Experimental Design Methodology Research Articles

Optimizing MEP Design in Pharmaceutical Cleanroom with the Integration of 2D To 7D in BIM

Abstract: This paper examines the optimization of pharmaceutical cleanroom design using 2D to 7D Modelling in Building Information Modelling (BIM). The goal is to enhance the reliability, different methods, statistical data, and performance of pharmaceutical production plants by effectively allocating components within design constraints. Mathematical optimization techniques improve facility design, ensuring robustness and operational efficiency. The study proposes a reliable optimal design for air-conditioning systems in pharmaceutical cleanrooms, considering uncertainties in design parameters and operational strategies. Resilient cleanroom systems are developed, and adaptable to various conditions while maintaining high performance standards. Additionally, the research explores the application of experimental design methodologies in optimizing drug delivery systems, such as factorial and etc, to enhance the quality and efficiency of pharmaceutical products and processes.

Effect of grinding media shape on the particle size distribution of UG2 ore using the response surface methodology central composite design

In this study, the effects of truncated ellipsoids and cubes on the particle size distribution of a UG2 ore were compared to the spheres using the response surface methodology for experimental design, modeling, and optimization. The response surface methodology demonstrated that it can be useful in optimizing grinding operations. It provides lenses to see and analyze the behavior of the ball filling, interstitial filling, and % solids parameters which are complex and interactive, and their effects on the production of the desired product (−75 µm) for different grinding media. Spherical grinding media outperformed truncated ellipsoids and cubes, producing the highest amount of the desired size class (82.58%), followed by 80.41% for truncated ellipsoids and 77.07% for cubes. Spheres also consumed the least power followed by cubes and lastly truncated ellipsoids. The differences in optimal grinding conditions were attributed to different contact mechanisms, surface area, and load behavior of the grinding media.

Production of β-cyclodextrin by immobilized CGTase in packed bed bioreactor through the medium component by employing Taguchi methodology

This study investigates the optimization of submerged culture conditions for the production of extracellular cyclodextrin glucanotransferase (CGTase) enzyme by Bacillus macerans ATCC 8244, employing Taguchi orthogonal array (OA) experimental design methodology. Five critical factors - starch, peptone, temperature, pH, and agitation speed - were examined at two levels using an OA layout of L8 for the experimental design. Analysis revealed a significant enhancement in CGTase yield, with the modified culture conditions resulting in a 21.2% increase from 513.416 U/ml to 622.5 U/ml. The successful application of Taguchi's design methodology underscores its efficacy in optimizing enzyme production processes. This study highlights Taguchi's design potential as a valuable tool for improving CGTase production efficiency while minimizing resource consumption. Additionally, the utilization of a packed bed bioreactor (PBR) facilitated continuous production of β-cyclodextrin (β-CD), demonstrating stability over 10 days with only a slight decrease in productivity. The Km,app value was determined to be 23.4 mg starch/ml, indicating a slight increase.

Utilizing experimental design and desirability function in optimizing RP-HPLC method for simultaneous determination of some skeletal muscle relaxants and analgesics

An experimental design and response surface methodologies using Plackett–Burman and Box-Behnken designs were applied for selecting and optimizing the most appropriate parameters which significantly affect the separation and quantitative estimation of five skeletal muscle relaxants and four analgesic drugs (baclofen, methocarbamol, dantrolene sodium, orphenadrine citrate, cyclobenzaprine hydrochloride, ketoprofen, etoricoxib, ibuprofen, and mefenamic acid) with a relatively short duration of analysis in a single run. For the separation of the nine drugs, an INERTSIL ODS-V3-5 µm C18 column (250 × 4.6 mm I.D.) was used with the optimum mobile phase conditions (45.15 mM ammonium acetate buffer pH 5.56 adjusted with acetic acid, acetonitrile, and methanol in a ratio of 30.5:29.5:40, v/v/v with a flow rate of 1.5 mL/min) and UV-detection at 220 nm. The optimized method was successfully subjected to the validation steps as described in ICH guidelines for linearity, precision, accuracy, robustness, and sensitivity. The optimized and validated method was effectively applied to determine the content of the studied drugs in their pharmaceutical preparations and to expand its applicability to the counterfeit estimation of etoricoxib in different brands of tablet dosage forms.

Prediction of the Performance and emission characteristics of diesel engine using diphenylamine Antioxidant and ceria nanoparticle additives with biodiesel based on machine learning

This study explores the impact of incorporating antioxidants diphenylamine (DPA) and nanoparticle ceria (CeO2) into Jatropha biodiesel (B30) blend on engine performance and exhaust emissions. The fuel blends utilized in this study consists of diesel, B30, B30 with 100 ppm of antioxidant diphenylamine (B30+DPA100), and B30 with 50 ppm of antioxidant diphenylamine and 50 ppm of nanoparticle ceria (B30+DPA50+CeO250). The experiments were designed using the design of experiment methodology, and they were conducted using various fuels to assess both engine performance and exhaust emissions characteristics. Further, machine learning algorithms (multilayer perceptron, random forest regression, and K-nearest neighbors) has been employed to develop a model for accurately predicting experimental outcomes. The K-nearest neighbors model surpassed the multilayer perceptron and random forest regression models, demonstrating a higher coefficient of determination value and precise outcome predictions. The experimental results reveal that adding antioxidants diphenylamine and nanoparticles ceria at 50 ppm to B30 significantly reduced nitrogen oxides emissions. Compared to B30, B30+DPA50+CeO250 showed a 6.35 % decrease in brake specific fuel consumption and an 8.68 % reduction in nitrogen oxides emissions. However, there was a slight increase of 5.74 % in brake thermal efficiency. Additionally, B30+DPA50+CeO250 exhibited a 2.54 % reduction in maximum cylinder pressure compared to B30.

Optimisation of dairy-based media using response surface modelling to increase yields of Lacticaseibacillus rhamnosus promoting an enhanced immune response

This study optimised biomass production of health-promoting bacteria by optimising cultivation media using design of experiments methodology. Screening of seven lactic acid bacteria were conducted, where Lacticaseibacillus rhamnosus LRH30 (L. rhamnosus LRH30) was selected as the most suitable candidate for further optimization. Response surface modelling was employed to optimise four dairy-based carbon sources: lactose, whey permeate (WP), demineralised whey (DW) and skim milk powder (SMP), in conjunction with yeast extract and peptone using a central composite design. SMP was identified as the best-suited carbon substrate for high biomass yields, with optimal media concentrations of 12.5 g L-1 lactose, 2.36 g L-1 yeast extract and 2.36 g L-1 peptone in shake flasks and later in batch bioprocess, resulting in biomass quantities of 6.5 g L-1. Additionally, the immunomodulatory effects of L. rhamnosus LRH30 were confirmed in an enzyme-linked immunosorbent assay (ELISA) that showed a significant reduction of tumour necrosis factor (TNF)-α.

An optimal experimental design framework for fast kinetic model identification based on artificial neural networks

The development of mathematical models to describe reaction kinetics is crucial in process design, control, and optimization. However, distinguishing between different candidate kinetic models presents a non-trivial challenge. Recent works on this topic introduced an approach that employs artificial neural networks (ANNs) to identify kinetic models. In this paper, the ANNs-based model identification approach is expanded by introducing an optimal experimental design procedure. The performance of the method is evaluated through a case study related to the identification of kinetics in a batch reaction system, where different combinations of experimental design variables and noise level on the measurements are compared to assess their impact on kinetic model identification. The proposed experimental design methodology effectively reduces the number of required experiments while enhancing the artificial neural network's ability to accurately identify the appropriate set of equations defining the kinetic model structure.

Performance analysis of solar photovoltaic thermal (PV/T) dryer for drying moringa leaf

Though its considerable solar energy potential prevents equal distribution throughout the region, Indonesia has not made the best use of it, particularly when it comes to integrated drying and electrical technologies. There are a number of drawbacks to traditional direct drying, particularly with regard to food goods. These include the possibility of contamination, discolouration, and animal disturbance. The purpose of this research is to develop, build, and evaluate an integrated drying technology—a solar photovoltaic thermal (PV/T) dryer—that can generate heat and electricity at the same time. The dried material used in this dryer is moringa leaves. This study employs an experimental design methodology, first developing and building a greenhouse-style PV/T dryer with three levels of shelves. Second, a test of the tool's functionality was conducted using 1000 grams of material on each shelf. Measuring devices such digital thermocouples, multimeters, solar power meters, and humidity meters are utilized to get test parameter data. The average daily solar intensity, according to the research findings, is 436.60 watts/m2. Shelf 1, which is next to the collector outlet, had the highest average temperature (53.9 °C), whereas shelf 3 had the lowest average temperature (37.1 °C). With an average voltage of 30.89 volts and an average current of 4.08 amperes, solar panels generate an average power of 85.65 watts. In the meantime, the material's mass decreased after drying for nine hours, on average by 72%, according to the drying data. With an average power of 86.65 watts, an average temperature on the third shelf between 37.1 °C and 40.6 °C, and an average mass reduction of 72%, it can also be stated that this PV/T dryer is quite successful for drying.

Study of Road Accidents by Analysis of Effects of Driving Parameters On Stopping Distance Using the Experimental Design Method and Driving Simulator

This paper aims to investigate the key factors contributing to road accidents and their interplay. A significant proportion of road accidents can be attributed to non-compliance with speed regulations, in conjunction with vehicle braking components (such as grip, road conditions, ..), as well as weather conditions. We conducted a speed measurement campaign on peri-urban roads in multiple Algerian cities to examine driving behavior and adherence to speed limits. To comprehensively understand the impact of speed, anti-lock systems, weather conditions, grip, and their interactions on a vehicle's braking distance, we employed an experimental design methodology. This approach was complemented with driving simulator tests. We expanded the experimental design to cover a range of speed variations, from 70 to 130 km/h, enabling us to explore the influence of various driving parameters on braking distance. Through statistical analysis of influence coefficients from 16 driving simulator tests, we established a model that represents the relationship between "objective functions" and "influence factors." This mathematical model was validated using simulator results. Our study revealed that braking distance is primarily affected by speed and weather conditions. Notably, the analysis of the road speed measurement campaign demonstrated that 55% of road users fail to adhere to speed limits.

Optimization of preparation conditions for Salsola laricifolia protoplasts using response surface methodology and artificial neural network modeling

BackgroundSalsola laricifolia is a typical C3–C4 typical desert plant, belonging to the family Amaranthaceae. An efficient single-cell system is crucial to study the gene function of this plant. In this study, we optimized the experimental conditions by using Box-Behnken experimental design and Response Surface Methodology (RSM)-Artificial Neural Network (ANN) model based on the previous studies.ResultsAmong the 17 experiment groups designed by Box-Behnken experimental design, the maximum yield (1.566 × 106/100 mg) and the maximum number of viable cells (1.367 × 106/100 mg) were obtained in group 12, and the maximum viability (90.81%) was obtained in group 5. Based on these results, both the RSM and ANN models were employed for evaluating the impact of experimental factors. By RSM model, cellulase R-10 content was the most influential factor on protoplast yield, followed by macerozyme R-10 content and mannitol concentration. For protoplast viability, the macerozyme R-10 content had the highest influence, followed by cellulase R-10 content and mannitol concentration. The RSM model performed better than the ANN model in predicting yield and viability. However, the ANN model showed significant improvement in predicting the number of viable cells. After comprehensive evaluation of the protoplast yield, the viability and number of viable cells, the optimal results was predicted by ANN yield model and tested. The amount of protoplast yield was 1.550 × 106/100 mg, with viability of 90.65% and the number of viable cells of 1.405 × 106/100 mg. The corresponding conditions were 1.98% cellulase R-10, 1.00% macerozyme R-10, and 0.50 mol L−1 mannitol. Using the obtained protoplasts, the reference genes (18SrRNA, β-actin and EF1-α) were screened for expression, and transformed with PEG-mediated pBI121-SaNADP-ME2-GFP plasmid vector. There was no significant difference in the expression of β-actin and EF1-α before and after treatment, suggesting that they can be used as internal reference genes in protoplast experiments. And SaNADP-ME2 localized in chloroplasts.ConclusionThe current study validated and evaluated the effectiveness and results of RSM and ANN in optimizing the conditions for protoplast preparation using S. laricifolia as materials. These two methods can be used independently of experimental materials, making them suitable for isolating protoplasts from other plant materials. The selection of the number of viable cells as an evaluation index for protoplast experiments is based on its ability to consider both protoplast yield and viability. The findings of this study provide an efficient single-cell system for future genetic experiments in S. laricifolia and can serve as a reference method for preparing protoplasts from other materials.

Analysis, construction, characterization, and application of copper nanowires loaded on activated carbon for removal of bromophenol blue in water samples

In the present work, a copper nanowire loaded on activated carbon (Cu-NW-AC) was fabricated and applied as an effective adsorbent for the removal of bromophenol blue (BPB) dye from aqueous solutions and then the percentage of removal was evaluated by UV–Vis spectrophotometer. The synthesized adsorbent was characterized and identified using techniques like Transmission electron microscopy (TEM) and Brunauer–Emmett–Teller (BET). The effective parameters of the removal process were investigated and optimized by experimental design methodology (EDM) based on response surface methodology (RSM) as a powerful optimization method. EDM is a unique method for following the effects of different factors on the removal process simultaneously. Analysis of variance (ANOVA) was used based on p-values and F-tests to investigate the accuracy and reliability of the used method. The optimized parameters were obtained as BPB concentration of 15 mg L-1, ultrasonic irradiation time of 14 min, adsorbent dosage of 0.018 g and pH= 5.5 under the desirability function. To evaluate the adsorption mechanism and calculation of maximum adsorption capacity, different adsorption isotherms were studied and according to the results, the Langmuir isotherm model showed the highest compatibility due to its higher R2 (0.9905). Also, the proposed adsorbent represented good adsorption capacity (123.45 mg g-1).

Optimization of Three-Dimensional-Printed Catalytic Electrodes for Alkaline Water Electrolysis Guided by the Experimental Design Methodology

Optimization of Three-Dimensional-Printed Catalytic Electrodes for Alkaline Water Electrolysis Guided by the Experimental Design Methodology

Academic support through tutoring, guided learning, and learning diaries in the context of the COVID-19 pandemic: an experimental model for master’s students

IntroductionEngaging students in co-planning their learning paths, peer cooperation, tutoring, and network building in an inclusive and project-based environment has been associated with higher academic and professional success. However, university teachers face challenges in offering guidance and academic supervision to optimize student learning, particularly in the context of online learning. This study aimed to investigate the effectiveness of adapting collaborative learning environments, peer tutoring, and academic supervision in the online learning space.MethodsAn experimental design methodology was employed to investigate the impact of collaborative learning environments, peer tutoring, and academic supervision on student engagement, interest, and dedication to learning.ResultsThe findings showed direct positive effects on the level of engagement (VI; M = 23.18, SD = 5), higher interest and dedication to the learning process (DE: M = 26.93, SD = 3.21), and better concentration and enthusiasm while studying (AB, M = 24.83, SD = 8.52).DiscussionThe results suggest that adapting to online learning by providing collaborative learning environments, peer tutoring, and academic supervision can enhance student engagement and motivation. Further research is needed to explore the long-term effects and scalability of these interventions in diverse educational contexts.

The Effects of Processing Parameters during the Wire Arc Additive Manufacturing of 308L Stainless Steel on the Formation of a Thin-Walled Structure.

Wire arc additive manufacturing (WAAM) excels in producing medium to large components with a high deposition rate. Process optimization is crucial for uniform, defect-free components. This research employs orthogonal experimental design and response surface methodology (RSM) to control TIG WAAM-ed 308L stainless steel components. Varied parameters, including tungsten electrode angle, welding current, and speed, target weld bead attributes. Analysis of variance (ANOVA) evaluates multi-processing parameter influence on weld bead formation. Comparison with experimental results confirms accurate modeling of the relationship between parameters and bead attributes. The study optimizes process parameters and swing to enhance dimensional accuracy in single-layer and multi-layer components, improving precision, quality, and accuracy in thin-walled structures.

Nanoparticle depressants in froth flotation – The effect of colloidal silica with different size and surface modifications on the selective separation of semi-soluble salt type minerals

Colloidal silica has been presented as a selective depressant in froth flotation separation of scheelite and calcite by our group in 2020. Here we analyze the effects of colloidal silica dosage, specific surface area (i.e. size), and modification with microflotation on pure scheelite, calcite, fluorite, and apatite minerals. In a reagents scale-up procedure batch flotation experiments were conducted on a low-grade scheelite ore using statistical experimental design methodology. Microflotation experiments showed colloidal silica selectively depressing calcite flotation with increasing intensity with reduced size of colloidal silica. The nanoparticulate depressant did not affect the recoveries of scheelite, apatite, and fluorite – typical ore minerals that occur associated with calcite. Batch flotation of a low-grade scheelite ore confirmed the observations made at the microscale: colloidal silica significantly improves the scheelite-calcite selectivity index. Here, we observe a series of relations between colloidal silica specific surface area and modification on different process indicators such as scheelite-calcite selectivity index, recoveries, and froth properties. These relations are assumed to be due to the different aggregation mechanisms of the colloidal silica dispersions. The aluminate-modified colloidal silica, which tends to form a coherent gel network in the presence of Ca2+, shows the strongest effect on reducing calcite recovery accompanied by a significant reduction in scheelite recovery compared to the non-functionalised and silane-modified colloidal silica.

Survey on the Compliance of the Assumptions of Variance Analysis of Experimental Designs in Undergraduate Theses of Peruvian Universities

The main objective of the research was to verify compliance with the assumptions of the analysis of variance, using some experimental design, in undergraduate theses from Peruvian universities. For this purpose, 200 undergraduate theses from professional degrees in Agricultural Sciences from 13 Peruvian universities were chosen at random. A descriptive textual analysis was carried out with the sentiment analysis classification algorithm and the text mining technique was used to discover patterns and trends. The result showed that, in almost all of the theses, the verification of the two most important assumptions of the analysis of variance, normality and homogeneity of variances, were not taken into consideration. Ignoring the importance of assumptions and the significance they have on the validity of research results, forces us to pay attention to the way in which the statistical methodology of experimental designs is being carried out in undergraduate theses.
 
 Received: 20 December 2023 / Accepted: 26 February 2024 / Published: 5 March 2024

The parallax: The possible errors in the quantification of human decision-making behavior in the experiment of neuroeconomics-A perspective from psychoanalysis.

With the development of neuroscience and technology, economics, which focuses on the selection and decision-making of human behavior, has also entered the field of neuroeconomics. Based on rational biological humans, neuroeconomics assumes that humans are rational biological individuals and behaviors of biological humans can be monitored and quantified in the nervous system through brain imaging technologies such as functional MRI and positron emission tomography. As an emerging discipline, neuroeconomics inevitably encounters obstacles in methodology and epistemology, showing excessive constraints in its understanding of rationality and facing basic conceptual disputes on the theoretical basis of "clear uncertainty monism." In neuroeconomics experiments, measurability will inevitably be traced back to quantification, but when faced with a complex and ever-changing social field, what can be predicted is only probability rather than behavior. Not all human behaviors and decisions can be quantified. Therefore, such research may be biased. Psychoanalysis, a major branch of psychology that is quite similar to neuroeconomics research in subject and object, can greatly correct these basic concepts. Seen from the perspective of psychoanalysis, the existence of these unquantifiable things conversely plays a decisive role and cannot be ignored. This article analyzes the core rational behavior detection of neuroeconomics from the perspective of psychoanalysis and proposes suggestions from the epistemology and methodology of experimental design as well as basic concept discrimination and so forth. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Experimental design using the Taguchi method for the development of conductive textiles used in flexible thermoelectric generators

This paper presents several aspects of the robust experimental design methodology using the Taguchi method to develop electrically conductive textiles. These conductive textiles will be used to make thermoelectric generators based on the Seebeck effect. Since the experimental development involves the use of the magnetron sputtering method with more than three variables, the Taguchi method was selected to observe how different parameters (5 independent variables such as argon flow, power, pressure, deposition surface area and metal type used for solid targets) influence the mean and variance of the process performance defined by the thickness of the metal film deposited (dependent variable). In the experimental design framework, the analysis using the Taguchi method, followed by optimisation, helped select the optimal experiments from the set of possible experiments. This methodology reduced the number of experiments by 21–42% and minimised resource consumption (e.g., metal targets, argon, energy).

A preliminary study to investigate the beneficiation of low-grade phosphate sludge using reverse flotation: Modeling and optimization through Box-Behnken design and response surface methodology.

This preliminary study explored a novel approach to enriching unexploited low-grade phosphate washing sludge (PS) using a combination of attrition scrubbing, desliming, and reverse flotation. By employing a Box-Behnken experimental design (BBD) and response surface methodology (RSM), the study optimized flotation parameters (pH of the pulp, Flotation time, Ester, and Amine dosage), to achieve significantly higher P2O5 grade and recovery. Key findings include the effective upgrading of P2O5 grade from 15.84% to 19.6% in the fraction +20 μm through attrition scrubbing and desliming, demonstrating the efficacy of pre-treatment. Additionally, BBD and ANOVA analysis revealed that flotation time, pulp pH, and ester dosage significantly influenced P2O5 grade and recovery, while amine dosage had a less pronounced effect. RSM optimization yielded optimum flotation conditions of 10 minutes flotation time, 481.81 g/T ester dosage, 400 g/T amine dosage, and 4.58 pulp pH. Under these optimized conditions, a P2O5 grade of 28.61% and a recovery of 71.5% were achieved, with deviations from predicted values of less than 2%, demonstrating the effectiveness of the RSM-based approach. These preliminary findings enhance the way for future studies aimed at validating the effectiveness of this treatment methodology under industrial conditions and assessing its long-term economic and environmental viability.

Investigation on carbon fiber-reinforced polymer combined with graphene nanoparticles subjected to drilling operation using response surface methodology and non-dominated sorting genetic algorithm-II

In this article, drilling of carbon fiber-reinforced plastics combined with graphene nanoparticles is investigated. These nanocomposites have the potential to be used widely in different industrial applications such as electrical, biomedical and aerospace engineering due to their superior mechanical and thermal specifications. Nonetheless, to be assembled on different sets, drilling nanocomposites is required especially for rivets and joints. In drilling carbon fiber-reinforced polymers (CFRPs), delamination is one of the main challenges that should be minimized in order to enhance the produced hole quality. Besides, machining force is a chief factor to control the drilling-induced damages, especially delamination. This article evaluates the effects of machining parameters on machining force and delamination using response surface methodology experimental design and non-dominated sorting genetic algorithm-II (NSGA-II) multiobjective optimization methodology. Examining delamination is conducted in two parts including peel-up and push-down delamination which are the principal drawbacks in drilling of fiber-reinforced composites. Analysis of variance is also carried out in order to examine the effects of the machining parameters. In addition, predictive quadratic correlations are established by regression analysis. Eventually, multiobjective optimization was implemented by NSGA-II and the optimal values were obtained from pareto-optimal solution set aimed at enhancing the drilling process.