Integration of different models in the design of chemical processes: Application to the design of a power plant

With advances in the synthesis and design of chemical processes there is an increasing need for more complex mathematical models with which to screen the alternatives that constitute accurate and reliable process models. Despite the wide availability of sophisticated tools for simulation, optimization and synthesis of chemical processes, the user is frequently interested in using the ‘best available model’. However, in practice, these models are usually little more than a black box with a rigid input–output structure. In this paper we propose to tackle all these models using generalized disjunctive programming to capture the numerical characteristics of each model (in equation form, modular, noisy, etc.) and to deal with each of them according to their individual characteristics. The result is a hybrid modular–equation based approach that allows synthesizing complex processes using different models in a robust and reliable way. The capabilities of the proposed approach are discussed with a case study: the design of a utility system power plant that has been decomposed into its constitutive elements, each treated differently numerically. And finally, numerical results and conclusions are presented.

Is socioeconomic position early in life associated with physical activity during adulthood following the accumulation of risk model with additive effects?

L'activité physique améliore la santé, mais seulement 4.8% des Canadiens atteignent le niveau recommandé. La position socio-économique est un des déterminants de l'activité physique les plus importants. Elle est associée à l’activité physique de manière transversale à l’adolescence et à l’âge adulte. Cette thèse a tenté de déterminer s'il y a une association à long terme entre la position socio-économique au début du parcours de vie et l’activité physique à l’âge adulte. S'il y en avait une, un deuxième objectif était de déterminer quel modèle théorique en épidémiologie des parcours de vie décrivait le mieux sa forme. Cette thèse comprend trois articles: une recension systématique et deux recherches originales. Dans la recension systématique, des recherches ont été faites dans Medline et EMBASE pour trouver les études ayant mesuré la position socio-économique avant l'âge de 18 ans et l'activité physique à ≥18 ans. Dans les deux recherches originales, la modélisation par équations structurelles a été utilisée pour comparer trois modèles alternatifs en épidémiologie des parcours de vie: le modèle d’accumulation de risque avec effets additifs, le modèle d’accumulation de risque avec effet déclenché et le modèle de période critique. Ces modèles ont été comparés dans deux cohortes prospectives représentatives à l'échelle nationale: la 1970 British birth cohort (n=16,571; première recherche) et l’Enquête longitudinale nationale sur les enfants et les jeunes (n=16,903; deuxième recherche). Dans la recension systématique, 10 619 articles ont été passés en revue par deux chercheurs indépendants et 42 ont été retenus. Pour le résultat «activité physique» (tous types et mesures confondus), une association significative avec la position socio-économique durant l’enfance fut trouvée dans 26/42 études (61,9%). Quand seulement l’activité physique durant les loisirs a été considérée, une association significative fut trouvée dans 21/31 études (67,7%). Dans un sous-échantillon de 21 études ayant une méthodologie plus forte, les proportions d’études ayant trouvé une association furent plus hautes : 15/21 (71,4%) pour tous les types et toutes les mesures d’activité physique et 12/15 (80%) pour l’activité physique de loisir seulement. Dans notre première recherche originale sur les données de la British birth cohort, pour la classe sociale, nous avons trouvé que le modèle d’accumulation de risque avec effets additifs s’est ajusté le mieux chez les hommes et les femmes pour l’activité physique de loisir, au travail et durant les transports. Dans notre deuxième recherche originale sur les données canadiennes sur l'activité physique de loisir, nous avons trouvé que chez les hommes, le modèle de période critique s’est ajusté le mieux aux données pour le niveau d’éducation et le revenu, alors que chez les femmes, le modèle d’accumulation de risque avec effets additifs s’est ajusté le mieux pour le revenu, tandis que le niveau d’éducation ne s’est ajusté à aucun des modèles testés. En conclusion, notre recension systématique indique que la position socio-économique au début du parcours de vie est associée à la pratique d'activité physique à l'âge adulte. Les résultats de nos deux recherches originales suggèrent un patron d’associations le mieux représenté par le modèle d’accumulation de risque avec effets additifs.

Mapping Whidbey Island and Possession Sound landslide susceptibility using an additive linear model

Senior thesis written for Oceanography 445

Statistical Hurdle Models for Single Cell Gene Expression: Differential Expression and Graphical Modeling

Thesis (Ph.D.)--University of Washington, 2016-06

A generalized Markov sampler

A recent development of the Markov chain Monte Carlo (MCMC) technique is the emergence of MCMC samplers that allow transitions between different models. Such samplers make possible a range of computational tasks involving models, including model selection, model evaluation, model averaging and hypothesis testing. An example of this type of sampler is the reversible jump MCMC sampler, which is a generalization of the Metropolis-Hastings algorithm. Here, we present a new MCMC sampler of this type. The new sampler is a generalization of the Gibbs sampler, but somewhat surprisingly, it also turns out to encompass as particular cases all of the well-known MCMC samplers, including those of Metropolis, Barker, and Hastings. Moreover, the new sampler generalizes the reversible jump MCMC. It therefore appears to be a very general framework for MCMC sampling. This paper describes the new sampler and illustrates its use in three applications in Computational Biology, specifically determination of consensus sequences, phylogenetic inference and delineation of isochores via multiple change-point analysis.

Forecasting with serially correlated regression models

In this article we investigate the asymptotic and finite-sample properties of predictors of regression models with autocorrelated errors. We prove new theorems associated with the predictive efficiency of generalized least squares (GLS) and incorrectly structured GLS predictors. We also establish the form associated with their predictive mean squared errors as well as the magnitude of these errors relative to each other and to those generated from the ordinary least squares (OLS) predictor. A large simulation study is used to evaluate the finite-sample performance of forecasts generated from models using different corrections for the serial correlation.

The specification of vector autoregressive moving average models

In this paper we propose a new identification method based on the residual white noise autoregressive criterion (Pukkila et al. , 1990) to select the order of VARMA structures. Results from extensive simulation experiments based on different model structures with varying number of observations and number of component series are used to demonstrate the performance of this new procedure. We also use economic and business data to compare the model structures selected by this order selection method with those identified in other published studies.

On minimal models of process systems

Minimal representations are known to have no redundant elements, and are therefore of great importance. Based on the notions of performance and size indices and measures for process systems, the paper proposes conditions for a process model being minimal in a set of functionally equivalent models with respect to a size norm. Generalized versions of known procedures to obtain minimal process models for a given modelling goal, model reduction based on sensitivity analysis and incremental model building are proposed and discussed. The notions and procedures are illustrated and compared on a simple example, that of a simple nonlinear fermentation process with different modelling goals and on a case study of a heat exchanger modelling. (C) 2004 Elsevier Ltd. All rights reserved.

Segmenting eukaryotic genomes with the generalized Gibbs sampler

Eukaryotic genomes display segmental patterns of variation in various properties, including GC content and degree of evolutionary conservation. DNA segmentation algorithms are aimed at identifying statistically significant boundaries between such segments. Such algorithms may provide a means of discovering new classes of functional elements in eukaryotic genomes. This paper presents a model and an algorithm for Bayesian DNA segmentation and considers the feasibility of using it to segment whole eukaryotic genomes. The algorithm is tested on a range of simulated and real DNA sequences, and the following conclusions are drawn. Firstly, the algorithm correctly identifies non-segmented sequence, and can thus be used to reject the null hypothesis of uniformity in the property of interest. Secondly, estimates of the number and locations of change-points produced by the algorithm are robust to variations in algorithm parameters and initial starting conditions and correspond to real features in the data. Thirdly, the algorithm is successfully used to segment human chromosome 1 according to GC content, thus demonstrating the feasibility of Bayesian segmentation of eukaryotic genomes. The software described in this paper is available from the author's website (www.uq.edu.au/similar to uqjkeith/) or upon request to the author.

A comparison of different choices for the regularization parameter in inverse electrocardiography models

Calculating the potentials on the heart’s epicardial surface from the body surface potentials constitutes one form of inverse problems in electrocardiography (ECG). Since these problems are ill-posed, one approach is to use zero-order Tikhonov regularization, where the squared norms of both the residual and the solution are minimized, with a relative weight determined by the regularization parameter. In this paper, we used three different methods to choose the regularization parameter in the inverse solutions of ECG. The three methods include the L-curve, the generalized cross validation (GCV) and the discrepancy principle (DP). Among them, the GCV method has received less attention in solutions to ECG inverse problems than the other methods. Since the DP approach needs knowledge of norm of noises, we used a model function to estimate the noise. The performance of various methods was compared using a concentric sphere model and a real geometry heart-torso model with a distribution of current dipoles placed inside the heart model as the source. Gaussian measurement noises were added to the body surface potentials. The results show that the three methods all produce good inverse solutions with little noise; but, as the noise increases, the DP approach produces better results than the L-curve and GCV methods, particularly in the real geometry model. Both the GCV and L-curve methods perform well in low to medium noise situations.

An upper bound on the Bayesian error bars for generalized linear regression

In the Bayesian framework, predictions for a regression problem are expressed in terms of a distribution of output values. The mode of this distribution corresponds to the most probable output, while the uncertainty associated with the predictions can conveniently be expressed in terms of error bars. In this paper we consider the evaluation of error bars in the context of the class of generalized linear regression models. We provide insights into the dependence of the error bars on the location of the data points and we derive an upper bound on the true error bars in terms of the contributions from individual data points which are themselves easily evaluated.

On the modulation of the effects of some 5 - HT - related agents in axiety models

The results of an investigation into how stressors interact with the action of serotonergic agents in animal models of anxiety are presented. Water deprivation and restraint both increased plasma corticosterone concentrations and elevated 5-HT turnover. In the elevated X-maze, water deprivation had a duration-dependent "anxiolytic" effect. The effect of restraint was dependent on the duration of restraint and was to inhibit maze exploration. Water-deprivation did not influence the action of diazepam or any 5-HT1A ligand in the X-maze. Restraint switched the "anxiogenic" effect of 8-0H-DPAT to either "anxiolytic" or inactive, depending on the time after the restraint when testing was performed. The Vogel conflict test detected an "anxiolytic" "anxiolytic"V"anxiolytic""anxiolytic" effect of buspirone which was additive with "anxiolytic" effects of pindolol and propranolol. Diazepam and fluoxetine were also active, but 8-0H-DPAT, ipsapirone, gepirone and yohimbine were inactive. In the elevated X-maze, "anxiogenic" responses to picrotoxin, flumazenil, RU 24969, CGS 12066B, fluoxetine and 8-0H-DPAT were detected. Other 5-HT1A ligands were inactive. Diazepam and corticosterone had "anxiolytic" effects. Increasing light intensity did not change behaviour on the elevated X-maze, but was able to reverse the effect of 8- OH-DPAT to an "anxiolytic" action. This effect was attributed to a presynaptic mechanism, because it was abolished by pCPA. The occurence of different behaviours in different reglons of the maze was shown to be susceptible to modulation by "anxiolytic" and "anxiogenic" drugs. These results are discussed in the context of there being at least two separate 5-HT mechanisms which are involved in the control of anxiety.

A new fuzzy additive model for determining the common set of weights in Data Envelopment Analysis

The main advantage of Data Envelopment Analysis (DEA) is that it does not require any priori weights for inputs and outputs and allows individual DMUs to evaluate their efficiencies with the input and output weights that are only most favorable weights for calculating their efficiency. It can be argued that if DMUs are experiencing similar circumstances, then the pricing of inputs and outputs should apply uniformly across all DMUs. That is using of different weights for DMUs makes their efficiencies unable to be compared and not possible to rank them on the same basis. This is a significant drawback of DEA; however literature observed many solutions including the use of common set of weights (CSW). Besides, the conventional DEA methods require accurate measurement of both the inputs and outputs; however, crisp input and output data may not relevant be available in real world applications. This paper develops a new model for the calculation of CSW in fuzzy environments using fuzzy DEA. Further, a numerical example is used to show the validity and efficacy of the proposed model and to compare the results with previous models available in the literature.

Mach bands and multiscale models of spatial vision:the role of first, second, and third derivative operators in encoding bars and edges

Ernst Mach observed that light or dark bands could be seen at abrupt changes of luminance gradient in the absence of peaks or troughs in luminance. Many models of feature detection share the idea that bars, lines, and Mach bands are found at peaks and troughs in the output of even-symmetric spatial filters. Our experiments assessed the appearance of Mach bands (position and width) and the probability of seeing them on a novel set of generalized Gaussian edges. Mach band probability was mainly determined by the shape of the luminance profile and increased with the sharpness of its corners, controlled by a single parameter (n). Doubling or halving the size of the images had no significant effect. Variations in contrast (20%-80%) and duration (50-300 ms) had relatively minor effects. These results rule out the idea that Mach bands depend simply on the amplitude of the second derivative, but a multiscale model, based on Gaussian-smoothed first- and second-derivative filtering, can account accurately for the probability and perceived spatial layout of the bands. A key idea is that Mach band visibility depends on the ratio of second- to first-derivative responses at peaks in the second-derivative scale-space map. This ratio is approximately scale-invariant and increases with the sharpness of the corners of the luminance ramp, as observed. The edges of Mach bands pose a surprisingly difficult challenge for models of edge detection, but a nonlinear third-derivative operation is shown to predict the locations of Mach band edges strikingly well. Mach bands thus shed new light on the role of multiscale filtering systems in feature coding. © 2012 ARVO.