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This article analyzes the performance of combining information from Scanning Electron Microscopy (SEM) micrographs with Static Light Scattering (SLS) measurements for retrieving the so-called Particle Size Distribution (PSD) in terms of experimental features. The corresponding data fusion is implemented using a novel Monte Carlo-based method consisting in a SMF (Sampling-Mapping-Filtering) approach. This approach provides an important reference to assess the strategy of the experiment for this specific problem by means of solving an inverse problem. Furthermore, low levels of volume fraction and a PSD represented by log-normal distributions are considered in order to reduce processing and model errors due to ill-posedness. The prior statistics corresponding to the SEM micrographs have been achieved by means of the Jackknife procedure used as a resampling technique. The likelihood term considers iid normal measurements generated from the Local Monodisperse Approximation (LMA) and also makes use of the same model as forward linear model, in an inversion case known as inverse crime. However, it has been proved that the LMA performs well in practice for low fraction volume systems as considered here. The PSD retrieval is measured in terms of improvement in precision with respect to one of the log-normal parameters in SEM micrographs, i.e., the desirability. Estimates are expressed as a function of a typical system parameter such as polydispersity, as well as experimental variables, i.e., number of particles per micrograph (PPM) and noise level $\varepsilon$ in the SLS measurements. These estimations are then analyzed by means of the Box-Behnken (BB) design and the response surface methodology (RSM) in order to generate a surrogate model from which rules for the optimization of the experiment are made when desirability is maximized. Finally, a Rule-Based System (RBS) is proposed for future use.

Soybeans are commonly known as a valuable source of biologically active compounds including isoflavones as well as allantoin and alpha-hydroxy acids. Since these compounds exhibit skin therapeutic effects, they are widely used in the cosmetic and pharmaceutical industries. The presented paper shows the optimization of three solvent systems (ethanol, water, and 1,3-propanediol) to increase the extraction efficiency of isoflavones (daidzin, genistin, 6″-O-malonyldaidzin, 6″-O-malonylglycitin, 6″-O-malonylgenistin), allantoin, and alpha-hydroxy acids (citric acid, malic acid) from soybean leaves. A simplex centroid mixture design for three solvents with interior points was applied for the experimental plan creation. Based on the obtained results of metabolite extraction yield in relation to solvent composition, polynomial regression models were developed. All models were significant, with predicted R-squared values between 0.77 and 0.99, while in all cases the model’s lack of fit was not significant. The optimal mixture composition enabling the maximization of extraction efficiency was as follows: 32.9% ethanol, 53.9% water, and 13.3% propanediol (v/v/v). Such a mixture composition provided the extraction of 99%, 91%, 100%, 92%, 99%, 70%, 92%, and 69% of daidzin, genistin, 6″-O-malonyldaidzin, 6″-O-malonylglycitin, 6″-O-malonylgenistin, allantoin, citric acid, and malic acid, respectively. The solvent mixture composition developed provides a good extraction efficiency of the metabolites from soybean leaves and high antioxidant properties.

The current paper aims to present a cooling concept for future centralized platforms of ECUs (Electronic Control Units) from the automotive industry that involves grouping multiple electronic devices into a single system and cooling them with forced convection dielectric coolant. The enhancement consists of replacing the inside air of the module with a dielectric coolant that has a higher thermal conductivity than air and employing an additional prototype system that aids in forced liquid cooling. To meet automotive requirements, the experiments were exposed to an ambient temperature of 85 °C. Temperature measurements on these solutions’ hot spots were compared to those on a thermal paste-only reference electronic module. This study used DFSS (Design for Six Sigma) techniques to determine the ideal pump flow rate, fan air flow rate, and liquid volume in the housing, leading to an optimization in heat dissipation. Finding a trustworthy transfer function that could forecast the impact of the crucial design parameters that had been found was the main goal. The electronics cooled by forced convection coolant improved heat dissipation by up to 60% when compared to the reference module. This demonstrates that the DoE (Design of Experiments) method, which is based on a limited number of measurements, can estimate the behavior of the ECU without the need for a more involved theoretical framework.

PVCremains one of the most challenging wastes for environmental activists and industrial recyclers because its decomposition products cause severe corrosion to equipment and our planet. The current study was designed to systematically investigate PVCdechlorinationusing the well-knownResponse Surface Methodology(RSM) to mature the process chemistry and assess the corrosion rate in the common SS-316 alloy. Undercover non-catalytic experiments, Cl removal, reactor wall corrosion rate, and H/C molar ratio of the products made the most argument to comprehend the chemistry and find the optimum operating condition. Based on data collected from different analytic techniques, like FESEM, SEM-EDX,TG, FT-IR, CHN, andAAS, it was found that water drove Cl removal through a combination of SN2 and Elimination pathways where the in-situ production ofhydrochloric acidat high Severity Factor (SF) resulted in a desirably high dechlorination rate of 89.42%. However, significant metal leakage into the aqueous phase, e.g., 278.01mg of Fe, in the Aqueous Phase (AP) necessitated the valorization of different approaches, including multistage dechlorination and application of various hetero and homogeneous catalysts, dealing with chloride and metal salts inside the reaction chamber. Among the solutions applied, the dual-stage and Ni-assisted dechlorination showed promising performances receptively with a corrosion rate as low as 0.034g and the highest Cl removal efficiency of 96.18%.

In this paper, we present the design and optimization of a centrifugal fan with requirements of maximizing the total-to-static pressure rise and total-to-static efficiency at two operating points and the maximum torque provided by the motor power using a 3D inverse design method, a DOE (design of experiment) study, an RSM (response surface model) and a MOGA (multi-objective genetic algorithm). The fan geometry is parametrized using 13 design parameters, and 120 different designs are generated. The fan performances of all the designs at two operating conditions are evaluated through steady-state CFD simulations. The resulting design matrix is used to create an RSM based on the Kriging method and MOGA is used to search the design space using the RSM and find the optimal design.

The tilted-wave interferometer (TWI) is a recent and promising technique for optically measuring aspheres and freeform surfaces and combines an elaborate experimental setup with sophisticated data analysis algorithms. There are, however, many parameters that influence its performance, and greater knowledge about the behavior of the TWI is needed before it can be established as a measurement standard. Virtual experiments are an appropriate tool for this purpose, and in this paper we present a digital twin of the TWI that was carefully designed for such experiments. The expensive numerical calculations involved combined with the existence of multiple influencing parameters limit the number of virtual experiments that are feasible, which poses a challenge to researchers. Experimental design is a statistical technique that allows virtual experiments to be planned such as to maximize information gain. We applied experimental design to virtual TWI experiments with the goal of identifying the main sources of uncertainty. The results from this work are presented here.

Exploring the feasibility of producing near-net-shape components with advanced properties and geometrical features via 3D printing has incrementally become the research focus of various studies. Digital light processing (DLP) technology can manufacture complex-structured components for various technical applications. The aims of this research were to investigate Al2O3 ceramic slurry preparation procedures to identify the ideal components to add to an in-house-developed ceramic slurry, to determine the optimal DLP printing parameters and conditions while understanding their effect on the green part properties and to evaluate the appropriate debinding regime to achieve fully dense crack-free fired parts capable of exhibiting translucent behaviours. The slurry obtained from the ball-milled powder at 800 rpm for 1 h, together with 2 wt.% BYK-145 as a dispersant and the highest achievable solid loading of 85 wt.%, showed the desired rheological and photopolymerisation properties. Full-factorial design of experiments (DOE) was employed to study the impact of the printing parameters on the density and the dimensions of the samples. Different debinding regimes were investigated and it was proven that the lowest debinding heat rate (0.2 °C/min) and longer holding times helped to reduce defects and promote densification (>99.0%), providing optimal grounds to obtain translucent fired parts.

The article is aimed at the mathematical and optimization modeling of technological processes of surface treatments, specifically the zincing process. In surface engineering, it is necessary to eliminate the risk that the resulting product quality will not be in line with the reliability requirements or needs of customers. To date, a number of research studies deal with the applications of mathematical modeling and optimization methods to control technological processes and eliminate uncertainties in the technological response variables. The situation is somewhat different with the acid zinc plating process, and we perceive their lack more. This article reacts to the specific requirements from practice for the prescribed thickness and quality of the zinc layer deposited in the acid electrolyte, which stimulated our interest in creating a statistical nonlinear model predicting the thickness of the resulting zinc coating (ZC). The determination of optimal process conditions for acid galvanizing is a complex problem; therefore, we propose an effective solving strategy based on the (i) experiment performed by using the design of experiments (DOE) approach; (ii) exploratory and confirmatory statistical analysis of experimentally obtained data; (iii) nonlinear regression model development; (iv) implementation of nonlinear programming (NLP) methods by the usage of MATLAB toolboxes. The main goal is achieved—regression model for eight input variables, including their interactions, is developed (the coefficient of determination reaches the value of R2 = 0.959403); the optimal values of the factors acting during the zincing process to achieve the maximum thickness of the resulting protective zinc layer (the achieved optimum value th* = 12.7036 μm), are determined.

Recycling plastic waste (RPW) benefits the ecological footprint. Therefore, the authors test its mixing by magnetic hydrodynamic MHD nanofluid materials such as alumina in two sizes (βcore,βskin) prepared into a new device called the incubator installed in the desktop injection machine to enhance its solubility by taking advantage of the mixture’s heat via defining the oblique stagnation-point slip flow (OSPSF) of a nanofluid in two dimensions. The paper has been innovative in mathematically identifying the operating parameters’ values for the injection flow mechanism (IFM) via controlling in Riga magnetic field and piston orifice pressure using a meta-heuristic algorithm called WSA. The proposed (IFM) is used to experimentally enhance the mixture properties via parameters’ control to meet the output quality and predict the control equation for the Riga plate. IFM controls the amount of pushed nanoparticles in the mixture with a ratio of plastic to aluminum approximate by 96.1%: 3.9%. The defects were reduced by approximately 23.21%, with an increasing system performance of 70.98%.

The co-pyrolysis of various biomasses mixed with two types of plastic waste was investigated in this study. Mixture M1 consisted of 30% m/m styrene–butadiene rubber (SBR), 40% m/m polyethylene terephthalate (PET), and 30% m/m polypropylene (PP). M2 consisted of 40% m/m PET, 30% m/m PP, and 30% m/m acrylonitrile–butadiene–styrene copolymer (ABS). The SBR, ABS, and PP used in this study were from the automotive industry, while the PET originated from scrap bottles. Co-pyrolysis was performed using wood biomass, agricultural biomass, and furniture trash. Thermal treatment was performed on samples from room temperature to 400 or 600 °C at a heating rate of 10 °C/min under N2 at a flow rate of 3 dm3/min. Based on the findings of the experiments, an acceptable temperature was found for the fixed-bed pyrolysis of biomass–plastic mixtures with varying ratios, and the raw materials were pyrolyzed under the same conditions. The composition of the derived gaseous fraction was investigated. The co-pyrolysis studies and variance analysis revealed that combining biomass with plastic materials had a good influence on the gaseous fraction, particularly in the presence of 6.6–7.5% v/v hydrogen and a lower heating value of 15.11 MJ/m3. This type of gaseous product has great potential for use as a replacement for coke oven gas in metallurgy and other applications.

Diese Arbeit konzentriert sich darauf, eine Lösung für die Schaffung einer spezifischen Methodik zu finden, die es ermöglicht, reale Daten zu sammeln, um KI-Systeme zu trainieren, die darauf ausgerichtet sind, Fertigungsprozesse durch die Entwicklung von Vorhersagemodellen zu verbessern.Dies geschieht durch standardisierte Experimentierprozesse, die die Methodik der Versuchsplanung (Design of Experiments, DOE) auf automatisierte Art und Weise nutzen, wodurch sichergestellt wird, dass die Durchführung dieser Experimente replizierbar und vollständig kontrolliert ist. Außerdem bietet es den Vorteil der Zeitoptimierung, sowohl für die nachfolgende Behandlung der Daten als auch für die Durchführung der Experimente selbst.Darüber hinaus beinhaltet dieses Projekt ein Datenmanagement durch Fertigungsfunktionen, um eine kontinuierliche Identifizierung der Daten während der verschiedenen Phasen des Projekts zu gewährleisten und gleichzeitig eine geschlossene Feedbackschleife für die Korrelation von Daten und Anwendungen der entwickelten KI-Systeme zu schaffen.Dieses Projekt hat gezeigt, dass die Anwendung der entwickelten Methodik zu strukturierten Daten führt, die für das Training von KI-Systemen verwendet werden können. Außerdem ermöglicht sie eine einfache Datenkorrelation und die Reproduzierbarkeit der Experimente., This thesis focuses on providing a solution for creating a specific methodology that allows the collection of real data to train AI systems focused on improving manufacturing processes by developing predictive models. This is carried out through standardized experimentation processes using the design of experiments (DOE) methodology in an automated way, ensuring that the execution of these experiments is replicable and fully controlled. It also provides the advantage of time optimization, both for the subsequent treatment of the data and for the execution of the experiments themselves. In addition, this work includes data management through manufacturing features to provide a continuous identification of data throughout the various stages of the project. This identification ensures control over the system while providing a feedback loop for the correlation of data and applications of the developed AI systems. This thesis has shown that using the developed solution results in structured data that can be used to train AI systems. It also allows for easy data correlation and enables the experiments to be replicable.

Automated Finite Element Analysis (FEA) requires sketches to cope effortlessly with dimensional changes to assess functional robustness. However, sketch robustness is often neglected and overlooked in today’s design processes and causes a waste of resources throughout the product development. The present study explores how CAD sketches are made less prone to regeneration failure when the dimensions in the sketch vary as much as ±80%. Three new Robust Sketch Principles (RSPs) to improve sketch robustness are developed based on commonly experienced failure modes. The first part of the study assesses the new RSPs’ impact by engaging nine M.Sc. engineering graduate students and five industrial CAD specialists in a comparative study. The participants are asked to constrain an existing sketch with and without knowledge of the new RSPs. The study shows that the initial practice of the participants leads to poor sketch robustness, while less than 30 minutes of training with RSPs enable all participants to achieve a statistically significant improvement in sketch robustness. The second part of the study explores the industrial application of the RSPs by redrawing existing product sketches and comparing the robustness with and without the new RSPs. Both parts of the study demonstrate that the RSPs improve robustness significantly and widen the parameter space for automated FEA simulations.

Quality by Design (QbD) is one of the most important tools for the implementation of Process Analytical Technology (PAT) in biopharmaceutical production. For optimal characterization of a monoclonal antibody (mAb) upstream process a stepwise approach was implemented. The upstream was divided into three process stages, namely inoculum expansion, production, and primary recovery, which were investigated individually. This approach enables analysis of process parameters and associated intermediate quality attributes as well as systematic knowledge transfer to subsequent process steps. Following previous research, this study focuses on the primary recovery of the mAb and thereby marks the final step toward a holistic characterization of the upstream process. Based on gained knowledge during the production process evaluation, the cell viability and density were determined as critical parameters for the primary recovery. Directed cell viability adjustment was achieved using cytotoxic camptothecin in a novel protocol. Additionally, the cell separation method was added to the Design of Experiments (DoE) as a qualitative factor and varied between filtration and centrifugation. To assess the quality attributes after cell separation, the bioactivity of the mAb was analyzed using a cell-based assay and the purity of the supernatant was evaluated by measurement of process related impurities (host cell protein proportion, residual DNA). Multivariate data analysis of the compiled data confirmed the hypothesis that the upstream process has no significant influence on the bioactivity of the mAb. Therefore, process control must be tuned towards high mAb titers and purity after the primary recovery, enabling optimal downstream processing of the product. To minimize amounts of host cell proteins and residual DNA the cell viability should be maintained above 85% and the cell density should be controlled around 15 × 106 cells/ml during the cell removal. Thereby, this study shows the importance of QbD for the characterization of the primary recovery of mAbs and highlights the useful implementation of the stepwise approach over subsequent process stages.

Detab is a company in Jönköping that develops systems for feeding parts for robots and packaging processes. To control the flow of parts on their vibratory feeders, Detab uses different carpets with angled straws. The two mainly used carpets have been discontinued and must now be replaced. A newly emerged option is a contender in replacing one of the carpets. The new white carpet is completely additively manufactured. To compare the old carpet and the new carpet, data is needed to show how different settings affect the flow of the parts. Through design of experiments and response surface methods, tests can be performed in a systematic and mathematical way compare the performance of different settings. Some basic parameters were defined to begin the investigation of which design of experiments method would be used to best compare the carpets. The parameters defined where the parts that would be used in the tests, the factors that were to be adjusted during the tests, the maximum and minimum values of the selected factors and how the results would be measured. Different methods were tested, and finally optimal design was chosen and executed using the program Design-Expert. The completed tests resulted in response surfaces that were constructed using Design-Expert. These response surfaces were analyzed and several similarities and differences between the different carpets were observed. Primarily, it was found that the green carpet had more settings that resulted in a flow above 0 parts/minute. Using Design-Expert, the response surfaces could be analyzed to find the optimal settings and their expected response. These points and more were then compared with tests performed afterwards to see if the expected values matched reality. It turned out that there were some uncertainties in how well the surfaces could predict the response in comparison with reality. Based on the experiments carried out, recommendations were made on how the company should proceed to be able to assess whether the new carpet is worth using in future construction of their machines.

The aim of this report was to present a case of exertional rhabdomyolysis in a fallow deer. The diagnosis was made on the basis of clinical and blood laboratory findings. Despite the treatment, the outcome was fatal. Gross anatomy and histopathological examinations were performed, detecting degenerative changes in the heart, skeletal muscles, the kidneys and the liver. On the basis of ante and post mortem findings providing explanations of the pathophysiological mechanism of severe tissue damage - hypoxaemia, severe lactic acidosis, muscle rupture, electrolyte imbalance and renal block, it was concluded that all they have led to the death of the animal.

Additive Manufacturing (AM) has freeform fabrication capabilities of complex geometries directly from feedstock material. This can potentially enhance the efficient use of material and the cost effectiveness. It also offers design flexibility, and it is versatile. However, there are challenges discouraging the various industries (such as the automotive sector) from adopting AM techniques, when considering suitability of AM components for specific applications. These barriers include the high cost of AM components due to low production speed, post processing requirements, and high initialcost. After cost, there is inferior quality performance compared to conventional fabrications, and inability of direct creation of many typical components such as those made by sheet metal forming. Rather than the use of AM as a tool for the final product, the thesis aims to develop cost- effectiveand sustainable plastic dies as a mid-chain tool via AM. Fused Deposition Modelling (FDM) is selected to fabricate the plastic dies at a reasonable cost. A laboratory experiment along with Finite Element Analyses (FEA) were conducted to compare metal and plastic dies for sheet metal V-bending. Furthermore, the influence of the process variables namely sheet metal thickness, punch radius,die radius, die width and the ratio of die width to sheet metal thickness on springback have been analyzed using Design of Experiments (DOE).The reliability of the proposed plastic and metal dies was investigated using FEA and experimental work. A proof of concept (POC), conclusions and recommendation discuss the feasibility of using plastic dies for sheet metal bending. Additiv tilleverkning -Additive Manufacturing (AM) har fria formtillvekningsmöjligheter av komplexa geometrier direkt från råmaterial. AM kan potentiellt förbättra den effektiva materialanvändningen och kostnadseffektiviteten. Dessutom erbjuder den designflexibilitet. Utmaningar hindrar dock industrier (såsom bilindustrin) från att anta AM-tekniker när det gäller AM-komponenters lämplighet för specifika tillämpningar. I detta ingår den höga kostnaden för AM-komponenter på grund av lågproduktionshastighet, efterbearbetningskrav och höga initiala kostnader. Jämfört med traditionell bearbetningsteknik, AM har sämre kvalitetsprestanda, oförmåga att skapa många typiska komponenter såsom de som tillverkas av plåtformning. Syftet med detta examensarbete är att utveckla kostnadseffektiva och hållbara plastformar (Plasticdies) som ett verktyg i produktionskedjan snarare än att använda AM som ett verktyg för slutprodukten. I detta examensarbete, väljs metoden ”Fused Deposition Modelling” (FDM) för att tillverkaplastformarna med en rimlig kostnad. Laboratorieexperiment och Finita Elementanalyser av processen (FEA) genomfördes för att göra jämförande studier mellan metalliska- och plastformar vid V-böjning av aluminium plåt. Dessutom har inverkan av processvariablerna, såsom plåttjocklek, stansradie, stansbredd, förhållandet mellan två variabler (stansbredd och plåttjocklek) på återfjädring (Spring back) analyserats med hjälp av ”Design of Experiments” (DOE).Tillförlitligheten hos de föreslagna plast och metalliskformarna undersöktes experimentellt samt med hjälp av FEA. Ett Proof of Concept (POC), slutsatser, rekommendationer diskuterar möjligheten att använda plastformar förböjning av plåt

Quality by Design is a life-cycle paradigm used by regulators to encourage pharmaceutical companies to consider product quality from the earliest development stages. The objective is to identify and document relationships between critical process parameters and productquality attributes via risk assessments and experimental results before validating for commercial manufacturing. This methodology has steadily gained traction over the last decade and applied statistical tools are increasingly leveraged to reach these goals quantitatively.Nonetheless, significant gaps remain between the methodological intent and the current state-of-the-art practices. For example, during the identification of critical process elements,latent variables have largely been overlooked due to over-reliance on process knowledge and the absence of relevant extraction and multivariate methods. Subsequent risk assessments and data-driven models are siloed in the individual process steps (unit operations) and are not linked to the patient-relevant outcome: drug substance specifications. Lastly, there is an absence of data feedback loops between the above procedures and the manufacturing data in the commercial life-cycle.This thesis addresses the above gaps via improvements and applications of an integratedprocess model; a framework centered on concatenating unit operation models and propagatingerror via Monte Carlo simulations. To realize this potential, novel procedures were first designed to uncover latent bioprocess variables via extraction and multivariate analysis.Once in place, an innovative Monte Carlo-based application was developed that establishes intermediate acceptance criteria for quality attributes via parameter sensitivity analysis. A further simulation procedure was created which, when combined with linearization techniques,enables the determination of parameter proven acceptable ranges and links these quantitatively to risk assessment severity rankings. Lastly, the integrated process model was substantially improved and inserted architecturally into manufacturing data feedback loops, enabling the model to react in real time to process conditions. The totality of these innovations depicts a major industry objective: a bioprocess digital twin.Leveraging the developments in this thesis, the proposed integrated process model now quantitatively links process parameters and quality attributes to patient-relevant outcomes.Moreover, it does so with a technology that can iteratively adapt to new manufacturing data,ensuring that it accompanies the process throughout its life-cycle, and thereby establishes an engine for a digital twin. Thus, with holistic process quality as a central goal, the industry will be able to better fulfill both the intention and the potential of the Quality by Designparadigm.

International audience; Nature has developed smart strategies to morph joint contact surfaces as a response to local mechanical stimuli. Hence, it is compelling to explore the advantages of mimicking these strategies to design mechanical pieces. Researchers and engineers have developed generative design strategies mostly focused on structural topological and shape optimization. However, these generative strategies are not well developed for joint contact surfaces, even though they are critical points in engineering structures and machinery. This work presents a computational tool for adaptive surface shaping in contact problems inspired by the morphogenesis of synovial joints. The algorithm behind follows the cartilage growth rules, in which the hydrostatic and shear octahedral stresses either promote or inhibit an isotropic expansion of the local domain. The implementation is based on the finite element method and it is freely available on GitHub. A parametric study was performed to tune the parameters of the algorithm and it was successfully tested for two elastic bodies in unilateral contact. It was evidenced that the cartilage growth rules adapted the geometry of the surfaces in such a way that the contact stresses got uniformly distributed. This study is a step forward in the scope of bio-inspired generative designs.

In this work, the photocatalytic degradation of the neonicotinoid insecticide acetamiprid was investigated using a UVA-LED radiation source and TiO2 nanoparticles in a batch slurry system. All experiments were performed with the same initial acetamiprid concentration, amount of TiO2, and solution pH. The influences of three parameters such as mixing rate, radiation intensity and hydrogen peroxide concentration were studied using response surface methodology. An experimental design was prepared using the software package Design-Expert. The obtained experimental results were tested with the kinetic model for the pseudo- first order reaction and showed a good fit. The ANOVA analysis was used to obtain a linear model for predicting the system behavior and to identify significant parameters. The experimental results showed that the radiation intensity had the greatest influence on the efficiency of the photocatalytic degradation of acetamiprid. The acetamiprid conversions increase with increasing radiation intensity and hydrogen peroxide concentration and with decreasing mixing rate.

One of the crucial steps for the conversion of CO2 into polymers is the catalytic formate to oxalate coupling reaction (FOCR). Formate can be obtained from the (electro)catalytic reduction of CO2, while oxalate can be further processed toward building blocks for modern plastics. In its 175 year history, multiple parameters for the FOCR have been suggested to be of importance. Yet, no comprehensive understanding considering all those parameters is available. Hence, we aim to assess the relative impact of all those parameters and deduce the optimal reaction conditions for the FOCR. We follow a systematic two-stage approach in which we first evaluate the most suitable categorical variables of catalyst, potential poisons, and reaction atmospheres. In the second stage, we evaluate the impact of the continuous variables temperature, reaction time, catalyst loading, and active gas removal within previously proposed ranges, using a response surface modeling methodology. We found KOH to be the most suitable catalyst, and it allows yields of up to 93%. Water was found to be the strongest poison, and its efficient removal increased oxalate yields by 35%. The most promising reaction atmosphere is hydrogen, with the added benefit of being equal to the gas produced in the reaction. The temperature has the highest impact on the reaction, followed by reaction time and purge rates. We found no significant impact of catalyst loading on the reaction within the ranges reported previously. This research provides a clear and concise multiparameter optimization of the FOCR and provides insight into the reaction cascade involving the formation and decomposition of oxalates from formate., Critical assessment of parameters in the formate to oxalate reaction using design of experiments and response surface model methodology in CO2 for materials processing.

Design of experiments (DOE)-based analytical quality by design (AQbD) method evaluation, development, and validation is gaining momentum and has the potential to create robust chromatographic methods through deeper understanding and control of variability. In this paper, a case study is used to explore the pros, cons, and pitfalls of using various chromatographic responses as modeling targets during a DOE-based AQbD approach. The case study involves evaluation of a reverse phase gradient HPLC method by a modified circumscribed central composite (CCC) response surface DOE.Solid models were produced for most responses and their validation was assessed with graphical and numeric statistics as well as chromatographic mechanistic understanding. The five most relevant responses with valid models were selected for multiple responses method optimization and the final optimized method was chosen based on the Method Operable Design Region (MODR). The final method has a much larger MODR than the original method and is thus more robust.This study showcases how to use AQbD to gain deep method understanding and make informed decisions on method suitability. Discoveries and discussions in this case study may contribute to continuous improvement of AQbD chromatography practices in the pharmaceutical industry.

In the present work, a statistical optimization of a sustainable coating for core–shell NPK (Nitrogen–Phosphorus–Potassium) fertilizers was investigated. The environmental green coating was enriched in nitrogen using a biomass and renewable source, namely the nitrogen rich fraction of black soldier fly larvae (BSFL) (Hermetia Illucens, Diptera: Stratiomyidae) reared on vegetable waste. A rational approach was proposed with the aim of calculating the best formulation of the coating, considering both its manufacturing behavior, such as adhesion to the core, and its physical properties, such as homogeneity or plasticity. From a circular economy perspective, together with the nitrogen-rich fraction from BSFL (from 51 to 90 wt.%), water and glycerol were considered for the coating formulation in different proportion: from 10 to 32 wt.% and from 0 to 17 wt.% respectively. The Design of Experiments technique was implemented to limit the total number of tests for the coating formulation (18 tests). ANOVA was employed, with the aim of obtaining mathematical models to derive a better precise and objective formulation. The results show that the use of glycerol can be avoided, as well as only a limited amount of water (11 wt.%) is necessary to obtain an optimized coating formulation, thereafter, satisfying the more relevant technological and physical properties for the coating manufacturing.

[EN] Experimental design following the statistical method of Taguchi for the resining of composite materials, with subsequent manufacture and testing of the material obtained. Following the scheme below: 1. Introduction to Design of Experiments - Taguchi method 2. Design of Experiment - Resing Moulding (calculation of factors, levels, process analysis) 3. Manufacturing 4. Testing 5. Data analysis 6. Conclusions., [ES] Diseño experimental siguiendo el método estadístico de Taguchi para el resinado de materiales compuestos, con posterior fabricación y ensayo del material obtenido. Siguiendo el esquema siguiente: 1. Introducción al Diseño de Experimentos - Método Taguchi 2. Diseño de Experimento - Resing Molding (cálculo de factores, niveles, análisis de procesos) 3. Fabricación 4. Pruebas 5. Análisis de datos 6. Conclusiones.

Submitted by Matheus Prevelato De Andrade (matheus.prevelato@unesp.br) on 2022-09-26T21:47:38Z No. of bitstreams: 1 Dissertacao_Defesa_Matheus_Prevelato_rev04_frente.pdf: 2414646 bytes, checksum: 851582a4ca22d16c6f755f16b0018a7c (MD5) Approved for entry into archive by Lucilene Cordeiro da Silva Messias null (lubiblio@bauru.unesp.br) on 2022-09-27T11:37:35Z (GMT) No. of bitstreams: 1 andrade_mp_me_bauru.pdf: 2414646 bytes, checksum: 851582a4ca22d16c6f755f16b0018a7c (MD5) Made available in DSpace on 2022-09-27T11:37:35Z (GMT). No. of bitstreams: 1 andrade_mp_me_bauru.pdf: 2414646 bytes, checksum: 851582a4ca22d16c6f755f16b0018a7c (MD5) Previous issue date: 2022-08-05 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Próteses odontológicas sobre-implantes são amplamente utilizadas no tratamento de pacientes com perda dentária. Apesar da alta taxa de sucesso, falhas mecânicas ainda ocorrem, muitas das quais causadas por falta de osseointegração, processo diretamente ligado às características geométricas e estruturais da prótese e da relação desta com o osso. Neste contexto, análises paramétricas e de otimização são utilizadas para identificar a influência de parâmetros construtivos no comportamento mecânico de próteses e encontrar uma combinação destes parâmetros que levam a melhores níveis de tensão no osso. O Método dos Elementos Finitos (MEF) é amplamente aplicado no desenvolvimento de novas próteses devido à capacidade de análise de estruturas complexas. O objetivo deste projeto é realizar uma análise paramétrica e um processo de otimização de uma prótese odontológica unitária sobre-implante. A análise paramétrica foi desenvolvida através de uma função matemática obtida a partir da Metodologia de Superfície de Resposta (RSM), que representa o comportamento estrutural da prótese em função de parâmetros pré-definidos, como a altura do implante, altura do abutment, Módulo de Elasticidade do osso cortical e carga aplicada. Para construir esta função, utilizou-se dos resultados de vinte e cinco modelos de Elementos Finitos com diferentes combinações dos parâmetros, as quais foram determinadas pela metodologia de Projeto por Experimentação (DOE). Através dessa função, analisou-se a influência de cada parâmetro na resposta mecânica, em termos de tensão de von Mises na região de interface entre osso cortical e implante. Esta função também foi utilizada no desenvolvimento de um processo de otimização que minimizou a tensão de von Mises no osso cortical. Constatou-se que dentro do domínio estudado todos os parâmetros têm comportamento linear e o mais influente é a carga aplicada. A tensão de von Mises é inversamente proporcional à altura do implante, e diretamente proporcional à altura do abutment, ao módulo de elasticidade do osso cortical e à força aplicada na coroa, portanto, o valor máximo da altura do implante juntamente com os valores mínimos da altura do abutment, módulo de elasticidade do osso cortical e de força aplicada é a combinação de parâmetros que minimiza a tensão de von Mises. Desta forma, apresenta-se além da influência dos parâmetros na resposta mecânica no osso cortical, um processo mais ágil na estimativa das tensões atuantes, através de uma função, e também a combinação de parâmetros obtida através da otimização que minimiza a tensão de von Mises no osso cortical. Dental prosthesis over-implants are widely used in the treatment of patients with tooth loss. Despite the high success rate, mechanical failures still occur, many of which are likely caused by a lack of osseointegration, process directly dependent on the geometric and structural characteristics of the prosthesis and its relationship with the bone. In this context, parametric and optimization analyzes are used to find the best combination of constructive parameters leading to better stress levels in the bone. The Finite Element Method (FEM) is widely applied to the development of new prostheses due to its ability to analyze complex structures. The objective of this project is to develop a parametric analysis and optimization process of a single dental prosthesis over-implant. The parametric analysis was developed through a mathematical function obtained from the Response Surface Methodology (RSM), which represents the structural behavior of the prosthesis as a function of pre-defined parameters, such as implant height, abutment height, Young’s modulus of cortical bone and applied load. To build this function, the results of twenty-five Finite Element models with different combinations of parameters was used, which were determined by Design of Experiments Methodology (DOE). Through this function, the influence of each parameter on the mechanical response was analyzed, in terms of von Mises stress in the region of interface between cortical bone and implant. This function was also used in the development of an optimization process that minimized von Mises stress in cortical bone. It was found that within the studied domain all parameters have linear behavior and the most influential is the applied load. The von Mises stress is inversely proportional to implant height, and directly proportional to the abutment height, the Young’s modulus of cortical bone and the applied load, therefore, the maximum value of implant height, together with the minimum values of the abutment height, Young’s modulus of cortical and applied load is the combination of parameters that minimizes von Mises stress. Thus, in addition to the influence of parameters on the mechanical response in cortical bone, a more agile process is presented in the estimation of the acting stresses, through a function, and the combination of parameters obtained through the optimization process that minimizes the von Mises stress in cortical bone.

Oleogels (defined as structured solid-like materials with a high amount of oil entrapped within a three-dimensional network of gelator molecules) represent a healthy alternative to fats that are rich in saturated and trans fatty acids. Given its fatty acids composition (oleic, linoleic, and linolenic acids), olive oil is an excellent candidate for the use of oleogels in the food industry. In this study, a D-optimal mixture design was employed to optimize the replacement of butter with olive oil-based oleogel in a type of sponge cake formulation: the plum cake. In addition, emulsifiers and whey proteins were used as recipe ingredients to extend the product’s shelf life by delaying staling phenomena and mold growth. In the experimental design, oleogel, emulsifier, and whey protein variables were set as the ingredients that change in specific ranges, while hardness, porosity, water activity, and moistness were used to characterize the obtained formulations. The experimental data of each response were fitted through polynomial regression models with the aim of identifying the best plum cake formulation. The results revealed that the best mixture was the formulation containing 76.98% olive oil-based oleogel, 7.28% emulsifier E471, and 15.73% whey protein. We stored the optimized plum cake for 3 months at room temperature and then checked for any hardness and moistness changes or mold spoilage.

Quality evaluation of micro injection molded products is a complex task, in particular when instruments basing on contact methods are used and issues in measurements could arise due to the contact tool dimension not fitting well with extremely narrow features. Therefore, in these cases, optical methods may be preferred for the evaluation of molded products’ dimensions and surface quality, especially for parts devoted to applications requiring functional purposes. In this context, the present paper proposes the use of surface parameters as a quality index for the evaluation of both the micro injection molding process and the resulting products. To this aim, two experimental procedures were implemented to allow for: (i) the evaluation of the most suitable surface parameters identified in relation to the process parameters; (ii) comparisons of the surface parameters findings with those obtained by classic dimensional quantity via a designed experimental plan (DoE). The results show that the surface parameters, evaluated in critical areas of the components, can ensure reliable estimates for the surface quality of the molded parts and can be preferred in comparison to linear measurements.

[ES] La fabricación aditiva ha supuesto una gran revolución en la industria permitiendo la manufactura de piezas complejas que de otra forma sería inviable. Sin embargo, todavía debe superar ciertas restricciones, como sucede en la rama de fabricación aditiva con materiales metálicos con el tamaño de las piezas y la posibilidad de utilizar un mayor abanico de materiales metálicos y aleaciones. Por otro lado, las técnicas tradicionales, como la fundición, no pueden afrontar las nuevas demandas donde se exige un desarrollo de productos cada vez más personalizados hacia el usuario final con la máxima calidad y precisión utilizando el menor tiempo posible. Por ello, surge como alternativa la combinación de fabricación aditiva con técnicas tradicionales para exceder las limitaciones que presentan por separado. En este proyecto, se ha considerado la combinación de la técnica de extrusión de material de fabricación por filamento fundido (FFF) con la técnica de fundición indirecta, donde el patrón desechable se ha realizado en PLA con la técnica de FFF, y mediante un estudio experimental secuencial planificado a través de Diseño de Experimento, se optimizó los parámetros de FFF para obtener una pieza final metálica con la mayor calidad dimensional, mayor calidad superficial y generación mínima de residuos. Para ello se analizó la influencia de los parámetros de altura de capa, densidad de relleno, número de capas iniciales y finales, espesor de pared y la aplicación de postproceso, llegando a la conclusión de que los parámetros que más afectan a la calidad dimensional es la acción de la densidad de relleno con el espesor de pared, el parámetro que más afecta a la rugosidad es la aplicación del postproceso, mientras que para la minimización de residuo finalmente no afecta ninguno de los parámetros. [EN] Additive manufacturing has been a major revolution in industry, enabling the manufacture of complex parts that would otherwise be unfeasible. However, it still must overcome certain restrictions, as is the case in additive manufacturing with metallic materials with the size of the parts and the possibility of using a wider range of metallic materials and alloys. On the other hand, traditional techniques, such as casting, cannot cope with the new demands for the development of increasingly customised products for the end user with the highest quality and precision in the shortest possible time. For this reason, the combination of additive manufacturing with traditional techniques has emerged as an alternative to exceed the limitations that they present separately. In this project, the combination of the extrusion technique of fused filament fabrication (FFF) with the indirect casting technique has been considered, where the expendable pattern has been made in PLA with the FFF technique, and by means of a sequential experimental study planned through Design of Experiment, the FFF parameters were optimised to obtain a final metallic part with the highest dimensional quality, greater surface quality and minimum waste generation. To this purpose, the influence of the parameters of layer height, filler density, number of initial and final layers, wall thickness and the application of post-processing were analysed, reaching the conclusion that the parameters that most affect the dimensional quality is the action of the filler density with the wall thickness, the parameter that most affects the roughness is the application of post-processing, while for the minimisation of residue, none of the parameters is affected in the end.

This paper presents an improved artificial neural network (ANN) training using response surface methodology (RSM) optimization for membrane flux prediction. The improved ANN utilizes the design of experiment (DoE) technique to determine the neural network parameters. The technique has the advantage of training performance, with a reduced training time and number of repetitions in achieving good model prediction for the permeate flux of palm oil mill effluent. The conventional training process is performed by the trial-and-error method, which is time consuming. In this work, Levenberg–Marquardt (lm) and gradient descent with momentum (gdm) training functions are used, the feed-forward neural network (FFNN) structure is applied to predict the permeate flux, and airflow and transmembrane pressure are the input variables. The network parameters include the number of neurons, the learning rate, the momentum, the epoch, and the training functions. To realize the effectiveness of the DoE strategy, central composite design is incorporated into neural network methodology to achieve both good model accuracy and improved training performance. The simulation results show an improvement of more than 50% of training performance, with less repetition of the training process for the RSM-based FFNN (FFNN-RSM) compared with the conventional-based FFNN (FFNN-lm and FFNN-gdm). In addition, a good accuracy of the models is achieved, with a smaller generalization error.

Copyright © 2022 The Author(s). Design of experiment (DoE) techniques are invaluable tools which readily allow for efficient optimisation of processes via simultaneous evaluation of a combination of input parameters. Such approaches can yield positive outcomes whilst minimising the number of resources and amount of time utilised, hence, achieving a more robust approach. Additionally, when designing the experiment intelligently information about the interaction between the variables could be gathered, therefore, allowing for a more in-depth understanding of the process and identification of the “key players”. This method of conducting an experimental campaign is, unfortunately, underused (or often misused) in academia. This review aims to technically scrutinise the employment of design of experiment techniques in the context of synthesis and deployment of carbonaceous sorbents and the optimisation of the adsorption processes in both gaseous and aqueous media for environmental applications. We have also discussed how the implementation of DoE techniques in interpreting the results and the underlying trends and/or adsorption mechanisms could help with a better understanding of such observations. Additionally, a brief description of the most popular experimental design techniques with an explanation and a simple visualisation is provided. This review aims to facilitate a greater understanding and appreciation of these powerful optimisation tools, and to depict the best practices upon their employment in academic research in the field of chemical and environmental engineering. UK Engineering and Physical Sciences Research Council (EPSRC) under the project titled “Multiphysics and multiscale modelling for safe and feasible CO2 capture and storage - EP/T033940/1”; UK Carbon Capture and Storage Research Centre (EP/P026214/1) through the flexible funded research programme “Techno-economics of Biomass Combustion Products in the Synthesis of Effective Low-cost Adsorbents for Carbon Capture”. The UKCCSRC is supported by the Engineering and Physical Sciences Research Council (EPSRC), UK, as part of the UKRI Energy Programme.

Two techniques, namely, optimized ultrasound-assisted extraction (UAE) and enzyme-assisted extraction (EAE), were used to promote the extraction of phenolic compounds from the pseudo-fruits of Rosa canina L. (RC). For UAE, an optimization process based on the design of experiment (DoE) principles was used for determining the dependence between three variables (i.e., time of extraction, ultrasound amplitude, and the material-to-water ratio) and the total phenolic content of the samples. For EAE, a 2:1:1 pectinase, cellulase, and hemicellulase enzymatic blend was used as pre-treatment for optimized UAE, inducing a higher total phenolic content. The untargeted phenolic profiling approach revealed a great abundance of lower molecular weight phenolics (1.64 mg Eq./g) in UAE-RC extracts, whilst gallic acid (belonging to hydroxybenzoic acid derivatives) was the most abundant individual compound of both extracts. The unsupervised multivariate statistics clearly discriminated the impact of enzymatic pre-treatment on the phenolic profile of RC pseudo-fruits. Finally, Pearson’s correlation coefficients showed that anthocyanins, phenolic acids, and tyrosol derivatives were those compounds mostly correlated to the in vitro antioxidant potential of the extracts, whilst negative and significant (p < 0.05) correlation coefficients were recorded when considering the enzymatic inhibition activities. The highest enzyme-inhibitory activity has been identified against α-glucosidase, which indicates an antidiabetic effect.

Bumper and crash boxes are vehicle structural elements that convert the collision (kinetic) energy at the collision into deformation energy. Failure of the bumper system to sufficiently absorb the collision energy in the event of a collision will result in the resulting forces being transferred directly to the driver and passenger area. It will result in deaths or injuries to people in the vehicle and further damage to the vehicle. In this study, the collision performance of an existing aluminum crash bumper system produced by the extrusion method with a 40% offset collision analysis is examined. A magnesium crash bumper with improved collision performance is designed by this reference. In this design process, the design of experiment method is used. With the combinations created with the Taguchi method, the long design and analysis process is completed in a shorter time. Taguchi experiment design process; four different design variables and three-level combinations of these variables are used. With the table's help created at the L9 level, models corresponding to the variables are prepared, crash analyzes are performed with finite element analysis, and crash performances are examined. After revealing the results, optimization is carried out with Minitab software; the optimum design is achieved in crash performance and lightness.

This study aimed to develop a biopredictive dissolution method for desvenlafaxine ER tablets using design of experiments (DoE) and physiologically based biopharmaceutics modeling (PBBM) to address the challenge of developing generic drug products by reducing the risk of product failure in pivotal bioequivalence studies. For this purpose, a PBBM was developed in GastroPlus® and combined with a Taguchi L9 design, to evaluate the impact of different drug products (Reference, Generic #1 and Generic #2) and dissolution test conditions on desvenlafaxine release. The influence of the superficial area/volume ratio (SA/V) of the tablets was observed, mainly for Generic #1, which presented higher SA/V than the others, and a high amount of drug dissolved under similar test conditions. The dissolution test conditions of 900 mL of 0.9% NaCl and paddle at 50 rpm with sinker showed to be biopredictive, as it was possible to demonstrate virtual bioequivalence for all products, despite their release-pattern differences, including Generic #3 as an external validation. This approach led to a rational development of a biopredictive dissolution method for desvenlafaxine ER tablets, providing knowledge that may help the process of drug product and dissolution method development.

The integration of energy harvesting systems into sensing technologies can result in novel autonomous sensor nodes, characterized by significant simplification and mass reduction. The use of piezoelectric energy harvesters (PEHs), particularly in cantilever form, is considered as one of the most promising approaches aimed at collecting ubiquitous low-level kinetic energy. Due to the random nature of most excitation environments, the narrow PEH operating frequency bandwidth implies, however, the need to introduce frequency up-conversion mechanisms, able to convert random excitation into the oscillation of the cantilever at its eigenfrequency. A first systematic study is performed in this work to investigate the effects of 3D-printed plectrum designs on the specific power outputs obtainable from FUC excited PEHs. Therefore, novel rotating plectra configurations with different design parameters, determined by using a design-of-experiment methodology and manufactured via fused deposition modeling, are used in an innovative experimental setup to pluck a rectangular PEH at different velocities. The obtained voltage outputs are analyzed via advanced numerical methods. A comprehensive insight into the effects of plectrum properties on the responses of the PEHs is attained, representing a new and important step towards the development of efficient harvesters aimed at a wide range of applications, from wearable devices to structural health monitoring systems.

Motivated by the challenge that manual glaucoma detection is costly and time consuming, and that existing automated glaucoma detection processes lack either good performance or any statistical robustness testing procedures, we proposed an effective, robust, and automated framework for glaucoma detection based on fundus images. The proposed framework using 1450 color fundus images provided by Kaohsiung Chang Gung (KCG) Memorial Hospital in Taiwan. The proposed framework combines the use of convolutional neural networks (CNN) with the proposed generalized loss function, robust design of experiment (DOE), and Retinex theory to improve the results of fundus photography flash by restoring the original colors via removing the light effect. The proposed framework outperformed most archival automatic glaucoma detection approaches in its effectiveness and simplicity. The effectiveness was demonstrated via the estimated sensitivity 0.95, specificity 0.98, and accuracy 0.97. The simplicity was shown via the adopted basic CNN model compared to deep CNNs such as GoogleLeNet and ResNet152. Further, the proposed framework outperformed all relevant archival work in terms of its robustness, illustrated in the associated standard errors (all less than 0.03). This paper demonstrated the proposed framework via intuitive graphs and clear mathematical notations to make it easy for others to reproduce our results. The proposed framework and demonstration have the potential to become the standard automated glaucoma detection approaches in practice.

The article presents the effect of the processing parameters on tribological properties of aluminum oxide coatings Al2O3 doped with fullerene-like tungsten disulfide (IF-WS2) by design of experiment (DOE). Anodic oxidation of aluminum alloy was carried out in a ternary solution of SAS (sulfuric, adipic and oxalic acids) with IF-WS2. The thickness, geometric structure of the surface (SGP) and the tribological properties such as friction coefficient of tribological pair: Al2O3/IF-WS2 with polieteroeteroketon filled with graphite, carbon fiber and PTFE (named PEEK/BG) were investigated. The influence of electrolysis time and temperature on the tribological properties of coatings was studied using 2 k factorial design. The stabilization of the friction coefficient indicates generation of steady anti-wear and anti-seizure Al2O3/IF-WS2 oxide coatings. DOE suggest i.e. high positive correlation between oxide thickness and time and temperature of the anodizing process.

The microstructure, mechanical properties, and wear behavior of three Al–Si alloys, namely: Al–1.3%Si, Al–1.5%Si and Al–13.5%Si were investigated. The specimens were examined by using an optical microscope to investigate the microstructural features of pin materials. Microhardness numbers and mechanical behavior parameters were determined by using a microhardness indenter and compression test, respectively. The dry sliding wear test was carried out using a pin-on-disc apparatus by varying the rotational speed (250, 350, and 450 rpm), normal load (5, 10 and 20 N) and test time (5, 10 and 15 min) at a constant sliding diameter of 300 mm. The surface roughness index (Ra) of the worn surfaces was determined by using a profilometer. The microstructure of Al–1.3%Si alloy was described as a fine eutectic colonizing in the FCC-Al phase matrix. Coarse eutectic dendrites surrounding the primary FCC-Al grains were observed in Al–1.5%Si alloy. The microstructure of Al–13.5%Si alloy showed a uniform layered structure of FCC-Al + Diamond Si eutectic. The average microhardness number was directly proportional to the Si concentration. Al–13.5%Si alloy with a high microhardness number (47.16 HV) showed excellent resistance to wear and exhibited a smoother surface at the end-of-wear test. The improved wear resistance in this case could be due to the presence of Diamond-Si hard phase in large quantities compared with other compositions. On the other hand, Al–1.5%Si alloy showed poorer resistance to wear because the mass loss action was dominated by a particle detachment mechanism. The response surfaces of the mass loss vs. speed, normal load and time showed increased mass loss when the three controlled parameters were increased. However, Pareto charts of the main effects of parametric interactions showed that the normal load was the main factor that must be considered when studying the tribological properties of Al–Si alloys.

Fused Deposition Modeling (FDM) is capable of producing complicated geometries and a variety of thermoplastic or composite products. Thus, it is critical to carry out the relationship between the process parameters, the finished part’s quality, and the part’s mechanical performance. In this study, the optimum printing parameters of FDM using oil palm fiber reinforced thermoplastic composites were investigated. The layer thickness, orientation, infill density, and printing speed were selected as optimization parameters. The mechanical properties of printed specimens were examined using tensile and flexural tests. The experiments were designed using a Taguchi experimental design using a L9 orthogonal array with four factors, and three levels. Analysis of variance (ANOVA) was used to determine the significant parameter or factor that influences the responses, including tensile strength, Young’s modulus, and flexural strength. The fractured surface of printed parts was investigate using scanning electron microscopy (SEM). The results show the tensile strength of the printed specimens ranged from 0.95 to 35.38 MPa, the Young’s modulus from 0.11 to 1.88 GPa, and the flexural strength from 2.50 to 31.98 MPa. In addition, build orientation had the largest influence on tensile strength, Young’s modulus, and flexural strength. The optimum printing parameter for FDM using oil palm fiber composite was 0.4 mm layer thickness, flat (0 degree) of orientation, 50% infill density, and 10 mm/s printing speed. The results of SEM images demonstrate that the number of voids seems to be much bigger when the layer thickness is increased, and the flat orientation has a considerable influence on the bead structure becoming tougher. In a nutshell, these findings will be a valuable 3D printing dataset for other researchers who utilize this material.

The Passiflora cincinnata Mast. has high flavor and potential for wine and other alcoholic beverages production. However, for wine production, the presence of pectin becomes undesirable, as it influences the efficiency of the fermentation process and the clarity of the final product. The objective of this study was to optimize the pectin hydrolysis process in Caatinga passion fruit must for wine production with pectinolytic enzymes, using a Central Composite Rotatable Design (CCRD). The 2³ factorial CCRD, E01 to E17 assays, was applied with six axial tests and three replications in the central point. Caatinga passion fruit must was obtained with pulp and distilled water (40:60 proporcion). Commercial enzyme with high concentration of pectinase was tested. Concentration of enzyme (0.014-0.056 g L-1), working temperature (43-57°C) and reaction time (12-139min) were the independent variables in the optimization process; and the soluble pectin content (mg 100g-1) corresponded to a process-dependent variable. Prior to the beginning of the assays, in order to provide the optimum working pH range for the pectinase, correction of the must pH to 3.9. The initial Caatinga passion fruit wine must had 24.06 mg 100g-1 of soluble pectin in its composition. After the optimization using CCRD, the treatments presented pectin contents ranging from 6.81 (E16: 0.014g L-1/50°C/75min) to 0.00 mg 100 g-1 (E05: 0.05 g L-1/45°C/ 30min and E06: 0.05 g L-1/45°C/120min). According to the results, to a satisfactory process of pectin hydrolysis in Caatinga passion fruit wine must be added 0.05 g L-1 of pectinase at 45°C for 30min. El Passiflora cincinnata Mast. tiene alto sabor y potencial para la producción de vino y otras bebidas alcohólicas. Sin embargo, para la producción de vino, la presencia de pectina se vuelve indeseable, ya que influye en la eficiencia del proceso de fermentación y la claridad del producto final. El objetivo de este estudio fue optimizar el proceso de hidrólisis de pectina en mosto de maracuyá Caatinga para la producción de vino con enzimas pectinolíticas, utilizando un Diseño Rotativo Compuesto Central (DRCC). Se aplicó el ensayo factorial 2³ DRCC, E01 a E17, con seis pruebas axiales y tres repeticiones en el punto central. El mosto de maracuyá caatinga se obtuvo con pulpa y agua destilada (proporción 40:60). Se probó enzima comercial con alta concentración de pectinasa. La concentración de enzima (0.014-0.056 g L-1), la temperatura de trabajo (43-57°C) y el tiempo de reacción (12-139min) fueron las variables independientes en el proceso de optimización; y el contenido de pectina soluble (mg 100g-1) correspondió a una variable dependiente del proceso. Previo al inicio de los ensayos, con el fin de proporcionar el rango de pH óptimo de trabajo para la pectinasa, corrección del pH del mosto a 3,9. El mosto de maracuyá Caatinga inicial tenía en su composición 24,06 mg 100g-1 de pectina soluble. Luego de la optimización mediante DRCC, los tratamientos presentaron contenidos de pectina que van desde 6.81 (E16: 0.014g L-1/50°C/75min) hasta 0.00 mg 100 g-1 (E05: 0.05 g L-1/45°C/30min y E06: 0,05 g L-1/45°C/120min). De acuerdo a los resultados, a un proceso satisfactorio de hidrólisis de pectina en vino de maracuyá Caatinga se le debe adicionar 0.05 g L-1 de pectinasa a 45°C por 30min. O Passiflora cincinnata Mast. apresenta elevado sabor e potencial para produção de vinho e outras bebidas alcoólicas. No entanto, para a produção de vinho, a presença de pectina torna-se indesejável, pois influencia na eficiência do processo de fermentação e na limpidez do produto final. O objetivo deste estudo foi otimizar o processo de hidrólise da pectina em mosto de maracujá da Caatinga para produção de vinho com enzimas pectinolíticas, utilizando um Delineamento Composto Central Rotacional (DCCR). O DCCR fatorial 2³, ensaios E01 a E17, foi aplicado com seis testes axiais e três repetições no ponto central. O mosto de maracujá da Caatinga foi obtido com polpa e água destilada (proporção 40:60). Enzima comercial com alta concentração de pectinase foi testada. Concentração da enzima (0,014-0,056 g L-1), temperatura de trabalho (43-57°C) e tempo de reação (12-139min) foram as variáveis ​​independentes no processo de otimização; e o teor de pectina solúvel (mg 100g-1) correspondeu a uma variável dependente do processo. Antes do início dos ensaios, a fim de fornecer a faixa de pH de trabalho ideal para a pectinase, corrigiu-se o pH do mosto para 3,9. O mosto inicial de maracujá da Caatinga continha 24,06 mg 100g-1 de pectina solúvel em sua composição. Após a otimização com DCCR, os tratamentos apresentaram teores de pectina variando de 6,81 (E16: 0,014g L-1/50°C/75min) a 0,00 mg 100 g-1 (E05: 0,05 g L-1/45°C/30min) e E06: 0,05 g L-1/45°C/120min). De acordo com os resultados, para um processo satisfatório de hidrólise de pectina em vinho de maracujá da Caatinga deve-se adicionar 0,05 g L-1 de pectinase a 45°C por 30min.

Bioindicator plants are species that have the capacity to linearly uptake some elements (metal and metalloids) from the growing substrate, thus reflecting their concentration in the soil. Many factors can influence the uptake of these elements by plants, among which is the simultaneous presence of several metals, a common situation in contaminated or natural soils. A novel approach that can be used to validate the bioindication ability of a species growing on a polymetallic substrate is the design of experiment (DoE) approach. The aim of the present study was to apply the DoE in full factorial mode to model the Cu, Cd, Pb, Zn, and Cr bioindication capacity of Polygonum aviculare, used as the model plant. The results showed that P. aviculare has the ability to bioindicate Cd and Cr with a linear uptake (from 0.35 to 6.66, and 0.1 to 3.4 mg kg−1, respectively) unaffected by the presence of other metals. Conversely, the uptake of Pb, Cu, and Zn is strongly influenced by the presence of all the studied metals, making their concentration in the plant shoot not proportional to that of the soil. In conclusion, these preliminary results confirmed that the DoE can be used to predict the bioindicator abilities of a plant for several elements at the same time and to evaluate the interactions that can be established between variables in the growing medium and in the plant itself. However, more studies including other plant species are needed to confirm the effectiveness of this method.

Additive technologies have undoubtedly become one of the most intensively developing manufacturing methods in recent years. Among the numerous applications, the interest in 3D printing also includes its application in pharmacy for production of small batches of personalized drugs. For this reason, we conducted multi-stage pre-formulation studies to optimize the process of manufacturing solid dosage forms by photopolymerization with visible light. Based on tests planned and executed according to the design of the experiment (DoE), we selected the optimal quantitative composition of photocurable resin made of PEG 400, PEGDA MW 575, water, and riboflavin, a non-toxic photoinitiator. In subsequent stages, we adjusted the printer set-up and process parameters. Moreover, we assessed the influence of the co-initiators ascorbic acid or triethanolamine on the resin’s polymerization process. Next, based on an optimized formulation, we printed and analyzed drug-loaded tablets containing mebeverine hydrochloride, characterized by a gradual release of active pharmaceutical ingredient (API), reaching 80% after 6 h. We proved the possibility of reusing the drug-loaded resin that was not hardened during printing and determined the linear correlation between the volume of the designed tablets and the amount of API, confirming the possibility of printing personalized modified-release tablets.

Submitted by Pedro Henrique Mainardi (pedro.h.mainardi@unesp.br) on 2022-03-05T14:08:37Z No. of bitstreams: 1 Mainardi_nd_Bidoia_2022_Fundamental concepts and recent applications of factorial statistical designs.pdf: 746147 bytes, checksum: f6c90695577fee43ac2f2707c4d9e142 (MD5) Approved for entry into archive by Marcia Correa Bueno Degasperi null (mcbueno@rc.unesp.br) on 2022-03-29T18:37:23Z (GMT) No. of bitstreams: 1 mainardi_ph_versaoeditor_rcla_fundamental.pdf: 746147 bytes, checksum: f6c90695577fee43ac2f2707c4d9e142 (MD5) Made available in DSpace on 2022-03-29T18:37:23Z (GMT). No. of bitstreams: 1 mainardi_ph_versaoeditor_rcla_fundamental.pdf: 746147 bytes, checksum: f6c90695577fee43ac2f2707c4d9e142 (MD5) Previous issue date: 2022-03-04 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Factorial designs have been increasingly used in scientific investigations and technological development. The designs, through the use of matrices with all the treatment combinations, have been capable to effectively characterize the relationships between the variables of multi-factor experiments, assess the experimental variabilities, and derive mathematical functions that represent the behavior of the responses. Factorial designs were fractionalized, which substantially reduced the number of treatments without the loss of relevant information. The addition of central and star points to the factorial arrays has given them the orthogonality and rotatability characteristics, frequently used to fit models with curvature and identify critical regions of interest. Literature reports indicated that factorial designs, also called factorial experiments, were successfully applied in different types of investigations, including in cost evaluations and time-series studies. They were capable to estimate important features of the experiments, like the individual and combined effects of factors, the magnitude of residuals, additionally to express the relationships of the variables in polynomial equations, draw response surface and contour plots, and determine optimal combinations of parameters. In this review, the fundamental aspects of the Complete, Fractional, Central Composite Rotational and Asymmetrical factorial designs were conceptualized, and recent applications of these powerful tools were described. Published Universidade Estadual Paulista Júlio de Mesquita Filho - UNESP, Instituto de Biociências, Departamento de Biologia Geral e Aplicada, Rio Claro, SP, Brasil. Capes: 001

Diffractive lens arrays with overlapping apertures can produce spots with high numerical apertures (NAs). Such diffractive optical elements (DOEs) can replace high-NA objectives and measure a large area with high resolution in transmission microscopes. However, in reflection microscopes for surface measurements, the axial resolution is still limited by the objectives. Direct-imaging DOEs are proposed to solve the problem. They can perform high-NA multi-spot measurement in reflection configurations in both lateral and axial directions. Experiments demonstrate a lateral non-vanishing contrast up to 1448 lp/mm and an axial response on a plane mirror with a full width at half maximum (FWHM) of 2.24 μm.

The CoCrMo alloy is a biomaterial that has been employed for the production of permanent orthopedic implants, however, the release of metallic ions from debris due to tribocorrosion is the main drawback for its extended application. In this paper, we propose the use of the ball burnishing process to improve the surface properties of the CoCrMo alloy. A 32 factorial design of experiments (DoE) was conducted to achieve a basic understanding of the relationship between the surface hardness, roughness and corrosion current (response variables) and the force and number of passes of the burnishing tool (input factors). Statistical analysis showed that force is the most significant factor for changing the surface properties. At the best experimental conditions, hardness was improved by 41.4%, the roughness was decreased by 72.7% and the corrosion rate was noticeably reduced by 92.4%. Microscopy and X-ray diffraction analysis showed that ball burnishing introduces changes in the microstructure, generating a surface layer with improved properties. It is concluded that the ball burnishing process is suitable for improving the surface properties of the CoCrMo alloy under an appropriate combination of experimental conditions.

Glass fiber–cement–polyester composites with different weight percentages (wt.%) of glass fiber and cement were fabricated in the present work through hand lay-up process. Composites were made to undergo different soaking conditions viz., water soaking at room temperature, and boiling water soaking. Unsoaked (as prepared) composites, water-soaked at room temperature, and boiling water soaked samples were tested as per ASTM standards for tensile and flexural strengths. The design of experiments (DOE) based on the full factorial design (33 = L27) is applied to optimize the response variables using MINITAB-18. Results revealed that increasing the wt.% of glass fiber enhanced flexural and tensile strengths. Increasing the wt.% of cement deteriorated the tensile strength but increased the flexural strength. However, specimen subjected to water soaking conditions exhibited a decline in both tensile and flexural strengths.

Highly polar trace organic compounds, which are persistent, mobile, and toxic (PMT) or are very persistent and very mobile (vPvM) in the aquatic environment, may pose a risk to surface water, ground water, and drinking water supplies. Despite the advances in liquid chromatography-mass spectrometry, there often exists an analytical blind spot when it comes to very polar chemicals. This study seeks to make a broad polarity range analytically accessible by means of serially coupling reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) to high-resolution mass spectrometry (HRMS). Moreover, a workflow is presented using optimized data processing of nontarget screening (NTS) data and subsequently generating candidate lists for the identification of very polar molecules via an open-access NTS platform and implemented compound database. First, key input parameters and filters of the so-called feature extraction algorithms were identified, and numerical performance indicators were defined to systematically optimize the data processing method. Second, all features from the very polar HILIC elution window were uploaded to the STOFF-IDENT database as part of the FOR-IDENT open-access NTS platform, which contains additional physicochemical information, and the features matched with potential compounds by their accurate mass. The hit list was filtered for compounds with a negative log D value, indicating that they were (very) polar. For instance, 46 features were assigned to 64 candidate compounds originating from a set of 33 samples from the Isar river in Germany. Three PMT candidates (e.g., guanylurea, melamine, and 1,3-dimethylimidazolidin-2-one) were illustratively validated using the respective reference standards. In conclusion, these findings demonstrate that polarity-extended chromatography reproducibly retards and separates (very) polar compounds from surface waters. These findings further indicate that a transparent and robust data processing workflow for nontarget screening data is available for addressing new (very) polar substances in the aqueous environment. Electronic supplementary material The online version of this article (10.1007/s00216-020-02743-0) contains supplementary material, which is available to authorized users.

Objectives A systematic design of experiment (DoE) based sensitive, robust high performance thin layer chromatographic (HPTLC) method was established for simultaneous estimation of gallic acid (GA), quercetin (QT), and rutin (RT) from ethanolic and aqueous leaf extracts of Moringa oleifera. Materials and Methods The chromatographic separation was carried on Merck TLC aluminum sheets of silica gel 60 F254 (10×10 cm) with mobile phase of toluene: ethyl acetate: methanol: formic acid (4.9:4.1:2:0.5, v/v/v/v) with densitometric scanning at 300 nm. The critical method parameters were initially identified by regular two level factorial design and further systematically optimized using a central composite design, evaluating the effect on selected critical analytical attributes, retention factor (RF), and peak area. Results The Pareto charts, 3D response surface plots, and polynomial equations for the generated models suggested significant influence of the selected factors on responses of QT, GA, and RT. The desirability and overlay plots employed provided appropriate solutions that were experimentally validated. Under the optimized conditions, the biomarkers were suitably resolved with RF values of 0.64±0.02, 0.80±0.03, and 0.22±0.02 for GA, QT, and RT, respectively, with wide linear dynamic range (200-1200 ng/band each), high accuracy (98.1-99.4%), and intra- and interday precision (%RSD

Device-to-Device (D2D) communication is a way to treat the User Equipments (UEs) not as terminals, but as a part of the network (helpers) for service provisioning. We propose a generic framework, namely Proximity as a Service (PaaS), formulate the helper selection problem, and design and prove a heuristic helper selection policy, ContAct based Proximity (CAP), which increases the service connectivity and continuity. Design Of Experiment (DOE) is a statistical methodology that rigorously designs and conducts an experiment, and maximizes the information obtained from that experiment. We apply DOE to explore the relationship (analytic expression) between four inputs (factors) and four metrics (responses). Since different factors have different regression levels, a unified four level full factorial experiment and cubic multiple regression analysis have been carried out. Multiple regression equations are provided to estimate the different contributions and the interactions between factors. Results show that transmission range and user density are dominant and monotonically increasing, but transmission range should be restricted because of interference and energy-efficiency. After obtaining the explicit close form expressions between factors and responses, optimal values of key factors are derived. A methodology (the e-constraint method) to solve the multiple-objective optimization problem has been provided and a Pareto-Optimal set of factors has been found through iteration. The fluctuation of the iterations is small and a specific solution can be chosen based on the particular scenarios (city center or countryside with different user density). The methodology of optimization informs the design rules of the operator, helping to find the optimal networking solution.

Povećana potreba za hranom rezultirala je intenzivnom primjenom pesticida. Nekontrolirana uporaba pesticida uzrokuje njihovu bioakumulaciju u okolišu što dovodi do onečišćenja tla, zraka i vode, što negativno djeluje na zdravlje ljudi. Budući da je primjenom konvencionalnih tehnika teško ukloniti pesticide iz voda i pritom postići stroge kriterije o dozvoljenim koncentracijama, za uklanjanje pesticida iz voda i otpadnih voda koriste se napredni oksidacijski procesi, od kojih se posebno ističe heterogena fotokataliza. U ovom radu istraživana je fotokatalitička razgradnja neonikotinoidnog insekticida imidakloprida u vodenoj otopini. Eksperimenti su provedeni u anularnom reaktoru s recirkulacijom reakcijske smjese primjenom imobiliziranog sloja TiO2 fotokatalizatora modificiranog prethodnim izlaganjem djelovanju UV-C zračenja. U kinetičkim istraživanjima ispitan je utjecaj početne koncentracije imidakloprida (5, 10, 15 ppm), recirkulacije reakcijske smjese (100, 200, 300 mL/min) te vrste primijenjenog izvora zračenja (UVA-8W, UVA-15W, Arcadia-8W) na učinkovitost razgradnje. Plan eksperimenata pripremljen je pomoću računalnog programa Design-Expert, a tijek fotorazgradnje imidakloprida praćen je pomoću tekućinske kromatografije visoke učinkovitosti (HPLC). Iz dobivenih eksperimentalnih podataka izračunate su konverzije i procijenjene su pripadajuće konstante brzine reakcije dobivene prema predloženom kinetičkom modelu za reakciju pseudo prvog reda. Dobiveni eksperimentalni rezultati pokazali su da početna koncentracija onečišćivala pokazuje najveći utjecaj na fotokatalitičku razgradnju. Najveća konverzija i najveća konstanta brzine reakcije postignute su pri koncentraciji onečišćivala 5 ppm te pri recirkulaciji reakcijske smjese od 200 mL/min. Increased need for food has resulted in intensive pesticide use. Uncontrolled use of pesticides causes their bioaccumulation in the environment, which leads to soil, air and water pollution and has a negative impact on human health. Since it is difficult to remove pesticides from water using conventional techniques and achieve strict criteria on permissible concentrations, advanced oxidation processes are used to remove pesticides from. water and wastewater, of which heterogeneous photocatalysis stands out. In this paper, the photocatalytic degradation of the neonicotinoid insecticide imidacloprid in aqueous solution was studied. The experiments were performed in an annular reactor with recirculation of the reaction mixture using an immobilized layer of TiO2 photocatalyst modified by previous exposure to UV-C radiation. The influence of the initial concentration of imidacloprid (5, 10, 15 ppm), recirculation of the reaction mixture (100, 200, 300 mL/min) and the type of applied radiation sources (UVA -8W, UVA -15W, Arcadia-8W) on the kinetic studies was investigated. The design of the experiments was prepared using the computer program Design-Expert and the course of photodegradation of imidacloprid was monitored using high performance liquid chromatography (HPLC). From the obtained experimental data, conversions were calculated and the corresponding reaction rate constants obtained according to the proposed kinetic model for the first order pseudo-reaction were estimated. The obtained experimental results showed that the initial concentration of pollutants shows the greatest influence on photocatalytic degradation. The highest conversion and the highest reaction rate constant were achieved at a pollutant concentration of 5 ppm and at a recirculation of the reaction mixture of 200 ml/min.

Crystallization is the main thermal process resulting in the formation of solid products and, therefore, is widely spread in all kinds of industries, from fine chemicals to foods and drugs. For these high-performance products, a quality by design (QbD) approach is applied to maintain high product purity and steady product parameters. In this QbD-context, especially demanded in the foods and drugs industry, the significance of models to deepen process understanding and moving toward automated operation is steadily rising. To reach these aspired goals, besides major process influences like crystallization temperature, other impacting parameters have to be evaluated and a model describing these influences is sought-after. In this work, the suitability of a population balance-based physico-chemical process model for the production of sugar is investigated. A model overview is given and the resulting model is compared to a statistical DoE scheme. The resulting process model is able to picture the effects of secondary process parameters, alongside temperature or temperature gradients, the influences of seed crystal size and amount, stirrer speed, and additives.