Modeling and Optimization of Stochastic Process Parameters in Complex Engineering Systems

For quality engineering researchers and practitioners, a wide number of statistical tools and techniques are available for use in the manufacturing industry. The objective or goal in applying these tools has always been to improve or optimize a product or process in terms of efficiency, production cost, or product quality. While tremendous progress has been made in the design of quality optimization models, there remains a significant gap between existing research and the needs of the industrial community. Contemporary manufacturing processes are inherently more complex - they may involve multiple stages of production or require the assessment of multiple quality characteristics. New and emerging fields, such as nanoelectronics and molecular biometrics, demand increased degrees of precision and estimation, that which is not attainable with current tools and measures. And since most researchers will focus on a specific type of characteristic or a given set of conditions, there are many critical industrial processes for which models are not applicable. Thus, the objective of this research is to improve existing techniques by not only expanding their range of applicability, but also their ability to more realistically model a given process. Several quality models are proposed that seek greater precision in the estimation of the process parameters and the removal of assumptions that limit their breadth and scope. An extension is made to examine the effectiveness of these models in both non-standard conditions and in areas that have not been previously investigated. Upon the completion of an in-depth literature review, various quality models are proposed, and numerical examples are used to validate the use of these methodologies

Models for Paired Comparison Data: A Review with Emphasis on Dependent Data

Thurstonian and Bradley-Terry models are the most commonly applied models in the analysis of paired comparison data. Since their introduction, numerous developments have been proposed in different areas. This paper provides an updated overview of these extensions, including how to account for object- and subject-specific covariates and how to deal with ordinal paired comparison data. Special emphasis is given to models for dependent comparisons. Although these models are more realistic, their use is complicated by numerical difficulties. We therefore concentrate on implementation issues. In particular, a pairwise likelihood approach is explored for models for dependent paired comparison data, and a simulation study is carried out to compare the performance of maximum pairwise likelihood with other limited information estimation methods. The methodology is illustrated throughout using a real data set about university paired comparisons performed by students.Comment: Published in at http://dx.doi.org/10.1214/12-STS396 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Koesuunnittelun hyödyntäminen ICP-MS -menetelmien optimoinnissa

Inductively coupled mass spectrometry (ICP-MS) is a state-of-the-art technique for elemental analysis. The technique allows fast and simultaneous analysis of multiple elements with a wide dynamic range and low detection limits. However, multiple adjustable parameters and the complex nature ICP-MS instruments can make the development of new analysis methods a tedious process. Design of experiments (DOE) or experimental design is a statistical approach for conducting multi- variate experiments in a way that gives maximal amount of information from each experiment. By using DOE the number of experiments needed for analytical method optimization can be minimized and information about interrelations of di↵erent experimental variables can be obtained. The aim of this thesis is to address the utilization of DOE for ICP-MS method developement as a more e cient mean to optimize analytical methods. The first part of this two part thesis gives an overview on the basics of ICP-MS and DOE. Then a literature review on applying experimental design for ICP-MS method optimization is given and the current state of the research is discussed. In the second part, two new ICP-MS methods for simultaneous determination of 28 elements from six middle distillate fuels, diluted with xylene or kerosine, are presented. The method developement involved optimization of the integration times and optimization of test sample dilution ratios and viscosities using univariate techniques. In addition, experimental designs were succesfully utilized together with desirability approach in multivariate optimizations of the plasma conditions and sample matrix compositions to achieve the best possible analyte recoveries from various matrices

ROBUST PARAMETER DESIGN IN COMPLEX ENGINEERING SYSTEMS:

Many industrial firms seek the systematic reduction of variability as a primary means for reducing production cost and material waste without sacrificing product quality or process efficiency. Despite notable advancements in quality-based estimation and optimization approaches aimed at achieving this goal, various gaps remain between current methodologies and observed in modern industrial environments. In many cases, models rely on assumptions that either limit their usefulness or diminish the reliability of the estimated results. This includes instances where models are generalized to a specific set of assumed process conditions, which constrains their applicability against a wider array of industrial problems. However, such generalizations often do not hold in practice. If the realities are ignored, the derived estimates can be misleading and, once applied to optimization schemes, can result in suboptimal solutions and dubious recommendations to decision makers. The goal of this research is to develop improved quality models that more fully explore innate process conditions, rely less on theoretical assumptions, and have extensions to an array of more realistic industrial environments. Several key areas are addressed in which further research can reinforce foundations, extend existing knowledge and applications, and narrow the gap between academia and industry. These include the integration of a more comprehensive approach to data analysis, the development of conditions-based approaches to tier-one and tier-two estimation, achieving cost robustness in the face of dynamic process variability, the development of new strategies for eliminating variability at the source, and the integration of trade-off analyses that balance the need for enhanced precision against associated costs. Pursuant to a detailed literature review, various quality models are proposed, and numerical examples are used to validate their use

Mechanical, optical, and physicochemical properties of HPMC-based doxazosin mesylate orodispersible films

In this study, orodispersible films formed from hydroxypropyl methylcellulose (HPMC) E6 (2, 2.5, and 3%) and plasticizers ((glycerin (Gly), propylene glycol (PP), or polyethylene glycol (PEG)), containing doxazosin mesylate, were prepared by the solvent casting method and characterized. Design of experiments (DoE) was used as a statistical tool to facilitate the interpretation of the experimental data and allow the identification of optimal levels of factors for maximum formulation performance. Differential scanning calorimetry (DSC) curves and X-ray powder diffraction (XRPD) diffractograms showed doxazosin mesylate amorphization, probably due to complexation with the polymer (HPMC E6), and the glass transition temperature of the polymer was reduced by adding a plasticizer. Fourier transformed infrared (FTIR) spectroscopy results showed that the chemical structure of doxazosin mesylate was preserved when introduced into the polymer matrix, and the plasticizers, glycerin and PEG, affected the polymer matrix with high intensity. The addition of plasticizers increased the elongation at break and adhesiveness (Gly > PEG > PP), confirming the greater plasticizer effect of Gly observed in DSC and FTIR studies. Greater transparency was observed for the orodispersible films prepared using PP. The addition of citric acid as a pH modifier was fundamental for the release of doxazosin mesylate, and the desirability formulation had a release profile similar to that of the reference product

RESEARCH AND DEVELOPMENT EFFORT IN DEVELOPING THE OPTIMAL FORMULATIONS FOR NEW TABLET DRUGS

Seeking the optimal pharmaceutical formulation is considered one of the most critical research components during the drug development stage. It is also an R&D effort incorporating design of experiments and optimization techniques, prior to scaling up a manufacturing process, to determine the optimal settings of ingredients so that the desirable performance of related pharmaceutical quality characteristics (QCs) specified by the Food and Drug Administration (FDA) can be achieved. It is widely believed that process scale-up potentially results in changes in ingredients and other pharmaceutical manufacturing aspects, including site, equipment, batch size and process, with the purpose of satisfying the clinical and market demand. Nevertheless, there has not been any single comprehensive research work on how to model and optimize the pharmaceutical formulation when scale-up changes occur. Based upon the FDA guidance, the documentation tests for scale-up changes generally include dissolution comparisons and bioequivalence studies. Hence, this research proposes optimization models to ensure the equivalent performance in terms of dissolution and bioequivalence for the pre-change and post-change formulations by extending the existing knowledge of formulation optimization. First, drug professionals traditionally consider the mean of a QC only; however, the variability of the QC of interest is essential because large variability may result in unpredictable safety and efficacy issues. In order to simultaneously take into account the mean and variability of the QC, the Taguchi quality loss concept is applied to the optimization procedure. Second, the standard 2×2 crossover design, which is extensively conducted to evaluate bioequivalence, is incorporated into the ordinary experimental scheme so as to investigate the functional relationships between the characteristics relevant to bioequivalence and ingredient amounts. Third, as many associated FDA and United States Pharmacopeia regulations as possible, regarding formulation characteristics, such as disintegration, uniformity, friability, hardness, and stability, are included as constraints in the proposed optimization models to enable the QCs to satisfy all the related requirements in an efficient manner. Fourth, when dealing with multiple characteristics to be optimized, the desirability function (DF) approach is frequently incorporated into the optimization. Although the weight-based overall DF is usually treated as an objective function to be maximized, this approach has a potential shortcoming: the optimal solutions are extremely sensitive to the weights assigned and these weights are subjective in nature. Moreover, since the existing DF methods consider mean responses only, variability is not captured despite the fact that individuals may differ widely in their responses to a drug. Therefore, in order to overcome these limitations when applying the DF method to a formulation optimization problem, a priority-based goal programming scheme is proposed that incorporates modified DF approaches to account for variability. The successful completion of this research will establish a theoretically sound foundation and statistically rigorous base for the optimal pharmaceutical formulation without loss of generality. It is believed that the results from this research will have the potential to impact a wide range of tasks in the pharmaceutical manufacturing industry

Aggregate Shocks vs Reallocation Shocks: an Appraisal of the Applied Literature

This paper critically appraises the di erent approaches that have characterized the literature on the macroeconomic e ects of job reallocations from Lilien's seminal work to recent developments rooted in structural general equilibrium models, nonlinear econometric techniques and the concepts of job creation and destruction. Despite a ourishing of empirical analysis no unifying theoretical framework has obtained consensus in the scienti c debate. We face a corpus of research which is heterogeneous in variables' selection and experimental design. This widespread heterogeneity makes the evaluation of results a daunting task. Reliability of outcomes becomes almost impossible to assess when, even within models of the same generation, the lack of a rigorous theoretical background hinders well de ned experimental design and makes comparisons di cult. The strong pace at which the empirical literature on the macroeconomic e ects of job reallocations has been growing in recent years suggests that a general assessment of the state of the art is valuable and maybe indispensable. As a guiding principle for our excursion we track down the methodological development of the proposed solutions to the crucial problem of observational equivalence. We do not linger on speci c econometric methods nor on strictly theoretical issues not relevant to our main purpose. We draw the conclusion that the asymmetric and non-directional nature of allocative shocks, which holds the key to the solution of the problem, is better captured by multivariate, non-linear, dynamic econometric models and numerical simulation techniques. Davis and Haltiwanger's perspective on job creation and destruction seems to us of paramount importance for future research because of its potential to encompass a wealth of micro-level data sets within a rigorous analytical framework.Sectoral shifts, methodology, measurement, assessment

Direct immersion solid-phase microextraction analysis of multi-class contaminants in edible seaweeds by gas chromatography-mass spectrometry

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.aca.2018.05.066 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/The present work aimed at the development of a simple and accurate direct immersion-solidphase microextraction-gas-chromatography-mass spectrometry (DI-SPME-GC-MS) method for simultaneous determination of PAHs, PCBs, and pesticide residues in edible seaweeds. As the target contaminants possess a wide range of physical-chemical properties, multivariate experimental design was used for method optimization. In particular, two different methods were optimized and validated: one that allows for simultaneous determination of all targets, and an ad hoc method for determination of hydrophobic analytes, a class that often poses a challenge for extraction from food matrices. Optimum conditions suitable for simultaneous quantitation of all targeted compounds, namely buffer at pH = 7.0, 20% acetone (v/v), 10% NaCl (w/w), 0.02% NaN3, 60 min DI extraction at 55 °C, and 20 min desorption at 270 °C, afforded limits of quantitation (LOQs) in the range of 1–30 μg kg−1, a wide linear range of 5–2000 μg kg−1, the attainment of satisfactory determination coefficients (R2˃0.99) with no significant lack of fit (p > 0.05) at the 5% level, and satisfactory accuracy and precision values. By modifying the extraction conditions to favor extraction of the most hydrophobic analytes (e.g. higher amount of organic modifier and pH, and lower salt content) lower LOQs were obtained for these compounds ranging from 0.2 to 13.3 μg kg−1. The established methods were then used for screening of commercial, edible dry seaweeds, with PCBs (≤16.0 ng g−1) and PAHs (≤15.5 ng g−1) detected in some samples. This method overcomes most challenges commonly encountered in dry sample analysis applications, and represents the first report of a DI-SPME method employing the matrix-compatible fiber for simultaneous multiclass and multiresidue analysis of seaweeds.Natural Sciences and Engineering Research Council of CanadaChina Sponsorship Council ["201606330026"]Fundação de Amparo à Pesquisa do Estado de São Paulo ["2016/16180–6"