Procedure Of Response Surface Methodology Research Articles

Radiating heat effect on Powell–Eyring blood-based hybrid nanofluid over a Riga plate with thermal stratification CattaneoChristov heat flux model

The contemporary investigation deliberate the influence of radiating heat on the flow of Powell-Eyring fluid over a Riga plate subjected to thermal stratification, utilizing the CattaneoChristov heat flux model. Additionally, the insertion of heat sources along with thermal radiation energies the study significantly. The basic equations that correspond to conservation principles of mass, momentum, energy are formulated for Powell-Eyring fluid. The CattaneoChristov heat flux model is incorporated for the non-Fourier effects arising due to thermal relaxation phenomena are also taken into account. The resulting nonlinear partial differential equations are transmuted into ordinary equations and numerically solved using the Runge-Kutta method combined with shooting approach, which is a reliable numerical methodology. The impacts of pertinent factors on the momentum and temperature profiles are scrutinized systematically. Additionally, the imposition of these factors on shear rate and Nusselt number are deployed, which are crucial for practical applications and heat transfer enhancement strategies. As a novelty, the optimization of share rate together with heat transmission rate are obtained by employing a innovative statistical procedure of response surface methodology followed by analysis of variance, a hypothetical test. The outcomes of this study of heat transmission processes in Powell–Eyring fluids under thermal radiation over a Riga plate involving thermal stratification have potential applications in various engineering and industrial processes including thermal management, materials processing, and energy conversion devices.

Evaluation of free convection cooling and entropy efficiency in a porous W-shaped cavity using RSM and CFD approaches

The cooling and entropy efficiencies of natural convection inside cavities within fully saturated metal foam play a vital role in thermal engineering applications systems. The current study employed response surface methodology (RSM) coupled with ANSYS FLUENT-CFD for a two-dimensional W-shaped cavity, which has not been examined previously. Several effective physical factors were considered, including the aspect ratio (W = h/H = 0, 0.2, 0.4, 0.6 and 0.8) and the applied temperature variation (ΔT = Th − Tc = 10, 20, 30 and 40 K). Additionally, the thermal performance (NumW/NumO and QW/QO ) and entropy generation (EGW/EGO ) improved under the optimum design of pore per inch (10 ≤ PPI ≤ 30), the number of wave amplitude (1 ≤ n ≤ 3) and aspect ratio (0.2 ≤ W ≤ 0.8) utilizing both RSM and CFD. The results showed that the optimal design of natural convection within a fully saturated metal foam W-shaped cavity at PPI = 10, n = 3 and W = 0.8 resulted in enhancements of approximately 2.875 times for both NumW/NumO and QW/QO while maintaining reasonable EGW/EGO at 1.193 times. Moreover, the novelty aspect of this research lies in the successful achievement of multi-objectives improving NumW/NumO , QW/QO and EGW/EGO by applying the RSM approach in combination with ANSYS FLUENT-CFD. Thus, the optimal working procedure of RSM and CFD is aligned with the desired goals, as the cooling and entropy efficiencies of the porous W-shaped cavity are considered acceptable and satisfactory.

Response Surface Methodology in Optimising the Extraction of Polyphenolic Antioxidants from Flower Buds of Magnolia × soulangeana Soul.-Bod. var. 'Lennei' and Their Detailed Qualitative and Quantitative Profiling.

A response surface methodology (RSM) with a central composite design (CCD) was developed to predict and apply the best ultrasound-assisted extraction (UAE) conditions, including the extraction time, the composition of aqueous-ethanolic extractants, and the solvent-to-plant-material ratio, for obtaining the highest yields of different types of polyphenolic components from the dried flower buds of Magnolia × soulangeana Soul.-Bod. var. 'Lennei' (MSL). The novel approach in the RSM procedure resulted from the simultaneous optimisation of UAE conditions to obtain extracts with the highest antioxidant and antiradical potential (examined as dependent variables), using appropriate spectrophotometric assays, with Folin-Ciocâlteu and 2,2-diphenyl-1-picrylhydrazyl reagents, respectively. The use of 66.8% (V/V) ethanol as the extraction solvent during the 55.2 min extraction protocol and the ratio of extractant volume to herbal substance of 46.8 mL/g gave the highest total yield of bioactive antioxidant phenolics in the extract obtained. For this herbal preparation, a qualitative and quantitative analysis was performed using combined chromatographic (LC), spectroscopic (PDA), and tandem mass spectrometric (ESI-QToF-MS/MS) techniques. A detailed phytochemical profiling, conducted for the first time, documented substantial amounts of various polyphenolic antioxidants, especially phenylethanoids and flavonoids, in the MSL flower buds. Their average total content exceeded 30.3 and 36.5 mg/g dry weight, respectively.

Response Surface Methodology Using Observational Data: A Systematic Literature Review

In the response surface methodology (RSM), the designed experiment helps create interfactor orthogonality and interpretable response models for the purpose of process and design optimization. However, along with the development of data-recording technology, observational data have emerged as an alternative to experimental data, and they contain potential information on design/process parameters (as factors) and product characteristics that are useful for RSM analysis. Recent studies in various fields have proposed modifications to the standard RSM procedures to adopt observational data and attain considerable results despite some limitations. This paper aims to explore various methods to incorporate observational data in the RSM through a systematic literature review. More than 400 papers were retrieved from the Scopus database, and 83 were selected and carefully reviewed. To adopt observational data, modifications to the procedures of RSM analysis include the design of the experiment (DoE), response modeling, and design/process optimization. The proposed approaches were then mapped to capture the sequence of the modified RSM analysis. The findings highlight the novelty of observational-data-based RSM (RSM-OD) for generating reproducible results involving the discussion of the treatments for observational data as an alternative to the DoE, the refinement of the RSM model to fit the data, and the adaptation of the optimization technique. Future potential research, such as the improvement of factor orthogonality and RSM model modifications, is also discussed.

Parametric optimization of a coiled agitated vessel with TiO2/water nanofluid

Abstract An effective procedure of response surface methodology (RSM) has been successfully developed for a coiled agitated vessel by finding the optimal values of working parameters to achieve the high heat transfer performance. Studies have been performed for two types of agitators, propeller and disk turbine respectively. TiO2/water nanofluid has been used as agitated medium. Various operating parameters, such as impeller speed (100–700 rpm for propeller and 100–300 rpm for disk turbine), TiO2/water nanofluid concentration (0–0.3 vol%), heat input (400–2200 W) to the agitated medium were explored by experiment. Experiments were performed at desired set of working conditions suggested by a standard RSM design called a Box-Behnken Design. Nusselt number was considered as performance parameter. The results identify the significant influence factors to achieve high coefficient of heat transfer. A set of 690 rpm, 0.28 vol%, 1480 W for propeller and 220 rpm, 0.15 vol%, 1330 W for disk turbine have been suggested by the model for achieving improved heat transfer performances. The obtained optimal working parameters have been predicted and verified by conducting validation experiments. A good agreement of discrepancy ±4% have been obtained between experimental and predicted values.

Cu(II)/Z4A as an Efficient Nanocomposite for Oxidative Degradation of Indole and Optimization of Effective Factors via RSM Procedure

One of the aromatic contaminants in the oil and fuel is indole, which is toxic even at low doses and is considered as air and water pollutant. In this research, the surface of Zeolite 4A (Z4A) was modified by Cu(II)nanoparticles to introduce a desirable nanocomposite (Cu(II)/Z4A) for indole oxidative degradation. Catalysts characterization was carried out by XRD, SEM, EDS, FTIR, and BET/BJH techniques. Response Surface Methodology (RSM) based on Box-Behnken Design (BBD) was employed for studying several effective factors influences in indole oxidation process, including pH, weight percentage of loaded copper (Cu(wt %)), mass of composite, and indole initial concentration (IND concentration). The obtained results by BBD revealed the solution pH was the most pivotal factor in indole oxidative degradation and predicted that under the optimum experimental conditions, the efficiency should be 98.91%. Moreover, GC-mass analysis was applied for evaluating side products, of which results led to some mechanisms and new productions to be found by using indole oxidative degradation. The results also demonstrated that due to indole oxidation and applying the proper solvent (ethanol), some side products were generated capable of acting as a fuel octane number enhancer, which may play a significant role in obtaining more valuable fuels.

Using DOE and RSM procedures to analyze and model a spray evaporation type solar still

The lower productivity of the conventional solar still requires bringing modifications to this system. The proposed modification in this work consists of coupling water spray and ambient air injection, which may enhance the water–air surface contact area as it plays a crucial role in the performance in such a device and, consequently, increases the still performances. Response surface methodology (RSM) model is derived from an experimental test set up using the design of experiment procedure to investigate the binary interaction effects of the operating parameters, i.e., spraying height, inlet water temperature, and water and air flowrates. Based on RSM results, a simple polynomial statistical model is stated in this investigation to determine and maximize the amount of evaporated water from solar still based on the four considered input factors. An excellent fitting is attained between the predicted results derived from the statistical model and the experimental results. The performance of this model was also validated using the variance analysis approach. The findings indicate that the main influencing parameters in the order of impact on the system productivity is ambient air mass flow, saltwater temperature, sprayed water mass flow and sprayer height. The binary interaction effects of the variables were considered and illustrated for design recommendations. Within the studied ranges, the hourly productivity varies from 1.91 to 7.9 kg/h m2 for a water temperature between 40 and 70 °C.

Effects of Various Processing Parameters on Polyphenols, Flavonoids, and Antioxidant Activities of Codonopsis javanica Root Extract

The root of a ginseng-like plant named Codonopsis javanica is a valuable ingredient in folk medicine with diverse biological properties and has been used in treatments for various diseases, including leukemia, inflammation, and hepatitis. This study aimed to optimize various parameters related to the extraction process of C. javanica root (CJR) with respect to total phenolic content (TPC), total flavonoid content, and antioxidant activities of the obtained CJR extract. We first performed a series of single-factor investigations taking drying conditions and different extraction parameters such as material size, extraction solvent, solvent concentration, extraction temperature, material/solvent ratio, extraction cycle, and stirring speed as varying factors. Then, a response surface methodology procedure was adopted with a central composite design to optimize further the extraction process in order to maximize the TPC. We found that the use of convection drying at 70 °C for 8 hours gave the extract with the highest TPC and antioxidant activities. Optimal extraction parameters were found as follows: ethanol with a concentration of 56.0% as the solvent, material-to-solvent ratio of 1/38.0 g/mL, extraction time of 67.2 minutes, material size ≤0.5 mm, temperature 60 °C, through 1 extraction cycle, and with a stirring speed of 300 rpm. Under the optimized conditions, the experimental value for TPC was 2.9 mg gallic acid equivalent (GAE)/g dry weight (DW), which is reasonably close to the value predicted by the model (2.8 mg GAE/g DW). The half-maximal inhibitory concentration (IC50) values determined by the 2,2-diphenyl-1-picrylhydrazyl and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid free radical tests of the CJR extract obtained under optimal conditions were 1042.3 and 299.0 µg/mL, respectively.

A stochastic methodology to evaluate the optimal multi-site investment solution for photovoltaic plants

The problem with evaluating the investment in a multi-site photovoltaic plant, such as that proposed in this paper (which can be viewed as a profitability analysis), is that it requires the development of an innovative methodological approach.By setting an upper limit of capital to invest, among sites with different Direct Normal Irradiations (DNI), those in which many photovoltaic systems can be installed are identified as is the optimal size within predefined ranges. The primary objective is to maximize the net return on investment for a multi-site plant with a lifespan of approximately twenty years. The proposed approach uses a stochastic business plan, through which data are generated via the Monte Carlo simulation. Subsequently, using the Response Surface Methodology procedure, it is possible to identify the regression meta-model that describes the optimum region and therefore the point (investment amount for individual sites and the corresponding size of plants) that maximizes the overall Net Present Value. The proposed approach is completely generalized and, as such, can be replicated. A properly conducted test case is presented and clearly illustrates the methodology.

Modeling and Analysis for Hardness and Structure of Nonferrous Alloy Castings Produced Using Zcast Metal Casting Process through Response Surface Methodology

The purpose of this investigation is to develop a mathematical model for predicting the influence of casting parameters on hardness of non ferrous alloy (NFA) castings produces using Zcast process by employing response surface methodology (RSM). An association has been proposed between hardness of castings and shell wall thickness (SWT), weight density (WD) and pouring temperature (PT) of the shell mould fabricated using three dimensional printer (3DP). The experiments were conducted using NFA materials like; aluminium, zinc, lead, copper, brass and bronze. An effective procedure of RSM has been utilized for the optimal values of casting parameters. Mechanical Micro-Vickers hardness was measured in which the indent is produced by applying a known load to the specimen and then measuring the size of the appropriate diagonals optically. Based on experimental data the investigation has led to conclusion as Quadratic model have been established for predicting the response in the form of hardness of castings.Further, the model developed was validated by analysis of variance (ANOVA) which indicated the significance of model. The Model F-value of 2477.66 implies that the model is significant. There is only a 0.01% chance that a Model F-Value could occur due to noise. Values of "Prob > F" less than 0.05 indicate model terms are significant. The statistical analysis of second order quadratic equation results showed that the most significant parameters, favoring the hardness are B, C and A2. The superiority of the quadratic mathematical models were developed in confidence intervals of 99.75% with prediction error sum of squares of 3.6%for the prediction of the harness.Microstructure analysis was also performed for justification of hardness data.

Modeling and Analysis for Hardness and Structure of Nonferrous Alloy castings produced using Zcast Metal casting Process through Response Surface Methodology

The purpose of this investigation is to develop a mathematical model for predicting the influence of casting parameters on hardness of non ferrous alloy (NFA) castings produces using Zcast process by employing response surface methodology (RSM). An association has been proposed between hardness of castings and shell wall thickness (SWT), weight density (WD) and pouring temperature (PT) of the shell mould fabricated using three dimensional printer (3DP). The experiments were conducted using NFA materials like; aluminium, zinc, lead, copper, brass and bronze. An effective procedure of RSM has been utilized for the optimal values of casting parameters. Mechanical Micro-Vickers hardness was measured in which the indent is produced by applying a known load to the specimen and then measuring the size of the appropriate diagonals optically. Based on experimental data the investigation has led to conclusion as Quadratic model have been established for predicting the response in the form of hardness of castings.Further, the model developed was validated by analysis of variance (ANOVA) which indicated the significance of model. The Model F-value of 2477.66 implies that the model is significant. There is only a 0.01% chance that a Model F-Value could occur due to noise. Values of "Prob > F" less than 0.05 indicate model terms are significant. The statistical analysis of second order quadratic equation results showed that the most significant parameters, favoring the hardness are B, C and A2. The superiority of the quadratic mathematical models were developed in confidence intervals of 99.75% with prediction error sum of squares of 3.6%for the prediction of the harness. Microstructure analysis was also performed for justification of hardness data.

Optimization of surface integrity in dry hard turning using RSM

This paper investigates the effect of different cutting parameters (cutting speed, feed rate, and depth of cut) on surface integrity defined in terms of surface roughness and microhardness in dry hard turning process. The workpiece material used was hardened alloy steel AISI 52100 and it was machined on a CNC lathe with coated carbide tool under different settings of cutting parameters. Three cutting parameters each at three levels were considered in the study. Central composite design (CCD) of experiment was used to collect experimental data for surface roughness and microhardness. Statistical analysis of variance (ANOVA) was performed to determine significance of the cutting parameters. The results were analysed using an effective procedure of response surface methodology (RSM) to determine optimal values of cutting parameters. Several diagnostic tests were also performed to check the validity of assumptions. The results indicated that feed rate is the dominant factor affecting the surface roughness whereas the cutting speed is found to be the dominant factor affecting the microhardness. Results also revealed that within the range investigated, good surface integrity is achieved when feed rate and depth of cut are near their low levels and cutting speed is at high level. Finally, the optimal cutting parameters and model equations to predict the surface roughness and microhardness are proposed.

Modeling and Microstructural Analysis of Induction Hardened Parts

In the present article, an effective procedure of response surface methodology (RSM) is utilized to find the optimal values of process parameters for induction hardening of AISI 1040 steel under three different conditions of the material to predict total case depth. The three material conditions are untreated as-received (rolled), normalized, and tempered. Various process parameters, such as feed rate, current, dwell time, and gap between the workpiece and induction coil are experimentally explored. The experimental results show that the proposed mathematical models can predict the total case depth within the limits of the factors being investigated. The optimal values of process parameters have been verified by confirmation experiments. After ascertaining the optimal sample (corresponding to the best setting of induction hardening process parameters), tensile strength tests were performed so that the comparison could be done between the optimal induction hardened material and material without subjecting to induction hardening. It was concluded that the tempered is the most favorable raw material for making shafts, axles, or other automobile components during induction hardening process as almost finely distributed martensite was observed during scanning electron microscope (SEM) analysis.

Optimization of processing parameters in induction hardening using response surface methodology

In this paper, an effective procedure of response surface methodology (RSM) has been utilized for finding the optimal values of process parameters while induction hardening of AISI 1040 under two different conditions of the material i.e., rolled and normalized. Various process parameters, such as feed rate (speed at which the induction coil moves, which is measured in mm/sec), current, dwell time (time after which heat intensity starts to heat work piece in seconds) and gap between the work piece and induction coil have been explored by experiments. Hardness at two different conditions has been considered as performance characteristic. The experiment plan was based on rotatable, central composite design (CCD). The experimental results showed that the proposed mathematical models suggested could describe the performance indicators within the limits of the factors being investigated. The obtained optimal process parameters have been predicted and verified by confirmation experiments. Microstructure and SEM (scanning electron microscope) analyses were also done for justification of the work.

English

This paper investigates the effect of different cutting parameters (cutting speed, feed rate, and depth of cut) on cutting forces in cryogenic hard turning processes. The workpiece material, hardened alloy steel AISI 52100, was machined on a CNC lathe with multilayer hard surface coatings (TiN/TiCN/Al2O3/TiN) insert under different settings of cutting parameters. Three cutting parameters each at three levels were considered in the study. Central composite design (CCD) of experiment was used to collect experimental data for cutting forces. The results were analyzed using an effective procedure of response surface methodology (RSM) to determine optimal values of cutting parameters. Statistical analysis of variance (ANOVA) was performed to determine significance of the cutting parameters. The results indicated that cutting forces are influenced principally by the cutting speed, feed rate and depth of cut. The ranges for best cutting parameters and model equations to predict the cutting forces were proposed. Confirmation experiments were performed to verify the pertinence of the developed empirical models. Finally, the optimum machining conditions to produce the minimum cutting forces under these experimental conditions were searched using desirability function approach for response factors optimization

Automated Response Surface Methodology for Simulation Optimization Models with Unknown Variance

Response Surface Methodology (RSM) is a collection of mathematical and statistical techniques useful for developing, improving, and optimizing processes. Applications of RSM can be found in e.g. chemical, engineering and clinical sciences. Still, there does not seem to be an established code of practice for the automated application of RSM in the field of simulation optimization. In this paper our aim is to find the best settings for an automated RSM procedure when there is very little information about the objective function. We present a framework of the RSM procedures for finding optimal solutions and emphasize the use of both stopping rules and restart procedures. Various versions of the RSM algorithms are compared on a number of test functions, including a simulation model for cancer screening. The results show that considerable improvement is possible over the proposed settings in the existing literature. Accordingly, we give general recommendations on the application of automated RSM algorithms in simulation optimization.

Application of response surface methodology in describing the thermal performances of a pin-fin heat sink

The heat sink of pin-fin array structure is widely applied in the cooling enhancement of current electronic equipment because of the advantage of non-sensitive to airflow direction and large surface area per given volume. In this study, a systematic experimental design based on the response surface methodology (RSM) is used to identify the effects of design parameters of the pin-fin heat sink (PFHS) on the thermal performance. Various design parameters, such as the height and diameter of pin-fin and the width of pitch between fins, are explored in the experiment. The thermal resistance R th and pressure drop Δ P are adopted as the thermal performance characteristics. A standard RSM design called a central composite design (CCD) is applied in this experimental plan. The results distinguish the significant influential factors for minimizing the thermal resistance R th and pressure drop Δ P. An effective procedure of response surface methodology (RSM) has been established for predicting and optimizing the thermal resistance R th and the pressure drop Δ P of PFHS with the design constraints. The experimental results also indicate that the model proposed in this study is reasonable and accurate and can be used for describing the thermal resistance R th and pressure drop Δ P with the limitations of the factors studied.

Modeling and optimization of designing parameters for a parallel-plain fin heat sink with confined impinging jet using the response surface methodology

The parallel-plain fin (PPF) array structure is widely applied in convective heat sinks in order to create extended surface for the enhancement of heat transfer. In the present study, for investigating the influences of designing parameters of PPF heat sink with an axial-flow cooling fan on the thermal performance, a systematic experimental design based on the response surface methodology (RSM) is used. The thermal resistance and pressure drop are adopted as the thermal performance characteristics. Various designing parameters, such as height and thickness of fin, width of passage between fins, and distance between the cooling fan and the tip of fins, are explored by experiment. Those parameters affect the structure arrangement, geometry of fins and the status of impinging jet from an axial-flow cooling fan installed over the heat sink. A standard RSM design called a central composite design is selected as experimental plan for the four parameters mentioned above. An effective procedure of response surface methodology (RSM) has been proposed for modeling and optimizing the thermal performance characteristics of PPF heat sink with the design constrains. The most significant influential factors for minimizing thermal resistance and pressure drop have been identified from the analysis of variance. The confirmation experimental results indicate that the proposed model is reasonably accurate and can be used for describing the thermal resistance and pressure drop with the limits of the factors studied. The optimum designing parameters of PPF heat sink with an axial-flow cooling fan under constrains of mass and space limitation, which are based on the quadratic model of RSM and the sequential approximation optimization method, are found to be fin height of 60 mm, fin thickness of 1.07 mm, passage width between fins of 3.32 mm, and distance between the cooling fan and the tip of fins of 2.03 mm.

Application of response surface methodology in the parametric optimization of a pin-fin type heat sink

In this paper, an effective procedure of response surface methodology (RSM) has been successfully developed finding the optimal values of designing parameters of a pin-fin type heat sink (PFHS) under constrains of mass and space limitation to achieve the high thermal performance (or cooling efficiency). Various design parameters, such as height and diameter of pin-fin and width of pitch between fins are explored by experiment. The thermal resistance and pressure drop are considered as the multiple thermal performance characteristics. Experiments are performed by a standard RSM design called a central composite design (CCD). The results identify the significant influence factors to minimize thermal resistance and pressure drop. The obtained optimal designing parameters have been predicted and verified by conducting confirmation experiments.

Automated Response Surface Methodology for Stochastic Optimization Models With Unknown Variance

Response Surface Methodology (RSM) is a tool that was introduced in the early 50's by Box and Wilson (1951). It is a collection of mathematical and statistical techniques useful for the approximation and optimization of stochastic models. Applications of RSM can be found in e.g. chemical, engineering and clinical sciences. In this paper we are interested in finding the best settings for an automated RSM procedure when there is very little information about the stochastic objective function. We will present a framework of the RSM procedures for finding optimal solutions in the presence of noise. We emphasize the use of both stopping rules and restart procedures. Good stopping rules recognize when no further improvement is being made. Restarts are used to escape from non-optimal regions of the domain. We compare different versions of the RSM algorithms on a number of test functions, including a simulation model for cancer screening. The results show that considerable improvement is possible over the proposed settings in the existing literature.