RSM Method Research Articles

Optimization of chloride ion removal from drinking water using graphene oxide modified with AgNO3 via CCD-based RSM method

Chloride (Cl−) is one of the pollutants that can cause significant health risks when its concentration is high in drinking water. Utilizing adsorption has a superiority in removing pollutants from water due to their large active surface area, small volume, and high adsorption capacity. Herin, graphene oxide (GO) modified with AgNO3 was used for Cl− removal. FESEM, EDAX, FTIR, zeta potential (ζ), and XRD analysis were employed for the characterization of synthesized adsorbent. The maximum Cl− removal (99.24 %) was achieved under optimized conditions by RSM based on CCD method (adsorbent dosage: 2 g/L, Cl− concentration: 30 mg/L, and reaction time: 17 min). The models of Langmuir and Freundlich isotherms were employed to determine the adsorption process. It was found that the Langmuir isotherm model with an R2 value of 0.9987 described the Cl−removal process appropriately. Finally, a comparison between the adsorbent's performance before and after modification with AgNO3 was made, demonstrating that the modified absorbent provided 87 % better efficiency than the unmodified adsorbent in the case of Cl−removal.

New insights of crude oil biodegradation construction by microbial consortium B10: Responded substrates, genomics, biodegradation mechanism and pathways

Crude oil pollution caused by production and living demands has existed for decades, which adverse to ecological security and human health. Microbial degradation of crude oil is a green and effective remediation method. This study is intended to develop a microbial consortium based on the commonness and differences of key genes and response substrates of the strains to efficiently degrade crude oil. Four crude oil-contaminated soil dwelling bacteria, CO1, CO2, CO6 and CO7, were isolated whose crude oil degradation and emulsification performance were evaluated. Subsequently, revealing their genomic characteristics and response substrates, the key genes of CO1 were mapped to genes alkB, hcaD; CO2 were located at genes andAd, andAc; CO6 were mapped to genes ladA, alkM, pcaH, benB-xylY and CO7 were located at gene cypd-E. Consortium of Ochrobactrum.sp, Pseudomonas aeruginosa and Bacillus.sp were selected as microbial consortium B10. Compared with single strain, the crude oil removal efficiency of B10 via RSM method optimizing is up to 51.58 % in 6 days, where the C11-C18 removal rate is up to 78.51 %. Besides, the biodegradation rates of C26-C29 and C33-35 increased by 36.20 % and 30.51 %, respectively. The possible metabolic pathways of crude oil were proposed, including aldehyde bond (H-C=O) and ketone bond (C-O-C) hydrodehydration, ring opening of cyclic ether, C-C bond oxidation of benzene ring and ring-opening hydrolysis of gentisic acid reactions. Altogether, this study provides a new insight into high-performance microbial consortia construction in crude oil biodegradation.

Optimizing fluid parameters of heat transfer and velocity of aluminum oxide nanoparticles and SWCNT passing through blades using RSM statistical method

This paper explores how the temperature and velocity change in a specific direction and the rotational velocity when nanofluids flow through triangular, rectangular, and chamfer baffles. The novelty of this article is to study and investigate the thermal and fluidic parameters of the velocity gradient and heat transferred between aluminum oxide and SWCNT nanofluids on the tensile surface. This study aims to increase the heat transfer coefficient by installing blades with different shapes. The Finite Element Method is chosen to solve the main equations. This paper utilized the RSM method to optimize the velocity of nanofluid and heat transfer as it passes through the stretching sheet. The main goal of this study mentioned in the article is to explore the impact of various vane shapes installed on the outer surface of a stretched sheet. To summarize, after analyzing the flow of SWCNT and Al2O3 nanofluids on various baffles and blades, it was found that the temperature of SWCNT nanofluid around the baffles was higher compared to the temperature of Al2O3 nanofluids. According to the results from the graphs of how fast something is turning and the factors that transfer heat in the software called Design-Expert, the best improvement happened when the velocity and temperature of the small particles in the liquid were at u = 1.12, and T = 20.18 and the turning velocity was N = 137.29.

Synthesis of activated carbon/magnetite nanocatalyst for sono-Fenton-like degradation process of 4-chlorophenol in an ultrasonic reactor and optimization using response surface method

Effective removal of 4-chlorophenol, a toxic and persistent organic pollutant, from wastewater is of utmost importance for environmental preservation and human well-being. The aim of this study is to design a new reactor for the quasi-Fenton process to increase the degradation efficiency of 4-chlorophenol using ultrasonic waves. For this purpose, activated carbon/magnetite nanocatalyst was synthesized using active carbon obtained from used tires and Fe3O4 nanoparticles prepared by the chemical coprecipitation method. The characterization of the active carbon/magnetite nanocatalyst showed a specific surface area of 172.46 m2g−1, and the size of the nanocatalyst was 10 nm. In addition, FTIR, EDX and XRD analyses were also investigated to identify the nanocatalyst. Evaluation of mechanical and chemical resistance confirmed the stability of the nanocatalyst. The degradation process of 4-chlorophenol was optimized using the RSM method. The experimental findings indicated that the optimal operating conditions for maximizing 4-chlorophenol removal efficiency were found to be pH 3, temperature 42 °C, nanocatalyst dose 2 gL−1, hydrogen peroxide concentration 25.58 mM ultrasonic power 320 W, and reaction time of 78 min. The validity of these optimal conditions was further confirmed through real-world experiments, resulting in remarkable removal efficiencies of 97.32 % and 93.47 % for 4-chlorophenol concentrations of 250 and 500 mgL−1, respectively.

Application of essential oils and optimizing storage conditions for control of postharvest diseases in apple

Postharvest loss in fruits and vegetables accounts for more than one third of the world production. On the other hand, using chemicals has raised food health concerns, and grown the demand for eco-friendly materials. Given the promising results of essential oil use to control and prevent postharvest decay, we conducted this research. In the present study, a two-step statistical method was used to determine and optimize application of essential oils along with parameters of storage conditions. Significant level of essential oils and storage conditions (temperature, ventilation, and relative humidity) were screened by using PBD method and the best concentration were determined by central composite design of response surface method. The results showed that 1000 and 1500 microgram/l (µg/l) of basil essential oils, and 1500 µg/l of peppermint essential oils reduced the lesion diameter in apple fruits infected with Penicillium expansum. All storage conditions had significant effect on postharvest decay. Based on the CCD of RSM method, the best concentration of essential oils and the optimal level of the storage condition were determined. Furthermore, basil and peppermint treatments reduced rate of the ethylene production during 56 days after treatment. The results of this study revealed and confirmed that basil essential oils as a postharvest treatment under optimized storage conditions can be utilized as a low-cost substrate for controlling postharvest decay in apple.

“Optimize the Mixed Proportions of Geopolymer Concrete to achieve Desired Strength by using RSM Method”

Abstract: GGBFS-FA-based GPC offers a clean and sustainable development technology alternative. In this study, the RSM method was used to optimize the mixed proportions of geopolymer concrete to achieve desired strength criteria. Four factors and four levels were considered: binder content, including four combinations of FA and GGFBS dosage, dosage of super plasticizer (0.5, 1.0, 1.5 and 2%), Na2SiO3/NaOH ratio (1.5, 2.0, 2.5 and 3), and molarity (6, 8, 10 and 12). Using these ingredients and factors, the effect of compressive strength was examined. The RSM approach using an L16 orthogonal array was employed to find the optimum condition of every factor while limiting the number of experiments

Optimasi Sudut Baji, Kecepatan Pemakanan & Rpm Terhadap Kekasaran Permukaan Material SCM 440 Dengan Metode Respon Surface (RSM)

The development of manufacturing technology has developed so rapidly. In this study, SCM 440 steel material was used, which is an alloy steel of chromium molybdenum. This steel has the advantages of stable composition,low harmful elements,high steel purity. Surface roughness is important, surface roughness is defined as the irregularity of surface configuration in an object resulting from a finishing process. This study aims to determine the influence of process variables on the surface roughness rate of SCM 440 material by varying the total cutting angles of 700,750 and 800 then variations in feeding speed of 0.08mm, 0.09mm, and 0.10mm and spindle speed or RPM, namely 160,220, and 280 puts / min in the RSM method. The Rspon Surface method is a set of mathematical and statistical techniques that are useful for analyzing problems where several independent variables affect the response variable and determine the optimal value of the process variable. From the results of the study, the value of the Second order yˆ = + 0.092224 X1² + 0.208515 X2² - 0.03661 X3² was obtained. With stationary point value X0 = ( total Cut angle ) = - 0.382409, X0 = (feeding) = 0.161715, X0 = (RPM) = 0.578250. and found the optimal values of Wedge Angle (78.212), Feeding Speed (0.095 mm) and RPM (278.29 put/min). Then it can be analyzed the optimal value that is different from the value of the RSM method framework and if the values are the same then the research fails.

Effect of basil seed and xanthan gum on physicochemical, textural, and sensory characteristics of low-fat cream cheese.

This study aims to produce fat-reduced cream cheese using the different levels (0.25%-0.5%) of basil seed and xanthan gum by a RSM method. The basil seed, xanthan gum, and fat levels did not significantly influence the cream cheese's pH and acidity. With the fat reduction, textural properties were lost; for example, hardness, gumminess, and adhesiveness increased, and cohesiveness decreased. In addition, low-fat cream cheese's sensory score (taste, mouthfeel, and overall acceptance score) was lower. However, adding basil seed and xanthan gum could improve water holding capacity (WHC), hardness, gumminess, cohesiveness, adhesiveness and scores of mouthfeel, and overall acceptance. Basil seed gum had a better impact than xanthan on fat-reduced cream cheese properties among the two gums. In general, results showed that adding 0.5% basil and 0.5% xanthan into cream cheese could manufacture a product with a reduced-fat level (19.04%). At the same time, its physicochemical, sensory, and textural attributes were similar to cream cheese with high fat (24%). In addition, the price of the obtained product was lower.

Thermal conductivity of hydraulic oil-GO/Fe3O4/TiO2 ternary hybrid nanofluid: Experimental study, RSM analysis, and development of optimized GPR model

In the present paper, the thermal conductivity (TC) of a hydraulic oil-based nanofluid in the presence of ternary nano-additives, graphene oxide (GO), iron oxide (Fe3O4), and titanium dioxide (TiO2), is analyzed in a wide range of volume fractions (VFs), temperatures, and mixing ratios (MRs). The stability of ternary hybrid nanofluids (THNFs) and size distribution of nanomaterial is obtained through zeta potential and dynamic light scattering (DLS) tests. Zeta potential and DLS tests indicated the remarkable stability of the samples with the GO(2): Fe3O4(1): TiO2(1) MR. Analysis of the measurements revealed that the enlargement in temperature and VFs improved the TC of THNFs for all MRs (1:1:1, 2:1:1, 1:2:1, 1:1:2). The highest TC enhancement is observed at the highest temperature (65 °C) and VF (1%), which for the MRs of 1:1:1, 1:1:2, 1:2:1, and 2:1:1 equal to 36.04%, 26.28%, 25.95%, and 33.86%, respectively. Furthermore, considering the average TC enhancement in the presence of nano-additives for various temperatures, MRs of GO(1): Fe3O4(1): TiO2(1) and GO(1): Fe3O4(1): TiO2(2) indicated the best and worst efficiency with 30.46% and 22.01%, respectively. The RSM method is applied to provide a simple and efficient formula-based model to describe the TC of THNFs in terms of input variables. In addition, a novel genetic algorithm-based optimization of training/structure parameters of Gaussian process regression (GPR) as a leading machine learning algorithm is developed, which provided thoroughly precise outcomes (Rtest2=0.9994 and Rtrain2=0.9998) for the prediction of TC of THNFs. The sensitivity analysis for the present THNFs revealed that the TC sensitivity is maximized at the highest VF and temperature.

Response surface methodology optimization of dynamical solutions of Lie group analysis for nonlinear radiated magnetized unsteady wedge: Machine learning approach (gradient descent)

When a consistent transverse magnetic field and heat radiation are present, the flow of boundary layer, Over porous wedge nanofluids, hybrid nanofluids, and ternary hybrid nanofluids have been studied., Water as base fluid, Hybrid nanofluid, Ternary Hybrid nanofluid, and nanofluid cases containing Case-1 Polyethylene Glycol-Water + AA7072, Case-2 Zirconium oxide + AA7072 + Polyethylene Glycol-Water Case-3 Magnesium oxide + Polyethylene Glycol-Water + AA7072 + Zirconium oxide is taken into consideration. Runge-Kutta (4th order) with the Shooting technique is used to solve the governing equations expressed in terms of Odes. For various values of the relevant parameters, the approximate relationship between temperature, velocity, rate of heat transfer, and shear stress at the wedge is depicted visually. It is found that the Nusselt number transfer rate is more in Ternary hybrid nanofluids than Hybrid and Nanofluids and the Skin friction rate is more in Hybrid nanofluids than in Ternary and Nanofluids. Table 9 shows the comparative study with recently published paper numerically having a good agreement of results. RSM method is useful to find the optimization conditions values based on the key factors that impact the Response Function. Simple linear regression machine learning with the Gradient descent method has been applied to some of the dimensionless parameters this method predicts the truth values accurately.

A Combinatorial Optimization Strategy for Performance Improvement of Stratum Ventilation Considering Outdoor Weather Changes and Metabolic Rate Differences: Energy Consumption and Sensitivity Analysis

Since occupants spend most of their time indoors, an energy-saving and comfortable indoor environment are particularly important. The differences in the metabolic rate of occupants make them have different requirements for their thermal environment. To save energy under the comprehensive needs of occupants for thermal environment, the combinatorial optimization strategy based on NSGA-II and improved the TOPSIS method is proposed in this study. Firstly, the physical model of the CFD simulation is verified by experiments. Secondly, the specific operation cases corresponding to combinations of different levels of factors are determined via the RSM method, and the ventilation performance prediction model considering the metabolic rate differences and outdoor weather changes is established. Thirdly, supply air velocities and temperatures are optimized by using Pareto-based NSGA-II; the Pareto optimal solution set under different outdoor temperatures is obtained. Finally, based on the Pareto optimal solutions at different outdoor temperatures, the optimal strategy under dynamic outdoor air temperature is obtained by improved TOPSIS by the CRITIC method. The optimization of ventilation parameters significantly improved the ventilation performance, and the results show that the predicted mean vote, energy consumption, vertical air temperature difference between head and ankle levels and the local mean age of air for different metabolic rates decrease by 64.1%, 4.74%, 24.83% and 7.39% on average, respectively. Moreover, the relative energy saving rate increases as the metabolic rate increases, and the strategy facilitates adaptation to outdoor weather changes and meets the individual needs of occupants for the indoor environment. This has important implications for achieving the global goal of energy efficiency and emission reduction.

Application of Response Surface Methodology for Optimization of the Biosorption Process from Copper-Containing Wastewater.

Copper-containing wastewater is a significant problem in the water industry. In this work, biosorption of copper ions on alginate beads have been considered as a promising solution. The effective diffusion coefficient De is the parameter describing the diffusion of copper ions in calcium alginate granules. Granules with a wide spectrum of alginate content from several to several dozen percent (0.6-20%) were tested. The granules with an alginate content of 20% were produced by a new method. The conductometric method was used to determine De. The study determined the De values depending on the process parameters (temperature and pH of copper solutions) and the alginate content in the granules. The RSM method was used to analyze the obtained results. The conducted research proved that all analyzed factors significantly affect the value of the diffusion coefficient (R2 = 0.98). The optimum operating conditions for biosorption of copper ions from CuCl2 salt, on alginate beads obtained by RSM were as follows: 0.57% of alginate content in the granules, temperature of 60.2 °C, and pH of 2. The maximum value of De was found to be 2.42·10-9 m2/s.

Performance Analysis of EDM on Grey Cast Iron Using RSM and TOPSIS Method

Performance Analysis of EDM on Grey Cast Iron Using RSM and TOPSIS Method

Sensitivity Analysis Using One-Factor-at-a-Time and Response Surface Method in Liquid-Dominated Geothermal Reservoir

In addition to extensive information that has been obtained from pre-feasibility, exploration, and drilling phase, we can improve our knowledge of reservoir behavior related to thermal extraction using sensitivity analysis. Such analysis is commonly applied to address technical uncertainty and risks in economic evaluation. The purpose of this study is to determine the parameters that have the most influence on thermal power generation using two different approaches named one-factor-at-a-time or OFAT and response surface method or RSM. Moreover, RSM analysis allowed us to make a predictive model for thermal power extracted in liquid-dominated geothermal reservoir. Literature study is conducted to understand various properties commonly encountered in a liquid-dominated geothermal reservoir including porosity, conductivity, reservoir temperature, and permeability. This information is then used to construct reservoir model in CMG STARS simulator with a single producer and injector. Two different sampling method, named OFAT and Box-Behnken are used to construct dataset, each contains different combination of levels of reservoir porosity, conductivity, temperature, permeability, and re-injection temperature. A total of 31 models using OFAT method with 7-level for each parameter are simulated to understand individual effect of each parameter. Meanwhile, 47 models are constructed using RSM method with 3-level for each parameter to evaluate the effect of interaction between parameters on thermal generation potential as well as constructing predictive model. Sensitivity analysis using both OFAT and RSM agree that the reservoir temperature is the most significant characteristic of geothermal reservoir to affect its thermal power potential. Meanwhile, re-injection temperature that initially expected to strongly effect the lifetime and sustainability of a liquid-dominated geothermal utilization is insignificant. This finding suggest that optimization re-injection temperature is solely for the purpose of maintaining sustainability of geothermal reservoir or cater the concern of environmental issue on wastewater management, and not for maximizing the thermal extraction.

Predictive modelling and optimization of performance and emissions of an auto-ignited heavy naphtha/n-heptane fueled HCCI engine using RSM

In this study, the effects of engine speed and lambda input parameters of a single-cylinder HCCI engine on the performance, combustion and emissions with the use of fuels with different concentrations were investigated. As finding of the best operating point of the engine perfomance is vital, therefore, in this research work the Response Surface Method waas used to model and optimize the process. The processes of determining the experimental sets, creating the model equations of the response parameters and performing the optimization were carried out with the RSM method. The engine speed was determined as 800–1600 rpm, the lambda value was 1.8–2.6 and the naphtha ratio in the mixed fuel was 0–100 %. As a result of the study, ANOVA tables, model equations, contour graphics of response parameters were created and the effects of input parameters were examined in detail. The accuracy of the model equations created by comparing the estimated and actual response parameter values has been strengthened. After the optimization, the optimum input parameters were calculated as 75 % naphtha ratio, 1166.75 rpm engine speed and 2.12 lambda value. The response parameter values obtained depending on the optimum input parameters are effective torque 6.26 Nm, indicated thermal efficiency 33.09 %, BSFC 196.79 g/kWh, CA10 0.77 °CA, CA50 5.6 °CA, combustion duration 28.84 °CA, COVimep 1.46 %, MPRR It was determined as 6.24 bar/°CA, UHC 375.96 ppm and CO 0.05 %.

Numerical and Analytical Investigation of the Surface Evaporation Rate of the Different Nanofluids and Optimization Results by Using the RSM Method

Numerical and Analytical Investigation of the Surface Evaporation Rate of the Different Nanofluids and Optimization Results by Using the RSM Method

Experimental study of quality respones during Gas metal arc welding in industrial welding robots by using Response surface method

Input parameters of GMAW can vary continuously at any point of time when used in an industrial welding robot and thus have a huge influence at output quality responses of the weld joint. In advent of welding automation, Industrial robots are used by most manufacturers to boost production rate & reduce operational cost. This publication tries to establish an optimum process parameter band for quality weld joint during continuous robot welding. To establish a relationship between both parameters RSM method has been used in an empirical approach. Input parameters of GMAW which are considered for this study are current, wirefeed rate and industrial robot arm speed, and the corresponding output response in form of weld bead geometry which defines the quality of the process. These all-input parameters have different effect on welding quality independent to each other. Stainless steel material (SUS409) has been used as experiment material.

Combined septum and chamfer fins on threated stretching surface under the influence of nanofluid and the magnetic parameters for rotary seals in computer hardware

In this paper, variation of temperature and velocity in the x-direction and the angular velocity of the nanofluids flow through triangular and rectangular and chamfer fins are investigated in the existence of a uniform magnetic field. The innovation of this paper is to investigate parameters of Mwcnt and TiO2 nanofluids passing from the different fins and blades on the stretching surface and compare their results together. Also by using FlexPDE and Design software, we were able to show all temperature and angular velocity counters in the form of diagrams and 3D images. The finite Element Method is selected for solving governing equations. The second part of the description relates to the use RSM method in the Design-Expert software. In this paper by using the RSM method, optimized the nanofluid velocity and heat transfer passing from the stretching sheet. Based on the results obtained from the angular velocity graphs and the heat transfer cantors in the Design-Expert software, the best optimization occurred for nanofluid velocity and nanofluid temperature, and angular velocity with u = 1.19 and T = 21.15 and N = 117.28. By comparison between the passing of MWCNT and TiO2 nanofluids flow on the different fins and blades, the results show that the temperature of MWCNT nanofluid around the fins has a greater value than the TiO2 nanofluids. The maximum heat transfer and temperature occurs near the rectangular fins for TiO2, and is 0.14% bigger than triangular and chamfer fins.

Optimization of Pancreatic Lipase Inhibition in Fermented Beverages Brewed from Katuk Leaves Using RSM

The inhibition of pancreatic lipase and the associated reduction of lipid absorption has become the most appropriate approach for treating obesity. Meanwhile, katuk (Sauropus androgynus) leaves are rich in polyphenols that act as natural bioactive compounds and are also responsible for the potential effect on metabolic diseases, including inhibition of pancreatic lipase activity. Naturally occurring polyphenols can inhibit pancreatic lipase and consequently affect fat digestion as well as energy intake. Therefore, this study aimed to optimize the inhibitory activity of pancreatic lipase, which plays an essential role in lipid absorption. In fermented katuk (Sauropus androgynus) leaves were brewed following RSM using a Box Behnken design. Data analysis was used to optimize the formulation with a response surface model consisting of three factors namely sucrose concentration ranging from 10-20% b/v, culture 10-20 b/v, as well as fermentation time of 1-5 days. The parameters tested were the percentage inhibition of pancreatic lipase, antioxidant activity, polyphenol, and total acid. Based on the RSM optimization results obtained from the three influencing factors, the optimum conditions were established namely 10.43% w/v sucrose, 10% v/v kombucha culture, and fermentation for 5 days. These conditions yielded the most optimal responses, with the percentage inhibition of pancreatic lipase, antioxidant activity, polyphenol, and total acid being 86.13%, 96.27%, 0.97 mg GAE/mL, and 1.11%, respectively. In general, the results demons-trated that the RSM method of the Box Behnken design and the parameter prediction values obtained using the model equation are in good agreement with the experimental values with at least R2 ≥ 0.8.

Effect of Process Parameters on Tensile Strength of FDM Printed Carbon Fiber Reinforced Polyamide Parts

Reinforcing the polymer with nanoparticles and fibers improves the mechanical, thermal and electrical properties. Owing to this, the functional parts produced by the FDM process of such materials can be used in industrial applications. However, optimal parameters’ selection is crucial to produce parts with optimal properties, such as mechanical strength. This paper focuses on the analysis of influential process parameters on the tensile strength of FDM printed parts. Two statistical methods, RSM and ANN, were applied to investigate the effect the layer thickness, printing speed, raster angle and wall thickness on the tensile strength of test specimens printed with a short carbon fiber reinforced polyamide composite. The reduced cubic model was developed by the RSM method, and the correlation between the input parameters and the output response was analyzed by ANOVA. The results show that the layer thickness and raster angle have the most significant influence on tensile strength. As for machine learning, among the nine different tested ANN topologies, the best configuration was found based on the lowest MAE and MSE test sample result. The results show that the proposed model could be a useful tool for predicting tensile strength. Its main advantage is the reduction in time needed for experiments with the LOSO (leave one subject out) k-fold cross validation scheme, offering better generalization ability, given the small set of learning examples.