Hybrid Response Surface Research Articles

Optimization of heat affected zone in laser cutting of Kevlar-29 fiber composite using hybrid response surface based grey wolf optimization (RSGWO) algorithm

The large strength-to-weight rate and superior mechanical qualities, Kevlar fiber reinforced polymers (KFRP) are commonly utilized in the aerospace, vehicle, and energy sectors. The fine machining of KFRPs in these sectors was required for specific applications. Because of low matrix cracking, burr development, negligible tool wear, and fiber delamination, laser beam cutting (LBC) is a promising alternative to traditional cutting processes in KFRP cutting. During pulsed laser cutting of neodymium-doped yttrium aluminum garnet of the K-29 (Kevlar composite laminate) for the 1.25 mm thickness, the heat affected zone (HAZ) was determined experimentally at various settings of the lamp current, pulse frequency, and cutting speed. A second-order regression model for HAZ was demonstrated by using experimentally obtained data. A novel response surface-based grey wolf optimization (RSGWO) algorithm has also been proposed and used to discover optimal levels of process parameter conditions for minimizing HAZ. The RSGWO technique has proposed ideal values for the specified parameters at low pulse frequency (20 Hz) and lamp current (160 A), as well as greater air pressure (10 kg/cm2) with cutting speed (200 mm/min). At optimal cutting parameter values, a 14.92% improvement in HAZ was observed. Furthermore, parametric effects have been studied, and it has been discovered that compressed air pressure is the valuable parameter for HAZ for laser-cut KFRP composite, while lamp current (I) is the least important.

Improved Hybrid Response Surface Method Based on Double Weighted Regression and Vector Projection

In order to increase the accuracy and stability of the classical response surface method and relevant method, a new improved response surface method based on the idea of double weighting factors and vector projection method is proposed. The response surface is fitted by the weighted regression technique, which allows the sampling points to be weighted by their distance from the true failure surface and that from the estimated design point. It uses the vector of the gradient projection method to get new sampling points in the process of iteration, in order to make the sampling points closer to the design point, and the value of deviation coefficient is constantly adjusted. To some extent, these strategies increase the accuracy and stability of the response surface method, while the calculation time is decreased. At last, the rationality and efficiency of the proposed method are demonstrated through five examples. Besides, as revealed from this investigation, compared with other conventional algorithms, this method has a few obvious advantages; this algorithm not only has high precision and efficiency, but also has solid stability.

Separate modeling technique for deformation monitoring of concrete dams

Deformation monitoring is an important aspect of safety control for concrete dams. Deformation monitoring models (such as statistical models and hybrid models) are extensively applied to predict concrete dam deformation and derive confidence interval of normal deformation for anomaly detection. Deformation monitoring models for concrete dams mainly consist of hydrostatic component, temperature component, and aging component. The optimum parameters of individual components are simultaneously determined by least square method for monitoring data fitting. Thus, significant over-fitting of deformation monitoring models may be induced by mutual compensation among the parameters of model components. In this paper, the Separate Modeling Technique (SMT) is proposed for mitigating the over-fitting problem of deformation monitoring models for concrete dams. Firstly, the Empirical Mode Decomposition (EMD) is adopted to extract and visualize the aging component of displacement sequence. Proper mathematical formulation of the aging component can be established, and the problem of improperly presupposing the mathematical form of aging component in the process of constructing traditional deformation monitoring models is well addressed. In this study, the hydrostatic component is represented by the Hybrid Response Surface (HRS), which is formulated using numerical simulation with varying water levels and material parameters. The displacement variation caused by water level fluctuation is identified in terms of isothermal conditions and is used to calibrate the material parameters in the HRS. The temperature component is separated through subtracting the hydrostatic and aging components from displacement time series and then is expressed with proper mathematical formulations. Finally, hybrid models for displacement monitoring of concrete dams are established by combining the separately formulated components. The Separate modeling technique is applied to formulate crest displacement of the YL concrete gravity dam. The false alarm rate of displacement monitoring and a new model selection criterion (namely over-fitting coefficient) are adopted to compare various deformation monitoring models. It is shown that the over-fitting levels of deformation monitoring models can be effectively reduced using the SMT. The deformation monitoring models constructed with the SMT are of better accuracy in displacement prediction and present no false alarm of displacement monitoring for the tested period.

Advanced Modeling of Production Time for Insole Orthotic Clubfoot Shoes Based on Hybrid Taguchi and Response Surface Methodology Approach

Advanced Modeling of Production Time for Insole Orthotic Clubfoot Shoes Based on Hybrid Taguchi and Response Surface Methodology Approach

An Experimental Study of ZrO2‐CeO2 Hybrid Nanofluid and Response Surface Methodology for the Prediction of Heat Transfer Performance: The New Correlations

This article experimentally and statistically reports the convective heat transfer performance of a cylindrical mesh‐type heat pipe apparatus filled with ZrO2‐CeO2/water‐ethylene glycol nanofluids. In this regard, ZrO2‐CeO2 nanoparticles were synthesized and characterized through the Scanning Electron Microscope and Powder X‐ray diffraction methods followed by the preparation of hybrid ZrO2‐CeO2 nanofluids of various concentrations ranging from 0.025 to 0.1%. The heat transfer features of a tubular heat pipe with a mixture of the ZrO2‐CeO2 nanofluid were evaluated. A 5.33% decrease in thermal resistance value and a 41.16% increase in heat transfer ability with increased power input were observed. The potent regression models were proposed to estimate heat transfer features of the heat pipe. The ANOVA statistical method has been employed to determine the P value and the F value of the models towards enhancing the reliability and accuracy of the developed models. The outcome revealed that the proposed models are reliable and have the best fit with the experimental data for 30–60 W power. The correlations’ results were validated against the experimental data and showed high accuracy. Moreover, the accuracy of the developed models was ensured through R‐squared and adjusted R‐squared values.

High-speed flow field prediction and process parameters optimization in a vertical MOCVD reactor based on a hybrid RSM-KNN model

Metal-Organic Chemical Vapor Deposition (MOCVD) is an important method of epitaxial growth used to obtain high-quality film and mass production. Computational Fluid Dynamics (CFD) method is widely used to simulate and study the growth process of MOCVD, but the complex process parameters and high computational cost have limited the real-time application. A hybrid Response Surface Methodology–K-Nearest Neighbor (RSM-KNN) model is developed to reach the goal of the high speed of flow field prediction and process parameters optimization. In this paper, a 3-dimensional ZnO-MOCVD model is built and then validated using the experimental and simulation results. The Design of Experiment (DOE) method is used to improve data collection performance. Results of deposition rate and uniformity are combined with the RSM model to analyze the influence of process parameters and to explore the relationship between deposition rate, uniformity and flow state. The results of the flow field are combined with the machine learning method KNN to study and predict the flow field and flow states in the reactor. The hybrid model is also presented and optimized to realize the growth of high deposition rate, and the uniformity is increased by 30% at a steady flow state of plug flow.

A DBN-PSOSA hybrid response surface method for bridge reliability

When solving bridge reliability problems, the traditional response surface method has a highly nonlinear implicit function, which results in a low fitting accuracy and difficulty in meeting the requirements. Therefore, the dynamic Bayesian network (DBN), which is suitable for solving a multi-state unit or system uncertainty problems, is selected to construct the response surface of the implicit function. In this study, the DBN model is combined with the particle swarm optimisation based on simulated annealing (PSOSA) algorithm to improve the optimisation efficiency of model parameters and allow the constructed implicit function to simulate the structure limit state function. The DBN-PSOSA hybrid response surface analysis method is proposed for bridge failure probability calculations. A numerical example is given to demonstrate the effectiveness of the proposed method, and the reliability of an actual bridge project is analysed. The results indicate that this method has a higher calculation accuracy and efficiency compared to the traditional response surface method, and is easy to combine with the existing general finite element analysis software to achieve the rapid analysis of bridge structure reliability.

Optimizing resiliency of train operations in an underground metro: A hybrid discrete-event simulation and response surface methodology

Avoiding the passengers extra waiting time is a vital task for rail planners. The current research focused on minimizing the passenger waiting time on the presence of real frequently random occurred disturbances. Details of the proposed model are on the 1st line of Tehran underground rail rapid transit. All fitness functions are validated using the analysis of variance (ANOVA) by applying the hypothesis testing method. Also, a validated discrete-event computer simulation model is applied to examine the average waiting time per passenger as the key performance measure under different scenarios generated using full factorial design of experiments. The validity of the obtained optimal solution, i.e., train headway times is confirmed at a 95% level of reliability. Also, simulation outcomes indicated that the proposed response surface meta-model could efficiently provide a more reliable train operation plan to ensure a desirable level of system resiliency on the presence of random disturbances. The numerical results indicated that wait time could be reduced by 14.8% for passengers as compared with the baseline train headway plan.

Optimization and prediction of safranin-O cationic dye removal from aqueous solution by emulsion liquid membrane (ELM) using artificial neural network-particle swarm optimization (ANN-PSO) hybrid model and response surface methodology (RSM)

The cationic dye safranin-O was removed from an aqueous solution using the Emulsion Liquid membrane (ELM) method. The ELM system consists of a diluent (hexane) and a surfactant (Span 80). Sulfuric acid (H2SO4) solution was used as an internal aqueous phase. The key parameters regulating the safranin-O dye removal were investigated. Twenty-nine (29) experiments were conducted, with various parameters impacting the removal of the cationic dye safranin-O from aqueous solution using the ELM approach, including surfactant content and internal phase concentration in H2SO4 dye concentration and stirring speed. The RSM with Box–Behnken Design (BBD) was used to optimize and predict the ELM safranin-O cationic dye removal experiments. The model produces 99.68% separated safranin-O in under 2 min of contact time with optimal operation conditions. Furthermore, for forecasting the removal of the cationic dye safranin-O from an aqueous solution by ELM technique, a novel optimization approach based on merging an artificial neural network (ANN) algorithm with a metaheuristic technique, namely PSO, has been proposed. The PSO technique was used to determine the optimal ANN parameter values.

Modeling and global optimization of biodiesel synthesis using hybrid response surface methodology‐crow search algorithm: Case study of papaya seed waste oil utilization

AbstractBiodiesel is potential renewable and clean energy, which can be produced form wide range of waste materials. This study employs a hybrid response surface methodology (RSM) and crow search algorithm (CSA) as novel tool for global optimization of transesterification reaction parameters to maximize biodiesel synthesis from papaya seed‐derived waste oil. Catalyst (NaOH) dose, methanol to oil molar ratio (M:O), and reaction time were considered independent factors, while biodiesel yield was taken as a dependent variable. The experimentally produced biodiesel was characterized by gas chromatography–mass spectrometry analysis. The experiments were developed based on RSM with Box–Behnken design matrix, which was subsequently used for modeling, optimization and model validation. Initially, a quadratic regression model was developed following RSM technique, correlating the transesterification reaction parameters and biodiesel yield. Afterward, the CSA coupled with RSM approach was employed to assess the global optimization. A highest biodiesel yield of 99.48% was attained with a catalyst (NaOH) dose of 0.5 wt%, M:O of 8.5:1 at a reaction time of 40 min. The results acquired by RSM‐CSA were also compared with the results achieved by desirability function‐based optimization technique. Further, the optimal set for maximizing biodiesel yield was validated experimentally with an error margin of 2.0%. These observations indicate that the hybrid RSM‐CSA is an efficient and economic approach to optimize the process conditions for biodiesel production from alternative sources.

Experimental study and parameters optimization of microalgae based heavy metals removal process using a hybrid response surface methodology-crow search algorithm

This study investigates the use of microalgae as a biosorbent to eliminate heavy metals ions from wastewater. The Chlorella kessleri microalgae species was employed to biosorb heavy metals from synthetic wastewater specimens. FTIR, and SEM/XRD analyses were utilized to characterize the microalgal biomass (the adsorbent). The experiments were conducted with several process parameters, including initial solution pH, temperature, and microalgae biomass dose. In order to secure the best experimental conditions, the optimum parameters were estimated using an integrated response surface methodology (RSM), desirability function (DF), and crow search algorithm (CSA) modeling approach. A maximum lead(II) removal efficiency of 99.54% was identified by the RSM–DF platform with the following optimal set of parameters: pH of 6.34, temperature of 27.71 °C, and biomass dosage of 1.5 g L−1. The hybrid RSM–CSA approach provided a globally optimal solution that was similar to the results obtained by the RSM–DF approach. The consistency of the model-predicted optimum conditions was confirmed by conducting experiments under those conditions. It was found that the experimental removal efficiency (97.1%) under optimum conditions was very close (less than a 5% error) to the model-predicted value. The lead(II) biosorption process was better demonstrated by the pseudo-second order kinetic model. Finally, simultaneous removal of metals from wastewater samples containing a mixture of multiple heavy metals was investigated. The removal efficiency of each heavy metal was found to be in the following order: Pb(II) > Co(II) > Cu(II) > Cd(II) > Cr(II).

Modeling and optimization of novel biodiesel production from non-edible oil with musa balbisiana root using hybrid response surface methodology along with african buffalo optimization

This research aims to produce a novel biodiesel fuel with standard quality from the non-edible oil with Lithium doped Calcium Oxide (Li–CaO) based heterogeneous nanocatalyst derived from Musa balbisiana Root ash. The characterization of the prepared nanocatalysts was achieved by X-ray diffractometer (XRD), Brunauer–Emmett–Teller (BET), Fourier transform infrared (FTIR), energy dispersive X-ray (EDX), scanning electron microscopy (SEM), X-Ray photoelectron spectroscopy (XPS) and transmission electron microscopy (TEM) techniques. Moreover, the production time and yield were optimized by a novel Hybrid Response Surface Methodology along with African Buffalo Optimization (HRSM-ABO) algorithm. The proposed method simulation was done on the Matlab platform. According to the simulation and experimental outcomes, the optimum biodiesel yield of nearly 97.8% was achieved at the conditions that the 4 wt% of catalyst amount, 15:1 methanol to the oil of molar ratio, reaction time of 150 min and the reaction temperature of 65 °C with Amplitude of 75%. Consequently, reusability investigation proved that there the catalytic action of the improved catalyst was moderately decreased after 7 cycles. The mechanism of the proposed transesterification process was understood by the estimation of kinetic study. Furthermore, the physiochemical properties of the proposed biodiesel were measured. This result signified that the newly prepared Li–CaO was the most suitable catalyst to make biodiesel, which can be utilized in diesel engines.

Hybrid Response Surface Method African Buffalo Optimization Technique for Ultrasonic Production of Biodiesel from Waste Cooking Oil using Li doped CaO Nan o catalyst

In the current era, there is an increasing emphasis on green fuels for a clean environment. Authors in this work have tried to devise an innovative method to optimize ultrasonic production of biodiesel from used cooking oil, using composite technique combining Response surface Methodology and African Buffalo optimization. In this research work, heterogeneous catalyst Lithium doped CaO has been obtained from a new natural source by high-temperature thermal decomposition of Musa Balbisiana root ash and tested its Conversion efficiency for conversion of waste cooking oil into methyl esters. It was observed that the catalyst is really effective for the production of biodiesel from even high Free Fatty Acid waste cooking oil. For optimization of production parameters authors have used ABO complemented with RSM to maximize the biodiesel production yield. The maximum biodiesel yield of 96.67% was achieved using ABO which is about 15% higher than provided by RSM which is 81.01%. The highest biodiesel yield of 96.67 % is obtained at 15:1 Molar Ratio with 3.5% catalyst wt. percent, 60 Degree C Temp. in 45 Minutes with an error of 2.5 % in yield prediction by ABO. The work may be utilized by industries and researchers to use ultrasonic reactors optimally to extract better biodiesel volume in very short time instead of presently used slow mechanical stirring tank reactors.

Train schedule optimization in a high-speed railway system using a hybrid simulation and meta-model approach

In urban metro systems, several factors such as passenger congestion may affect the movement of trains, resulting in significant deviations from the planned timetable. This study proposes a meta-model simulation-based optimization approach for minimizing the passenger wait time under random disturbances. The modeling framework is based on a hybrid discrete-event simulation and response surface methodology (RSM), thereby capturing the benefits of both methods. The verification process is handled using the calculation of second-order derivate along with an Analysis of variance (ANOVA) test. Simulation meta-data that is most consistent with the data collected from simulation experiments are used to describe the relationship between response variables, i.e., average waiting time per passenger, and input variable, i.e., time intervals between two departures. The optimal parameters of the RSM are decided using a factorial design. The validity of the results is confirmed using the real instances of a high-speed rail transit line.

Electrocoagulation of Pb2+, Co2+, and Mn2+ from simulated wastewater: An algorithmic optimization using hybrid RSM–GA–PSO

AbstractEfficiency of electrocoagulation process for the removal of heavy metals (Pb2+, Mn2+, and Co2+) from simulated wastewater was investigated using iron electrode pair at five operational variables: pH (2–10), current density (0.076–0.189 A/cm2), inter‐electrode distance (3–7 cm), solution temperature (30–70°C), and charging time (5–20 min). Experiments were conducted as per central composite design (CCD), and the data was used for model building. Algorithmic optimization via hybrid response surface methodology (RSM)–genetic algorithm (GA)–particle swarm optimization (PSO) was studied where Pb2+, Mn2+, and Co2+ removal efficiency were monitored as responses from the finding equations of the CCD. Transition to the optimum removal (≥90%) of Pb2+, Mn2+, and Co2+ ions was achieved with current density of 0.19 A/cm2, solution temperature of 70°C, mean electrode distance of 3 cm and pH of 6.6 and charging time of 25 min for PSO relative to GA and RSM. Corresponding experiments conducted with the global optimal conditions show that the actual results (98.5, 90, 99%) were in reasonable agreement with the theoretical results of RSM (94.09, 90.82, 85.08%), GA (102.08, 87.91, 101.55%), and PSO (100.58, 90.91, 101.05%) for Pb2+, Mn2+, and Co2+, respectively. The mean error indices of 6.43, 2.83, and 1.73% for RSM, GA, and PSO, respectively, indicate that PSO guarantees best convergences to true optimal.

Multi-objective optimisation for minimum quantity lubrication assisted milling process based on hybrid response surface methodology and multi-objective genetic algorithm

Parametric modelling and optimisation play an important role in choosing the best or optimal cutting conditions and parameters during machining to achieve the desirable results. However, analysis of optimisation of minimum quantity lubrication–assisted milling process has not been addressed in detail. Minimum quantity lubrication method is very effective for cost reduction and promotes green machining. Hence, this article focuses on minimum quantity lubrication–assisted milling machining parameters on AISI 1045 material surface roughness and power consumption. A novel low-cost power measurement system is developed to measure the power consumption. A predictive mathematical model is developed for surface roughness and power consumption. The effects of minimum quantity lubrication and machining parameters are examined to determine the optimum conditions with minimum surface roughness and minimum power consumption. Empirical models are developed to predict surface roughness and power of machine tool effectively and accurately using response surface methodology and multi-objective optimisation genetic algorithm. Comparison of results obtained from response surface methodology and multi-objective optimisation genetic algorithm depict that both measured and predicted values have a close agreement. This model could be helpful to select the best combination of end-milling machining parameters to save power consumption and time, consequently, increasing both productivity and profitability.

Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change

ABSTRACTUnderstanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size.

Thermal boundary condition optimization of ball screw feed drive system based on response surface analysis

Abstract To improve the simulation accuracy of the traditional transient thermal characteristics analysis model (TCAM) of the ball screw feed drive system (BSFDS), the optimization method of the thermal boundary conditions (TBCs), including the thermal loads, the convective heat transfer coefficient and the thermal contact resistance (TCR), was proposed. A multi-objective and multi-parameter optimization model was established based on the hybrid response surface (HRS) and a multi-objective genetic algorithm (MOGA) was developed to optimize the above TBCs. To simulate the thermal characteristics of the BSFDS under different working conditions, the generality of the method was extended to predict the TBCs under different feed rates. To validate the effectiveness of the method, the thermal characteristic experiments of BSFDS were conducted. The results showed that the simulation error for the temperature field was reduced from 25% to 10% and that the simulation error for the thermal elongation was reduced from 30% to 11%.

Determination of biochemical oxygen demand and dissolved oxygen for semi-arid river environment: application of soft computing models.

Surface and ground water resources are highly sensitive aquatic systems to contaminants due to their accessibility to multiple-point and non-point sources of pollutions. Determination of water quality variables using mathematical models instead of laboratory experiments can have venerable significance in term of the environmental prospective. In this research, application of a new developed hybrid response surface method (HRSM) which is a modified model of the existing response surface model (RSM) is proposed for the first time to predict biochemical oxygen demand (BOD) and dissolved oxygen (DO) in Euphrates River, Iraq. The model was constructed using various physical and chemical variables including water temperature (T), turbidity, power of hydrogen (pH), electrical conductivity (EC), alkalinity, calcium (Ca), chemical oxygen demand (COD), sulfate (SO4), total dissolved solids (TDS), and total suspended solids (TSS) as input attributes. The monthly water quality sampling data for the period 2004-2013 was considered for structuring the input-output pattern required for the development of the models. An advance analysis was conducted to comprehend the correlation between the predictors and predictand. The prediction performances of HRSM were compared with that of support vector regression (SVR) model which is one of the most predominate applied machine learning approaches of the state-of-the-art for water quality prediction. The results indicated a very optimistic modeling accuracy of the proposed HRSM model to predict BOD and DO. Furthermore, the results showed a robust alternative mathematical model for determining water quality particularly in a data scarce region like Iraq.

Tolerance Sensitivity Analysis and Robust Optimal Design Method of a Surface-Mounted Permanent Magnet Motor by Using a Hybrid Response Surface Method Considering Manufacturing Tolerances

This paper presents a robust optimal design method using a hybrid response surface method (H-RSM) which directly finds an optimal point satisfying a target Z-value or a probability of failure. Through three steps, this paper achieves the goal that is to increase the open-circuit airgap flux (OCAF) in a surface-mounted permanent magnet motor and decrease its variation caused by variations of the airgap lengths including an additional one between permanent magnets and rotor back yoke. First, the OCAF equation is derived from the magnetic equivalent circuit (MEC) considering the additional airgap. Then, the equation is validated by comparing its results with those of the finite element method (FEM) modeled by the slotless stator. Next, the tolerance sensitivity analysis, using the partial derivative of the OCAF equation with respect to the airgap length, is performed to investigate the effects of design variables on the OCAF. It is shown that increasing the magnet thickness is effective for both increasing mean of the OCAF and reducing its variation. Finally, robust optimal design is performed using the H-RSM, in which all data are obtained from the FEM modeled by the slotted stator. The results of the robust optimal design are verified using the FEM.