Integrating Response Surface Methodology Research Articles

Insights into water-lubricated transport of heavy and extra-heavy oils: Application of CFD, RSM, and metaheuristic optimized machine learning models

With diminishing light crude oil reserves, the focus shifts to heavy and extra-heavy crude oil, posing challenges with high viscosity impeding flow. Water-lubricated technology addresses this issue in oil transmission lines. This study introduces a novel method integrating response surface methodology (RSM), computational fluid dynamics (CFD), and optimized machine learning (ML) models to analyze pipeline pressure gradients (PG) in oil–water two-phase flows downstream of T-junctions. The present study uses the D-optimal technique for simulation design to optimize CFD computational demands efficiently. This study breaks new ground by proposing a framework that leverages support vector machines (SVMs). The proposed framework incorporates metaheuristic optimization algorithms (genetic algorithm (GA) and particle swarm optimization (PSO)) to achieve superior PG prediction accuracy. The optimized ML models outperformed RSM models for predicting PG. Results indicated that oil-to-water viscosity ratio and oil inlet velocity significantly affect PG, followed by water inlet velocity and surface tension between phases. In contrast, the oil-to-water density ratio, oil entry angle at the T-junction, and wall contact angle have minimal impact. Furthermore, statistical metrics and visual comparison tools identified the PSO-optimized SVM model based on linear kernel function as the most effective (MAPE = 13.2 % and R = 0.9949). The hybrid methodology presented in this research holds significant promise for optimizing heavy oil transfer efficiency in applications involving water-lubricated technologies.

Enhanced biosynthesis of coated silver nanoparticles using isolated bacteria from heavy metal soils and their photothermal-based antibacterial activity: integrating Response Surface Methodology (RSM) Hybrid Artificial Neural Network (ANN)-Genetic Algorithm (GA) strategies.

This study explores the biosynthesis of silver nanoparticles (AgNPs) using the Streptomyces tuirus S16 strain, presenting an eco-friendly alternative to mitigate the environmental and health risks of chemical synthesis methods. It focuses on optimizing medium culture conditions, understanding their physicochemical properties, and investigating their potential photothermal-based antibacterial application. The S16 strain was selected from soils contaminated with heavy metals to exploit its ability to produce diverse bioactive compounds. By employing the combination of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)-Genetic Algorithm (GA) strategies, we optimized AgNPs synthesis, achieving an improvement of nearly 2.45 times the initial yield under specific conditions (Bennet's medium supplemented with glycerol [5g/L] and casamino-acid [3g/L] at 30°C for 72h). A detailed physicochemical characterization was conducted. Notably, the AgNPs were well dispersed, and a carbonaceous coating layer on their surface was confirmed using energy-dispersive X-ray spectroscopy. Furthermore, functional groups were identified using Fourier-transform infrared spectroscopy, which helped enhance the AgNPs' stability and biocompatibility. AgNPs also demonstrated efficient photothermal conversion under light irradiation (0.2 W/cm2), with temperatures increasing to 41.7°C, after 30min. In addition, treatment with light irradiation of E. coli K-12 model effectively reduced the concentration of AgNPs from 105 to 52.5µg/mL, thereby enhancing the efficacy of silver nanoparticles in contact with the E. coli K-12.

Parametric Optimization of FDM Process for PA12-CF Parts Using Integrated Response Surface Methodology, Grey Relational Analysis, and Grey Wolf Optimization.

Efficiently managing multiple process parameters is critical for achieving optimal performance in additive manufacturing. This study investigates the relationship between eight key parameters in fused deposition modeling (FDM) and their impact on responses like average surface roughness (Ra), tensile strength (TS), and flexural strength (FS) of carbon fiber-reinforced polyamide 12 (PA 12-CF) material. The study integrates response surface methodology (RSM), grey relational analysis (GRA), and grey wolf optimization (GWO) to achieve this goal. A total of 51 experiments were planned using a definitive screening design (DSD) based on response RSM. The printing process parameters, including layer thickness, infill density, and build orientation, significantly affect Ra, TS, and FS. GRA combines responses into a single measure, grey relational grade (GRG), and a regression model is developed. GWO is then employed to optimize GRG across parameters. Comparison with GRA-optimized parameters demonstrates GWO's ability to discover refined solutions, reducing average surface roughness to 4.63 μm and increasing tensile strength and flexural strength to 88.5 MPa and 103.12 MPa, respectively. Practical implications highlight the significance of GWO in industrial settings, where optimized parameters lead to reduced costs and improved product quality. This integrated approach offers a systematic methodology for optimizing FDM processes, ensuring robustness and efficiency in additive manufacturing applications.

Applications of integrated response surface methodology statistic techniques and artificial neural network‐based machine learning to optimize residual chlorine production and energy consumption

AbstractA multifactor interaction study was performed using the combined response surface methodology and an artificial neural network on the operational parameters and their influence on residual chlorine production. The operating variables, sodium chloride concentration, electrical potential, electrolysis time, and electrode gap, were evaluated over the response, residual chlorine and energy consumption. The results indicated that the optimum value for residual chlorine was 2450 mg/L achieved at an electrical potential of 8.8 V for 25 min in the presence of 25 g/L of sodium chloride and an electrode distance of 1 cm, and the optimum corresponding energy consumption was measured at 21.76 kWh/L. The study reveals that electric potential, sodium chloride concentration, and electrolysis time positively influence residual chlorine production. ANN models showed superior prediction ability compared with RSM models. This suggests electrolysis can be used for active chlorine production from saline solutions, potentially for industrial applications and water disinfection.

Enhanced production of mycelium biomass and exopolysaccharides of Pleurotus ostreatus by integrating response surface methodology and artificial neural network

This study aimed to enhance the production of mycelium biomass and exopolysaccharides (EPS) of Pleurotus ostreatus in submerged fermentation. Response Surface Methodology (RSM)sought to optimize culture conditions, whereas Artificial Neural Network (ANN)aimed to predict the mycelium biomass and EPS. After optimization of RSM model conditions, the maximum biomass (36.45 g/L) and EPS (6.72 g/L) were obtained at the optimum temperature of 22.9 °C, pH 5.6, and agitation of 138.9 rpm. Further, the Genetic Algorithm (GA) was employed to optimize the cultivation conditions in order to maximize the mycelium biomass and EPS production. The ANN model with an optimized network structure gave the coefficient of determination (R2) value of 0.99 and the least mean squared error of 1.9 for the validation set. In the end, a graphical user interface was developed to predict mycelium biomass and EPS production.

Integrating response surface methodology and machine learning for analyzing the unconventional machining properties of hybrid fiber‐reinforced composites

AbstractThe aim of this investigation was to delve into the impact of abrasive water jet machining (AWJM) process variables on the surface roughness (Ra) and kerf angle (Ka) of hybrid fiber‐reinforced polyester composites. Utilizing both response surface methodology (RSM) and artificial neural network (ANN) prediction models, the study sought to optimize the input parameters for abrasive water jet machining, specifically in the context of paddy straw and PALF‐reinforced polyester hybrid composites. The process parameters targeted for optimization included the abrasive flow rate, traverse rate, and standoff distance during AWJM. The investigation identified an optimal combination of AWJM parameters that effectively meets the practical requirements for machining hybrid fiber‐reinforced polyester composites. According to the RSM, the suggested optimal values for the process parameters are an abrasive flow rate set at 300 g/min, traverse speed at 110 mm/min, and standoff distance at 1 mm. The ANN exhibited robust predictive capabilities, achieving high R2 scores of 0.932 and 0.962 for surface roughness and kerf angle, respectively. To enhance the performance of abrasive water jet machining and minimize surface roughness and kerf angle, the researchers conducted an optimization of the process parameters. Subsequently, confirmation experiments were executed to validate the predictive model and fine‐tune the set of process parameters for practical application.Highlights Investigated AWJM impact on Ra value and kerf angle of hybrid composites. Used RSM and ANN models for parameter optimization in biocomposite. Optimal AWJM parameters: AFR (300 g/min), TS (110 mm/min), and SOD (1 mm). ANN showed strong predictions: R2 scores 0.932 (Ra) and 0.962 (Ka). Confirmation experiments validated the predictive model for applications.

Integrating response surface methodology and finite element analysis for model updating and damage assessment of multi-arch gallery masonry bridges

Integrating response surface methodology and finite element analysis for model updating and damage assessment of multi-arch gallery masonry bridges

Parametric Optimization In Drilling Process Parameters For Machining Of Glass Fibre Reinforced Composites Using Grey Relational Grade Analysis

The aim of the research is to optimize the drilling process parameters while drilling of glass fi bre reinforced composites using solid carbide drill by integrating response surface methodology and Taguchi technique. Drilling process parameters includes cutting speed, Feed rate and point angle with three levels. The responses measured against these drilling parameters are; material removal rate, surface roughness, oversize and delamination factor. The ANOVA analysis is carried out for the responses and the regression equations are developed and checked for its adequacy. The R-squared value for material removal rate is found to be 99.40%, for surface roughness is found to be 96.81% , for over size is found to be 98.2% and for delamination factor is found to be 96.46% and these values are acceptable. Also Taguchi grey relational grade analysis is carried and the grey relational coeffi cients are determined and they are ranked. The optimal parameter is fi nally determined using the grey relational analysis and the experiments are validated.

A hybrid DMAIC framework for integrating response surface methodology and multi-objective optimization methods

A hybrid DMAIC framework for integrating response surface methodology and multi-objective optimization methods

Performance optimization of preheated palm oil-diesel blends using integrated response surface methodology and analysis of variance

Performance enhancement is one of the major research topics where researchers are focusing on various biofuels, blending ratios and optimization. Whereas previous researches have not optimized the performance of preheated palm oil (PHPO) with diesel, this research optimizes the performance of PHPO with diesel at 90°C. Investigations were made with various volume fractions i.e. 0.05, 0.1, 0.15 and 0.2 of PHPO in diesel to form O5, O10, O15 and O20, respectively. Regression modelling employing RSM is adopted to find the behavioural variation of input parameters on the performance of PHPO. Central composite rotatable designs with 31 experimental runs were conducted. Blending ratio (BR), compression ratio (CR), load and water flow were used as input parameters. Theoretical modelling was carried out to correlate the control parameters with performance parameters like brake thermal efficiency (BTE), specific fuel consumption (SFC) and exhaust gas temperature (EGT). There was an increase in BTE, decrease in SFC and EGT on increasing the load and CR. A maximum BTE of 25% was achieved by varying the CR and load from 18.5 to 20 and 7–10 kg, respectively. Regarding SFC and EGT, a 10% decrement in SFC and a 3% decrement in EGT was achieved by varying the CR from 16 to 20. All the tests with higher CR gave lower emissions for all PHPO blends. Water flow rate does not have any influence on performance. In particular, O10 blend gave better performance at maximum load with a CR 18 and was found to be a potential choice for diesel engine.

Optimization of Activated Tungsten Inert Gas Welding Process Parameters Using Heat Transfer Search Algorithm: With Experimental Validation Using Case Studies

The Activated Tungsten Inert Gas welding (A-TIG) technique is characterized by its capability to impart enhanced penetration in single pass welding. Weld bead shape achieved by A-TIG welding has a major part in deciding the final quality of the weld. Various machining variables influence the weld bead shape and hence an optimum combination of machining variables is of utmost importance. The current study has reported the optimization of machining variables of A-TIG welding technique by integrating Response Surface Methodology (RSM) with an innovative Heat Transfer Search (HTS) optimization algorithm, particularly for attaining full penetration in 6 mm thick carbon steels. Welding current, length of the arc and torch travel speed were selected as input process parameters, whereas penetration depth, depth-to-width ratio, heat input and width of the heat-affected zone were considered as output variables for the investigations. Using the experimental data, statistical models were generated for the response characteristics. Four different case studies, simulating the real-time fabrication problem, were considered and the optimization was carried out using HTS. Validation tests were also carried out for these case studies and 3D surface plots were generated to confirm the effectiveness of the HTS algorithm. It was found that the HTS algorithm effectively optimized the process parameters and negligible errors were observed when predicted and experimental values compared. HTS algorithm is a parameter-less optimization technique and hence it is easy to implement with higher effectiveness.

Numerical Analysis of Multiple Factors Affecting Hydraulic Fracturing in Heterogeneous Reservoirs Using a Coupled Hydraulic-Mechanical-Damage Model

Hydraulic fracturing performance, affected by multiple factors, was essential to the economic exploitation of oil and gas in heterogeneous unconventional reservoirs. Multifactor analysis can gain insight into the fracturing response of reservoirs and in turn optimize the treatment design. Based on characterizations of the geological setting of a heterogeneous glutenite reservoir, the hydraulic fracture (HF) initiation and propagation process, as well as the stimulated reservoir volume (SRV), were simulated and analyzed using a coupled hydraulic-mechanical-damage model. The Weibull distribution was employed to describe rock heterogeneity. The numerical model was verified with microseism (MS) interpretation results of HF geometry. A multifactor analysis and optimization workflow integrating response surface methodology, central composite design (CCD), and numerical simulations was proposed to investigate the coupling effects of multiple geomechanical and hydrofracturing factors on SRV and identify the optimum design of fracturing treatment. The results showed that the horizontal stress difference and injection rate were the most significant factors to control the SRV. Increasing the injection rate and reducing fluid viscosity may contribute to improving the SRV. It is more difficult to increase the SRV at higher horizontal stress difference than at lower horizontal stress difference. The multifactor analysis and optimization workflow introduced in this work was a practical and effective method to control the HF geometry and improve the SRV. This study provided a deep understanding of the hydraulic fracturing mechanism and possessed theoretical significance for treatment design.

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).

Integrating artificial neural network and scoring systems to increase the prediction accuracy of patient mortality and organ dysfunction.

The aim of this study was to develop and compare techniques to increase the prediction accuracy of patient mortality and organ dysfunction in the Intensive Care Units (hereinafter ICU) of hospitals. Patient mortality was estimated with two models of artificial neural network (ANN)-backpropagation (BP) and simplified acute physiology score (SAPS). Organ dysfunction was predicted by coupled ANN self-organizing map (SOM) and logistic organ dysfunction score (LODS) method on the basis of patient conditions. Input dataset consisted of 36 features recorded for 4,000 patients in the ICU. An integrated response surface methodology (RSM) and genetic algorithm (GA) was developed to achieve the best topology of the ANN-BP model. Although mortality prediction of the best ANN-BP (MSE=0.0036, AUC=0.83, R2=0.81) was more accurate than that of the SAPS score model (MSE=0.0056, AUC=0.82, R2=0.78), the execution time of the former (=45min) was longer than that of the latter (=20min). Therefore, the principal component analysis (PCA) was used to reduce the input feature dimensions, which, in turn, reduced the execution time up to 50%. Data reduction also helped to increase the network accuracy up to 90%. The likelihood of organ dysfunction determined by coupled ANN and scoring method technique can be much more efficient than the LODS model alone because the SOM could successfully classify the patients in 64 classes. The primary patient classification plays a major role in increasing the efficiency of an estimator.

Fuzzy prediction and RSM optimization of CI engine performance analysis: aegle marmelos non-edible seed cake pyrolysis oil as a diesel alternative

ABSTRACT The present study focuses on performance analysis of a Compression Ignition (CI) engine, fueled with Aegle marmelos (AM) de-oiled seed cake bio-oil opus. The experimentation carried out on a single-cylinder diesel engine under various engine load (W) and fuel opus (F) at the standard compression ratio (17.5:1). The obtained experimental data are used to analyze the performance analysis of the CI engine by using fuzzy prediction and Taguchi integrated response surface methodology (RSM) optimization. The experimental, and Taguchi integrated RSM results represent that F20 modified fuel provided the enriched performance as compared with plain diesel (F0) and another altered fuel opus. In the predicted fuzzy model and RSM results offered the average correlation coefficient (R) of 0.9787 and the average coefficient of a determination (R2) of 0.9119 correspondingly.

Attaining optimized A-TIG welding parameters for carbon steels by advanced parameter-less optimization techniques: with experimental validation

Enhanced penetration potential of activated TIG (A-TIG) welding process over conventional TIG welding has made the former process preferred for welding in recent times. The quality and shape of the weld in A-TIG are not only influenced by the chemical composition of the flux, but also by the selection of welding parameters. As a variety of process parameters influence the results, a proper understanding of process performance and identification of favorable welding conditions (optimum setting of process parameters) are indeed necessary to improve quality. The present work highlights the application potential of multi-response optimization route by integrating response surface methodology with the JAYA optimization algorithm, particularly for optimizing the A-TIG welding process parameters for carbon steels. Systematic experiments were carried out considering welding current, arc gap and travel speed as input parameters, whereas the depth of penetration, depth-to-width ratio, heat input and total width of heat-affected zone were considered as output performance characteristics. An attempt has been made in the current study to recognize the precise setting of selected input process parameters for simultaneous optimization of the aforementioned performance characteristics. The result of JAYA algorithm has also been compared with the teaching–learning-based optimization technique. While fairly similar results were achieved, the implementation of JAYA algorithm was computationally efficient. Experimental validation of the single-objective as well as multi-objective optimization results indicates that the empirical models for the response parameters as well as the proposed optimization framework are accurate tools in A-TIG welding research.

Integrated response surface methodology and UHPLC coupled with triple quadrupole time‐of‐flight MS quantitation to investigate the salt‐processing chemistry of traditional Chinese medicines: A case study on Achyranthes bidentata

AbstractA sensitive ultra high performance liquid chromatography with tandem mass spectrometry approach for the quantification of eight bioactive constituents, including β‐ecdysterone, 25S‐inokosterone, achyranthoside D, ginsenoside Ro, chikusetsusaponin IV, achyranthoside C, chikusetsaponin IVa, and 13‐hydroxyoctadecadienoic acid, in raw and salt‐processed Achyranthes bidentata was developed and validated. Response surface methodology was employed for optimization of three parameters of ultrasound‐assisted extraction process, including the extraction time, methanol percentage, and solvent volume, to give optimum extraction conditions as follows: extraction time of 40.2 min, methanol percentage 67.2% v/v, and solvent volume of 48.4 mL. The established approach was interrogated in terms of linearity, sensitivity, precision, repeatability as well as recovery. Intra‐ and inter‐assay variability for all analytes ranged from 1.6 to 3.1% and from 2.0 to 3.2%, respectively. The standard addition method determined recovery rates for each analyte (99.2–103.6%). In addition, the developed approach was applied to 20 batches of collected raw samples of Achyranthes bidentata and their salt‐processed products. After salt‐processing, the contents of β‐ecdysterone, 25S‐inokosterone, achyranthoside D, chikusetsusaponin IV and achyranthoside C were elevated, while the content of ginsenoside Ro was decreased. The results showed that experimental design in combination with ultra high performance liquid chromatography with tandem mass spectrometry quantification provided insights into the chemistry alteration between raw Achyranthes bidentata and its salt‐processed product.

Optimization of Hydrogen-Enriched Biogas by Dry Oxidative Reforming with Nickel Nanopowder Using Response Surface Methodology

Today, the worldwide research is focused on the development of alternative energy sources for power generation; thus, the present study aims to optimize the dry oxidative reforming (DOR) process parameters for H2-enriched biogas production by integrating response surface methodology with a three-level, three-factor Box–Behnken design in the presence of commercial Ni nanopowder. First, the effect of CH4/CO2 and O2/CH4 ratios on the catalytic performance of DOR was assessed in the temperature range of 800–900 °C. The reactant (CH4 and CO2) conversions, product (H2 and CO) yields, selectivities of H2 and CO, H2/CO ratio, and specific energy consumption were chosen as responses. The empirical regression models were developed to identify the influential and most significant parameters. More than a 95% value of determination coefficients by analysis of variance proved that the developed regression models were highly satisfactory. Experimentally, a maximum H2 enrichment of 38.7% with 82.9 and 90.8% CH4 and CO2 c...

Optimization of Machining Parameters for Improving Energy Efficiency using Integrated Response Surface Methodology and Genetic Algorithm Approach

Machine tools consume enormous amount of energy during machining, build-up to machining, post machining and idling condition to drive motors and auxiliary equipments in the manufacturing system. Reduction of energy consumption during the machining phase is extremely important to improve the environmental performance over the entire life cycle. This paper presents a predictive and optimization model based on integrated response surface methodology and genetic algorithm approach to predict the energy consumption and the corresponding machining parameters during the turning of AISI 1045 steel with a tungsten carbide tool. Experiments using Taguchi design are performed to develop the predictive model. The developed predictive model is used to formulate the objective function for genetic algorithm. The confirmation experiments are performed to validate the developed model and the results are found within 4% error. The statistical significance of the developed model has been tested by the analysis of variance test. This research will be beneficial for a number of manufacturing industries for selection of machine tools on the basis of energy consumption. The reduction of peak load through optimization will results in lowering the energy consumption of the machine tools during non-cutting time.

Fabrication of 6061 aluminum alloy reinforced with Si3N4/n-Gr and its wear performance optimization using integrated RSM-GA

ABSTRACTIn this research work the dry sliding wear behavior of a hybrid aluminum metal matrix composite is evaluated. Al 6061 is used as a matrix material while Si3N4 and nanographite powder (3–15 wt%) are used as reinforcements. These two reinforcements (50 wt% of each) were blended in a high-energy ball mill for homogeneous mixing to derive the sound aluminum matrix composite (AMC). The hybrid composite is made by the stir casting route and its wear rate was investigated against an EN32 steel disc surface, using a pin-on-disc tribometer. Integrated response surface methodology (RSM) and genetic algorithm (GA) are used to optimize the pin-on-disc process parameters. Analysis of variance (ANOVA) shows that sliding distance plays a major role on the dry sliding wear rate followed by load, sliding speed and reinforcements. Two-factor interactions and quadratic terms have also significant contributions. GA suggested a minimum wear rate value of 0.827 mg at optimized setting. Microstructural analysis by scanning electron microscopy (SEM) reveals that very fine grooves are obtained at optimized settings while at other settings severe ploughing is observed. Transition of wear mechanism takes place with the increase of speed (i.e., temperature between the two rubbing surfaces) from abrasive to adhesive.