Availability estimation and management for complex processing systems

“Availability” is the terminology used in asset intensive industries such as petrochemical and hydrocarbons processing to describe the readiness of equipment, systems or plants to perform their designed functions. It is a measure to suggest a facility’s capability of meeting targeted production in a safe working environment. Availability is also vital as it encompasses reliability and maintainability, allowing engineers to manage and operate facilities by focusing on one performance indicator. These benefits make availability a very demanding and highly desired area of interest and research for both industry and academia. In this dissertation, new models, approaches and algorithms have been explored to estimate and manage the availability of complex hydrocarbon processing systems. The risk of equipment failure and its effect on availability is vital in the hydrocarbon industry, and is also explored in this research. The importance of availability encouraged companies to invest in this domain by putting efforts and resources to develop novel techniques for system availability enhancement. Most of the work in this area is focused on individual equipment compared to facility or system level availability assessment and management. This research is focused on developing an new systematic methods to estimate system availability. The main focus areas in this research are to address availability estimation and management through physical asset management, risk-based availability estimation strategies, availability and safety using a failure assessment framework, and availability enhancement using early equipment fault detection and maintenance scheduling optimization.

Student Independent Projects Environmental Studies 2011: The Humber River Basin Human Values Project: Designing a Sampling Strategy for a Pilot Survey in the Corner Brook Area

Sustainability can be indicated by a number of factors. Populations need to be aged evenly, ensuring a healthy equilibrium. Job opportunities must be numerous and of wide varieties to balance incomes from different employment sectors. Regions must also sustain vital natural resources in the area which are directly related to a place being self-sustaining. These indicators prove to be true, especially in Newfoundland, where people have struggled to remain in the small traditional communities that they consider being there 'home.' The population of Corner Brook and the surrounding areas can be stratified according to the values people hold to their special place. Even though people in western Newfoundland hold strong ties to their home, some parts of the region even though people in western Newfoundland hold strong ties to their home, some parts of the region struggle with employment, low incomes, out-migration, and dependency on declining natural resources. The aim of this paper is to present the process of designing a sample strategy for a human values pilot survey conducted in the city of Corner Brook. It will present a theoretical background over the period 2002-2006 to be used for sampling strategy.

Estimation of sea surface temperatures and salinity of ODP Site 177-1089 (Appendix A)

ODP Site 1089 is optimally located in order to monitor the occurrence of maxima in Agulhas heat and salt spillage from the Indian to the Atlantic Ocean. Radiolarian-based paleotemperature transfer functions allowed to reconstruct the climatic history for the last 450 kyr at this location. A warm sea surface temperature anomaly during Marine Isotope Stage (MIS) 10 was recognized and traced to other oceanic records along the surface branch of the global thermohaline (THC) circulation system, and is particularly marked at locations where a strong interaction between oceanic and atmospheric overturning cells and fronts occurs. This anomaly is absent in the Vostok ice core deuterium, and in oceanic records from the Antarctic Zone. However, it is present in the deuterium excess record from the Vostok ice core, interpreted as reflecting the temperature at the moisture source site for the snow precipitated at Vostok Station. As atmospheric models predict a subtropical Indian source for such moisture, this provides the necessary teleconnection between East Antarctica and ODP Site 1089, as the subtropical Indian is also the source area of the Agulhas Current, the main climate agent at our study location. The presence of the MIS 10 anomaly in the delta13C foraminiferal records from the same core supports its connection to oceanic mechanisms, linking stronger Agulhas spillover intensity to increased productivity in the study area. We suggest, in analogy to modern oceanographic observations, this to be a consequence of a shallow nutricline, induced by eddy mixing and baroclinic tide generation, which are in turn connected to the flow geometry, and intensity, of the Agulhas Current as it flows past the Agulhas Bank. We interpret the intensified inflow of Agulhas Current to the South Atlantic as responding to the switch between lower and higher amplitude in the insolation forcing in the Agulhas Current source area. This would result in higher SSTs in the Cape Basin during the glacial MIS 10, due to the release into the South Atlantic of the heat previously accumulating in the subtropical and equatorial Indian and Pacific Ocean. If our explanation for the MIS 10 anomaly in terms of an insolation variability switch is correct, we might expect that a future Agulhas SSST anomaly event will further delay the onset of next glacial age. In fact, the insolation forcing conditions for the Holocene (the current interglacial) are very similar to those present during MIS 11 (the interglacial preceding MIS 10), as both periods are characterized by a low insolation variability for the Agulhas Current source area. Natural climatic variability will force the Earth system in the same direction as the anthropogenic global warming trend, and will thus lead to even warmer than expected global temperatures in the near future.

Bayesian adaptive estimation of arbitrary points on a psychometric function

Bayesian adaptive methods have been extensively used in psychophysics to estimate the point at which performance on a task attains arbitrary percentage levels, although the statistical properties of these estimators have never been assessed. We used simulation techniques to determine the small-sample properties of Bayesian estimators of arbitrary performance points, specifically addressing the issues of bias and precision as a function of the target percentage level. The study covered three major types of psychophysical task (yes-no detection, 2AFC discrimination and 2AFC detection) and explored the entire range of target performance levels allowed for by each task. Other factors included in the study were the form and parameters of the actual psychometric function Psi, the form and parameters of the model function M assumed in the Bayesian method, and the location of Psi within the parameter space. Our results indicate that Bayesian adaptive methods render unbiased estimators of any arbitrary point on psi only when M=Psi, and otherwise they yield bias whose magnitude can be considerable as the target level moves away from the midpoint of the range of Psi. The standard error of the estimator also increases as the target level approaches extreme values whether or not M=Psi. Contrary to widespread belief, neither the performance level at which bias is null nor that at which standard error is minimal can be predicted by the sweat factor. A closed-form expression nevertheless gives a reasonable fit to data describing the dependence of standard error on number of trials and target level, which allows determination of the number of trials that must be administered to obtain estimates with prescribed precision.

Energy estimation of cosmic rays with the Engineering Radio Array of the Pierre Auger Observatory

The Auger Engineering Radio Array (AERA) is part of the Pierre Auger Observatory and is used to detect the radio emission of cosmic-ray air showers. These observations are compared to the data of the surface detector stations of the Observatory, which provide well-calibrated information on the cosmic-ray energies and arrival directions. The response of the radio stations in the 30-80 MHz regime has been thoroughly calibrated to enable the reconstruction of the incoming electric field. For the latter, the energy deposit per area is determined from the radio pulses at each observer position and is interpolated using a two-dimensional function that takes into account signal asymmetries due to interference between the geomagnetic and charge-excess emission components. The spatial integral over the signal distribution gives a direct measurement of the energy transferred from the primary cosmic ray into radio emission in the AERA frequency range. We measure 15.8 MeV of radiation energy for a 1 EeV air shower arriving perpendicularly to the geomagnetic field. This radiation energy-corrected for geometrical effects-is used as a cosmic-ray energy estimator. Performing an absolute energy calibration against the surface-detector information, we observe that this radio-energy estimator scales quadratically with the cosmic-ray energy as expected for coherent emission. We find an energy resolution of the radio reconstruction of 22% for the data set and 17% for a high-quality subset containing only events with at least five radio stations with signal.

Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

Essays in Empirical Operations Management: Bayesian Learning of Service Quality and Structural Estimation of Complementary Product Pricing and Inventory Management

This dissertation contributes to the rapidly growing empirical research area in the field of operations management. It contains two essays, tackling two different sets of operations management questions which are motivated by and built on field data sets from two very different industries --- air cargo logistics and retailing.

The first essay, based on the data set obtained from a world leading third-party logistics company, develops a novel and general Bayesian hierarchical learning framework for estimating customers' spillover learning, that is, customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. We then apply our model to the data set to study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. We find that customers indeed borrow experiences from similar but different services to update their quality beliefs that determine future purchase decisions. Also, service quality beliefs have a significant impact on their future purchasing decisions. Moreover, customers are risk averse; they are averse to not only experience variability but also belief uncertainty (i.e., customer's uncertainty about their beliefs). Finally, belief uncertainty affects customers' utilities more compared to experience variability.

The second essay is based on a data set obtained from a large Chinese supermarket chain, which contains sales as well as both wholesale and retail prices of un-packaged perishable vegetables. Recognizing the special characteristics of this particularly product category, we develop a structural estimation model in a discrete-continuous choice model framework. Building on this framework, we then study an optimization model for joint pricing and inventory management strategies of multiple products, which aims at improving the company's profit from direct sales and at the same time reducing food waste and thus improving social welfare.

Collectively, the studies in this dissertation provide useful modeling ideas, decision tools, insights, and guidance for firms to utilize vast sales and operations data to devise more effective business strategies.

Time-series of snow spectral albedo and superficial snow specific surface area at Dome C in Antarctica, 2012-2015

Spectral albedo has been measured at Dome C since December 2012 in the visible and near infrared (400 - 1050 nm) at sub-hourly resolution using a home-made spectral radiometer. Superficial specific surface area (SSA) has been estimated by fitting the observed albedo spectra to the analytical Asymptotic Approximation Radiative Transfer theory (AART). The dataset includes fully-calibrated albedo and SSA that pass several quality checks as described in the companion article. Only data for solar zenith angles less than 75° have been included, which theoretically spans the period October-March. In addition, to correct for residual errors still affecting data after the calibration, especially at the solar zenith angles higher than 60°, we produced a higher quality albedo time-series as follows: In the SSA estimation process described in the companion paper, a scaling coefficient A between the observed albedo and the theoretical model predictions was introduced to cope with these errors. This coefficient thus provides a first order estimate of the residual error. By dividing the albedo by this coefficient, we produced the "scaled fully-calibrated albedo". We strongly recommend to use the latter for most applications because it generally remains in the physical range 0-1. The former albedo is provided for reference to the companion paper and because it does not depend on the SSA estimation process and its underlying assumptions.

Land cover classification of Samoylov Island and Landsat subpixel water cover of Lena River Delta, Siberia, with links to ESRI grid files

Ignoring small-scale heterogeneities in Arctic land cover may bias estimates of water, heat and carbon fluxes in large-scale climate and ecosystem models. We investigated subpixel-scale heterogeneity in CHRIS/PROBA and Landsat-7 ETM+ satellite imagery over ice-wedge polygonal tundra in the Lena Delta of Siberia, and the associated implications for evapotranspiration (ET) estimation. Field measurements were combined with aerial and satellite data to link fine-scale (0.3 m resolution) with coarse-scale (upto 30 m resolution) land cover data. A large portion of the total wet tundra (80%) and water body area (30%) appeared in the form of patches less than 0.1 ha in size, which could not be resolved with satellite data. Wet tundra and small water bodies represented about half of the total ET in summer. Their contribution was reduced to 20% in fall, during which ET rates from dry tundra were highest instead. Inclusion of subpixel-scale water bodies increased the total water surface area of the Lena Delta from 13% to 20%. The actual land/water proportions within each composite satellite pixel was best captured with Landsat data using a statistical downscaling approach, which is recommended for reliable large-scale modelling of water, heat and carbon exchange from permafrost landscapes.

Permanent open water surfaces in central Siberia from ENVISAT ASAR wide swath data 2003/2004, link to shapefile

Permanent water bodies not only store dissolved CO2 but are essential for the maintenance of wetlands in their proximity. From the viewpoint of greenhouse gas (GHG) accounting wetland functions comprise sequestration of carbon under anaerobic conditions and methane release. The investigated area in central Siberia covers boreal and sub-arctic environments. Small inundated basins are abundant on the sub-arctic Taymir lowlands but also in parts of severe boreal climate where permafrost ice content is high and feature important freshwater ecosystems. Satellite radar imagery (ENVISAT ScanSAR), acquired in summer 2003 and 2004, has been used to derive open water surfaces with 150 m resolution, covering an area of approximately 3 Mkm**2. The open water surface maps were derived using a simple threshold-based classification method. The results were assessed with Russian forest inventory data, which includes detailed information about water bodies. The resulting classification has been further used to estimate the extent of tundra wetlands and to determine their importance for methane emissions. Tundra wetlands cover 7% (400,000 km**2) of the study region and methane emissions from hydromorphic soils are estimated to be 45,000 t/d for the Taymir peninsula.

Melt climatology estimates for small to giant Antarctic icebergs, links to NetCDF files

We present four melt climatology estimates based on a simulation of Antarctic iceberg drift and melting that includes small, medium-sized, and giant tabular icebergs with a realistic size distribution. Drift and meltdown is simulated using vertical profiles of ocean currents, temperature, and salinity, which goes beyond the present standard in iceberg modeling. The climatology estimates based on simulations of small (SMA), 'small-to-medium'-sized (MED12 & MED123), and small-to-giant icebergs (ALL) exhibit differential characteristics: successive inclusion of larger icebergs leads to a reduced seasonality of iceberg melt and a shift of the mass input to the area north of 58°S, while less melt water is released into the coastal areas. This highlights the necessity to account for larger and giant icebergs in order to obtain accurate melt climatologies. The four monthly melt climatologies [mm/day] are available as netCDF files with 1°x1° spatial resolution and can be used, e.g., for sensitivity studies with uncoupled sea ice-ocean models, or as spatio-temporal templates for the redistribution of land ice from the Antarctic ice sheet over the Southern Ocean in climate models.

Annotated record of the detailed examination of Mn deposits on the coral banks of the continental shelf in the south-western coastal area of the Gulf of Mexico off Texas

During U.S. Department of Interior, Bureau of Land Management (BLM) public hearings held in 1973, 1974 and 1975 prior to Texas Outer Continental Shelf (OCS) oil and gas lease sales, concern was expressed by the National Marine Fisheries Service, scientists from Texas A&M and the University of Texas and private citizens over the possible environmental impact of oil and gas drilling and production operations on coral reefs and fishing banks in or adjacent to lease blocks to be sold. As a result, certain restrictive regulations concerning drilling operations in the vicinity of the well documented coral reefs and biostromal communities at the East and West Flower Gardens were established by BLM, and Signal Oil Company was required to provide a biological and geological baseline study of the less well known Stetson Bank before a drilling permit could be issued. Considering the almost total lack of knowledge of the geology and biotic communities associated with the South Texas OCS banks lying in or near lease blocks to be offered for sale in 1975, BLM contracted with Texas A&M University to provide the biological and geological baseline information required to facilitate judgments as to the extent and nature of restrictive regulations on drilling near these banks which might be required to insure their protection. In pursuit of this, scientists from Texas A&M University were to direct their attention toward assessments of ground fish populations, unique biological and geological features, substratum type and distribution, and the biotic and geologic relationships between these banks and those farther north.

UTILIZATION OF GIS AND SPATIAL ANALYSIS TECHNIQUES IN ARTISANAL AND SMALL SCALE MINING TO LOCATE A CENTRALIZED PROCESSING CENTRE

One of the global phenomena with threats to environmental health and safety is artisanal mining. There are ambiguities in the manner in which an ore-processing facility operates which hinders the mining capacity of these miners in Ghana. These problems are reviewed on the basis of current socio-economic, health and safety, environmental, and use of rudimentary technologies which limits fair-trade deals to miners. This research sought to use an established data-driven, geographic information (GIS)-based system employing the spatial analysis approach for locating a centralized processing facility within the Wassa Amenfi-Prestea Mining Area (WAPMA) in the Western region of Ghana. A spatial analysis technique that utilizes ModelBuilder within the ArcGIS geoprocessing environment through suitability modeling will systematically and simultaneously analyze a geographical dataset of selected criteria. The spatial overlay analysis methodology and the multi-criteria decision analysis approach were selected to identify the most preferred locations to site a processing facility. For an optimal site selection, seven major criteria including proximity to settlements, water resources, artisanal mining sites, roads, railways, tectonic zones, and slopes were considered to establish a suitable location for a processing facility. Site characterizations and environmental considerations, incorporating identified constraints such as proximity to large scale mines, forest reserves and state lands to site an appropriate position were selected. The analysis was limited to criteria that were selected and relevant to the area under investigation. Saaty’s analytical hierarchy process was utilized to derive relative importance weights of the criteria and then a weighted linear combination technique was applied to combine the factors for determination of the degree of potential site suitability. The final map output indicates estimated potential sites identified for the establishment of a facility centre. The results obtained provide intuitive areas suitable for consideration

Axis Mapping: The Estimation of Surface Orientations and its Applications in Vehicle Localization and Structural Geology

The map representation of an environment should be selected based on its intended application. For example, a geometrically accurate map describing the Euclidean space of an environment is not necessarily the best choice if only a small subset its features are required. One possible subset is the orientations of the flat surfaces in the environment, represented by a special parameterization of normal vectors called axes. Devoid of positional information, the entries of an axis map form a non-injective relationship with the flat surfaces in the environment, which results in physically distinct flat surfaces being represented by a single axis. This drastically reduces the complexity of the map, but retains important information about the environment that can be used in meaningful applications in both two and three dimensions. This thesis presents axis mapping, which is an algorithm that accurately and automatically estimates an axis map of an environment based on sensor measurements collected by a mobile platform. Furthermore, two major applications of axis maps are developed and implemented. First, the LiDAR compass is a heading estimation algorithm that compares measurements of axes with an axis map of the environment. Pairing the LiDAR compass with simple translation measurements forms the basis for an accurate two-dimensional localization algorithm. It is shown that this algorithm eliminates the growth of heading error in both indoor and outdoor environments, resulting in accurate localization over long distances. Second, in the context of geotechnical engineering, a three-dimensional axis map is called a stereonet, which is used as a tool to examine the strength and stability of a rock face. Axis mapping provides a novel approach to create accurate stereonets safely, rapidly, and inexpensively compared to established methods. The non-injective property of axis maps is leveraged to probabilistically describe the relationships between non-sequential measurements of the rock face. The automatic estimation of stereonets was tested in three separate outdoor environments. It is shown that axis mapping can accurately estimate stereonets while improving safety, requiring significantly less time and effort, and lowering costs compared to traditional and current state-of-the-art approaches.