Estimation procedures and optimal designs for estimation of the individual parameters and of the global parameters are discussed under various conditions of prior knowledge. The extension to nonlinear parametrization of the response function ís based on the asymptotical validity of the results for the linear parametrization. For the case where the error variance and the dispersion matrix are unknown, an iterative estimation procedure is suggested. An example based on dental plaque pH profiles demonstrates the improvement that is achieved (a) through using the optimal design or a design that ís close to the optimal, and (b) through taking into account prior information. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

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In this thesis we introduce and study an A-optimal minimax design criterion for two-level fractional factorial designs, which can be used to estimate a linear model with main effects and some interactions. The resulting designs are called A-optimal minimax designs, and they are robust against the misspecification of the terms in the linear model. They are also efficient, and often they are the same as A-optimal and D-optimal designs. Various theoretical results about A-optimal minimax designs are derived. A couple of search algorithms including a simulated annealing algorithm are discussed to search for optimal designs, and many interesting examples are presented in the thesis. / Graduate / 0463 / yinyue@uvic.ca

Thesis (Ph.D.)--University of Nebraska-Lincoln, 2006. / Title from title screen (sites viewed on August 11, 2006). PDF text of dissertation: 76 p. : ill. ; 1.32Mb. UMI publication number: AAT 3208130. Includes bibliographical references. Also available in microfilm, microfiche and paper format.

De nos jours, de nouvelles technologies sont développées pour produire efficacement des produits dérivés de matières premières autresque le pétrole, comme par exemple la biomasse. En effet, la biomasse et plus spécifiquement la biomasse non alimentaire possède un fort potentielcomme substitut aux ressources fossiles pour des raisons environnementales, économiques et politiques. Dans ce contexte, l’étude des bioraffineries offre de nouvelles opportunités pour le Process System Engineering et plus particulièrement pour des activités de recherche quivisent la conception de systèmes constitués d’entités interconnectés. En effet, le verrou principal se concentre sur la modélisation et l’optimisation multi-échelle de la bioraffinerie qui permet l’intégration de plusieurs échelles spatiales allant de l’échelle moléculaire à celle de l’unité de production. Ces différentes échelles sont essentielles pour décrire correctement le système puisqu’elles interagissent en permanence. La forte dilution des courants est le meilleur exemple pour illustrer ces interactions. En effet, la présence d’eau induit de nombreux problèmes thermodynamiques (azéotropes, etc.) à l’échelle moléculaire, ce qui impacte fortement la topologie du procédé notamment sur les étapes de séparation, de purification et detraitement des purges (pour limiter les pertes en produits). Ainsi, la performance de la séquence d’opérations unitaires de l’étape de purification dépend entièrement de la concentration en eau. De plus dans la conception de bioraffinerie, il est fréquent de coupler fermentation et séparation afin d’améliorer les performances de la fermentation et de limiter la présence d’eau dans l’étapede purification. Par ailleurs, la grande quantité d’eau à chauffer ou refroidir entraine la nécessité de réaliser l’intégration énergétique du réseaud’échangeurs du procédé afin de minimiser le coût les dépenses énergétiques. L’objectif de ce travail est alors de proposer une méthodologie générique et les outils associés afin de lever certains verrous de la modélisation et l’optimisation multi-échelle de la bioraffinerie. Basée sur une approche par superstructure, la finalité de la méthodologie est d’évaluer les performances des alternatives étudiées en termes technico-économiques, environnementaux et d’efficacité énergétique en vue de son optimisation multi-objectifs pour trouver la voie de traitement optimale pour le(s) bioproduit(s) d’intérêt. Le cas d’application retenu se focalise sur la production de biobutanol à partir du système Acétone-Butanol-Ethanolet d’une biomasse d’origine forestière. La première étape de la méthodologie proposée concerne la création de la superstructure de la bioraffineriebasée sur une décomposition de cette dernière en 5 étapes principales : le prétraitement, la fermentation, la séparation, la purification et letraitement des purges. Ensuite, la seconde étape consiste à modéliser chaque alternative de procédé. Cette modélisation utilise un modèlethermodynamique à coefficients d’activité afin de décrire le comportement fortement non-idéal des molécules du milieu. De plus, l’intégration du traitement des purges et de l’intégration énergétique durant cette étape permet d’améliorer le procédé. Enfin, la dernière étape s’intéresse à l’optimisation multiobjectif qui se focalise sur différents aspects : maximisation de la production, minimisation des coûts, du prix minimal de vente des bioproduits, des pertes en produits et de l’impact environnemental. Cette dernière étape inclut également des études de sensibilité sur les différents paramètres de la méthodologie : opératoires, économiques, environnementaux... A l’issu de l’optimisation, un compromis seratrouvé afin d’obtenir une bioraffinerie durable. / Nowadays, to replace chemical products derived from petrol, new technologies are developed to produce products derived from others feedstock than crude oil like biomass. Indeed, biomass and especially nonfood biomass has a high potential as substitute due to its environmental, economic and political interests. Inthis context, the study of biorefineries offers new opportunities in the Process System Engineering and especially in research activities which aim to design systems with interlinked compounds. Indeed, the main hurdle focuses on the modeling and the multiscale optimization of thebiorefinery that allows integratingseveral spatial scales from the molecular scale to the plant scale. These scales are essential to describe accurately the system because they interact. The large dilution of flows is the best example to show these interactions. Indeed, water induces many thermodynamic problems (azeotropes, etc.) at the moleculescale, that impact on the process design and mainly on the separation, the purification and the treatment of purges (to limit losses of products). In consequence, the sequence of unit operations of the purification step depends of the water concentration. Furthermore, in the design of the biorefinery, the fermentation and theseparation are usually combined in order to improve performances of the fermentation and limit the water concentration in the purification step. Moreover, the large amount of water that needs to be heated or cooled induces the need of the energy integration of the heat exchangers network to minimize energy consumption. The aim of this work is to propose a generic methodology with connected tools in order to overcome some hurdles caused by the modeling and the multiscaleoptimization of the biorefinery. Based on the superstructure approach, the purpose of the methodology is to estimate performances of considered alternatives in the technical, economic, environmental and energy efficient aspects in preparation for the multiobjective optimization which finds the optimal process for the productionof the interesting bioproduct. This work focuses especially on the production of biobutanol through the Acetone-Butanol-Ethanol system from forest biomass. The methodology begins with the creation of the superstructure of the biorefinery composed by 5 major steps: the pretreatment, the fermentation, the separation, the purification and the treatment of purges. Next, the methodology consists in modeling each alternative of process. It integrates a thermodynamic model with activity coefficients in order to describe accurately the greatly nonideal behavior of molecules. Moreover, the treatment of purges and the energy integration are integratedat this step in order to improve the process. Finally, the last step interests to the multiobjective optimization which focuses on different aspects: the maximization of production and the minimization of the costs, the minimal selling price of bioproducts, the losses of bioproducts and the environmental impact. This step includes also sensitivity analysis on different parameters of the methodology: operating, economic, environmental… After the optimization, a compromise is made in order to obtain sustainable biorefinery.

abstract: The majority of research in experimental design has, to date, been focused on designs when there is only one type of response variable under consideration. In a decision-making process, however, relying on only one objective or criterion can lead to oversimplified, sub-optimal decisions that ignore important considerations. Incorporating multiple, and likely competing, objectives is critical during the decision-making process in order to balance the tradeoffs of all potential solutions. Consequently, the problem of constructing a design for an experiment when multiple types of responses are of interest does not have a clear answer, particularly when the response variables have different distributions. Responses with different distributions have different requirements of the design. Computer-generated optimal designs are popular design choices for less standard scenarios where classical designs are not ideal. This work presents a new approach to experimental designs for dual-response systems. The normal, binomial, and Poisson distributions are considered for the potential responses. Using the D-criterion for the linear model and the Bayesian D-criterion for the nonlinear models, a weighted criterion is implemented in a coordinate-exchange algorithm. The designs are evaluated and compared across different weights. The sensitivity of the designs to the priors supplied in the Bayesian D-criterion is explored in the third chapter of this work. The final section of this work presents a method for a decision-making process involving multiple objectives. There are situations where a decision-maker is interested in several optimal solutions, not just one. These types of decision processes fall into one of two scenarios: 1) wanting to identify the best N solutions to accomplish a goal or specific task, or 2) evaluating a decision based on several primary quantitative objectives along with secondary qualitative priorities. Design of experiment selection often involves the second scenario where the goal is to identify several contending solutions using the primary quantitative objectives, and then use the secondary qualitative objectives to guide the final decision. Layered Pareto Fronts can help identify a richer class of contenders to examine more closely. The method is illustrated with a supersaturated screening design example. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2016

abstract: Accelerated life testing (ALT) is the process of subjecting a product to stress conditions (temperatures, voltage, pressure etc.) in excess of its normal operating levels to accelerate failures. Product failure typically results from multiple stresses acting on it simultaneously. Multi-stress factor ALTs are challenging as they increase the number of experiments due to the stress factor-level combinations resulting from the increased number of factors. Chapter 2 provides an approach for designing ALT plans with multiple stresses utilizing Latin hypercube designs that reduces the simulation cost without loss of statistical efficiency. A comparison to full grid and large-sample approximation methods illustrates the approach computational cost gain and flexibility in determining optimal stress settings with less assumptions and more intuitive unit allocations. Implicit in the design criteria of current ALT designs is the assumption that the form of the acceleration model is correct. This is unrealistic assumption in many real-world problems. Chapter 3 provides an approach for ALT optimum design for model discrimination. We utilize the Hellinger distance measure between predictive distributions. The optimal ALT plan at three stress levels was determined and its performance was compared to good compromise plan, best traditional plan and well-known 4:2:1 compromise test plans. In the case of linear versus quadratic ALT models, the proposed method increased the test plan's ability to distinguish among competing models and provided better guidance as to which model is appropriate for the experiment. Chapter 4 extends the approach of Chapter 3 to ALT sequential model discrimination. An initial experiment is conducted to provide maximum possible information with respect to model discrimination. The follow-on experiment is planned by leveraging the most current information to allow for Bayesian model comparison through posterior model probability ratios. Results showed that performance of plan is adversely impacted by the amount of censoring in the data, in the case of linear vs. quadratic model form at three levels of constant stress, sequential testing can improve model recovery rate by approximately 8% when data is complete, but no apparent advantage in adopting sequential testing was found in the case of right-censored data when censoring is in excess of a certain amount. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2014

Multiple drive belt conveyors are being increasingly incorporated in mining plans worldwide because of their high economic performance and the ease of moving these installations around, especially in underground mines. A typical modern multi-drive conveyor system consists of one or more intermediate drive stations positioned along the upper stretch of the conveyor and a single drive station situated in the lower stretch. Despite the acknowledged cost saving potential of the multiple drive technology, no previous work was reported on the methodology to realize a cost-effective design of multi-drive belt conveyors. This study investigates a design approach for multiple drive belt conveyors with the objective to achieve the lowest life cycle cost of multi-drive belt conveyors for a specified material transport task. For this purpose, an optimization model for the cost-effective design of multi-drive conveyor systems is formulated on the basis of the recommendations of the DIN 22101 and SANS 1313 standards. For a given number of intermediate drive stations, the proposed model optimizes a set of design parameters so that the minimum equivalent annual cost of a conveyor can be attained whilst handling the transport requirements and design conditions. The conveyor parameters optimized in this study are the rated powers of motors, the rated torques of gear reducers, the diameters and wrap angles of drive pulleys, the belt width, the belt speed, the lengths of the belt sections not nestled between drive pulleys, the spacings between idler rolls and the shell diameters and shaft diameters of idler rolls. For benchmark analysis purposes, a similar optimization model is also developed for the single drive technology. Described as mixed integer nonlinear programming (MINLP) problems, the two optimization models are solved using the MIDACO solver embedded in the MATLAB environment. The results of this study show the validity and effectiveness of the design model proposed for multi-drive belt conveyors. The results also indicate that the multiple drive technology is more beneficial for the conveying over long distances. The impact of the possible instability of inflation throughout the project lifetime is also investigated through three hypothetical scenarios, which involve a fixed inflation rate, a higher fluctuating inflation rate and a lower fluctuating inflation rate, respectively. The results of this sensitivity analysis show that the most cost-effective multi-drive belt conveyors obtained under a fixed inflation rate is robust enough against limited fluctuations of this parameter. / Dissertation (MEng)--University of Pretoria, 2017. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

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