the four possible combinations. The methods used in mathematical models building were the ordinary least squares performed in Minitab (v. 17) and genetic programming performed in Eureqa Formulize (v. 1.24.0). The assessment of the best method for building mathematical models was performed using the Akaike Information Criterion. The responses agglutination were performed using the desirability and modified desirability functions. In all MRO cases, the optimization step was performed by generalized reduced gradient method on Microsoft ExcelTM software. The average percentage distance between predicted and experimental results was used to both assess the best agglutination function and verify the effect of the method used in the building of the mathematical models about its fitness to estimate the best condition close to that one obtained on experimental validation step. The obtained results suggest as the better strategy for multiple responses optimization the use, jointly, of genetic programming to mathematical models building and the modified desirability function to responses agglutination.

Optimization of Continuous Electro-Fenton and Photo electro-Fenton Processes to Treat Iraqi Oilfield Produced Water Using Surface Response Methodology

Considerable amounts of produced water (PW) is usually accompanied with the production of oil. Most countries with oilfields are generally water stressed countries. This study proposed electro-Fenton (EF) as alternative for the degradation of organic pollutants in PW. Continuous electro-Fenton processes was investigated using dimensionally stable anode Ti-RuO2/IrO2 and activated carbon fiber felt (ACFF) cathode. The effect of crucial process variables, namely, initial ferrous ions concentration (0.1-0.5 mM), current intensity (100-500 mA), and residence time (22-81 minutes) on the removal efficiency of COD was studied using contour and response surface plots. The experimental results were analyzed by analysis of variance (ANOVA). Multiple response optimization for continuous EF experiments reveals that at optimum conditions (initial ferrous ion concentration of 0.306 mM, current intensity of 156.6 mA, and residence time of 81.0 min) the COD removal efficiency was 73.33% and electrical energy consumption was 0.901 kWh/kg COD. Improvement of continuous EF process was investigated using UVA irradiation (Photoelectron-Fenton). It was found that COD removal efficiency for continuous EF was increased to 81.1% and 86.0% when using one and two UVA lamps (3 Watt each) respectively. It is concluded that EF is an effective process for treating produced water and further improvement can be achieved by photo assisting the process.

Single and Multi-Response Optimization and Validation of Mechanical Properties in Dissimilar Friction Stir Welded AA2219-T87 and AA7075-T73 Alloys Using T-GRA

A study of dissimilar friction stir welding of two aerospace aluminum alloys AA2219-T87 and AA7075-T73 is carried out with five friction stir welding (FSW) parameters namely rotational speed, welding speed, tool profile, tilt angle and position of material. The welding was performed as per the L16 Orthogonal array formed by Taguchi mixed factorial design matrix. Taguchi and Grey Relational Analysis was used for single and multiple response optimizations respectively for optimizing tensile strength, yield strength and percentage elongation. Tensile testing was carried out and analysis was done for getting the optimum setting parameters level. Then the results for L16 was ranked and the optimized parameter set was predicted to be A1B1C2D1E1. Further experimental validation weld was performed and the results correlate with the predicted value. A joint efficiency of 71.8% which is even higher than the predicted values was obtained. Microstructural and hardness studies were performed on the validated welds. Fractography was done to understand the mode of failure occurs in the weld region and most fracture occurs with an offset towards AA2219 side. The presence of intermetallic is found in EDS analysis and XRD further validates the intermetallic composition.

A synergic multivariate statistical process control framework for monitoring, diagnosis, and adjustment of multiple response abrasive machining processes

In various abrasive machining processes, output quality is defined in terms of multiple critical responses and their deviations from target values. These multiple responses are often interacting and changing the process conditions for improving or controlling one response may deteriorate the quality of another. Thus, there is a need to simultaneously consider all responses and recommend a trade-off operating condition for process control and optimisation. Two important fields that consider simultaneous control and optimisation of multiple responses are the multivariate statistical process control (MSPC) and multiple response optimisation (MRO). Although various MSPC and MRO approaches have been proposed by researchers, there is limited prior research on the integration of MSPC and MRO approaches to ensure the stability and process capability. In this study, a synergic MSPC and MRO approach is proposed based on Mahalanobis-Taguchi system and nonlinear optimisation to ensure the stability and capability of the abrasive machining process.

Modification of pineapple starch from stem and rhizome using multiple desired response optimization and its characterization

Modification of pineapple starch from stem and rhizome using multiple desired response optimization and its characterization

An evolutionary-based hybrid approach for simultaneous optimisation of multiple responses in self-propelled rotary turning process

An evolutionary-based hybrid approach for simultaneous optimisation of multiple responses in self-propelled rotary turning process

An evolutionary-based hybrid approach for simultaneous optimisation of multiple responses in self-propelled rotary turning process

This investigation focused on optimisation of self-propelled rotary turning process conditions through an integrated multi-objective optimisation approach. The machining experiments are conducted for machining hardened EN24 (SAE4340) steel with TiN-coated tungsten carbide rotary insert. Two important machining responses such as surface roughness and metal removal rate were measured by varying the machining variables such as depth of cut, inclination angle of the rotary tool, feed rate and spindle speed. To deal with the simultaneous optimisation of two conflicting process characteristics governed by four process variables, an evolutionary-based hybrid optimisation approach is proposed. The method integrated gray relational analysis (GRA) for deriving the overall process performance index, response surface methodology (RSM) to analyse the significance and variation process variables and genetic algorithm (GA) to derive the optimal values of the process variables which will give maximum process performance. The derived optimal machining conditions were confirmed through validation machining experiments. [Submitted 15 August 2017; Accepted 16 March 2018]

Application Of WPCA &amp; CQL Methods In The Optimization Of Mutiple Responses

Abstract In this work, an investigational study has been carried out to optimize the multiple responses of the material removal rate (MRR) and surface roughness parameters (Ra, Rq and Rz). The investigating experiments were done with the CNC turret machine according to the standard Taguchi's L9 orthogonal exhibit plan. Cutting velocity, speed and depth of cut were taken as the three control factors at three distinct levels to turn the AA7075 steel. The weighted principal component analysis (WPCA) along with combined-quality-loss (CQL) idea is utilized for the optimization of multiple responses and after that CQL has been enhanced utilizing Taguchi technique. From the Taguchi outcomes, the ideal mix of cutting parameters for greatest material expulsion rate and least surface hardness is acquired at 1500 rpm of speed, 0.2 mm/rev of speed and 0.5 mm of depth of cut. The ANOVA outcomes demonstrate that the feed has superior impact in affecting the multiple responses.

An experimental investigation on the formation of burrs in micro milling of Zr-based amorphous bulk metallic glass

Fabrication of precision micro components demands part quality having burr free edges and acceptable surface finish to comply with its requirements of proper functionality. Machining conditions influence process performances in different ways. The present study examines the effect of cutting parameters like cutting speed, feed per tooth (feed rate) and axial depth of cut on the formation of top burrs and surface finish produced in micro-channels made through full immersion micro end milling process carried out on a workpiece made of amorphous bulk metallic glass (BMG). The process modelling is done based on response surface methodology and parametric analysis is performed to examine the influence of the process parameters on the process responses, namely the burr size formed in up-milling and down-milling sides and the surface roughness of the bottom surface of the micro-channels. The study also undertakes multiple response optimisation analysis that determines the optimal values of the cutting parameters that yield lower burr sizes and lower surface roughness.

An experimental investigation on the formation of burrs in micro milling of Zr-based amorphous bulk metallic glass

Fabrication of precision micro components demands part quality having burr free edges and acceptable surface finish to comply with its requirements of proper functionality. Machining conditions influence process performances in different ways. The present study examines the effect of cutting parameters like cutting speed, feed per tooth (feed rate) and axial depth of cut on the formation of top burrs and surface finish produced in micro-channels made through full immersion micro end milling process carried out on a workpiece made of amorphous bulk metallic glass (BMG). The process modelling is done based on response surface methodology and parametric analysis is performed to examine the influence of the process parameters on the process responses, namely the burr size formed in up-milling and down-milling sides and the surface roughness of the bottom surface of the micro-channels. The study also undertakes multiple response optimisation analysis that determines the optimal values of the cutting parameters that yield lower burr sizes and lower surface roughness.

Experimental study on electrical discharge machining of Inconel using RSM and NSGA optimization technique

This work presents the optimization while machining of Inconel superalloy as base material and copper as the electrode using ZNC electrical discharge machine with positive polarity. Formation of complex shapes by this material alloy with reasonable speed and surface finish is not an easy task with the traditional machining. Moreover, the edge profile (which is circular in this work) formed by the energy released by the spark does not result with expected profile. After machining, it is observed that the edge of the profile gets disintegrated because of which the material will lose its fatigue strength. Central composite design of response surface methodology (RSM) is used with six blocks for the analysis. For experimentation, peak voltage (Vin), peak current (Iin) and pulse on-time (Ton) are taken as key input parameters based on the literature support. MRR: metal removal rate, SR: surface roughness, TWR: tool wear rate and edge disintegration (overcut OC) have been taken as performance measures. Then, the RSM is employed over the input parameters to get the suitable mathematical model, and the outcome reveals that the suggested model helps to predict the factor values within the limit of investigation. At 95% CI, the results show that the input current value has the greatest influence on the disintegration factor, followed by the input voltage and then spark-on on-time the MRR and OC of measurements. It is observed that almost near optimal is observed at voltage level of 1 mV, current value of 17 mA and on-time of 850 μs. The desirability is 0.489 for MRR, 0.703 for TWR, 0.649 for SR and 0.649 for OC at the optimal setting of the parameters. Finally, non-sorting genetic algorithm is utilized for optimization of multiple responses.

An Improved Robust Regression Model for Response Surface Methodology

In production, manufacturing and several other allied industries, appropriate tool is applied in the analysis of data in order to enhance the opportunity for product and process optimization. A statistical tool that has successfully been used to achieve this goal is Response Surface Methodology (RSM). A recent trend in the modeling phase of RSM involves the use of semi-parametric regression models which are hybrids of the Ordinary Least Squares (OLS) and the Local Linear Regression (LLR) models. In this paper, we propose a modification in the current structure of the semi-parametric Model Robust Regression 2 (MRR2) with a view to improving its sensitivity to local trends and patterns in data. The proposed model is applied to two multiple response optimization problems from the literature. The results of goodness-of-fits and optimal solutions confirm that the proposed model performs better than the MRR2.

PCA based hybrid Taguchi philosophy for optimization of multiple responses in EDM

The present study is aimed at a multi-response optimization problem by applying Principal Component Analysis (PCA) combined with Taguchi method. The investigation has been carried out through a case study in Electric Discharge Machining (EDM) of D2 steel by using copper, brass and Direct Metal Laser Sintered (DMLS) electrode produced by direct metal laser sintering using Directmetal20. The research work has been carried out to evaluate the best parametric combination which could fulfill multiple responses like lower Tool Wear Rate (TWR), higher Material Removal Rate (MRR) and lower Surface Roughness (Ra). Unlike the use of single-objective optimization problem in traditional Taguchi method, a hybrid Taguchi method has been developed in combination with PCA to solve multi-objective problem. Taguchi method assumes that the quality characteristics should be uncorrelated or independent which is not always fulfilled in actual condition. PCA is applied to remove response correlation and to calculate independent (uncorrelated) quality indices known as principal components. These principal components combined with weighted principal component analysis (WPCA) are used to calculate overall quality index denoted as Multi-response Performance Index (MPI). This investigation combines WPCA and Taguchi method for forecasting optimal setting. The predicted result by this method was validated through confirmatory test proving the efficacy of the process. Out of five input process parameters considered, tool electrode has been found to be the most significant factors through the Analysis of Variance (ANOVA).

Optimization of spray-drying process for concentrated orange juice

Response surface methodology was used to optimize spray-drying process for concentrated orange juice. Independent variables were: inlet air temperature (130–170 ºC) and maltodextrin 12DE content (60–75% wet basis (wb)). Responses variables were powder yield, moisture, and ascorbic acid retention. Moisture content was negatively affected by inlet air temperature, while ascorbic acid retention was directly related. Powder yield and ascorbic acid retention increased with the rise in maltodextrin content, while moisture content was negatively affected by maltodextrin content. Multiple response optimisation indicated that an inlet air temperature of 155 ºC and maltodextrin content of 74% wb were predicted to provide 77% powder yield, 3.7% wb moisture content, and 89% ascorbic acid retention.

Multiple response optimizations to improve performance and reduce emissions of Argemone Mexicana biodiesel-diesel blends in a VCR engine

The characteristics of multi-fuel VCR engine fuelled with Argemone Mexicana methyl ester, prepared by two step trans-esterification process and its diesel blends (20%, 40%, 60% and 100%) were evaluated with variation of load (3–12 kg) and compression ratio (16–18). In the current analysis engine load, compression ratio and biodiesel blends were taken as input parameters. Response Surface Methodology of Full Factorial Design was used for modelling and analyzing the response parameters with Minitab-14.0 software. Data regression, significance analysis and individual model coefficients were studied for the developed models and presented for validation of the model. Multi objective optimization was carried out for the responses by using the desirability function. Confirmation experiments were executed for validation of optimization results by setting input parameters (Load = 9.8 kg, CR = 18.0, Blend = 20%). Output responses from the mathematical modeling such as BTE 26.77%, BSFC 0.284 kg/kW h, CO 0.0059%, HC 114.84 ppm, NOx 905.6 ppm respectively were obtained using D-optimal test with composites desirability of 0.97009. The predictions of RSM results were obtained in concurrence with the experimental ones, with errors less than 5% excepting for CO model.

Estudo comparativo entre métodos de otimização de problemas com múltiplas respostas

Este trabalho tem o objetivo de avaliar o desempenho conjunto das funções aglutinadoras Desirability e Desirability Modificada, utilizadas com dois métodos de busca por melhores resultados, o Gradiente Reduzido Generalizado e a metaheurística Recozimento Simulado. O desempenho das possíveis combinações foi avaliado por meio da Distância Absoluta e do Desvio Médio Percentual. Para a implementação dos métodos de otimização foram selecionados, na literatura, três casos de estudo envolvendo problemas de múltipla resposta. Os resultados obtidos para os casos de estudo selecionados indicam um melhor desempenho da função aglutinadora Desireability Modificada, independente do método de busca utilizado, para a otimização simultânea de múltiplas respostas, especialmente quando essas respostas são modeladas por equações contendo termos quadráticos, independentemente do número de termos, do tipo de respostas e do número de variáveis que possam conter. A metodologia e os resultados apresentados neste trabalho visam colaborar com a pesquisa, o desenvolvimento e a avaliação de técnicas avançadas para a otimização multirrespostas que são relevantes na busca de especificações para o aumento da eficiência em processos industriais.