Statistical Design Of Experiments Approach Research Articles

Design of Low-Cost Hydroponic Technology for Cultivation of Leafy Vegetables for Human Food Security in a Cold Desert Area of the Trans-Himalayan Range

For many decades, food security has been a worldwide concern. The present research aimed to examine an efficient technique for planting system i.e. hydroponic (a soil less system of farming) and statistical experimental design approach was used to compare the growth of different Leafy Vegetables (Spinach and Lettuce Varieties) in the Cold Desert Region of Trans Himalaya under the designed hydroponic system. The results showed that the leafy vegetable variety that were grown in the NFT system, exhibited significant differences in above attributes. The BC ratio of nutrient solution was found to be four times higher.

Experimental statistical modeling of tensile properties and flexural stiffness of recycled high-density polyethylene (rHDPE) thermoplastic using response surface methodology (RSM)

The widespread use of thermoplastic polymer products, particularly high-density polyethylene (HDPE), in daily consumer goods has led to the generation and accumulation of large amounts of plastic waste. This waste poses a significant threat to the environment due to the challenges and limitations in managing plastic waste effectively. Establishing new large volume applications of recycled HDPE (rHDPE) would incentivize recycling by creating new revenue streams. Before rHDPE (specifically rHDPE-talc blends) can be used more heavily in lieu of or in addition to virgin HDPE (vHDPE), its material properties in relation to application temperatures must be better understood. This study focused on characterizing tensile and flexural properties of rHDPE-talc blends across broad talc filler content and temperature ranges. Experimentally, tensile strength, elastic modulus, storage modulus, and nominal yield strain data were collected. Tensile strength and elastic modulus were observed to increase with an increase in talc content and decrease with an increase in temperature. Conversely, yield strain was observed to decrease with an increase in talc content and increase with an increase in temperature. Response Surface Methodology (RSM) was applied using the Central Composite Design (CCD) statistical experimental design approach to study the stiffness and strength of rHDPE and the effects that temperature and talc filler content have on these mechanical properties. The surfaces generated in this analysis fit the data well, with coefficients of determination greater than 0.94 for all but one property, storage modulus, which was 0.80. The evaluation of model performance revealed that the surfaces yielded good correlation and statistical significance. The results provide useful relationships that can be considered for use in practical applications involving rHDPE-talc blends, including engineering applications.

Experimental Investigation to Improve Heat Transfer Rate of Transformer Oil Using Cuo Nanoparticles

In this research, we employed a statistical experimental design approach, specifically the Taguchi method using an L9 orthogonal array robust design, to optimize experimental conditions. Our aim was to maximize the Heat Transfer coefficient of CuO-Transformer oil Nano fluid. We carefully selected three controllable factors with different levels: Concentration of Nanoparticles (0.1, 0.2, and 0.3g/lit), Size of nanoparticles (10, 15, and 20 Nm), and Ratio of Surfactant weight to Nano particles weight (0.1, 0.5, and 1).Our analysis of the results highlighted the significant role played by the Size of nanoparticles in influencing the heat transfer coefficient of the Nano fluid, accounting for approximately 88.852% of the variation. We determined the optimal settings for these three factors to maximize the heat transfer coefficient: Concentration of Nano particlesof0.2g/lit, Size of Nano particles of 10 Nm, and Ratio of Surfactant weight to Nano particles weight of 1. The Heat transfer coefficient predicted by ANOVA under these optimized conditions was 559.443W/m2 K and by regression model was 549.45 W/m2 K.To validate our findings, a confirmation test was conducted at these optimal conditions, and the results demonstrated a high degree of consistency between the experimental and predicted values.

The Impact of Pulp Temperature on the Flotation Performance of Copper-Lead-Zinc Sulphide Ore

Flotation performance can significantly be affected by seasonal variations due to the changes in water temperature and pulp temperature, type and concentration of dissolved ions in process water. Extreme temperature conditions could be the major factor affecting flotation performance and mask the influence of water chemistry. Therefore, the interactive effects of the temperature and water chemistry should be taken into consideration, particularly for mine sites experiencing extreme temperature conditions. In this paper, effects of temperature, sulphate (SO42−), thiosulphate (S2O32−) and calcium (Ca2+) ions on the flotation performance of a Cu-Pb-Zn complex sulfide ore were investigated using a statistical experimental design and modelling approach. The results were evaluated using ANOVA and regression analysis to determine the significant parameters and derive individual regression models for each flotation response using Design Expert software version 6.0.8. Individual regression models were developed for mass pull, water recovery, grade and recovery of the sulfide minerals using the statistically significant main effects and their interactions. The models were used to determine the concentration of the dissolved ions and pulp temperature required to achieve the maximum zinc recovery, maximum zinc grade or the optimum zinc grade and recovery. The results showed that the water chemistry (i.e., the concentrations of Ca2+, SO42− and S2O32−) affected the flotation performance significantly at low temperature (25 °C). At high pulp temperature (60 °C), however, the temperature was the dominant parameter and masked the effects of water chemistry. Details of the statistical experimental design, discussions of the effects of experimental factors and their interactions on flotation performance, and the development of regression models are presented in this paper.

Mathematical Modelling of Fused Deposition Modeling (FDM) 3D Printing of Poly Vinyl Alcohol Parts through Statistical Design of Experiments Approach

This paper explores the 3D printing of poly vinyl alcohol (PVA) using the fused deposition modeling (FDM) process by conducting statistical modeling and optimization. This study focuses on varying the infill percentage (10–50%) and patterns (Cubic, Gyroid, tri-hexagon and triangle, Grid) as input parameters for the response surface methodology (DOE) while measuring modulus, elongation at break, and weight as experimental responses. To determine the optimal parameters, a regression equation analysis was conducted to identify the most significant parameters. The results indicate that both input parameters significantly impact the output responses. The Design Expert software was utilized to create surface and residual plots, and the interaction between the two input parameters shows that increasing the infill percentage (IP) leads to printing heavier samples, while the patterns do not affect the weight of the parts due to close printing structures. On the contrary, the discrepancy between the predicted and actual responses for the optimal samples is below 15%. This level of error is deemed acceptable for the DOE experiments.

Optimizing growth conditions in vertical farming: enhancing lettuce and basil cultivation through the application of the Taguchi method

This paper reports on the findings of an experimental study that investigated the impact of various environmental factors on the growth of lettuce and basil plants in vertical farms. The study employed the Taguchi method, a statistical design of experiments approach, to efficiently identify the optimal growth conditions for these crops in a hyper-controlled environment. By reducing the time and cost of designing and running experiments, this method allowed for the simultaneous investigation of multiple environmental factors that affect plant growth. A total of 27 treatments were selected using the Taguchi approach, and the signal to noise ratio was calculated to predict the optimal levels of each environmental condition for maximizing basil and lettuce growth parameters. The results showed that most of the parameters, except for EC and relative humidity for certain growth parameters, were interrelated with each other. To validate the results, confirmation tests were conducted based on the predicted optimal parameters. The low error ratio between expected and predicted values (1–3%) confirmed the effectiveness of the Taguchi approach for determining the optimal environmental conditions for plant growth in vertical farms.

Chemometric study for the performances of deep eutectic solvents during the recovery of high-added-value substances from Moringa oleifera leaves: Principal component analysis.

Moringa oleifera is a plant with high antioxidant content in its leaves, flowers and seeds. It attracts the attention of researchers with the effect of its nutritional and medical advantages. The purpose of the current study is to propose a deep eutectic solvent (DES)-based ultrasound-assisted extraction of bioactive substances from M. oleifera leaves by the application of a chemometric study. A total of 18 different choline chloride-based DESs were prepared by using several hydrogen bond donors (glucose, sucrose, glycerol, ethylene glycol, urea and dimethyl urea) with various molar ratios (1:1, 1:2 and 2:1) by addition of diluents (water and 50% methanol) or alone. In order to decide the best DES combination, principal component analysis (PCA) was applied. The response surface method (RSM) was used as statistical experimental design approach through the Box-Behnken design. The best phenolic (TPC), flavonoid (TFC) and antioxidant activity yields of M. oleifera leaf extract were found to be 19.102 mg-GAE, 10.47 mg-CE and 24.404 mg-TEAC per gram dried leaf under the optimal conditions (50% water content, 20% amplitude, 15 min time). The model fitting has been also found reliable depending on the statistical indicators such as p-value (<0.0001), coefficients of determination (R2 = 0.9827, 0.9916 and 0.9864) and root mean square error (RMSE = 1.0562, 2.4656 and 0.7713). A chemometric study through PCA was carried out to determine the similarities and differences between the solvent groups, and the ethylene glycol-based DES (1:2, molar ratio) with the addition of water showed the best performance.

Development of an Encapsulation Method for Trapping the Active Materials from Sour Cherry Biowaste in Alginate Microcapsules

This study aims to contribute to those valorization approaches for the recovery process of high-value-added substances in environmentally friendly ways. In this study, one of the most consumed juice products was selected for providing waste byproducts (peel). Sour cherry peels were subjected to automatic solvent extraction using a GRAS solvent (aqueous 80% ethanol, v/v). Then, encapsulation for the preservation of the related extract was performed by ionic gelation in alginate beads. The process conditions (gelling medium concentration, wall material concentration, and hardening time) were optimized by a Box-Behnken design (statistical experimental design approach). An almost 80% encapsulation efficiency was achieved under the proposed method (7.8% CaCI2, 1.3% alginate, and 26 min). The inhibition effect of the produced capsules against DPPH (2,2-diphenyl-1-picrylhydrazil) radicals also shows that the current products might represent potential alternative natural antioxidants for food formulations. The morphological properties were also measured.

Optimization of extraction conditions for LC-ToF-MS analysis of mevalonate pathway metabolites in engineered E. coli strain via statistical experimental designs

Isoprenoids give rise to many functional products used today such as flavours, fragrances and even pharmaceutical compounds. Mevalonate pathway metabolites are the key intermediates that affect the production yield of isoprenoids. With increasing demand and benefit of isoprenoids, the present study adopts Analytical Quality-by-Design (AQbD) approach to establish an efficacious extraction protocol prior to the determination of mevalonate pathway metabolites in an engineered Escherichia coli model. The statistical experimental design approach, described in this work, has successfully validated an optimised sample preparation method i.e., using acetonitrile: 50 mM ammonium formate (pH 9.5) (7:3) (ACN73) at -20 °C for 10 min without solvent evaporation to retain the targeted mevalonate metabolites in engineered E. coli strain. The study also demonstrates the use of liquid chromatography paired with a Time-of-Flight Mass Spectrometer (LC-ToF-MS) for the quantitative analysis of the mevalonate pathway metabolites in E. coli. The analytical method was validated in accordance with guidelines in Metabolomics Standards Initiative and ICH Q2 (R1) with analyte spike recoveries at 80% and above. In short, the present study overcomes the one-variable-at-a-time (OVAT) limitations in analytical development, minimises metabolite losses and gives better cost and time efficiencies by eliminating the solvent evaporation and swapping process. This work highlights the importance of analytical methods development in microbial metabolomics studies.

Alginate-based hydrogels for trapping the polyphenols of Hibiscus sabdariffa: use of a statistical experimental design approach

Alginate-based hydrogels for trapping the polyphenols of Hibiscus sabdariffa: use of a statistical experimental design approach

Understanding scandium leaching from bauxite residues of different geological backgrounds using statistical design of experiments

The leaching behavior of scandium (Sc) from bauxite residues can differ significantly when residues of different geological backgrounds are compared. The mineralogy of the source rock and the physicochemical environment during bauxitization affect the association of Sc in the bauxite i.e., how Sc is distributed amongst different mineral phases and whether it is incorporated in and/or adsorbed onto those phases. The Sc association in the bauxite is in turn crucial for the resulting Sc association in the bauxite residue. In this study systematic leaching experiments were performed on three different bauxite residues using a statistical design of experiments approach. The three bauxite residues compared originated from processing of lateritic and karstic bauxites from Germany, Hungary, and Russia. The recovery of Sc and Fe was determined by ICP-OES measurements. Mineralogical changes were analyzed by X-ray-diffraction and subsequent Rietveld refinement. The effects of various parameters including temperature, acid type, acid concentration, liquid-to-solid ratio and residence time were studied. A response surface model was calculated for the selected case of citric acid leaching of Hungarian bauxite residue. The investigations showed that the type of bauxite residue has a strong influence. The easily leachable fraction of Sc can vary considerably between the types, reaching ~20–25% in German Bauxite residue and ~50% in Russian bauxite residue. Mineralogical investigations revealed that a major part of this fraction was released from secondary phases such as cancrinite and katoite formed during Bayer processing of the bauxite. The effect of temperature on Sc and Fe recovery is strong especially when citric acid is used. Based on the exponential relationship between temperature and Fe-recovery it was found to be particularly important for the selectivity of Sc over Fe. Optimization of the model for a maximum Sc recovery combined with a minimum Fe recovery yielded results of ~28% Sc recovery at <2% Fe recovery at a temperature of 60 °C, a citric acid normality of 1.8, and a liquid-to-solid ratio of 16 ml/g. Our study has shown that detailed knowledge about the Sc association and distribution in bauxite and bauxite residue is key to an efficient and selective leaching of Sc from bauxite residues.

Dexamethasone encapsulated PLGA nanoparticles for the treatment of neuroinflammation

Neuroinflammation is a condition that contributes significantly to the pathogenesis and progression of several neurodegenerative disorders. Targeting neuroinflammation is a novel therapeutic approach for the treatment of these disorders. Dexamethasone is a steroid-based anti-inflammatory drug with the potential to treat neuroinflammation. However, to maintain the efficacy of the drug, dexamethasone needs to be coupled with an effective drug delivery vehicle to be able to be transported across the central nervous system. Poly(lactic-co-glycolic acid) (PLGA) nanoparticles have been used as drug delivery vehicles for the transport of drugs into the central nervous system. This paper describes the preparation and encapsulation of dexamethasone-loaded PLGA nanoparticles by using the solvent evaporation method. The statistical experimental design approach was performed, wherein response surface methodology was carried out to optimize the parameters associated with the synthesis process. Further, the kinetic model and drug release profile were also determined. The drug encapsulated nanoparticles were validated for their effectiveness in vitro. Toxicity studies revealed the nanodrug to be non-cytotoxic, and the Griess assay highlighted its ability to lower neuroinflammation. Further, genetic studies revealed that the anti-inflammatory properties of the nanodrug were successful in modulating neuroinflammation.

Laser free-form fabrication of dual phase DP600 steel using water atomized feedstock powder

Wrought dual-phase (DP) steels are exploited extensively within the automotive sector in the manufacture of components critical to occupant safety. Despite the widespread deployment of these alloys in wrought form, there exists an acute lack of knowledge on their performance/applicability in the context of additive manufacturing (AM). To address this knowledge gap, a preliminary study into the feasibility of utilizing laser directed energy deposition as a means to fabricate prototype structural components from a water atomized dual-phase (DP600) steel powder was completed. A statistical design of experiments approach was used to parametrically model the effects of laser power, traverse rate, hatch spacing, z-step size and powder feed rate on the density, tensile properties, microstructure, and impurity content (O-H-N) of the deposited products produced from each powder. Using optimized parameters, specimen with densities of up to 7.867 g/cm 3 (were obtained while maintaining an equiaxed ferritic microstructure. Intercritical heat treatment developed the requisite dual phase microstructure and elevated tensile properties to effectively match wrought DP600. The prototyping capability of this material and system were then demonstrated through the fabrication of large format tubular specimen coupled with quasi-static axial compression tests.

Investigating mechanical and thermo-physical properties of binder jet 3D printed elements using a statistical experiment approach

PurposeWith a growing list of available materials and processes, the inherent mechanical and thermophysical properties of three-dimensional (3D) prints are important design targets. This paper aims to study the functionality of binder jet 3D printed objects for thermally activated building construction elements and recyclable formwork for concrete structures.Design/methodology/approachBinder jet printed sand samples with various material and post-processing parameters (infiltration and baking) are prepared and studied. Using a statistical experiment design, the mechanical (flexural and compressive strength) and thermal (conductivity and specific capacity) characteristics are quantified.FindingsRelative to the unprocessed “green” print samples, post-processing improved the flexural and compressive strength of the samples by factors of 6.9 and 21.6, respectively; the thermal conductivity and specific heat capacity were improved by factors of 7.7 and 1.2, respectively. For the investigated temperature range (20°C–200°C), the “green” prints showed excellent stability while the stability of post-processed samples depended on the infiltrate used. Microscopic images of the microstructures offered evidence to support improvement in the mechanical and thermo-physical characteristics of the 3D printed sand elements.Research limitations/implicationsThe literature review concluded that optimal printing parameters and infiltration under vacuum could further improve the mechanical and thermo-physical properties of the binder jet printed elements. However, both these factors were not explored in this research. The statistical experimental design approach provided more flexibility to choose the number of experiments for a fixed amount of time and resources. However, for future work, a more extensive number of experiments and reproducibility testing for each combination of binder-infiltrate is recommended.Practical implications3D printing has been identified as a promising opportunity to reduce material usage and improve construction efficiency in the field of architecture and building engineering. The emerging fabrication technologies are further expected to significantly reduce the operational energy of buildings through performance integration, i.e. multi-functional building elements with integrated heat- and mass-transfer capabilities to replace conventional systems.Originality/valueThis study has quantified the impact of infiltration on the mechanical and thermo-physical characteristics of sand-printed elements and, as such, reports reproducible functional performance maps for sand-print applications. The research demonstrates a way to achieve the desired functional characteristics of 3D prints through combinations of material selection and process/post-processing parameters.

Optimized preparation and characterization of Co-N codoped TiO2 with enhanced visible light activity: An insight into effect of dopants on surface redox reactions of photogenerated charge carriers for hydroxyl radical formation

In this study, a statistical experiment design approach using response surface methodology was applied in the optimization of preparing Co-N codoped TiO2 photocatalyst. The prepared Co-N codoped TiO2 significantly enhanced the photocatalytic degradation of dye Congo Red under visible light irradiation, as compared to undoped and N, Co monodoped TiO2. As proved by various characterizations, the enhanced photocatalytic activity of Co-N codoped TiO2 can be attributed to the narrowing of energy band structure and the existence of surface oxygen vacancies (OVs) and Co ions as the charge traps, thus resulting in the improvement of visible light absorption and charge transfer. The electron spin resonance and fluorescence measurements confirmed that the formation of hydroxyl radicals (•OH) can be promoted during Co-N codoped TiO2 photocatalysis, due to the more efficient generation and separation of electron–hole pairs. Moreover, it was found that the oxidation of H2O by holes is the relatively major pathway of •OH formation, and OVs could effectively reduce the electron competition between dissolved O2 and Co dopants to ensure the hole- and electron-induced surface redox reactions to proceed smoothly. Based on the above results, a more comprehensive mechanism of Co-N codoping for enhancing TiO2-assisted photocatalysis was thereby proposed.

Optimization of the Microwave Assisted Glycosylamines Synthesis Based on a Statistical Design of Experiments Approach.

Glycans carry a vast range of functions in nature. Utilizing their properties and functions in form of polymers, coatings or glycan derivatives for various applications makes the synthesis of modified glycans crucial. Since amines are easy to modify for subsequent reactions, we investigated regioselective amination conditions of different saccharides. Amination reactions were performed according to Kochetkov and Likhoshertov and accelerated by microwave irradiation. We optimized the synthesis of glycosylamines for N-acetyl-d-galactosamine, d-lactose, d-glucuronic acid and l-(−)-fucose using the design of experiments (DoE) approach. DoE enables efficient optimization with limited number of experimental data. A DoE software generated a set of experiments where reaction temperature, concentration of carbohydrate, nature of aminating agent and solvent were investigated. We found that the synthesis of glycosylamines significantly depends on the nature of the carbohydrate and on the reaction temperature. There is strong indication that high temperatures are favored for the amination reaction.

Development of a continuous process: a perspective for Mitsunobu reaction

The developments around flow processing technology have paved the way for new avenues and perspectives to consider in the field of organic chemistry and engineering. In this study, customized flow equipment was utilized to develop and optimize the Mitsunobu reaction. The flow reactor was a prototype of a simple tubular reactor based on the plug flow reactor concept. The experimentation methodology was designed through the statistical design of experiments approach to minimize the number of experiments. The molar ratios of cyclohexanol (1) and o-cresol (2) and interaction effects of triphenylphosphine, diisopropyl azodicarboxylate were studied in detail. The reaction profile of flow experiments agreed with the batch conditions adding noteworthy improvements to the overall reaction time, selectivity, and yield towards the desired product 1-(cyclohexyloxy)-2-methylbenzene (3). The Mitsunobu reaction in batch condition would take on an average of 3 to 5 h, which was effectively accomplished in 30 to 45 mins in this flow reactor. The generated mathematical model is in good agreement with the reaction conditions. We believe that the process could be executed continuously without a break, readily scaled to kilogram quantities in a short time without further development.

A Statistical Design of Experiments Approach to Machine Learning Model Selection in Engineering Applications

Abstract An important but insufficiently addressed issue for machine learning in engineering applications is the task of model selection for new problems. Existing approaches to model selection generally focus on optimizing the learning algorithm and associated hyperparameters. However, in real-world engineering applications, the parameters that are external to the learning algorithm, such as feature engineering, can also have a significant impact on the performance of the model. These external parameters do not fit into most existing approaches for model selection and are therefore often studied ad hoc or not at all. In this article, we develop a statistical design of experiment (DOEs) approach to model selection based on the use of the Taguchi method. The key idea is that we use orthogonal arrays to plan a set of build-and-test experiments to study the external parameters in combination with the learning algorithm. The use of orthogonal arrays maximizes the information learned from each experiment and, therefore, enables the experimental space to be explored extremely efficiently in comparison with grid or random search methods. We demonstrated the application of the statistical DOE approach to a real-world model selection problem involving predicting service request escalation. Statistical DOE significantly reduced the number of experiments necessary to fully explore the external parameters for this problem and was able to successfully optimize the model with respect to the objective function of minimizing total cost in addition to the standard evaluation metrics such as accuracy, f-measure, and g-mean.

Optimized precipitation process for efficient and size-controlled synthesis of hydroxyapatite–chitosan nanocomposite

It is known that the physicochemical characteristics of nanocomposites strongly affected by synthesis route and conditions. In this study, hydroxyapatite–chitosan nanocomposite as a substance with extensive medical application has been prepared by precipitation method under controlled conditions. To evaluation of the main synthesis parameters, including mixing ratio of precursors, pH and drying condition and investigation of their effect on the final products characteristics, a statistical design of experiments approach via Minitab 18 has been applied. For this purpose 12 sets of experimental run have been designed and performed based on various combinations of aforementioned parameters and the characteristics of the prepared samples have been elucidated by means of Fourier transformed infrared spectroscopy, X-ray diffraction, thermogravimetric analysis, scanning electron microscopy, transmission electron microscopy and the particle size analysis. The results represent the potential impact of synthesis parameters, their interaction with each other and estimation of optimum synthesis conditions. It is revealed that mean particle size of HA-CS nanocomposite reduced by increasing the content of hydroxyapatite and increment of reaction pH to values higher than 10. Also it is founded that freeze drying process, can be used as a superior drying method for the preparation of HA-CS nanocomposite with uniform particle size.

A detailed study on the sorption characteristics of humic acid onto calcined dolomite

Removal of unsafe materials in wastewater is of great significance to the environment. In this study, humic acid has been investigated as the research material for its adsorption characteristics (equilibirium, kinetics and thermodynamics). Dolomite has been used as adsorbent after calcination process of dolomite by heating to 1100 °C. The calcined material has been analyzed by Fourier Transform Infrared Spectroscopy (FT-IR) and X-ray Diffraction (XRD) methods for characterization reasons. On the other hand, conditions of the adsorption process have been optimized with Central composite face-centered (CCFC) design of Response Surface Approach (RSA). The findings of the current study will be the first to optimize the process conditions for the humic acid adsorption by a statistical experimental design approach. Moreover, equilibrium, kinetic and thermodynamic parameters have been determined to be able to interpret the nature of the current adsorption process.