Статті в журналах: "RSM-BBD"

1

Nayeemuddin, Mohammed, PUGANESHWARY PALANIANDYA, Feroz Shaik, and Hiren Mewada. "EXPERIMENTAL AND COMPUTATIONAL ANALYSIS FOR OPTIMIZATION OF SEAWATER BIODEGRADABILITY USING PHOTO CATALYSIS." IIUM Engineering Journal 24, no. 2 (July 4, 2023): 11–33. http://dx.doi.org/10.31436/iiumej.v24i2.2650.

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Seawater pollution is a significant global environmental problem. Various technologies and methods have been used to remove the contaminants found in saltwater. This experimental study investigates the degradation of contaminants present in seawater using solar photocatalysis, where a combination of TiO2 and ZnO was used. The effects of catalyst dosage, pH, and reaction duration were assessed using percentage removal efficiencies of total organic carbon (TOC), chemical oxygen demand (COD), biological oxygen demand (BOD), and biodegradability (BOD/COD). Biodegradability is essential for removing pollutants from saltwater and plays a vital role. The higher the biodegradability, the more efficient the treatment procedure will be. The most effective percentage reduction rates from the experimental data obtained were TOC=59.80%, COD=75.20%, BOD=23.94%, and biodegradability=0.055. For modeling, optimizing, and assessing the effects of parameters, the Design Expert based on Box Behnken design (RSM-BBD) and a predictive model based on the MATLAB adaptive neuro-fuzzy inference system (ANFIS) tools were used. The coefficient of determination R2 was found to be 0.977 for the RSM-BBD model and 0.99 for the ANFIS model. According to the RSM-BBD design, the maximum percentage pollutant elimination efficiencies were found to be TOC=55.4, COD=73.4, BOD=23.70%, and BOD/COD=0.054, but for the ANFIS model, they were TOC=59.4, COD=75.4, BOD=24.1%, and BOD/COD=0.055. It was discovered that the ANFIS model outperformed RSM-BBD in process optimization. ABSTRAK: : Pencemaran air laut adalah masalah alam sekitar global yang ketara. Pelbagai teknologi dan kaedah telah digunakan bagi menyingkirkan pencemaran yang dijumpai dalam air laut. Kajian eksperimen ini menilai degradasi pencemaran yang hadir dalam air laut menggunakan fotopemangkin, di mana kombinasi TiO2 dan ZnO digunakan. Kesan dos pemangkin, pH, dan tempoh reaksi dipantau menggunakan peratus kecekapan penyingkiran jumlah karbon organik (TOC), keperluan kimia oksigen (COD), keperluan biologi oksigen (BOD), dan kebolehdegradasian (BOD/COD). Kebolehdegradasian adalah sangat penting bagi menyingkirkan bahan cemar dari air laut dan berperanan penting. Semakin tinggi kebolehdegradasian, semakin cekap prosedur rawatan. Peratus kadar pengurangan yang paling berkesan daripada data eksperimen adalah didapati pada TOC=59.80%, COD=75.20%, BOD=23.94%, dan biodegradasi=0.055. Bagi mengkaji kesan parameter terhadap model, kadar optimum, dan memantau keberkesanan parameter, kaedah Pakar Reka Bentuk pada rekaan Kotak Behnken (RSM-BBD) dan model ramalan berdasarkan sistem pengaruh menggunakan sistem MATLAB iaitu Inferens Neural-Fuzi Boleh Suai (ANFIS) digunakan. Pekali penentu R2 terhasil pada 0.977 bagi model RSM-BBD dan 0.99 pada model ANFIS. Berdasarkan reka bentuk RSM-BBD, peratus maksimum keberkesanan penyingkiran bahan cemar dijumpai pada TOC=55.4, COD=73.4, BOD=23.70%, dan BOD/COD=0.054, tetapi bagi model ANFIS, TOC=59.4, COD=75.4, BOD=24.1%, dan BOD/COD=0.055. Model ANFIS adalah lebih berkesan daripada model RSM-BBD dalam proses pengoptimuman.

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Jawad, Ali, Mohd shafiq Bin Aazmi, and Ali Jawad. "Statistical Optimization for Dye Removal from Aqueous Solution by Cross-linked Chitosan Composite." Science Letters 14, no. 2 (June 1, 2020): 1. http://dx.doi.org/10.24191/sl.v14i2.9537.

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Response surface methodology-Box–Behnken design (RSM-BBD) was employed to optimize the methyl orange (MO) dye removal efficiency from aqueous solution by cross-linked chitosan-tripolyphosphate/nano-titania composite (Chi-TPP/NTC). The influence of pertinent parameters, i.e. A: TiO2 loading (0- 50 %), B: dose (0.04-0.14 g), C: pH (4-10), and D: temperature (30-50 oC) on the MO removal efficiency were tested and optimized using RSM-BBD. The F-values of BBD model for MO removal efficiency was 93.4 (corresponding p-value < 0.0001). The results illustrated that the highest MO removal efficiency (87.27 %) was observed at the following conditions: TiO2 loading (50% TiO2), dose (0.09 g), pH 4.0, and temperature of 40 oC.

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Oramahi, Hasan Ashari, Tsuyoshi Yoshimura, Elvi Rusmiyanto, and Kustiati Kustiati. "Optimization and Characterization of Wood Vinegar Produced by Shorea laevis Ridl Wood Pyrolysis." Indonesian Journal of Chemistry 20, no. 4 (June 10, 2020): 825. http://dx.doi.org/10.22146/ijc.45783.

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In this study, the Box-Behnken Design (BBD) was employed to investigate the effects of wood particle size, pyrolysis temperature, and pyrolysis time on the production of wood vinegar from Indonesia “bengkirai” wood (Shorea laevis Ridl). Characterization of wood vinegar was conducted by gas chromatography-mass spectrometry (GC-MS). Three variable designs consisting of wood particle size (2.00, 2.38, and 3.36 mm), pyrolysis temperature (350, 400, and 450 °C), and pyrolysis time (105, 120, and 135 min) were employed in a BBD response surface methodology (RSM-BBD). RSM-BBD results suggested that maximum wood vinegar yield would be obtained with a wood particle size of 3.85 mm, pyrolysis temperature of 400 °C, and pyrolysis time of 93 min. In addition, the mathematical model indicated the maximum wood vinegar yield would be 30.31%. The main compounds in wood vinegar were acetic acid, 1-hydroxy-2-propanone, furfural, 2,3-pentanedione, phenol, 2-methoxy phenol, 2-methoxy-4-methyl phenol, 2,6-dimethoxy phenol, and 1,2,4-trimethoxybenzene.

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Barah, Obinna O. "Estimating the Tensile Strength Properties of Plantain Fiber Ash Particulate and Silumin using Box-Behnken Design." IDOSR JOURNAL OF SCIENCE AND TECHNOLOGY 9, no. 2 (June 3, 2023): 100–106. http://dx.doi.org/10.59298/idosr/jst/03.1.12008.

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This study utilizes response surface methodology (RSM) to estimate the engineering parameters of PFAP/silumin composites. The tensile strengths of the developed composites were evaluated using a Box-Behnken design (BBD), considering factors such as weight fraction, particle size, soaking time, plantain fiber ash particulate concentration, and silumin. The results indicate that the weight fraction of fibers has the greatest influence on tensile strength, with interaction effects being more significant than linear and quadratic effects. The predicted tensile strengths of the PFAP/silumin composites, obtained through RSM, closely matched the experimental values, validating the reliability of the software. The range of predicted tensile strengths was found to be 44.66 MPa to 64.05 MPa, while the obtained experimental values ranged from 40.31 MPa to 75.98 MPa. This study demonstrates the effectiveness of the BBD method in quickly obtaining optimum values of tensile strength for PFAP/silumin composites. Furthermore, this research highlights the promising potential of utilizing waste materials in the automotive industry, particularly in East Africa. Keywords: Response surface methodology (RSM), Natural fibers, Hybrid composites, Tensile strength Agro-waste, Cellulose fiber, Box-Behnken design (BBD), Automotive industry.

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Bhandare, Snehal B., and Kirti S. Laddha. "OPTIMIZATION OF EXTRACTION PARAMETERS FOR TOTAL FLAVONOIDS FROM GARDENIA GUMMIFERA GUM RESIN BY RESPONSE SURFACE METHODOLOGY." International Journal of Pharmacy and Pharmaceutical Sciences 8, no. 11 (October 28, 2016): 64. http://dx.doi.org/10.22159/ijpps.2016v8i11.11134.

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Objective: To optimize the extraction parameters for determining the highest yield of Total Flavonoids from Gardenia gummifera gum resin.Methods: In the present study, response surface methodology (RSM) with three level Box Behenken design (BBD) was performed to optimize extraction parameters for total flavonoids. Solvent concentration(A), extraction time (B) and extraction temperature(C) were considered for single factor experiment.Results: The highest flavonoid concentration was obtained with acetone 45.00% v/v, time 101.46 min, temperature 41.57 °C. The average experimental TFC under optimal conditions was 161.14 mg/g which was in close agreement with the predicted value of 163.42 mg/g.Conclusion: RSM-BBD is successfully applied in optimizing extraction conditions for extraction of total flavonoids from G. gummifera gum resin.

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Yu, Aixin, Yuankun Liu, Xing Li, Yanling Yang, Zhiwei Zhou, and Hongrun Liu. "Modeling and Optimizing of NH4+ Removal from Stormwater by Coal-Based Granular Activated Carbon Using RSM and ANN Coupled with GA." Water 13, no. 5 (February 26, 2021): 608. http://dx.doi.org/10.3390/w13050608.

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As a key parameter in the adsorption process, removal rate is not available under most operating conditions due to the time and cost of experimental testing. To address this issue, evaluation of the efficiency of NH4+ removal from stormwater by coal-based granular activated carbon (CB-GAC), a novel approach, the response surface methodology (RSM), back-propagation artificial neural network (BP-ANN) coupled with genetic algorithm (GA), has been applied in this research. The sorption process was modeled based on Box-Behnben design (BBD) RSM method for independent variables: Contact time, initial concentration, temperature, and pH; suggesting a quadratic polynomial model with p-value < 0.001, R2 = 0.9762. The BP-ANN with a structure of 4-8-1 gave the best performance. Compared with the BBD-RSM model, the BP-ANN model indicated better prediction of the response with R2 = 0.9959. The weights derived from BP-ANN was further analyzed by Garson equation, and the results showed that the order of the variables’ effectiveness is as follow: Contact time (31.23%) > pH (24.68%) > temperature (22.93%) > initial concentration (21.16%). The process parameters were optimized via RSM optimization tools and GA. The results of validation experiments showed that the optimization results of GA-ANN are more accurate than BBD-RSM, with contact time = 899.41 min, initial concentration = 17.35 mg/L, temperature = 15 °C, pH = 6.98, NH4+ removal rate = 63.74%, and relative error = 0.87%. Furthermore, the CB-GAC has been characterized by Scanning electron microscopy (SEM), X-ray diffraction (XRD) and Brunauer-Emmett-Teller (BET). The isotherm and kinetic studies of the adsorption process illustrated that adsorption of NH4+ onto CB-GAC corresponded Langmuir isotherm and pseudo-second-order kinetic models. The calculated maximum adsorption capacity was 0.2821 mg/g.

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Buasri, Achanai, Phensuda Sirikoom, Sirinan Pattane, Orapharn Buachum, and Vorrada Loryuenyong. "Process Optimization of Biodiesel from Used Cooking Oil in a Microwave Reactor: A Case of Machine Learning and Box–Behnken Design." ChemEngineering 7, no. 4 (July 21, 2023): 65. http://dx.doi.org/10.3390/chemengineering7040065.

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In the present investigation, response surface methodology (RSM) and machine learning (ML) are applied to the biodiesel production process via acid-catalyzed transesterification and esterification of triglyceride (TG). In order to optimize the production of biodiesel from used cooking oil (UCO) in a microwave reactor, these models are also compared. During the process, Box–Behnken design (BBD) and an artificial neural network (ANN) were used to evaluate the effect of the catalyst content (3.0–7.0 wt.%), methanol/UCO mole ratio (12:1–18:1), and irradiation time (5.0–9.0 min). The process conditions were adjusted and developed to predict the highest biodiesel yield using BBD with the RSM approach and an ANN model. With optimal process parameters of 4.94 wt.% catalyst content, 16.76:1 methanol/UCO mole ratio, and 8.13 min of irradiation time, a yield of approximately 98.62% was discovered. The coefficient of determination (R2) for the BBD model was found to be 0.9988, and the correlation coefficient (R) for the ANN model was found to be 0.9994. According to the findings, applying RSM and ANN models is advantageous when optimizing the biodiesel manufacturing process as well as making predictions about it. This renewable and environmentally friendly process has the potential to provide a sustainable route for the synthesis of high-quality biodiesel from waste oil with a low cost and high acid value.

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Nandiwale, Kakasaheb Y., Ashwini M. Pande, and Vijay V. Bokade. "One step synthesis of ethyl levulinate biofuel by ethanolysis of renewable furfuryl alcohol over hierarchical zeolite catalyst." RSC Advances 5, no. 97 (2015): 79224–31. http://dx.doi.org/10.1039/c5ra13520f.

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Response surface methodology (RSM) with Box–Behnken experimental design (BBD) demonstrated Hierarchical-HZ-5 as potential catalyst for ethanolysis of renewable furfuryl alcohol to ethyl levulinate biofuel with 99% FAL conversion and 73% EL yield.

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Huiqing, Zhang, Ye Chunsong, Zhang Xian, Yang Fan, Yang Jun, and Zhou Wei. "Optimization of photo-Fenton process of RO concentrated coking wastewater using response surface methodology." Water Science and Technology 66, no. 4 (August 1, 2012): 816–23. http://dx.doi.org/10.2166/wst.2012.254.

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The objective of this study was aimed at investigating the removal of chemical oxygen demand (COD) from reverse osmosis (RO) concentrated coking wastewater by the photo-Fenton process. The optimum extraction conditions for the photo-Fenton process by Box–Behnken design (BBD) and response surface methodology (RSM) to establish a predictive polynomial quadratic model were discussed based on a single factor test. Optimized parameters validated by the analysis of variances (ANOVA) were found to be H2O2 concentration of 345.2 mg/L, pH value of 4.1 and reaction time of 103.5 minutes under ultraviolet irradiation. The experimental results of the COD removal under the optimized conditions presented better agreement with the predicted values with deviation error of 3.2%. The results confirmed that RSM based on BBD was a suitable method to optimize the operating conditions of RO concentrated coking wastewater.

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Luo, Meng, Jiao-Yang Hu, Zhuo-Yue Song, Jiao Jiao, Fan-Song Mu, Xin Ruan, Qing-Yan Gai, Qi Qiao, Yuan-Gang Zu, and Yu-Jie Fu. "Optimization of ultrasound-assisted extraction (UAE) of phenolic compounds from Crataegus pinnatifida leaves and evaluation of antioxidant activities of extracts." RSC Advances 5, no. 83 (2015): 67532–40. http://dx.doi.org/10.1039/c5ra07445b.

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In this study, a highly efficient BBD–RSM optimized ultrasound-assisted extraction combined with HPLC method has been established for the simultaneous extraction and determination of CA, VG, VR, ORT, RT, VIT and HYP from Crataegus pinnatifida leaves.

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Tian, Shuang-Qi, Zi-Liang Wang, Xin-Wei Wang, and Ren-Yong Zhao. "Development and digestion of resistant malate starch produced by l-malic acid treatment." RSC Advances 6, no. 98 (2016): 96182–89. http://dx.doi.org/10.1039/c6ra12238h.

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Resistant starch (RS) was esterified by l-malic acid, and the optimum conditions determined using a response surface methodology (RSM) based on a Box–Behnken design (BBD) were: temperature, 146 °C; pH, 1.46; time, 5.52 h; and mass ratio, 0.60.

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Mohamad, Nor Syahirah, and Salmiah Kasolang. "Optimized characterization of response surface methodology on lubricant with titanium oxide nanoparticles." Industrial Lubrication and Tribology 69, no. 3 (May 8, 2017): 387–92. http://dx.doi.org/10.1108/ilt-09-2016-0214.

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Purpose An optimized model is often deployed to reduce trial and error in experimental approach and obtain the multi-variant correlation. In this study, response surface methodology (RSM), namely, Box–Behnken design (BBD) approach, has been used to optimize the characterization of lubricant with additives. BBD is based on multivariate analysis whereby the effects of different parameters are considered simultaneously. It is a non-linear system which is more representative of the actual phenomenon. This study aims to investigate the effect of three independent variables, namely, speed, load and concentration of TiO2, on the coefficient of friction (CoF). Design/methodology/approach RSM was applied to get the multiplicity of the self-determining input variables and construct mathematical models. Mathematical models were established to predict the CoF and to conduct a statistical analysis of the independent variables’ interactions on response surface using Minitab 16.0 statistical software. Three parameters were regulated: speed (X1), load (X2) and concentration of TiO2 (X3). The output measured was the CoF. Findings The result obtained from BBD has shown that the most influential parameter was speed, followed by concentration of TiO2 nanoparticles and then normal load. Analysis of variance indicated that the proposed experiment from the quadratic model has successfully interpreted the experimental data with a coefficient of determination R2 = 0.9931. From the contour plot of BBD, the optimization zone for interacting variables has been obtained. The zone indicates two regions of lower friction values (<0.04): concentration between 0.5 to 1.0 Wt.% for a speed range of 1,000 to 2,000 rpm, and load between 17 to 20 kg for a speed in the range of 1,200 to 1,900 rpm. The optimized condition shows that the minimum value of CoF (0.0191) is at speed of 1,782 rpm, load of 20 kg and TiO2 concentration of 1.0 Wt.%. Originality/value In general, it has been shown that RSM is an effective and powerful tool in experimental optimization of multi-variants.

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Singh, Hariraj, Brijesh Kumar Mishra, and Aditya Prakash Yadav. "Application of Response Surface Methodology for Enhanced Electrochemical Process for the Removal of Phenol from Aqueous Solution using Graphite Electrodes." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 9, no. 02 (December 25, 2017): 97–107. http://dx.doi.org/10.18090/samriddhi.v9i02.10869.

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The aim of the present work was to investigate the removal of phenol from a synthetic solution by the enhanced electrochemical oxidation process using graphite electrodes. Central composite design (CCD) and Box Behnken Design (BBD) under Response Surface Methodology (RSM) tool were used to investigate the effects of major operating variables viz. Current density (mA/ cm2): (2.27 to 4.54), pH: (5.5 to 7.5) and electrolysis time (min): (30 to 90). The predicted values of BBD responses obtained using RSM were more significant than the CCD model in terms of reaction time, whereas under the desirability test CCD model was found more appropriate in terms of phenol removal and power consumption. The optimal result shows that the CCD model predicted and experimental values of phenol removal and power consumption are 92.87 %; 0.866 kWh/m3 and 86.34 %; 1.12 kWh/m3 respectively under optimized variable conditions, current density: 2.78 mA/cm2, pH: 6.98 and electrolysis time: 88.02 minutes at high desirability level.

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Stamenković, Olivera S., Milan D. Kostić, Dragana B. Radosavljević, and Vlada B. Veljković. "Comparison of Box-Behnken, Face Central Composite and Full Factorial Designs in Optimization of Hempseed Oil Extraction by n-Hexane: a Case Study." Periodica Polytechnica Chemical Engineering 62, no. 3 (January 24, 2018): 359–67. http://dx.doi.org/10.3311/ppch.11448.

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Statistical multivariate methods like Box-Behnken, face central composite and full factorial designs (BBD, FCCD and FFD, respectively) in combination with the response surface methodology (RSM) were compared when applied in modeling and optimization of the hempseed oil (HSO) extraction by n-hexane. The effects of solvent-to-seed ratio, operation temperature and extraction time on HSO yield were investigated at the solvent-to-seed ratio of 3:1, 6.5:1 or 10:1 mL/g, the extraction temperature of 20, 45 or 70 °C and the extraction time of 5, 10 or 15 min. All three methods were efficient in the statistical modeling and optimization of the influential process variables and led to almost the same optimal process conditions and predicted HSO yield. Having better statistical performances and being economically advantageous over the FFD with repetition, the BBD or FCCD combined with the RSM is recommended for the optimization of liquid-solid extraction processes.

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Okewale, Akindele, Olusola Adesina, and Mustapha Oloko-Oba. "Comparative Study of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) on Optimization of Ethanol Production from Sawdust." International Journal of Engineering Research in Africa 30 (May 2017): 125–33. http://dx.doi.org/10.4028/www.scientific.net/jera.30.125.

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This work focused on optimization of production of ethanol from saw dust using two empirical methods, the ANN and the RSM. It further investigated the modeling and optimization efficiencies of RSM and ANN in separate hydrolysis and fermentation of sawdust for ethanol production. Box - Behnken Design (BBD) was used to generate 17 individual experiments which were carried out, RSM and Genetic Algorithm (GA) of ANN which were used to optimize the production which was then compared. The optimum concentrations of ethanol yield predicted were 56.968 wt. % and 57. 387263 wt. % for RSM and ANN models respectively. R2 value obtained for ANN model was 0.9989 while R2 value of 0.9046 was obtained for RSM model. The Root Mean Square Error (RMSE) value for ANN was found to be 0.143 while the RMSE value for RSM was 2.17. It showed that ANN had relatively higher predictive model ability and thus shows to be a better optimization tool for the ethanol from saw dust compared to RSM which also a good modelling tool.

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Sangal, Vikas, Vineet Kumar, and Mani Mishra. "Optimization of a divided wall column for the separation of C4-C6 normal paraffin mixture using Box-Behnken design." Chemical Industry and Chemical Engineering Quarterly 19, no. 1 (2013): 107–19. http://dx.doi.org/10.2298/ciceq121019047s.

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In the present study, simulation of a divided wall column (DWC) was carried out to study the product quality and energy efficiency as a function of reflux rate, liquid spilt and vapour split for the separation of C4-C6 normal paraffin ternary mixture. Rigorous simulation of the DWC was carried out using Multifrac model of ASPEN Plus software. Box-Behnken design (BBD) was used for the optimization of parameters and to evaluate the effects and interaction of the process parameters such as reflux rate (r), liquid split (l) and vapour split (v). It was found that the number of simulation runs reduced significantly for the optimization of DWC by BBD. Optimization by BBD under response surface methodology (RSM) vividly underscores interactions between variables and their effects. The predictions agree well with the results of the rigorous simulation.

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Oramahi, H. A., Farah Diba, and Rizka Diah Permana. "Optimasi Produksi Asap Cair Dari Kayu Medang (Cinnamomum sp.) Menggunakan Metode Permukaan Respon." EnviroScienteae 16, no. 1 (August 18, 2020): 37. http://dx.doi.org/10.20527/es.v16i1.8998.

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In this work medang wood (Cinnamomum spp.) was pyrolyzed to produces wood vinegar. The effect of several parameters including the wood particle size, pyrolysis temperature, and pyrolysis time on the pyrolysis efficiency was tested to identify the optimal wood vinegar production conditions. The efficient response surface methodology (RSM) with the Box-Behnken design (BBD) was used for modeling and optimization of the process parameters. RSM used three variable designs namely particle size of wood of 2.38, 3.36, and 4.76 mm, respectively, pyrolysis temperature of 370, 400 and 430°C, respectively, and pyrolysis times 90, 120 and 150 minutes, respectively, with the total of 15 experimental runs. The responses of the BBD fitted with a second-order polynomial equation, illustrating the wood vinegar yield was Y = 15.20 + 0.31 X1 + 5.00 X2 + 0.48 X3 – 1.84 X12 – 1.18 X22 + 0.63 X1.X2 + 0.35 X1.X3 + 0.31 X2.X3 – 0.27 X3.X3. The optimal conditions found to be at the particle size of wood, pyrolysis temperature, and pyrolysis time were 4.76 mm, 475 °C, 195 minutes, respectively, and yield of wood vinegar was 22.90%.

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Xu, Ying Cao, and Hong You. "A Statistical Experimental Design to Degradation of Aqueous Methyl Orange Solution with Photocatalyst of Ti-TiO₂ Nanotube Array." Advanced Materials Research 479-481 (February 2012): 221–25. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.221.

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A statistical experimental design to the degradation of aqueous methyl orange, by using the Ti/TiO2 nanotube array photocatalyst, under UV irradiation, was investigated. Concentration of methyl orange(5mg/L,15mg/L,25mg/L), catalytic time (0.5h,1h,1.5h) and UV light source (6w,11w,15w) were selected as major operating variables. To investigate the effects of variables to degradation rate, the statistical experiment of Box-Behnken design(BBD) and Response Surface methodology(RSM) was employed. Regression analysis showed that the experiment data accorded with the predicted values obtained from quadratic regression equation in BBD with R-Squared of 0.9806 and F-value of 39.34.

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Xu, Ying Cao, Zhi Biao Feng, and Chun Hong Liu. "A Statistical Experimental Design to Plastein Synthesis with the Mixture of Soy Protein Isolate(SPI) Hydrolysate and Whey Protein Isolate(WPI) Hydrolysate." Advanced Materials Research 236-238 (May 2011): 2773–79. http://dx.doi.org/10.4028/www.scientific.net/amr.236-238.2773.

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A statistical experimental design to plastein synthesis which was catalyzed by transglutaminase, using the mixture of soy protein isolate(SPI) hydrolysate and whey protein isolate (WPI) hydrolysate, was investigated. Enzyme/Substrate(E/S:5-25U/g), pH(5-9) and temperature (35-65°C) were selected as major operating variables. To investigate the effects of variables to yield of plastein, the statistical experiment of Box-Behnken design(BBD) and Response Surface methodology(RSM) was employed. Regression analysis showed that the experiment data accorded with the predicted values obtained from quadratic regression equation in BBD with R-Squared of 0.9866 and F-value of 102.51. The optimum results estimated by BBD were as follows: E/S(19.5U/g), pH(6.8), and temperature(50.0°C), gave a maximum plastein yield of 54%. In the present experiment, the preliminary study on plastein functions such as foaming, emulsifying, were showed that plastein had a good biological function.

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Ahmadi, Shabnam, Soumya Ghosh, Alhadji Malloum, Charné Bornman, Christian Osagie, Leili Mohammadi, and Chinenye Adaobi Igwegbe. "Modeling the Liquid-Phase Adsorption of Cephalexin onto Coated Iron Nanoparticles Using Response Surface and Molecular Modeling." Adsorption Science & Technology 2022 (May 12, 2022): 1–16. http://dx.doi.org/10.1155/2022/7619063.

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In order to assess the interactions between process factors, the experiments involving the liquid-phase adsorption of cephalexin (CEX) onto silicon-coated iron nanoparticles (Fe3O4@SIO2) were designed using the Box-Behnken Design-Response surface methodology (BBD-RSM). Optimal circumstances were used to investigate the synergistic influence on the process’s efficiency. In addition, the data was used to test and fit an artificial neural network (ANN) model. Molecular-level DFT calculations on the CEX molecule were carried out. The PW6B95D3/Def2-TZVP level of theory was used to build DFT-based descriptors for the CEX molecule. At 25°C, pH 5.83, 37.67 min, a dosage of 0.8 g Fe3O4@SIO2 and 118.01 mg/L CEX, the removal efficiency achieved a maximum of 99.01 percent. For example, we found that OH --- O, NH --- O, CH --- O hydrogen bonds, NH --- π, OH --- π, CH --- π interactions as well as dipole-dipole interactions between CEX and the nanoparticles could all be used to connect the CEX and the nanoparticles. There is a strong correlation between the output and target values acquired by BBD-RSM and ANN fits. Fe3O4@SIO2 proved to be an excellent tool for eliminating CEX.

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Sarangapani, Chaitanya, Y. Dixit, Vladimir Milosavljevic, Paula Bourke, Carl Sullivan, and P. J. Cullen. "Optimization of atmospheric air plasma for degradation of organic dyes in wastewater." Water Science and Technology 75, no. 1 (October 27, 2016): 207–19. http://dx.doi.org/10.2166/wst.2016.471.

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This study optimises the degradation of a cocktail of the dyes methyl orange and bromothymol blue by atmospheric air plasma. Response surface methodology (RSM) was employed to investigate the efficacy of the plasma process parameters on degradation efficiency. A Box–Behnken design (BBD) was employed to optimise the degradation of dyes by air plasma discharge. A second order polynomial equation was proposed to predict process efficiency. It was observed that the predicted values are significant (p<0.001) with coefficients of determination 0.98, 0.96, 0.98 for dye degradation, pH value and ozone concentration, respectively. The analysis of variance results showed that the coefficients of the polynomials for the percentage degradation and ozone concentration responses indicated positive linear effects (p<0.001), whereas a negative linear effect was found for pH. The positive linear effect of variable emphasises that voltage and treatment time were the most dominant factors (p<0.001), meaning that higher degradation efficiencies are achieved with an increase in treatment duration. This study showed that a BBD model and RSM could be employed to optimize the colour degradation parameters of non-thermal plasma treated model dyes while minimising the number of experiments required.

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Mohamed Isa, Eleen Dayana, Haslina Ahmad, and Mohd Basyaruddin Abdul Rahman. "Optimization of Synthesis Parameters of Mesoporous Silica Nanoparticles Based on Ionic Liquid by Experimental Design and Its Application as a Drug Delivery Agent." Journal of Nanomaterials 2019 (March 24, 2019): 1–8. http://dx.doi.org/10.1155/2019/4982054.

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Optimization is a process utilized to discover the best condition to generate the best possible outcome. One of the common optimization method used in the field of chemistry is response surface methodology (RSM). This method consists of mathematical and statistical techniques which relate the responses with the variables of interest. There are many experimental designs in RSM, and one of the most common one is the Box-Behnken design (BBD). In this work, BBD was employed to analyze the main effects and interactions of the reaction temperature, amount of template, and amount of triethanolamine (TEA) on the two responses which are the surface area (SA) and particle size (PS) of ionic liquid templated mesoporous silica nanoparticles (MSNs). It was found that the SA and PS were fitted with linear and quadratic models, respectively. MSNs with the highest surface area (999.051 m2 g-1) was chosen for the application of drug delivery; thus, drug loading and drug release experiments were conducted. From these studies, it was found that 37% of drug (quercetin) was successfully encapsulated in MSN and, in 48 hours, 32% of the drug was released.

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Laxmi Deepak Bhatlu, M., P. S. Athira, Neethu Jayan, Debabrata Barik, and Milon Selvam Dennison. "Preparation of Breadfruit Leaf Biochar for the Application of Congo Red Dye Removal from Aqueous Solution and Optimization of Factors by RSM-BBD." Adsorption Science & Technology 2023 (February 16, 2023): 1–17. http://dx.doi.org/10.1155/2023/7369027.

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In this work, biochar produced from breadfruit leaves was utilized to remove the toxic Congo red dye. XRD, FTIR, and FESEM-EDX were implemented to characterize the biochar. Response surface methodology (RSM) and the Box-Behnken design (BBD) techniques were used to evaluate Congo red’s optimum adsorption efficiency. The adsorption of Congo red was studied by varying dye concentrations (5–50 mg/L), times (30–240 min), pH (6–9), and dosages (0.5–2 g/100 mL). X-ray diffractometer results show that the structure of biochar is amorphous. The biochar exhibited unbounded OH, aliphatic CH group, and C=O stretch, as shown by the band peaks at 3340 cm−1, 2924 cm−1, and 1625 cm−1 intensities. RSM-BBD design results showed maximum removal efficiency of 99.96% for Congo red at pH 6.37, dye concentration 45 mg/L, time 105 min, and dosage 1.92 g, respectively. The adsorption of Congo red by biochar was successfully modeled using the Langmuir model and pseudo-second-order model. The biochar produced from breadfruit leaves exhibited a high adsorption capacity of 17.81 mg/g for Congo red adsorption. It suggests that the adsorption is both homogenous monolayer and physicochemical.

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Sharon, Abimbola Bowofoluwa, Eze Frank Ahuekwe, Elughi Gift Nzubechi, Olubukola Oziegbe, and Margaret Oniha. "Statistical optimization strategies on waste substrates for solving high-cost challenges in biosurfactants production: a review." IOP Conference Series: Earth and Environmental Science 1197, no. 1 (June 1, 2023): 012004. http://dx.doi.org/10.1088/1755-1315/1197/1/012004.

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Abstract Biosurfactants are bio-based amphiphilic molecules with extensive applications in various industries. These eco-friendly alternatives possess numerous advantages over chemical surfactants. However, high production costs hinder market competitiveness of biosurfactants. Production costs of synthetic surfactants range between $1-3/kg, while biosurfactants cost between $20-25/kg. Principal challenges hindering commercialization of biosurfactants are high costs of media constituents and downstream processing, accounting for 30% and 60-80% of production costs, respectively. Thus, cost-effective biosurfactant production would depend on the utilization of environment-friendly low-cost substrates and efficient product recovery. To this end, statistical tools such as Factorial Designs (FD) and Response Surface Methodology (RSM), are employed to optimize the production processes. FD as effective screening models comprise Plackett-Burman Design (PBD) and Taguchi design; and involves quantification of various significant factor effects including the main effect and level of dependency of one factor on the level of one or more factors. RSM predicts appropriate proportions of media constituents and optimal culture conditions; and is reportedly effective in reducing production cost and consequently, market price. Central Composite Design (CCD) and Box-Behnken Design (BBD) are common RSM for optimizing biosurfactants production. CCD assesses the relationship between one factor or more and a set of experimental variables. BBD is considered more proficient than CCD as it requires fewer experimental runs. Most recently, Artificial Neural Network which uses artificial intelligence-based tools to predict biosurfactant production using dependent variables of the process is gaining attention.

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Chu, Xudong, Huaifang Wang, Shuying Sui, Ping Zhu, and Qingdao University. "Modeling and Optimization of Durable Press Finishing of Cotton Fabric with BTCA by Response Surface Methodology." AATCC Journal of Research 7, no. 4 (June 1, 2020): 37–44. http://dx.doi.org/10.14504/ajr.7.4.6.

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Durable press (DP) finishing process of cotton fabric with 1,2,3,4-butanetetracarboxylic acid (BTCA) was investigated and optimized using the Box-Behnken design (BBD) with a response surface methodology (RSM). BBD experimental data were fitted to create the response surface regression models describing wrinkle recovery angle (WRA) and breaking strength of the finished cotton fabric. Analysis of variance (ANOVA) revealed that the concentration of BTCA was the most significant variable affecting the WRA, followed by the curing time, curing temperature, and concentration of sodium hypophosphite (SHP). Regarding breaking strength, curing time was the most significant variable, followed by the curing temperature, concentration of BTCA, and concentration of SHP. After optimal treatment, the WRA of the finished fabric was 252°, while the retention rate of breaking strength was 69%.

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Sari, Bina Lohita, Triastinurmiatiningsih Triastinurmiatiningsih, and Tri Saptari Haryani. "Optimasi Metode Microwave-Assisted Extraction (MAE) untuk Menentukan Kadar Flavonoid Total Alga Coklat Padina australis." ALCHEMY Jurnal Penelitian Kimia 16, no. 1 (February 27, 2020): 38. http://dx.doi.org/10.20961/alchemy.16.1.34186.38-49.

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Metode Microwave-Assisted Extraction (MAE) digunakan untuk mengekstraksi senyawa flavonoid dari simplisia alga coklat Padina australis. Sampel diperoleh dari Pantai Bayah, Banten Indonesia. Kondisi optimal ekstraksi ditentukan dengan Response Surface Methodology (RSM). Desain Box-Behnken (BBD) digunakan untuk mengevaluasi pengaruh 3 faktor dengan 3 level yaitu daya microwave (300, 450, 600 watt), konsentrasi etanol (30, 50, 70%), dan waktu ekstraksi (7, 8 dan 9 menit) dengan 15 perlakuan yang berbeda. Hasil disain eksperimen dengan BBD menunjukkan kondisi ekstraksi optimum yaitu pada daya microwave 414 watt, konsentrasi etanol 50,33% dan waktu ekstraksi 7,89 menit menghasilkan kadar prediksi flavonoid sebesar nilai 0,2963%. Nilai ini mendekati nilai kadar flavonoid yang diperoleh secara eksperimental sebesar 0,2961%, pada kondisi menggunakan daya microwave sebesar 450 watt, konsentrasi etanol 50% dengan lama ekstraksi 8 menit. Berdasarkan hasil penelitian, kadar flavonoid total dapat meningkat secara signifikan dengan melakukan optimasi proses MAE menggunakan RSM.Optimization of Microwave-Assisted Extraction for Total Flavonoid Content of Padina australis Brown Algae. Microwave-Assisted Extraction (MAE) was done to extraction flavonoid from Padina australis brown algae simplicial. The sample collected from Bayah coastal waters, Banten Indonesia. Optimum extraction condition was determined by the response surface methodology (RSM). The Box-Behnken design (BBD) was used to evaluate the influence of 3 factors with 3 levels extraction that is microwave power (300, 450, 600 watts), ethanol concentration (30, 50, 70%), and extraction time (7, 8 and 9 minutes) with 15 different runs. The research showed that optimum extraction condition was at 414 watts of microwave power, 50.33% ethanol concentration, and time extraction of 7.89 minutes yielded a predicted value of total flavonoid content of 0.2963%. This value approaches to the flavonoid content obtained experimentally at 0.2961% under condition of 450 watts microwave power, 50% ethanol concentration, and time extraction of 8 minutes. Based on the result, total flavonoid content can be significantly increased by optimizing the MAE process use RSM.

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Abidin, Lubna, Mohammad Mujeeb, and Showkat R. Mir. "Maximized Extraction of Flavonoid Luteolin from V.negundo L. Leaves: Optimization Using Box-Behnken Design." Current Bioactive Compounds 15, no. 3 (May 7, 2019): 343–50. http://dx.doi.org/10.2174/1573407214666180731120014.

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Background: Luteolin is a flavonoid unveiling various therapeutic activities, found in Vitex negundo L. Thus, there is a need to present process parameters at which maximum amount of luteolin can be extracted from V. negundo L. leaves in “one-run”. Objective: Response surface methodology (RSM) was employed for optimizing the process parameters for the extraction of luteolin from V. negundo L. leaves. The study also compared the efficacy of various traditional and modern extraction methods for luteolin extraction. Methods: Extraction conditions (solvent to drug ratio, extraction temperature and extraction time) were optimized by RSM, Box-Behnken Design (BBD). Quantification of luteolin in various extracts was done through High Performance Liquid Chromatography (HPLC). Results: Hot solvent extraction by reflux technique stood out to be the best technique and methanol was found to be the most effective solvent for luteolin extraction.Through the use of BBD, the optimal conditions for luteolin extraction were established as: solvent to drug ratio- 17.7 mL/g, extraction temperature- 55.5°C and extraction time-2.04 hours. Under such conditions 7.32 %w/w of luteolin was yielded which was close to predicted value of 7.29 %w/w. Conclusion: Reflux technique stood out to be the best among all the studied modes of extraction and methanol proved to be the most effective solvent. Moreover, all the three variables significantly affected the luteolin extraction. Our study shows the applicability of a statistical technique, RSM in phytocompound extraction field. This makes the optimization technique cheap and less laborious than the traditional optimization method.

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Jawad, Jasir, Alaa Hawari, and Syed Zaidi. "Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques." Membranes 11, no. 1 (January 19, 2021): 70. http://dx.doi.org/10.3390/membranes11010070.

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The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.

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Cho, Seung-Young, Min-Seob Kim, Young-Sik Pyun, and Do-Sik Shim. "Strategy for Surface Post-Processing of AISI 316L Additively Manufactured by Powder Bed Fusion Using Ultrasonic Nanocrystal Surface Modification." Metals 11, no. 5 (May 20, 2021): 843. http://dx.doi.org/10.3390/met11050843.

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Ultrasonic nanocrystal surface modification (UNSM) technology was applied to the surfaces of specimens additively manufactured by powder bed fusion (PBF). The changes in roughness and hardness due to the UNSM were set as objective functions, and the optimal conditions for the main parameters were derived through the response surface method (RSM) and Box–Behnken design (BBD). Regression analysis-based mathematical models for predicting the surface hardness and roughness are presented and validated. The RSM results show that the surface roughness is highly dependent on the load and ball tip diameter, and the surface roughness significantly improves as the inter-pass interval and ball tip diameter decrease. Through BBD and ANOVA, the optimal conditions for the improvement of surface characteristics were found to be a load of 40 N, inter-pass interval of 10 μm, and ball tip diameter of 2.38 m. The surface treated under these optimal conditions exhibited a hardness of 497 Hv and surface roughness of 1.32 μm, which were significantly improved compared to the values for an untreated specimen. In addition, it was confirmed that the grains are significantly refined after UNSM, and scratch resistance increases for the top layer of the surface directly affected by the UNSM horn.

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Huaccallo-Aguilar, Ysabel, Silvia Álvarez-Torrellas, Johanny Martínez-Nieves, Jonathan Delgado-Adámez, María Victoria Gil, Gabriel Ovejero, and Juan García. "Magnetite-Based Catalyst in the Catalytic Wet Peroxide Oxidation for Different Aqueous Matrices Spiked with Naproxen–Diclofenac Mixture." Catalysts 11, no. 4 (April 19, 2021): 514. http://dx.doi.org/10.3390/catal11040514.

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Magnetite supported on multiwalled carbon nanotubes catalysts were synthesized by co-precipitation and hydrothermal treatment. The magnetic catalysts were characterized by X-ray diffraction, Fourier-transform infrared spectrometry, thermogravimetric analysis and N2 physisorption. The catalysts were then tested for their ability to remove diclofenac (DCF) and naproxen (NAP) from an aqueous solution at different conditions (pH, temperature, and hydrogen peroxide) to determine the optimum conditions for chemical oxidation. The optimization of the process parameters was conducted using response surface methodology (RSM) coupled with Box–Behnken design (BBD). By RSM–BBD methodology, the optimal parameters (1.75 mM H2O2 dosage, 70 °C and pH 6.5) were determined, and the removal percentages of NAP and DCF were 19 and 54%, respectively. The NAP–DCF degradation by catalytic wet peroxide oxidation (CWPO) was caused by •OH radicals. In CWPO of mixed drug solutions, DCF and NAP showed competitive oxidation. Hydrophobic interactions played an important role during the CWPO process. On the other hand, the magnetic catalyst reduced its activity after the second cycle of reuse. In addition, proof of concept and disinfection tests performed at the operating conditions showed results following the complexity of the water matrices. In this sense, the magnetic catalyst in CWPO has adequate potential to treat water contaminated with NAP–DCF mixtures.

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Goyal, Kapil K., Neeraj Sharma, Rahul Dev Gupta, Gurpreet Singh, Deepika Rani, Harish Kumar Banga, Raman Kumar, Danil Yurievich Pimenov, and Khaled Giasin. "A Soft Computing-Based Analysis of Cutting Rate and Recast Layer Thickness for AZ31 Alloy on WEDM Using RSM-MOPSO." Materials 15, no. 2 (January 15, 2022): 635. http://dx.doi.org/10.3390/ma15020635.

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In the present research, the AZ31 alloy is machined by wire-cut electric discharge machining (WEDM). The experiments were designed according to the Box-Behnken design (BBD) of response surface methodology (RSM). The input process variables, namely servo feed (SF), pulse on-time (Ton), servo voltage (SV), and pulse off-time (Toff), were planned by BBD, and experiments were performed to investigate the cutting rate (CR) and recast layer thickness (RCL). The analysis of variance (ANOVA) was performed to determine the influence of machining variables on response characteristics. The empirical models developed for CR and RCL were solved using Multi-Objective Particle Swarm Optimization (MOPSO). Pareto optimal front is used for the collective optimization of CR and RCL. The optimal solution suggested by the hybrid approach of RSM-MOPSO is further verified using a confirmation test on the random setting indicated by the hybrid algorithm. It is found that the minimum RCL (6.34 µm) is obtained at SF: 1700; SV: 51 V; Toff: 10.5 µs; and Ton: 0.5 µs. However, maximum CR (3.18 m/min) is predicted at SF: 1900; SV: 40 V; Toff: 7 µs; and Ton: 0.9 µs. The error percentage of ±5.3% between the experimental results and predicted solutions confirms the suitability of the proposed hybrid approach for WEDM of AZ31.

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Pei, Pei, Lemin Guo, Rui Zou, Chunlan Liu, Xiaoyu Deng, Lulu Liu, Xinyao Wang, Jinyan Liu, Menghui Yu, and Shizhong Li. "Preparation and optimization of chemically modified corn straw/chitosan/PLA composite using RSM." BioResources 18, no. 4 (October 5, 2023): 7943–62. http://dx.doi.org/10.15376/biores.18.4.7943-7962.

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In this study, an optimized composite was prepared based on chemically modified corn straw, chitosan, and poly(lactic acid) using Response Surface Methodology (RSM). The composite was produced by screw extruding and hot pressing. The Box Behnken Design (BBD) software was used to design the test to optimize the composite composition. The optimum ratio of 3 factors, e.g. chemically modified corn straw (0.10 to 0.40 g), chitosan (0.25 to 0.75 g), and poly(lactic acid) (2.00 to 3.00 g) on the response value (bending strength) of the composite was investigated. RSM-BBD provided the optimum combination of composites. The novel composite prepared under the optimized factors was characterized by Fourier transform infrared spectroscopy, mechanical testing, water absorption tests, contact angle tests, and scanning electron microscopy. The results showed that the mechanical strength, e.g., bending strength, impact strength, and tensile strength of chemically modified corn straw based composite were 21.6 MPa, 4.43 kJ/m2, and 20.0 MPa, respectively, which increased by 23.5%, 13.9%, and 18.7% compared to native corn straw based composite. Improved mechanical strength and hydrophobicity for chemically modified corn straw/chitosan/poly(lactic acid) demonstrated that chemically modified biomass fibers and bio-based degradable polymers have the potential to produce environmentally friendly composites.

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Kaskah, Saad E., Gitta Ehrenhaft, Jörg Gollnick, and Christian B. Fischer. "Prediction Model for Optimal Efficiency of the Green Corrosion Inhibitor Oleoylsarcosine: Optimization by Statistical Testing of the Relevant Influencing Factors." Eng 4, no. 1 (February 15, 2023): 635–49. http://dx.doi.org/10.3390/eng4010038.

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Optimization and statistical methods are used to minimize the number of experiments required to complete a study, especially in corrosion testing. Here, a statistical Box–Behnken design (BBD) was implemented to investigate the effects of four independent variables (inhibitor concentration [I], immersion time t, temperature ϑ, and NaCl content [NaCl]) based on the variation of three levels (lower, middle, and upper) on the corrosion protection efficiency of the green inhibitor oleoylsarcosine for low-carbon steel type CR4 in salt water. The effects of the selected variables were optimized using the response surface methodology (RSM) supported by the Minitab17 program. Depending on the BBD analytical tools, the largest effects were found for ϑ, followed by [I]. The effect of interactions between these variables was in the following order: [I] and ϑ > t and ϑ > [I] and [NaCl]. The second-order model used here for optimization showed that the upper level (+1) with 75 mmol/L for [I], 30 min for t, and 0.2 mol/L [NaCl] provided an optimal protective effect for each of these factors, while the lower level (−1) was 25 °C for ϑ. The theoretical efficiency predicted by the RSM model was 99.4%, while the efficiency during the experimental test procedure with the best-evaluated variables was 97.2%.

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Masmoudi, Fatma, Abdulrahman Mallah, and Mohamed Masmoudi. "Optimizing Experimental Immersion Protocol for SEBS Coating Formation on Copper Surfaces Using Response Surface Methodology." Coatings 13, no. 10 (October 5, 2023): 1734. http://dx.doi.org/10.3390/coatings13101734.

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Polystyrene-block-poly (ethylene-ran-butylene)-block-polystyrene (SEBS) was successfully deposited on the copper surface with an optimal condition of immersion protocol. Response surface methodology (RSM), particularly Box–Behnken Design (BBD), was used to study the combination of three environmental factors that minimize corrosion rate (CR), evaluated by voltammetry around the open circuit potential (OCP). The BBD analysis calculates the contribution value of each parameter in changing the value of the CR in both individual and synergistic cases. The optimized parameters were found to be 2.17% of SEBS ratio, 20 min of immersion time 1, and 21 min of immersion time 2. The empirical model result was confirmed by studying the electrochemical behavior of the SEBS coating on copper under optimal conditions (Cu-SEBS-Opt-Cond) exposed in a 3 wt% NaCl solution.

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Zhang, Qingtao, Lingxiao Shangguan, Tao Li, Xianyong Ma, Yunfei Yin, and Zejiao Dong. "Tire–Pavement Interaction Simulation Based on Finite Element Model and Response Surface Methodology." Computation 11, no. 9 (September 18, 2023): 186. http://dx.doi.org/10.3390/computation11090186.

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Acquiring accurate tire–pavement interaction information is crucial for pavement mechanical analysis and pavement maintenance. This paper combines the tire finite element model (FEM) and response surface methodology (RSM) to obtain tire–pavement interaction information and to analyze the pavement structure response under different loading conditions. A set of experiments was initially designed through the Box–Behnken design (BBD) method to obtain input and output variables for RSM calibration. The resultant RSM was evaluated accurately using the analysis of variance (ANOVA) approach. Then, tire loading simulations were conducted under different magnitudes of static loading using the optimal parameter combination obtained from the RSM. The results show that the deviations between the simulations and the real test results were mostly below 5%, validating the effectiveness of the tire FEM. Additionally, three different dynamic conditions—including free rolling, full brake, and full traction—were simulated by altering the tire rolling angle and translational velocities. Finally, the pavement mechanical response under the three rolling conditions was analyzed based on the tire–pavement contact feature.

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Huang, Mingqing, Lin Chen, Ming Zhang, and Shulin Zhan. "Multi-Objective Function Optimization of Cemented Neutralization Slag Backfill Strength Based on RSM-BBD." Materials 15, no. 4 (February 20, 2022): 1585. http://dx.doi.org/10.3390/ma15041585.

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Tailings produced in the beneficiation of Carlin-type gold deposits are characterized by fine particle size and high mud content. When neutralized with wasted acid generated by pressurized pre-oxidation, the tailings turn to neutralized slag and perform as a novel backfill material. To understand the influential behavior of variable factors on the strength and its optimization of cemented neutralization slag backfill, RMS-BBD design test was carried out with 56–60% slurry mass fraction, 12.5–25% cement/(neutralization slag + waste rock) (i.e., C/(S+R)) and 30–40% waste rock content. A modified three-dimensional quadratic regression model was proposed to predict the strength of cemented neutralization slag backfill. The results showed that backfill strength predicted by the modified ternary quadratic regression model was in high coincidence with the data of backfill mixture tests. C/(S+R) was predominant in backfill strength with regard to every single influential factor throughout the curing age, and the mass fraction of slurry had a significant effect on the later strength. From the perspective of economic and engineering operation, a multi-objective function method was further introduced to optimize the backfill strength. The optimal mixture proportion of cemented neutralized slag backfill slurry was: 58.4% slurry mass fraction, 32.2% waste rock content, and 20.1% C/(S+R). The backfill strength of this mixture proportion on days 7, 28 and 56 was verified as 0.42, 0.64 and 0.85 MPa, respectively. RSM-BBD design and multi-objective function optimization proposed a reliable way to evaluate and optimize the strength of neutralized slag backfill with high mud content.

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Mazloom, Golshan. "Modeling and Optimization of Propane Selective Oxidation to Acrylic Acid Over Mo 1 V 0.3 Te 0.23 NB 0.12 O X Catalyst Using Artificial Neural Network and Box-Behnken Design." Chemical Engineering Research Bulletin 21, no. 1 (June 4, 2020): 1–19. http://dx.doi.org/10.3329/cerb.v21i1.47368.

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The prediction capability of response surface methodology (RSM) and artificial neural network (ANN) models for propane selective oxidation to acrylic acid (AA) over Mo1V0.3Te0.23Nb0.12Ox catalyst was investigated in this work. 15 experimental runs based on the Box-Behnken design (BBD) were employed to study the effects of temperature (380 to 500 °C), superficial velocity (33.3 to 66.7 mL (min gcat)-1), (O2)/(C3H8) ratio (1 to 3) and their interactions on propane conversion, AA selectivity and COx selectivity. The quadratic polynomial BBD equations and the feed-forward back propagation ANN models were developed based on the designed experimental data. Statistical analysis; coefficient of determination (R2), mean absolute error (MAE) and analysis of variance (ANOVA) illustrated that there was acceptable adjustment between BBD and ANN predicted responses as compared to experimental data. While, the ANN model showed a clear preference and generalization capability over BBD model in the case of experimental data set which were not used to training the models. In addition the optimum conditions were found to be temperature (461.7 °C), GHSV (51.9 mL (min gcat)-1) and (O2)/(C3H8) ratio (2.1) which were determined by desirability function approach. In these conditions, propane conversion of 15.2%, AA selectivity of 32% and COx selectivity of 44% which obtained experimentally were in reasonable agreement with predicted responses. Chemical Engineering Research Bulletin 21(2019) 1-19

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Thirugnanasambandham, K., and V. Sivakumar. "Preparation of Chitosan based Nanofibers: Optimization and Modeling." International Journal of Chemical Reactor Engineering 14, no. 1 (February 1, 2016): 283–88. http://dx.doi.org/10.1515/ijcre-2015-0029.

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AbstractThe main objective of the present study is to prepare a chitosan based nanofiber and model the electrospinning process using response surface methodology (RSM). The electrospinning parameters such as collector distance, polymer solution concentration and applied voltage were optimized by using three-variable-three-level Box–Behnken design (BBD). Based on RSM analysis, second order polynomial equation was formed and it indicated good correspondence between experimental and predicted values. 3D response surface plots were used to study the individual and interactive effects of process variables on chitosan based nanofiber diameter. The optimum process conditions for the minimum chitosan based nanofiber diameter (0.3 µm) were found to be collector distance of 12 cm, polymer solution concentration of 25% and applied voltage of 6 kV.

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39

Zhang, Fengyan, Yonglu Dong, Shudong Lin, Xuefeng Gui, and Jiwen Hu. "Optimization of production process of epoxidized soybean oil with high oxygen content through response surface methodology." Polish Journal of Chemical Technology 25, no. 2 (June 1, 2023): 21–29. http://dx.doi.org/10.2478/pjct-2023-0013.

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Abstract The epoxidation process of soybean oil (SBO) and peracid produced by 50 wt% hydrogen peroxide (H2O2) and formic acid (FA) was studied with sulfuric acid (H2SO4) as a catalyst. Three reaction parameters, including reaction temperature, FA-to-SBO ratio, and H2O2-to-SBO ratio, were investigated, along with the combined effect on oxirane value (OV). Based on response surface methodology (RSM), the Box-Behnken design (BBD) was used to optimize the process parameters. According to the results, the calculated OV (7.34%) and the experimental OV (7.31%) were signifi cantly in agreement. The product was confi rmed as epoxidized soybean oil (ESO) by IR and NMR characterization methods. These results demonstrated the reliability of RSM to optimize the SBO reaction to produce ESO with high oxygen content.

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40

Maalihan, Reymark D. "Modelling the Toughness of Nanostructured Polyhedral Oligomeric Silsesquioxane Composites Fabricated by Stereolithography 3D Printing: A Response Surface Methodology and Artificial Neural Network Approach." Materials Science Forum 1053 (February 17, 2022): 41–46. http://dx.doi.org/10.4028/p-6s4jp4.

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In this study, stereolithography (SLA) 3D printing was used to prepare toughened composites by facile blending of chemically compatible nanoscale polyhedral oligomeric silsesquioxanes (POSS) to commercial photoreactive resin. Due to the complex nature of 3D printing, the mechanical performance of the final parts cannot be simply determined or even estimated until they are manufactured and tested. Thus, response surface methodology (RSM) and artificial neural network (ANN) were used to build regression models for determining the toughness of fabricated composites as function of toughener (POSS) amount and printing conditions (layer thickness and annealing temperature). The influence of the mentioned process parameters on toughness were investigated through a 17-run three-factor three-level Box-Behnken RSM design (BBD). The same experimental design was also used to acquire a data set for ANN. Finally, both the modeling methodologies were compared by coefficient of determination (R2) and residual distribution values. Results reveal that ANN possesses a better data fitting and predictive power as compared to RSM.

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41

Belachew, Neway, Redeat Fekadu, and Amare Ayalew Abebe. "RSM-BBD Optimization of Fenton-Like Degradation of 4-Nitrophenol Using Magnetite Impregnated Kaolin." Air, Soil and Water Research 13 (January 2020): 117862212093212. http://dx.doi.org/10.1177/1178622120932124.

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In this work, we have reported a low-cost and environmentally friendly Fe3O4-modified activated kaolin (AK-Fe3O4) composite for efficient Fenton-like degradation of 4-nitrophenol (4-NP) and optimization of the degradation variables. The AK-Fe3O4 composites were characterized by Fourier transform infrared spectroscopy, powder x-ray diffraction, scanning electron microscopy (SEM), and vibrating sample magnetometer (VSM). X-ray diffraction confirms the syntheses of pure phases of Fe3O4 and AK-Fe3O4. The SEM image of the AK-Fe3O4 composite reveals the formation of a highly porous surface. The room temperature VSM analysis describes the superparamagnetic nature of AK-Fe3O4 composites with 25 emu/g magnetization values. Response surface methodology coupled with Box-Behnken design was used to optimize the 4-NP degradation (%) variables such as contact time (10-90 minutes), 4-NP concentration (10-30 mg/L), and pH (3-8). The high regression value ( R² = 0.9964 and adjusted R² = 0.9917) and analysis of variance ( P < .0001) show that the quadratic model can sufficiently explain the 4-NP degradation (%). The optimum 4-NP degradation was found to be 96.01% ± 1.89% using 1 mg/mL of AK-Fe3O4, 20 mg/L of 4-NP, 97.9 mmol/L of H2O2, and pH of 3 at the end of 75 minutes of reaction time. Moreover, the catalyst shows good recyclability and stability after 5 successive degradations of 4-NP. In general, a low-cost and magnetically separable AK-Fe3O4 composite is an effective Fenton-like catalyst for the degradation of 4-NP.

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42

Inam, Muhammad Ali, Rizwan Khan, Ick Tae Yeom, Abdul Salam Buller, Muhammad Akram, and Muhammad Waleed Inam. "Optimization of Antimony Removal by Coagulation-Flocculation-Sedimentation Process Using Response Surface Methodology." Processes 9, no. 1 (January 7, 2021): 117. http://dx.doi.org/10.3390/pr9010117.

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Coprecipitation-adsorption plays a significant role during coagulation-flocculation-sedimentation (C/F/S) of antimony (Sb) in water. This work uses a Box–Behnken statistical experiment design (BBD) and response surface methodology (RSM) to investigate the effects of major operating variables such as initial Sb(III, V) concentration (100–1000 µg/L), ferric chloride (FC) dose (5–50 mg/L), and pH (4–10) on redox Sb species. Experimental data of Sb(III, V) removal were used to determine response function coefficients. The model response value (Sb removal) showed good agreement with the experimental results. FC showed promising coagulation behavior of both Sb species under optimum pH (6.5–7.5) due to its high affinity towards Sb species and low residual Fe concentration. However, a high dose of 50 mg/L of FC is required for the maximum (88–93%) removal of Sb(V), but also for the highest (92–98%) removal of low initial concentrations of Sb(III). Furthermore, BBD and RSM were found to be reliable and feasible for determining the optimum conditions for Sb removal from environmental water samples by a C/F/S process. This work may contribute to a better understanding and prediction of the C/F/S behavior of Sb(III, V) species in aqueous environments, to reduce potential risks to humans.

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43

Ye, Xingxing, Yexun Shi, Liming Shen, Peng Su, and Ningzhong Bao. "Optimization of Spray-Drying Process with Response Surface Methodology (RSM) for Preparing High Quality Graphene Oxide Slurry." Processes 9, no. 7 (June 27, 2021): 1116. http://dx.doi.org/10.3390/pr9071116.

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The “Drying-redissolution” method is promising for the industrial production of high-concentration well-dispersed graphene oxide slurry (GOS). As the potential key step in this method, the spray drying process requires a statistical investigation which guides the large-scale preparation of graphene oxide powder (GOP). This work systematically studies the effects of operating parameters, including nozzle airflow rate (439–895 L·h−1), atomization pressure (0.5–0.7 MPa), and liquid feed rate (3.0–9.0 mL·min−1), by using the response surface methodology integrated Box–Behnken design (RSM–BBD), aiming to produce GOP with high yield and easy re-dispersion. The optimized spray drying condition is predicted to be 439 L·h−1, 0.59 MPa, and 9.0 mL·min−1, at which a powder yield of 70.45% can be achieved. The experimentally obtained GOP has an average particle size of 11.65 μm and the low crumpling degree of the particle morphology results in the good re-dispersibility (97.95%) and excellent adsorption performance (244.1 mg·g−1) of GOP. The GOS prepared by the spray-dried GOP possess low viscosity and high exfoliation efficiency with a single-layer fraction up to 90.8%, exhibiting good prospects for application. This work first applied the RSM–BBD model on the spray drying process of GO, and evidenced the possibility of producing high-quality GO slurry with the “drying-redissolution” method.

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44

Inam, Muhammad Ali, Rizwan Khan, Ick Tae Yeom, Abdul Salam Buller, Muhammad Akram, and Muhammad Waleed Inam. "Optimization of Antimony Removal by Coagulation-Flocculation-Sedimentation Process Using Response Surface Methodology." Processes 9, no. 1 (January 7, 2021): 117. http://dx.doi.org/10.3390/pr9010117.

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Анотація:

Coprecipitation-adsorption plays a significant role during coagulation-flocculation-sedimentation (C/F/S) of antimony (Sb) in water. This work uses a Box–Behnken statistical experiment design (BBD) and response surface methodology (RSM) to investigate the effects of major operating variables such as initial Sb(III, V) concentration (100–1000 µg/L), ferric chloride (FC) dose (5–50 mg/L), and pH (4–10) on redox Sb species. Experimental data of Sb(III, V) removal were used to determine response function coefficients. The model response value (Sb removal) showed good agreement with the experimental results. FC showed promising coagulation behavior of both Sb species under optimum pH (6.5–7.5) due to its high affinity towards Sb species and low residual Fe concentration. However, a high dose of 50 mg/L of FC is required for the maximum (88–93%) removal of Sb(V), but also for the highest (92–98%) removal of low initial concentrations of Sb(III). Furthermore, BBD and RSM were found to be reliable and feasible for determining the optimum conditions for Sb removal from environmental water samples by a C/F/S process. This work may contribute to a better understanding and prediction of the C/F/S behavior of Sb(III, V) species in aqueous environments, to reduce potential risks to humans.

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45

Im, Jong-Kwon, Moon-Kyung Kim, and Kyung-Duk Zoh. "Optimization of photolysis of diclofenac using a response surface methodology." Water Science and Technology 67, no. 4 (February 1, 2013): 907–14. http://dx.doi.org/10.2166/wst.2012.634.

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This study investigates the effects of environmental parameters such as UV intensity (X1, 2.1 ∼ 6.3 mW/cm2), Fe(III) (X2, 0 ∼ 0.94 mg/L), NO3− (X3, 0 ∼ 20 mg/L) and humic acid (X4, 0 ∼ 30 mg/L) on the removal efficiency of diclofenac (DCF, Y), and optimization using a response surface methodology (RSM) based on Box–Behnken design (BBD). According to analysis of variance and t-test results (p < 0.001), the proposed quadratic BBD model based on a total of 29 experimental runs fitted well to the experimental data. Moreover, the determination coefficient (R2 = 0.990) and adjusted determination coefficient (Ra2 = 0.981) indicated that this model is adequate with a high goodness-of-fit. Variables of X1, X2 and X3 had significant positive contributions (p < 0.001), while X4 had significant negative contribution to the DCF removal (p < 0.001). A Pareto analysis showed that X4 was the most important factor (57.18%) in DCF photolytic removal. The predicted and observed DCF removal were 94.98 and 94.2% under optimal conditions (X1 = 6.29 mW/cm2, X2 = 0.75 mg/L, X3 = 15.65 mg/L and X4 = zero), respectively. The RSM not only gives valuable information on the interactions between these photoreactive species (UV intensity, Fe(III), NO3−, and humic acid) that influence DCF removal, but also identifies the optimal conditions for effective DCF removal in water.

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46

Wei, Xiuyan, Xinyue Song, Dong Dong, Nemat O. Keyhani, Lindan Yao, Xiangyun Zang, Lili Dong, et al. "Efficient production of Aschersonia placenta protoplasts for transformation using optimization algorithms." Canadian Journal of Microbiology 62, no. 7 (July 2016): 579–87. http://dx.doi.org/10.1139/cjm-2015-0770.

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The insect pathogenic fungus Aschersonia placenta is a highly effective pathogen of whiteflies and scale insects. However, few genetic tools are currently available for studying this organism. Here we report on the conditions for the production of transformable A. placenta protoplasts using an optimized protocol based on the response surface method (RSM). Critical parameters for protoplast production were modelled by using a Box–Behnken design (BBD) involving 3 levels of 3 variables that was subsequently tested to verify its ability to predict protoplast production (R2 = 0.9465). The optimized conditions resulted in the highest yield of protoplasts ((4.41 ± 0.02) × 107 cells/mL of culture, mean ± SE) when fungal cells were treated with 26.1 mg/mL of lywallzyme for 4 h of digestion, and subsequently allowed to recover for 64.6 h in 0.7 mol/L NaCl–Tris buffer. The latter was used as an osmotic stabilizer. The yield of protoplasts was approximately 10-fold higher than that of the nonoptimized conditions. Generated protoplasts were transformed with vector PbarGPE containing the bar gene as the selection marker. Transformation efficiency was 300 colonies/(μg DNA·107 protoplasts), and integration of the vector DNA was confirmed by PCR. The results show that rational design strategies (RSM and BBD methods) are useful to increase the production of fungal protoplasts for a variety of downstream applications.

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47

Ji, Yan Ping, Cheng Ping Li, Xing Xu, Lan Tang, and Qian Tong. "Optimization of Extraction Technology of Favonoids from Kandelia Candel Leaves by Using Response Surface Methodology." Advanced Materials Research 704 (June 2013): 332–37. http://dx.doi.org/10.4028/www.scientific.net/amr.704.332.

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This work is to extract flavonoids using ethanol refluxing method from the Kandelia candel leaves which are rich in flavonoids. Ethanol concentration, solid-liquid ratio, temperature and extraction time on the extraction rate were investigated by the single factor experiments. Box-Behnken design (BBD) based on response surface methodology (RSM) was adopted to optimize the parameters. The results showed the best extracting rate was more than 7.74%, when the extraction parameters were ethanol concentration 60%, extraction time 0.5 h and temperature 80°C.

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48

Azhari, Ayu Wazira, Kamaruzzaman Sopian, Dewi Suriyani Che Halin, Abdul Haqi Ibrahim, and Saleem H. Zaidi. "Response Surface Methodology (RSM) in Fabrication of Nanostructured Silicon." Materials Science Forum 857 (May 2016): 151–55. http://dx.doi.org/10.4028/www.scientific.net/msf.857.151.

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In this paper, a respond surface methodology (RSM) model has been developed using three levels Box-Benkhen experimental design (BBD) technique to study the influence of several metal-assisted chemical etching (MACE) process variables on the properties of nanostructured silicon (Si) wafer. Five process variables are examined i.e. concentrations of silver (Ag), hydrofluoric acid (HF), deposition time, H2O2 concentration and etching time as a function of etching rate. Design-Expert® software (version 7.1) is used in formulating the RSM model of five factors with 46 experiments. A regression quadratic model is developed to correlate the process variables where the most significant factors are identified and validated using analysis of variance (ANOVA). The model for etching rate is found to be significant with R2 of 0.8, where both Ag concentrations and etching time are the major influence.

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49

Mushtaq, Faizan, Zhian Huang, Syyed Adnan Raheel Shah, Yinghua Zhang, Yukun Gao, Marc Azab, Sajid Hussain, and Muhammad Kashif Anwar. "Performance Optimization Approach of Polymer Modified Asphalt Mixtures with PET and PE Wastes: A Safety Study for Utilizing Eco-Friendly Circular Economy-Based SDGs Concepts." Polymers 14, no. 12 (June 19, 2022): 2493. http://dx.doi.org/10.3390/polym14122493.

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Eco-friendly waste utilization helps in the development of sustainable infrastructures. Recently, researchers have focused on the production of road infrastructures using the circular economy concept of human safety. The objective of this study is to investigate and explore the utilization of optimum polymer waste content for the development of polymer-modified asphalt mixtures using response surface methodology (RSM). RSM based on Box–Behnken design (BBD) was employed to optimize experimental design and included three factors: X1, polymer type; X2, polymer contents; and X3, testing day. The optimized responses determined by the RSM were as follows: MS of 42.98 kN, MF of 5.08 mm, and MQ of 8.66 kN/mm, indicating a favorable and consistent precision in comparison with experimental values. Moreover, the Marshall characteristics of samples prepared with PE were quite improved compared to PET. In conclusion, the incorporation of such polymer wastes in road construction is a sustainable and cost-effective way of improving their engineering properties. This study will help in the development of sustainable road infrastructures supporting human safety and environmentally friendly practices.

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50

Wang, Yong, Xia He, Xi Zhang, Dong Wang, and Xianjun Li. "Optimization Strategies for Improved Performance of Nano-Hybrid Wood/Polymer Composites Through Response Surface Methodology." Journal of Biobased Materials and Bioenergy 15, no. 5 (October 1, 2021): 648–55. http://dx.doi.org/10.1166/jbmb.2021.2100.

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Response surface methodology (RSM) was used to optimize the preparation conditions of nano-hybrid wood/polymer composites with montmorillonite (MMT) and furfuryl alcohol (FA). The effects of MMT amount, impregnation pressure and impregnation time on weigh percent gain (WPG) of treated samples were evaluated with Box-Behnken design (BBD) of a 3-level-3-factor from RSM. The predictive model for the response was extremely significant (p < 0.01). The determination coefficient (√R2) and the adjusted determination coefficient (VR2) of this model were 0.9651 and 0.9203, respectively. The optimal preparation conditions obtained by RSM design with the assistance of Design Expert were determined as follows: 4 wt% MMT amount, 0.8 MPa impregnation pressure, and 80 min impregnation time. It could be concluded from the SEM images that MMT and FA coated the cell walls and filled cell lumens. Moreover, the thermal stability was also investigated. The effects of preparation conditions were further validated by analyzing the water uptake ratio (WU), modulus of elasticity (MOE) and modulus of rupture (MOR) of the pristine samples and the treated samples prepared under optimized conditions.

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