Box-Behnken Method Research Articles

Optimizing roads for sustainability: Inverted pavement design with life cycle cost analysis and carbon footprint estimation

Inverted pavements have proven performance across the world and there is a need to optimize the layer thickness and material properties of the pavement addressing the critical failures in the Mechanistic-Empirical Pavement Design. The present study is made on bituminous concrete pavement with traffic up to 50 MSA and studying the critical factors affecting the pavement performance. The Minitab’s Response Surface Methodology − Box Behnken method was used for the Design of Experiments which includes critical factors and responses obtained from ANSYS finite element modeling of the inverted pavement. The critical factors and responses are normally distributed and indicate a linear relationship with the least error. The composite desirability for minimum stress and strains in the pavement layers was found to be 0.89. The optimized pavement thickness and layer material properties were validated with two pavement field cross sections of different Indian National Highways and it is observed that the optimized cross-section is safe. Further, this research paper carried out Life Cycle Cost Analysis and Life Cycle Assessment of inverted pavement with optimized pavement cross-section obtained including the carbon footprint during the vehicle operation phase. The study demonstrated the benefits of inverted pavement with reduced costs and carbon emissions. Thus, this approach paves the way towards sustainable and long-lasting pavements.

Towards enhanced surface roughness modeling in machining: an analysis of data transformation techniques

Data transformation methods are utilized to convert datasets into non-integer formats, potentially altering their distribution patterns. This implies that the variance and standard deviation of the dataset may be altered after the dataset undergoes data transformation operations. Improving model accuracy is a primary application of these methods. This study compares the efficacy of three data transformation techniques: square root transformation, logarithmic transformation, and inverse transformation. The comparison is conducted within the context of developing a surface roughness model for a turning process. Eighteen experiments are performed using the Box-Behnken method, with surface roughness chosen as the response variable. The surface roughness dataset undergoes transformation using the mentioned methods. Four surface roughness regression models are then built: one without transformation, one with square root transformation, one with logarithmic transformation, and one with inverse transformation. Evaluation metrics include coefficient of determination (R-Sq), adjusted coefficient of determination (R-Sq(adj)), Mean Absolute Error (%MAE), and Mean Squared Error (%MSE). Results indicate logarithmic transformation as the most effective, followed by square root transformation, in enhancing model accuracy. The surface roughness model utilizing data transformation exhibits high R-Sq and R-Sq(adj) values, at 0.8792 and 0.7434 respectively. On the other hand, this model has %MAE and %MSE values of only 10.33 and 2.05 respectively. Conversely, inverse transformation exhibits the least effectiveness among the three methods

Helical airfoil corrugated tubes: A numerical comparison with common corrugated profiles and geometrical optimization by multi-objective algorithm

In this study, helical airfoil corrugated tube (HACT) has suggested to improve the heat transfer capabilities of the tube heat exchanger. Firstly, thermal-hydraulic performance of HACT is numerically compared with common helical corrugated tube (HCTs) such as helical semi-ellipse corrugated tube (HSECT), helical semi-circle corrugated tube (HSCCT) and smooth tube based on thermal performance parameters: Nusselt number (Nu), friction factor (f) and performance evaluation criteria (PEC). Helium under high Reynolds number (Re) is the working fluid of this study. By analyzing the numerical outcomes, it is found that HACT has the heat transfer coefficient roughly 4 % higher than that of HSECT, around 5 % higher than that of HSCCT and more than 60 % higher than that of smooth tube. Then, a multi-objective optimization (MOO) is carried out to optimize the geometrical configuration of HACT. Initially, a numerical experimental design is constructed using the Box-Behnken method within the response surface methodology (RSM), focusing on two objectives and three factors. Subsequently, significant regression models (SRM) are derived via analysis of variance (ANNOVA). Then, leveraging these significant regression models of Nu and PEC, the geometric optimization of HACT is executed using Non-dominated Sorting Genetic Algorithm (NSGA-II), ultimately leading to the attainment of the Pareto front. Finally, based on Pareto optimal set, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was applied for decision-making involving multiple objectives.

Evaluation of the efficiency of thermostable L-asparaginase from B. licheniformis UDS-5 for acrylamide mitigation during preparation of French fries.

A thermostable L-asparaginase was produced from Bacillus licheniformis UDS-5 (GenBank accession number, OP117154). The production conditions were optimized by the Plackett Burman method, followed by the Box Behnken method, where the enzyme production was enhanced up to fourfold. It secreted L-asparaginase optimally in the medium, pH 7, containing 0.5% (w/v) peptone, 1% (w/v) sodium chloride, 0.15% (w/v) beef extract, 0.15% (w/v) yeast extract, 3% (w/v) L-asparagine at 50 °C for 96h. The enzyme, with a molecular weight of 85kDa, was purified by ion exchange chromatography and size exclusion chromatography with better purification fold and percent yield. It displayed optimal catalysis at 70°C in 20mM Tris-Cl buffer, pH 8. The purified enzyme also exhibited significant salt tolerance too, making it a suitable candidate for the food application. The L-asparaginase was employed at different doses to evaluate its ability to mitigate acrylamide, while preparing French fries without any prior treatment. The salient attributes of B. licheniformis UDS-5 L-asparaginase, such as greater thermal stability, salt stability and acrylamide reduction in starchy foods, highlights its possible application in the food industry.

Hydroxyapatite-Gold Modified Screen-Printed Carbon Electrode for Selective SARS-CoV-2 Antibody Immunosensor.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or coronavirus disease 2019 (COVID-19), is still spreading worldwide; therefore, the need for rapid and accurate detection methods remains relevant to maintain the spread of this infectious disease. Electrochemical immunosensors are an alternative method for the rapid detection of the SARS-CoV-2 virus. Herein, we report the development of a screen-printed carbon electrode immunosensor using a hydroxyapatite-gold nanocomposite (SPCE/HA-Au) directly spray-coated with the immobilization receptor binding domain (RBD) Spike to increase the conductivity and surface electrode area. The HA-Au composite synthesis was optimized using the Box-Behnken method, and the resulting composite was characterized by UV-vis spectrophotometry, TEM-EDX, and XRD analysis. The specific interaction of RBD Spike with immunoglobulin G (IgG) antibodies was evaluated by differential pulse voltammetry and electrochemical impedance spectroscopy methods in a [Fe(CN)6]4-/3- solution redox system. The IgG was detected with a detection limit of 0.0561 pg mL-1, and the immunosensor had selectivity and stability of 103-122% and was stable until week 7 with the influence of storage conditions. Also, the immunosensor was tested using real samples from human serum, where the results were confirmed using the chemiluminescent microparticle immunoassay (CMIA) method and showed satisfactory results. Therefore, the developed electrochemical immunosensor can rapidly and accurately detect SARS-CoV-2 antibodies.

An experimental investigation of F53 super duplex stainless steel using wire cut EDM process

This research focuses on the machinability of F53 super duplex stainless steel using the Wire Electrical Discharge Machining (WEDM) process. F53 Super Duplex Stainless Steel is known for its exceptional corrosion resistance and mechanical properties. It is a popular choice for critical applications in various industries, such as offshore, chemical, and petrochemical. The wire EDM process is chosen for its ability to achieve precise and intricate cuts in hard-to-machine materials like super duplex stainless steel. This study considered important process variables like wire feed rate, servo voltage, pulse-on and pulse-off time, and material removal rate and surface roughness as output responses. Box Behnken method is used to analyse all possible interactions between input and output variables are taken into account, and three different levels of parameter conditions are adopted. It was found that MRR increased by 51.30 % with a gradual increase in pulse on time and applied wire feed and SR decreased by 62.86 % gradually with increasing servo voltage and pulse off time. The optimized values are found to be Ton = 11 µs, WF = 13 m/min, SV = 40 V and Toff = 8 µs. The microhardness study confirms the formation of the melted layer on the machined surface. This research contributes to a deeper understanding of the wire EDM process's applicability to the challenging material properties of F53 super duplex stainless steel, offering insights for the manufacturing and engineering industries aiming to improve the efficiency and precision of machining operations for this advanced material.

Unlocking the potential of Tunisian dam sediment: optimizing zeolite X synthesis via Taguchi and Box-Behnken methods for sustainable resource recovery and versatile applications.

The Tunisian Lebna dam sediment was utilized to create the zeolite faujasite type Na-X. The aim of this investigation is to optimize the yield of Na-X zeolite using alkaline fusion hydrothermal treatment. Taguchi orthogonal array design was employed with nine trials to explore operating parameters including fusion temperature and time, activator type, and sediment type. The efficiency of alkaline fusion was evaluated using acid solubility. After dissolving the optimal alkali-fused sample in water, the Box-Behnken plan was used to identify the influence of L/S ratio, crystallization temperature, and time on zeolite Na-X yield. Rietveld analysis identified the mineral phases in the sediment as quartz (82.0%), calcite (8.8%), kaolinite (6.0), and illite (1.2%). With a NaOH activator, 850°C fusion temperature for 30min, 15 L/S ratio, and 75°C crystallization temperature for 4days, highly crystalline zeolite Na-X was created. FTIR, TGA, N2 adsorption-desorption isotherm, and X-ray diffraction were used to thoroughly describe this sample. The findings reveal the substantial zeolitization potential of the raw Lebna dam sediment, resulting in a high yield of zeolite Na-X.

Enhancing the Formulation of Chemically Modified Starch Bionanoparticles Through Ultrasonic Assisted Emulsification: A Parameter Study

AbstractThe design of polymeric nanoparticles has gained significant attention for drug delivery and controlled release applications. Biocompatible and degradable polyesters have emerged as promising materials for this purpose. However, achieving precise control over the size of nanoparticles (NPs) is crucial to ensure selective delivery and avoid clearance by the reticuloendothelial system. This study investigates the impact of external parameters, including polymer concentration, sonication amplitude, and sonication time, on the preparation of polymeric NPs using starch butyrate (SB) via the single emulsification‐solvent evaporation (SE‐SE) technique with ultrasound assistance. Employing a Box–Behnken method, this study optimizes the conditions for nanoformulation, obtaining response surfaces that reveal the effects of these parameters on particle size. These findings highlight that sonication time is the most influential parameter, followed by sonication amplitude and polymer concentration. By utilizing the statistical model, this study predicts and controls the variables to achieve particle diameters ranging from 152 to 346 nm. The optimal conditions for NPs preparation involve a polymer concentration of 1 mg mL−1, 40% sonication amplitude, and a 30‐min sonication time, resulting in particle sizes of 142 nm. Furthermore, this model demonstrates the capability to develop nanoformulations with excellent size homogeneity, enhancing their attractiveness and efficacy in drug delivery applications.

Optimizing nutrients from fly ash-amended soil through microbial-assisted phytoremediation using response surface methodology.

Nutrients are vital ingredients to boost plant health. The availability of nutrients is limited in fly ash (FA) waste to properly implement phytoremediation. The research explored the integration of microbes and treated wastewater irrigation in phytoremediation to provide the necessary nutrients for plant growth in fly ash-amended soils. The Box-Behnken method was used to design the experimental layout for the pot study. Response surface methodology (RSM) was applied as the optimization approach to model predictions for nutrient accumulation. The implemented pot study attained the highest morphological indicators with a plastochron index of 33.40, an absolute growth rate of 2.63 cm/day, and a leaf area of 2681.68 cm2 and attained maximum biomass of 24.91 g for the treatments that included a mid-range of the variables. The combination of FA 14.98%, microbial dose 4.07 mL, and treated wastewater as the irrigation source was found to be the optimized combination for nitrogen and phosphorus accumulation of 212.4 and 8.867 mg/L.

Application of natural fungi in bioconversion of lignocellulosic waste to second-generation ethanol

The second generation (2G) or cellulosic ethanol can help with diversification of the use of fossil energy sources. However, as bioconversion of plant waste into 2G bioethanol requires expensive additional steps of pre-treatment/hydrolysis of lignocellulosic materials, and this technology has not yet reached the technological readiness level which would allow it to be scaled-up, this process needs more interdisciplinary and comprehensive studies. This work was aimed at experimental study of a full cycle of successive processes of pre-treatment/saccharification using cellulolytic enzymes of filamentous fungi and fermentation of obtained syrups by xylose-fermenting yeast, using selected natural microorganisms for the fungal-based bioconversion of lignocellulosic agricultural waste to 2G ethanol. Using the Plackett-Burman and Box-Behnken methods of mathematical statistics, the optimal conditions for pre-treatment and enzymatic hydrolysis of wheat straw by a hemi- and cellulolytic multi-enzyme complex of the selected fungal strain Talaromyces funiculosus UCM F-16795 were established: microwave-assisted alkali pre-treatment with sodium hydroxide (NaOH) solutions (concentration range 4.6–4.8%), and saccharification conditions of medium pH 4, temperature 40 °С, hydrolysis duration 18 hours, and dilution of culture liquid with a buffer solution 1:1. The total energy of microwave irradiation 1.2 kJ and the ratio of substrate/enzyme solution 100 mg/1 mL were used. Under optimized conditions, wheat straw hydrolysates contained 5.0–7.5 g/L of reducing sugars, which, according to HPLC assessment, contained 0.7–1.0 g/L of glucose, 2.2–2.9 g/L of xylose and 0.7–0.8 g/L cellobiose. We used the selected strain of xylose-fermenting yeast in fermentation of mixtures of the most important monosaccharides in hydrolysates, xylose and glucose, in the concentration range relevant for syrups obtained by us during the optimized saccharification of lignocellulosic substrates with T. funiculosus enzymes. Based on sequencing and phylogenetic analysis, strain UCM Y-2810 was confirmed as Scheffersomyces stipitis; its nucleotide sequences of ITS region and 28S gene rDNA were deposited in GenBank under the accession numbers OP931914 and OP931915, respectively. The ethanologenic process for S. stipitis UCM Y-2810 was studied according to Box-Behnken design, assessing ethanol concentration by gas chromatography-mass spectrometry. Yeast fermentation under static microaerophilic conditions showed a 1.5 times higher rate of bioethanol production and 1.7 times greater efficiency of ethanologenesis per yeast biomass than for submerged cultivation. Optimization of the process of ethanologenesis resulted in the maximum rate of fermentation mixture of sugars, being 11.30 ± 0.36 g/L of ethanol, with optimal values of factors: 30 g/L of xylose, 5.5 g/L of glucose and cultivation for 5.5 days. It was revealed that the tested glucose concentrations did not significantly affect the process of xylose-fermentation by yeast, and non-competitive inhibition of xylose transport by glucose into yeast cells did not occur. This study demonstrated the potential of a full cycle bioconversion of lignocellulosic waste to 2G ethanol based on use of natural fungal strains and optimization of conditions for all steps.

Effect of papain enzyme surface modification on hydrophilic and comfort properties of polyester/cotton blend fabric

In this study, the authors used an enzyme called papain sourced from the Carica Papaya to improve the comfort and water-absorbing properties of a fabric made from a blend of polyester and cotton (65/35). The experiment was designed using the Box Behnken method to determine the most important variable and the best levels of parameters. The focus was on testing the wettability, moisture regain, and surface characteristics of the material. The results showed that all the comfort properties of the fabric improved after treatment with papain enzyme. After testing different parameters, the best conditions for treating the fabric with papain enzyme were determined to be a temperature of 30 °C, a papain concentration of 14%, and a treatment time of 50 min. Under these optimized conditions, the moisture regain and wettability of the polyester/cotton blend fabric treated with papain enzyme improved to 1.9 ± 0.02% and 6 cm capillary rise (measured with a 2-s drop test and 2-s sinking time) within just 3 min of wicking time. The Polyester/cotton blend fabrics treated with papain enzyme exhibited several noteworthy characteristics, including a significantly reduced susceptibility to fabric pilling (4–5), a limited capacity to attract oily impurities, and a high oil-soil-release capability with a stain removal index of 85%. Additionally, the fabrics showed a one-order-of-magnitude decrease in surface resistivity under normal conditions, with a half-life decay time of 513 s. Observations of the treated fabrics revealed the presence of cracks, grooves, nanostructures, and a high degree of roughness on the surfaces that were treated with papain enzyme. To further evaluate the effects of the lipase enzyme treatment on the fabric properties, several tests were conducted, including Fourier Trasform Infrared spectroscopy (FT-IR), Thermogravimetric Analysis (TGA), Differential scanning calorimetry (DSC), Moisture Regain, Tensile Strength, Stain Repellency, pilling resistance, and Anti-static charge generation.

Formulation Optimization of Topical Simvastatin Emulgel by Box Behnken Method

Formulation Optimization of Topical Simvastatin Emulgel by Box Behnken Method

Otimização do Extrato Bruto Hidroetanólico das Folhas de Eugenia dysenterica DC. pelo Método Box‑Behnken e Avaliação das suas Atividades Antioxidante e Antimicrobiana

Otimização do Extrato Bruto Hidroetanólico das Folhas de Eugenia dysenterica DC. pelo Método Box‑Behnken e Avaliação das suas Atividades Antioxidante e Antimicrobiana

Optimization of hydrogen generation rate with Co/MMT catalyst from NaBH4 using box-behnken method

Optimization of hydrogen generation rate with Co/MMT catalyst from NaBH4 using box-behnken method

Fluoride removal from phosphogypsum: A study on pre-industrial scale and its mathematical analysis

This study discusses fluoride removal efficiency from phosphogypsum (PG) on a lab-scale experiment matrix designed by the Box-Behnken method. Temperature, solid/liquid ratio, and time were supposed to influence fluoride removal efficiency from PG by various salt solution media. Experiment matrices were designed according to salt solution types: seawater, 5% NaCl, and 10% NaCl solutions. The factor-response analysis showed a direct proportionality between fluoride removal efficiency and temperature. The optimum fluoride removal conditions based on the experimental data obtained by the multi-variable design matrix were determined by the Design Expert v.12 software. The optimum temperature, time, and solid/liquid ratio were 80 ?C, 3 h, and 0,174 for seawater. The software predicted a 73,31% fluoride removal efficiency at the optimum conditions, whereas the experimental value was 74,99%. Since the actual vs. predicted data show high consistency, results might also be useful when industrial-scale fluoride removal to a predetermined level is required prior to a particular use of PG. PG has a high potential as an alternative raw material, and fluoride removal might be important in recycling applications. This study provides a novel pre-industrial scale fluoride removal inventory, especially for the fertilizer and cement industry.

A kinetic-assisted growth curve prediction method for Chlamydomonas reinhardtii incorporating transfer learning

Traditional predictions of microalgal growth states rely on empirical or easily implementable kinetic models, leading to significant biases and elevated cost. This study proposes a kinetic-assisted machine learning method for predicting the growth curve of microalgal biomass under small sample conditions. Firstly, a microalgae growth kinetic model is constructed based on the logistic model. A two-stage kinetic fitting strategy is specified to account for the light–dark ratio. The Box-Behnken method is employed for experimental design. Then, using Two-stage TrAdaboost.R2 algorithm, the kinetic model is utilized as the source domain, and the experimental design data serves as the target domain for training machine learning models. The results indicate that the proposed method outperforms a single machine learning model in terms of prediction and has the potential to rapidly estimate microalgal growth trends under different conditions and accurately predict harvested biomass, potentially reducing the need for laborious, expensive, and time-consuming laboratory trials.

Statistical-experimental modeling of the effect of process parameters on geometric characteristics of laser cladding of stellite 6 on SS316 using second-order regression

Applications of 316 stainless steel can be developed by laser cladding of the Co-based superalloys, which has multiple enhancement properties and creates a small heat affected zone. Quantitive investigation of the effect of process parameters on geometrical features and providing reliable models to achieve the desirable metallurgical bond, minimal structural defects, and predicting the dimensions of the geometry without conducting excessive experimental tests are an important issue. This study evaluate the effects of the main process parameters of laser cladding on geometric characteristics of stellite 6 using the response surface methodology. A quadratic model was developed for each of the geometric characteristics. Laser power, scanning speed, and powder feed rate ranged between 400 and 600 W, 6–10 mm/s and 12–20 gr/min, respectively, were considered as the input variables and height, width, penetration depth and dilution were considered as responses. The experiments were designed using the box-behnken method. The accuracy and fitness of the models were evaluated using analysis of variance method. The results indicated the R2 factors obtained for height, width, penetration depth and dilution are equal to 0.89, 0.98, 0.98 and 0.96 respectively. After obtaining the desirability factor of 0.795, the optimal set of process parameters were selected as laser power of 510 W, scanning speed of 10 mm/s and powder feed rate of 13 gr/min. Verification of the optimization was done by performing experimental test,which maximum 13 % difference between actual values and the predicted values was obtained.

Quality by Design and Optimization of Favipiravir Nanoparticles Using 3-Level Factorial Design by Box-Behnken Method

Quality by Design and Optimization of Favipiravir Nanoparticles Using 3-Level Factorial Design by Box-Behnken Method

Process design and optimization of EDTA-biojarosite – a treatment approach in the Box–Behnken framework

Abstract A study was conducted to investigate the performance of biojarosite as a catalyst in Fenton oxidation with and without the presence of the chelating agent ethylene diamine tetra acetic acid (EDTA). The addition of EDTA resulted in increased iron dissolution, confirming the role of EDTA as a chelating agent. The heterogeneous catalytic properties of jarosite were confirmed by the removal efficiencies of 93.5 and 83.4%, with and without EDTA, respectively. The Box–Behnken method was employed as a Design of Experiments tool to identify suitable experimental runs. Both Fenton oxidation and EDTA-based Fenton oxidation were examined separately, with PCP removal being the response variable. In the case of Fenton oxidation, the optimization process resulted in the selection of 0.1–1 g/L of iron catalyst, 100–1,000 mg/L of H2O2, and a pH range of 2.5–3.5. On the other hand, for EDTA-based Fenton oxidation, the optimal conditions were determined to be 0.1–1 g/L of iron catalyst, 100–1,000 mg/L of H2O2, and a pH range of 6.5–7.5. ANOVA was conducted to analyze the results, and the model fit was examined.

Analysis and Design of Embedded High-Temperature Doubly Salient Electro-Magnetic Starter/Generator for Aviation

In view of the high temperature operation characteristics of the embedded starter/generator for the more electric engine (MEE), the influence of material property change on motor performance at high temperature is studied. Furthermore, considering the influence of high temperature, the electromagnetic calculation of doubly salient electro-magnetic machine (DSEM) is carried out, and the excitation design principle of DSEM under high temperature is pointed out. The performance of DSEMs using DW310 silicon steel and 1J22 iron-cobalt alloy material was compared. Based on the silicon steel material, the structural parameters of DSEM are analyzed. According to the key parameters obtained from structural parameter analysis, Box-Behnken method and genetic algorithm were used to carry out the multi-parameter and multi-objective optimization design. The optimization objectives include excitation current, voltage ripple and cogging torque, to meet the torque and power quality requirements of MEE. Then the performance of DSEM at room temperature and high temperature is analyzed by the finite element method, which verifies the effectiveness of the design. Finally, a high-temperature and high-speed DSEM prototype was processed. At the same time, in order to cooperate with the test, a DSEM ground simulation test system platform for MEE was built to test the prototype. Its design experience lays a foundation for the embedded DSEM design of more electric aircraft.