Multiple Response Optimization Research Articles

Multiple responses optimization in the development of a headspace gas chromatography method for the determination of residual solvents in pharmaceuticals

An efficient generic static headspace gas chromatography (HSGC) method was developed, optimized and validated for the routine determination of several residual solvents (RS) in drug substance, using a strategy with two sets of calibration. Dimethylsulfoxide (DMSO) was selected as the sample diluent and internal standards were used to minimize signal variations due to the preparative step. A gas chromatograph from Agilent Model 6890 equipped with flame ionization detector (FID) and a DB-624 (30m×0.53mm i.d., 3.00µm film thickness) column was used. The inlet split ratio was 5:1. The influencing factors in the chromatographic separation of the analytes were determined through a fractional factorial experimental design. Significant variables: the initial temperature (IT), the final temperature (FT) of the oven and the carrier gas flow rate (F) were optimized using a central composite design. Response transformation and desirability function were applied to find out the optimal combination of the chromatographic variables to achieve an adequate resolution of the analytes and short analysis time. These conditions were 30°C for IT, 158°C for FT and 1.90mL/min for F. The method was proven to be accurate, linear in a wide range and very sensitive for the analyzed solvents through a comprehensive validation according to the ICH guidelines.

Effects of homogenization process parameters on physicochemical properties of astaxanthin nanodispersions prepared using a solvent-diffusion technique.

Nanodispersion systems allow incorporation of lipophilic bioactives, such as astaxanthin (a fat soluble carotenoid) into aqueous systems, which can improve their solubility, bioavailability, and stability, and widen their uses in water-based pharmaceutical and food products. In this study, response surface methodology was used to investigate the influences of homogenization time (0.5–20 minutes) and speed (1,000–9,000 rpm) in the formation of astaxanthin nanodispersions via the solvent-diffusion process. The product was characterized for particle size and astaxanthin concentration using laser diffraction particle size analysis and high performance liquid chromatography, respectively. Relatively high determination coefficients (ranging from 0.896 to 0.969) were obtained for all suggested polynomial regression models. The overall optimal homogenization conditions were determined by multiple response optimization analysis to be 6,000 rpm for 7 minutes. In vitro cellular uptake of astaxanthin from the suggested individual and multiple optimized astaxanthin nanodispersions was also evaluated. The cellular uptake of astaxanthin was found to be considerably increased (by more than five times) as it became incorporated into optimum nanodispersion systems. The lack of a significant difference between predicted and experimental values confirms the suitability of the regression equations connecting the response variables studied to the independent parameters.

The optimisation of bi-signal multi-response systems with stochastic target profiles using goal programming

There are many real world applications consisting of finding optimal design variables that yields desirable values for stochastic responses. Finding the levels of controllable factors is an important process control stage to achieve desired specification of the output. On other hand, finding the optimal setting will be more important when there are more than one signal in dynamic systems. Also difficulties may arise particularly for multiple responses when there are stochastic target profiles in each level of signal factor. In this regard, linear regression models for response mean, variance and covariance of multiple responses in each level of signals are obtained. Then optimal settings of controllable factors are extracted by the goal programming applied in an example from the literature. Then optimal setting of controllable factors is determined by stochastic optimisation. Also, the sensitivity analysis is performed to show efficiency of the proposed approach in case of existing stochastic target profiles in multiple response optimisation.

Simultaneous Optimization of Multiple Quality Characteristics In Turning EN-31Steel

This paper optimizes multiple characteristics (surface roughness and tool wear) in turning of EN-31 steel using tungsten carbide inserts. Five controllable factors of the turning process viz. cutting velocity, feed rate, depth of cut, tool nose radius and different concentrations of solid-liquid lubricants, were studied. A factorial composite design (25 + 8) was used for experimentation. Response surface methodology was used for modelling the responses. Desirability function was used for simultaneous optimization of multiple quality responses.

Multiple Response Optimization of Wire EDM on Aluminium HE30 by using Grey Relational Analysis

Wire Electrical Discharge Machining (WEDM) allowed success in the production of newer materials, especially for the aerospace and medical industries. Using WEDM technology, complicated cuts can be made through difficult-to-machine electrically conductive components. The high degree of the obtainable accuracy and the fine surface quality make WEDM valuable. WEDM is so complex in nature that the selection of appropriate input parameters is not possible by the trial-and-error method. The selection of machining parameters in any machining process significantly affects production rate, product quality and production cost of a finished component. WEDM process involves a large number of variables that affect its performance. In this paper, an attempt is made to study the effect of various process parameters such as pulse on time, pulse off time, wire tension, current, upper flush and lower flush for Aluminium HE30. The experimentation has been completed with the help of Taguchi grey relational analysis. Taguchi grey relational analysis is used to optimize the process parameters on multiple performance characteristics such as Material removal rate, surface finish. The experimental result analysis showed that the combination of pulse on time, pulse off time, wire tension, lower flush, wire tension and upper flush is essential to achieve maximization of material removal rate and minimization of surface roughness and kerf width.

IQF 굴(Crassostrea gigas) 복합엑스분의 추출 및 풍미개선

To develop an effective use for poor-quality individually quick-frozen (IQF) oysters Crassostrea gigas stored for a long period, the extract conditions, quality characteristics, and optimum reaction flavoring (RF) conditions of a complex extract from these IQF oysters were investigated. The moisture, pH, and volatile basic nitrogen contents of IQF oysters stored for 18 months (18M-IQFO) were 77.9%, 6.32, and 17.9 mg/100 g, respectively. Three different kinds of extract were prepared from 18M-IQFO: a hot-water extract (HE), scrap enzymatic hydrolysate (EH), and complex extract (CE). The respective extracts contained 5.5, 8.6, and 6.6% crude protein and 281.7, 366.0, and 343.0 mg/100 g amino nitrogen, and had 811, 359, and 1,170 mL/kg extraction yields. The CE was superior to the traditional HE in terms of the extraction yield, amino-nitrogen content, and organoleptic qualities, except for the odor. To improve flavor via the Maillard reaction, the reaction system used to produce a desirable flavor comprised CE (Brix 30°), 0.4 M glucose, 0.4 M glycine, and 0.4 M cysteine solution (4:2:1:1, v/v). The reaction time and pH were the independent variables, and the sensory scores for baked potato odor, masking shellfish odor, and boiled meat odor were the dependent variables. The surface response methodology (RSM) analysis of the multiple responses optimization gave a reaction time of 120.6 minutes and pH 7.33 at 120°C. The reaction improved the flavor of CE considerably, as compared to that of the unreacted extract.

High Voltage Electrical Discharges, Pulsed Electric Field, and Ultrasound Assisted Extraction of Protein and Phenolic Compounds from Olive Kernel

The study was aimed at improvement of recovery of intracellular valuable compounds from olive kernels (Olea europaea). High voltage electrical discharges (HVED), pulsed electric field (PEF), and ultrasound (US) were applied as pretreatments before extraction. The influence of HVED energy input (0–109 kJ/kg), pH (2.5–12), and ethanol (0–50 %) on the efficiency of the extraction was studied. The extracts obtained immediately after pretreatments were analyzed for total phenolic compounds, antioxidant activity, proteins, and pigments. HVED treatment was demonstrated to be more effective than ultrasound and pulsed electric field in terms of energy input and effective treatment time to extract phenolic compounds and proteins. Moreover, the application of HVED increased significantly the aqueous and hydro-ethanolic extractions of total phenolic content (TPC), and proteins of the recovered extracts when energy input was augmented. pH and ethanol percentage had also a significant influence in TPC, protein, and antioxidant recovery. The interesting observation is that pH 2.5 resulted in the optimum conditions to recover TPC and antioxidant capacity. However, the higher protein content was found when pH 12 was used. Multiple response optimization showed that TPC, content of proteins, and antioxidant capacity (Trolox equivalent antioxidant capacity (TEAC) and 1,1-diphenyl-2-picrylhydrazyl (DPPH) values) of the sample were further maximized after HVED pretreatment at energy input 66 kJ/kg at pH 2.5 followed by extraction in 49 % ethanol. TPC, content of proteins, TEAC, and DPPH values under such conditions of extraction were 626.6 mg GAE/L, 0.225 mg/mL, 9.80 mM TE, and 7.61 mM TE, respectively.

A comparison of the different multiple response optimization techniques for turning operation of AISI O1 tool steel

In this article, the effect of several process parameters such as tool nose radius, speed,feed and depth of cut on the machining performance of turning operation has beenstudied using AISI O1 tool steel as a work material. The machining characteristicsthat are being studied are material removal rate (MRR) and surface roughness (SR)of machined surface. Taguchi method is utilized for single response optimization. Formulti-response optimization, weighted signal-to-noise ratio (WSN), grey relationalanalysis (GRA), utility concept and technique for order preference by similarity toideal solution (TOPSIS) method have been utilized and their performance is evaluated.WSN method has been found to produce best results for multi-response optimizationfor this study.

Multi-response optimization and modeling of trim cut WEDM operation of commercially pure titanium (CPTi) considering multiple user's preferences

In this research work, development of a multi response optimization technique has been undertaken, using traditional utility method in conjunction with the weight assignment concept (for multiple customer's priorities) in trim cut wire electrical discharge machining (WEDM). Pure titanium has been selected as work material for experimentation. The effect of key process parameters such a wire type (zinc coated and uncoated brass wire), pulse on time (TON), pulse off time (TOFF), peak current (IP), wire feed (WF), servo voltage (SV) and wire offset (WOFF) were investigated on material removal rate (MRR), surface roughness and wire weight consumption (eroded weight of wire after machining) in finish cut WEDM operation. Two different types of wire electrodes were taken for experimental research (uncoated, zinc coated). Further, the variation of the MRR was modeled semi-empirically through dimensional analysis. The developed model is mechanistic, as it can be used by the machinists to predict the MRR over a wide range of input parameters. The optimization of multiple responses has been done for satisfying the priorities of multiple users, in contrast to the traditional multi-response techniques where the optimized process setting is realized without giving any attention to the priorities of different users.

Robust Estimation of Multi-response Surfaces Considering Correlation Structure

Response surfaces express the behavior of responses and can be used for both single and multi-response problems. A common approach to estimate a response surface using experimental results is the ordinary least squares (OLS) method. Since OLS is very sensitive to outliers, some robust approaches have been discussed in the literature. Although there are many methods available in the literature for multiple response optimizations, there are a few studies in model building especially robust models. Assuming correlated responses, in this paper, a robust coefficient estimation method is proposed for multi response problem based on M-estimators. In order to illustrate the performance of the proposed procedure, a contaminated experimental design using a numerical example available in the literature with some modifications is used. Both the classical multivariate least squares method and the proposed robust multivariate approach are used to estimate regression coefficients of multi-response surfaces based on this example. Moreover, a comparison of the proposed robust multi response surface (RMRS) approach with separate robust estimation of single response show that the proposed approach is more efficient.

An Optimality Probability Index for Correlated Multiple Responses

In the real problems, there are many cases which have correlated quality characteristics so multiple response optimization can be more realistic if we can consider correlation structure of responses. In this study we propose a new method which uses multivariate normal probability to find the optimal treatment in an experimental design. Moreover, a heuristic method is used to find better factors’ level in all possible combinations in the designs with large number of controllable factors and their levels. Some simulated numerical examples and a real case were studied by the proposed approach and the comparison of the results with previous methods show efficiency of the proposed method.

Optimization and economic analysis of small scale nanofiltration and reverse osmosis brackish water system powered by photovoltaics

Integration of renewable energy with desalination technologies is a strongly emerging field in many regions of the world having drinking water and energy crisis. This study presents the results of a techno-economical investigation of a small scale, photovoltaic (PV) powered hybrid nanofiltration (NF) and reverse osmosis (RO) membrane system for the brackish water treatment. Optimization experiments of six commercially available small scale RO and NF membranes were carried out using central composite design (CCD) of response surface methodology (RSM). Experiments employing optimized input conditions validate the developed RSM model. Predictive model, using multiple response optimizations, revealed that CSM RO and NF250 membranes showed the optimal efficiency with 20.24% and 18.98% water recovery, 90.22% and 70.64% salt rejection and 17.87 and 9.35kWh/m3 of SEC respectively. Comparison of membranes was also carried out by membrane characterization duly supported by experimental observations. Membrane surface was characterized by AFM, contact angle measurement and FTIR. Hybrid experiments were performed with NF and RO membranes in concentrate and permeate staging configurations. Results also suggested that techno-economic performance of the hybrid PV–NF/RO system was affected by factors like mode of integration of NF and RO membranes, recovery ratio, daily average operating hours and government subsidy.

Optimisation of material compositions for flammability characteristics in rice husk/polyethylene composites

A parametric study on the flammability characteristics of rice husk-reinforced polyethylene composites with various material compositions was conducted to find the “best” composites’ formulation for fire retardancy. Composites were manufactured using rice husk, maleated anhydride polyethylene and linear medium density polyethylene. The blends for manufacturing of composites were selected using mixture design approach. The individual effects of each constituent material on the fire performance of composites by cone calorimeter were studied using trace and contour plots for the various thermal and flammability properties. Regression coefficients were also estimated for each measured response. The cone calorimetry results show that the addition of rice husk improved fire retardancy of composites. The addition of maleated anhydride polyethylene did not influence the flammability properties much, except for mass loss rate and specific extinction area. The optimum mixture of rice husk, maleated anhydride polyethylene and linear medium density polyethylene for overall “best” flammability properties of the composites was also determined by multiple response optimisation using the regression models in Design Expert software. The optimum mixture for overall “best” fire retardant properties was found to be 50 wt% of rice husk, 5.6 wt% of maleated anhydride polyethylene and 44.4 wt% of linear medium density polyethylene. The flammability properties measured from composites manufactured with this formulation closely matched the values predicted by the model.

Multiple response optimization of Bacillus subtilis EA-CB0015 culture and identification of antifungal metabolites

The low yields of biomass and antimicrobial metabolites obtained in fermentation processes are limiting factors for implementing biological control agents in the field -- In this context, optimization of the culture medium for the biological control agent Bacillus subtilis EA-CB0015 was conducted in submerged culture to maximize the biomass production and antifungal activity -- Additionally, the active metabolites against the phytopathogen Mycosphaerella fijiensis produced under optimized conditions were identified -- Carbon and nitrogen constituents of the culture medium were optimized using a 22 factorial design with central point followed by a multiple response optimization coupled to the desirability function approach -- The optimized medium (33.4 g/L of glucose and 32.5 g/L of yeast extract), showed significant increases in both, cell density by 3.6 folds (13.271.0 g/L) and antifungal activity by 1.2 folds (77.770.3% inhibition) -- To gain insight into the type of active compounds, they were purified and identified by mass spectrometry (MS) -- The MS analysis revealed the presence of three families of lipopeptides: surfactin, fengycin and iturin -- The high antifungal activity was associated with the novel fengycin C and with iturin -- A which were partially growth-associated with a maximum concentration of 781.4 and 355.4 mg/L at 36 h of growth, respectively -- These results indicate the potential of B. subtilis EA-CB0015 to produce high concentrations of biomass and lipopeptides that can be exploited for biotechnological application

A new desirability function-based method for correlated multiple response optimization

Design of process parameters to meet the required specification of quality characteristics has become a crucial issue for many industries. The underlying relationships between process inputs and outputs are one of the main tasks in quality engineering. Most of researches assume independency and consider same relative importance of quality characteristics with constant variances over experimental space. This study represents a novel robust approach based on desirability function and global criterion methods that not only obtains the parameter design but also considers different variance, correlation, and relative importance level of outputs. The suggested method enforces all quality measurements to fall within specification limits. To illustrate computational aspects of the proposed method, two realistic examples have been conducted. The obtained results demonstrate the superiority of the proposed approach with respect to the existing approaches.

Erratum to: Multiple Response Optimization of a HPLC Method for the Determination of Enantiomeric Purity of S-Ofloxacin

Erratum to: Multiple Response Optimization of a HPLC Method for the Determination of Enantiomeric Purity of S-Ofloxacin

Optimization of Multiple Responses in Dry Turning Process Using Taguchi-Grey Relational Analysis

The present study investigates the optimization of multiple responses in turning of EN-8 steel by the Taguchi and grey relational analysis. The performance characteristics considered are tangential force, feed force and radial force. Grey relational theory is adopted to determine the best process parameters that give lower magnitude of tangential, feed, radial forces and optimal cutting parameters. An orthogonal array L18 is used for the experimental design. The setting of process parameters— nose radius, 0.8mm; cutting speed, 60.65 m/min; feed rate, 0.04 mm/rev; and depth of cut, 0.60 mm— has highest grey relational grade and therefore produces best turning performance in terms of cutting forces.

Multiple Response Optimization with Grey Relational Analysis of Friction Stir Welding Parameters in Joining Dissimilar Aluminium Alloys by Taguchi Method

This paper presents a novel approach for the optimization of welding parameters on dissimilar friction stir welded joints between AA6061T-651 and AA7075T-651 aluminium alloys with multiple responses based on orthogonal array by grey relational analysis. Experiments are conducted by varying the welding parameters related to three pin profiles. In this study, welding parameters namely rotational speed, welding speed and pin profiles are optimized with the considerations of multi responses such as ultimate tensile strength and hardness. A grey relational grade is obtained from the grey analysis. Based on the grey relational grade, optimum levels of parameters have been identified and significant contribution of parameters is determined by ANOVA (Analysis of Variance). Confirmation test is conducted to validate the test result. Experimental results have shown that the responses in welding process can be improved effectively through this novel approach.

A new approach for optimization of small-scale RO membrane using artificial groundwater

The present study aims at evaluating a small-scale brackish water reverse osmosis (RO) process using parameter optimization. Experiments were carried out using formulated artificial groundwater, and a predictive model was developed by using response surface methodology (RSM) for the optimization of input process parameters of brackish water RO process to simultaneously maximize water recovery and salt rejection while minimizing energy demand. The result of multiple response optimization along with analysis of variance for RSM predictions showed that the optimal water recovery (19.18%), total dissolved solids rejection (89.21%) and specific energy consumption (17.60 kWh/m3) occurred at 31.94 °C feed water temperature, 0.78 MPa feed pressure, 1500 mg/L feed salt concentration and 6.53 pH. Furthermore, confirmation of RSM predictions was carried out by an artificial neural network (ANN) model trained by RSM experimental data. Predicted values by both RSM and ANN modelling methodologies were compared and found within the acceptable range. Finally, a membrane validation experiment was carried out successfully at proposed optimal conditions, which proves the accuracy of employed RSM and ANN models. Present methodology can be used as a generalized way for the optimization of different RO membranes available in the market in terms of increased water recovery and salt rejection with least energy consumption to make it commercially competent.

Multiple Response Optimization of a HPLC Method for the Determination of Enantiomeric Purity of S-Ofloxacin

The aim of the study was to develop a new HPLC method for direct chiral separation of Ofloxacin enantiomers using polar non-aqueous mobile phase by application of response surface methodology. Rotatable central composite design (CCD) with eight factorial points, six axial points and six replications in central point was used to evaluate the influence of three independent variables (concentration of methanol, diethylamine and flow rate) on the output responses (capacity factor of first peak, tailing factors of both the enantiomers, resolution between the Ofloxacin enantiomers, retention time of the last peak and chromatographic optimization function). Further, CCD data were combined with multiple response optimization in order to obtain a set of optimal experimental conditions (% methanol/hexane/acetonitrile-43.33/10/46.62 (v/v), % acetic acid/diethylamine-0.4/0.2 and flow rate as 1.4 mL min−1) leading to the most desirable compromise between resolution and analysis time. The method demonstrated good correlation between observed and predicted responses. The developed method was validated according to ICH guidelines and applied for quantitative analysis of two commercially available tablets Zenoflox (Ofloxacin) and Glevo (Levofloxacin). Good agreement was found between the assay results and the label claim of the marketed formulations by showing good %recovery and %CV. The study resulted in a better chromatographic system for the determination of Ofloxacin enantiomers.