Mathematical models of water distribution systems (WDS) serve as tools to represent the real systems for many different purposes. Calibration is the process of fine tuning the model parameters so that the real system is well-represented. In practice, calibration is performed considering all information is deterministic. Recent researches have incorporated uncertainties caused by field measurements into the calibration process. Parameter (D-optimality) and predictive (I-optimality) uncertainties have been used as indicators of how well a system is calibrated.This study focuses on a methodology that extends previous work by considering the impact of uncertainty on decisions that are made using the model. A new sampling strategy that would take into account the accuracy needed for different model objectives is proposed.The methodology uses an optimization routine that minimizes square differences between the observed and model calculated head values by adjusting the model parameters. Given uncertainty in measurements, the parameters from this nonlinear regression are imprecise and the model parameter uncertainties are computed using a first order second moment (FOSM) analysis. Parameter uncertainties are then propagated to model prediction uncertainties through a second FOSM analysis. Finally, the prediction uncertainty relationships are embedded in optimization problems to assess the effect of the uncertainties on model-based decisions. Additional data is collected provided that the monetary benefits of reducing uncertainties can be addressed.The proposed procedure is first applied on a small hypothetical network for a system expansion design problem using a steady state model. It is hypothesized that the model accuracy and data required calibrating WDS models with different objectives would require different amount of data. A real-scale network for design and operation problems is studied using the same methodology for comparison. The effect of a common practice, grouping pipes in the system, is also examined in both studies.Results suggest that the cost reductions are related to the convergence of the mean parameter estimates and the reduction of parameter variances. The impact of each factor changes during the calibration process as the parameters become more precise and the design is modified. Identification of the cause of cost changes, however, is not always obvious.

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The detection of biologically infectious agents, such as bacteria, in water is often a cumbersome process that can only be performed by trained personnel in a well-equipped laboratory and may require sufficient time (sometimes days) until detectable concentration levels of the pathogen are reached. Surface-based biosensors, on the other hand, have shown great promise as miniaturized devises that can provide efficient and de-centralized detection of bacteria. Their operation involves the selective capture of the target pathogen from a liquid sample to a surface (“sampling”) and their subsequent detection via signal transduction. Sampling currently presents a major bottleneck in the successful operation of these sensors as it relies on the extremely slow diffusive transport of the target particle from the bulk of the sample to the capture surface. The present work investigates how alternating current (AC) electric field effects produced by planar microelectrode arrays embedded into a solid surface can improve capture of bacteria. Specifically, numerical and experimental work is combined toward the analysis of a host of electric field generated phenomena (electrophoresis, electroosmosis, and electrothermal fluid flow) that can potentially guide and enhance the transport of K12 E. coli bacteria to the capture surface inside an electric field to find the effective frequency/voltage combination for an efficient capture. Bacteria capture at concentrations as low as 104 particles/mL was experimentally achieved. Proof of principle demonstrations on how antibody-functionalized microelectrode arrays can accelerate and selectively capture K12 E. coli bacteria (target pathogens) from samples containing debris or mixed bacteria populations are also demonstrated. / Thesis (Master, Chemical Engineering) -- Queen's University, 2014-02-28 14:53:46.437

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This study was conducted to determine and compare indoor mold concentrations of total mold, five target taxa, and unidentified mold taxa using culturable/viable mold sampling (on DG-18 and MEA) and total mold spore sampling concurrently. Samples were taken within two locations of 22 single-family dwellings. Paired comparisons of culturable/viable mold concentrations revealed that DG- 18 samples had significantly higher total colony counts than MEA samples and near significantly higher counts of Aspergillus. Total mold spore concentrations were an average of 16-21 times greater than culturable/viable mold concentrations. The use of both sampling techniques concurrently allowed apparent viability ratios to be calculated. Significant differences in apparent viability were observed on the two media for total mold and Cladosporium, and near significance for Aspergillus; higher ratios were observed using DG-18. These studies indicate that DG-18 may be a superior medium for culturable/viable mold sampling and significant apparent viability differences exist among mold taxa quantified. / Department of Natural Resources and Environmental Management

Darbe nagrinėjame ėmimo planus, kurių priklausymo imčiai tikimybės yra dviejų komponenčių mišiniai. Pirmoji komponentė yra proporcinga papildoma informacija nusakytam populiacijos elemento dydžiui, o antroji yra vienoda visiems elementams. Ieškome tokių mišinių, kurie minimizuoja įvairių populiacijos sumos įvertinių dispersijas ir parodome kaip, naudojantis papildoma informacija, apytiksliai nustatyti optimalų mišinį. Pateikiame teorinius ir kompiuterinio modeliavimo rezultatus Poisson'o imtims, renkamoms iš populiacijų, kurios yra generuotos naudojant tiesinės regresijos modelį. / We consider sampling designs, where inclusion (to sample) probabilities are mixtures of two components. The first component is proportional to the size of a population unit (described by means of an auxiliary information available). The second component is the same for every unit. We look for mixtures that minimize variances of various estimators of the population total and show how auxiliary information could help to find an approximate location of such mixtures. We report theoretical and simulation results in the case of Poisson samples drawn from populations which are generated by a linear regression model.

Thesis (Ph. D.)--University of Toronto, 2004. / Adviser: Frank Wania. Includes bibliographical references (p. 154-163).