Thailand possesses high potential for solar energy, which has been given more attention from both government and the private sector lately. While the major investment in solar energy has gradually shifted from governments to private sectors, this paper aims to compare private investment's net present value (NPV) and production risks of grid-connected photovoltaic (PV) projects. The scope of this paper includes PV projects on three different scales: 3 kW, 5 MW and 73 MW installed capacity in 74 locations across Thailand. The study utilizes RETscreen software to estimate means and variability in electricity productions in Thailand. Cost-benefit analysis is the method used to measure projects’ NPV and profitability, while portfolio theory is applied to capture the profit variability or production risk. The analysis also includes 3 feed-in-tariff (FIT or adder) scenarios that are likely to happen in the solar energy policy in Thailand; which are 8 baht/kWh, 6.5 baht/kWh and the case where there is no support at all. The study finds that 3 kW PV projects yield negative NPV, which means investment losses, in all scenarios and locations in the country. On the other hand, 5 MW PV projects result in positive NPV in all scenarios and locations and it is concluded to be the most profitable scale among three. 73 MW PV projects are considered as policy dependenceas the projectsyield negative NPV without the supports. Sensitivity analysis shows that the costs of 3 kW projects have to reduce at least by 60% in order to turn the projects to profits. With respect to allocation of risk, the production risks of 73 MW projects are slightly higher compared to the other two scales. Also, it is shown that the technologies located in north of Thailand have higher production risks, but yield approximately the same expected net benefits as technologies located in the rest of the country. As for conclusions, the future of PV technology in Thailand is still bright, but the investors should intensely consider about scale and location of the implementation. Further researches can examine other scales of PV technology and the drives behind the risk characteristics in Thailand.

Parameters associated with the detection and quantification of microorganisms (or discrete particles) in water such as the analytical recovery of an enumeration method, the concentration of the microorganisms or particles in the water, the log-reduction achieved using a treatment process, and the sensitivity of a detection method cannot be measured exactly. There are unavoidable random errors in the enumeration process that make estimates of these parameters imprecise and possibly also inaccurate. For example, the number of microorganisms observed divided by the volume of water analyzed is commonly used as an estimate of concentration, but there are random errors in sample collection and sample processing that make these estimates imprecise. Moreover, this estimate is inaccurate if poor analytical recovery results in observation of a different number of microorganisms than what was actually present in the sample. In this thesis, a statistical framework (using probabilistic modelling and Bayes’ theorem) is developed to enable appropriate analysis of microorganism concentration estimates given information about analytical recovery and knowledge of how various random errors in the enumeration process affect count data. Similar models are developed to enable analysis of recovery data given information about the seed dose. This statistical framework is used to address several problems: (1) estimation of parameters that describe random sample-to-sample variability in the analytical recovery of an enumeration method, (2) estimation of concentration, and quantification of the uncertainty therein, from single or replicate data (which may include non-detect samples), (3) estimation of the log-reduction of a treatment process (and the uncertainty therein) from pre- and post-treatment concentration estimates, (4) quantification of random concentration variability over time, and (5) estimation of the sensitivity of enumeration processes given knowledge about analytical recovery. The developed models are also used to investigate alternative strategies that may enable collection of more precise data. The concepts presented in this thesis are used to enhance analysis of pathogen concentration data in Quantitative Microbial Risk Assessment so that computed risk estimates are more predictive. Drinking water research and prudent management of treatment systems depend upon collection of reliable data and appropriate interpretation of the data that are available.

Hurricanes have caused severe damage to the electric power system throughout the Gulf coast region of the U.S., and electric power is critical to post-hurricane disaster response as well as to long-term recovery for impacted areas. Managing hurricane risks and properly preparing for post-storm recovery efforts requires rigorous methods for estimating the number and location of power outages, customers without power, and damage to power distribution systems. This dissertation presents a statistical power outage prediction model, a statistical model for predicting the number of customers without power, statistical damage estimation models, and a physical damage estimation model for the gulf coast region of the U.S. The statistical models use negative binomial generalized additive regression models as well as negative binomial generalized linear regression models for estimating the number of power outages, customers without power, damaged poles and damaged transformers in each area of a utility company’s service area. The statistical models developed based on transformed data replace hurricane indicator variables, dummy variables, with physically measurable variables, enabling future predictions to be based on only well-understood characteristics of hurricanes. The physical damage estimation model provides reliable predictions of the number of damaged poles for future hurricanes by integrating fragility curves based on structural reliability analysis with observed data through a Bayesian approach. The models were developed using data about power outages during nine hurricanes in three states served by a large, investor-owned utility company in the Gulf Coast region.

The objective of this thesis is to develop a decision support tool to assess the feasibility of a hydro electrical power plant (HEPP) investment option by estimating its profitability under various scenarios. The decision support tool may help the decision makers to understand critical parameters that affect the internal rate of return (IRR) of a HEPP investment, create realistic scenarios by assigning different values to these parameters and monitor profitability under various scenarios. The information and the assumptions to construct the proposed decision support tool have been collected by conducting interviews with experts and its reliability has been tested by a real case study.

There has been growing public concern regarding the threat to people and environment from industrial activities, thus more rigorous regulations. The investigation of almost all the major accidents shows that we could have avoided those tragedies with effective risk analysis and safety management programs. High-quality risk analysis is absolutely necessary for sustainable development. As a powerful and systematic tool, fault tree analysis (FTA) has been adapted to the particular need of chemical process quantitative risk analysis (CPQRA) and found great applications. However, the application of FTA in the chemical process industry (CPI) is limited. One major barrier is the manual synthesis of fault trees. It requires a thorough understanding of the process and is vulnerable to individual subjectivity. The quality of FTA can be highly subjective and variable. The availability of a computer-based FTA methodology will greatly benefit the CPI. The primary objective of this research is to develop a computer-aided fault tree synthesis methodology for CPQRA. The central idea is to capture the cause-and-effect logic around each item of equipment directly into mini fault trees. Special fault tree models have been developed to manage special features. Fault trees created by this method are expected to be concise. A prototype computer program is provided to illustrate the methodology. Ideally, FTA can be standardized through a computer package that reads information contained in process block diagrams and provides automatic aids to assist engineers in generating and analyzing fault trees. Another important issue with regard to QRA is the large uncertainty associated with available failure rate data. In the CPI, the ranges of failure rates observed could be quite wide. Traditional reliability studies using point values of failure rates may result in misleading conclusions. This dissertation discusses the uncertainty with failure rate data and proposes a procedure to deal with data uncertainty in determining safety integrity level (SIL) for a safety instrumented system (SIS). Efforts must be carried out to obtain more accurate values of those data that might actually impact the estimation of SIL. This procedure guides process hazard analysts toward a more accurate SIL estimation and avoids misleading results due to data uncertainty.

Ports play a critical role in transportation infrastructure, but are vulnerable to seismic hazards. Downtime and reduced throughput from seismic damage in ports results in significant business interruption losses for port stakeholders. Current risk management practices only focus on the effect of seismic hazards on individual port structures. However, damage and downtime of these structures has a significant impact on the overall port system's ship handling operations and the regional, national, and even international economic impacts that result from extended earthquake-induced disruption of a major container port. Managing risks from system-wide disruptions resulting from earthquake damage has been studied as a central element of a Grand Challenge project sponsored by the National Science Foundation Network for Earthquake Engineering Simulation (NEES) program. The following thesis presents the concepts and methods developed for the seismic risk management of a port-wide system of berths. In particular the thesis discusses the framework used to calculated port losses: the use of spatially correlated ground motion intensity measures to estimate damage to pile-supported marginal wharves and container cranes of various configurations via fragility relationships developed by project team members, repair costs and downtimes subsequently determined via repair models for both types of structures, and the impact on cargo handling operations calculated via logistical models of the port system. Results are expressed in the form of loss exceedance curves than include both repair/replacement costs and business interruption losses. The thesis also discusses how the results from such an analysis might be used by port decision makers to make more informed decisions in design, retrofit, operational, and other seismic risk management options.

Für zwei ausgewählte sächsische Betriebe wurden quantitative Risikoanalysen auf der Grundlage der vorliegenden Sicherheitsberichte mit einem von der Bundesanstalt für Materialforschung und -prüfung (BAM) entwickelten Verfahren erstellt. Nach der Darstellung von bekannten Verfahren zur Durchführung von Risikoanalysen wird die daraus abgeleitete Methodik unter Berücksichtigung der Randbedingungen in Deutschland erläutert. Nach der Beschreibung der Modelle für die Auswirkungsbetrachtungen werden die möglichen Ansätze für die Bewertung der Auswirkungen von Stoff- und Energiefreisetzungen diskutiert. Ein wesentlicher Teil der Risikoanalyse umfasst die Berechnung der Eintrittshäufigkeiten der Szenarien. Hierfür sind Angaben über die Ausfallhäufigkeit von Anlagenteilen, insbesondere von Störfall verhindernden und -begrenzenden Einrichtungen erforderlich. Ausführlich werden die in der Literatur gefundenen Angaben miteinander verglichen und ein einheitlicher Datensatz für die Berechnungen zusammengestellt.

<p>Safety analysis is a process involving several techniques.The purpose of this thesis is to test and develop methodssuitable for the safety analysis of railway risks and railwaysafety measures. Safety analysis is a process comprisingproblem identification, risk estimation, valuation of safetyand economic analysis. The main steps are described in separatechapters, each of which includes a discussion of the methodsand a review of previous research, followed by the contributionof this author. Although the safety analysis proceduredescribed can be used for analysing railway safety, it has suchgeneral foundations that it can be used wherever safety isimportant and wherever safety measures are evaluated. Itcombines cost benefit analysis with criteria for thedistribution and the absolute levels of risk.</p><p>Risks are estimated with both statistical and risk analysismethods. Historical data on railway accidents are analysed andstatistical models fitted to describe trends in accident ratesand consequences. A risk analysis model is developed usingfault tree and event tree techniques, together with Monte Carlosimulation, to calculate risks for passenger train derailments.The results are compared with the statistical analysis ofhistorical data.</p><p>People's valuation of safety in different contexts isanalysed, with relative values estimated in awillingness-to-pay study. A combination of focus groups andindividual questionnaires is used. Two different methods areused to estimate the value of safety and the results arecompared. Comparisons are also made with other studies.</p><p>Different approaches for safety analysis and methods foreconomic analysis of safety are reviewed. Cost-benefit analysisas a decision criterion is discussed and a study on theeconomic effectsof a traffic control system is presented.</p><p>There are several results of the work. Historical data showsa decrease in the accident rate. The average consequence ofeach accident has not changed over time. The risk analysismodel produces comparable results and enables analysis ofvarious safety measures. The valuation study shows that peopleprefer the prevention of small-scale accidents over theprevention of larger, catastrophic accidents. There are onlysmall differences in the valuation of safety in differentcontexts.</p>

The historic use of asbestos-containing materials during the manufacturing of automobiles has resulted in a perception of an increased risk of asbestos-related pulmonary diseases within mechanics. This study was conducted to assess the potential asbestos exposures mechanics encounter while servicing vehicles assembled with parts containing asbestos, in addition to compare the cumulative lifetime asbestos exposures for different maintenance activities against theorical threshold exposures for asbestosis, lung cancer and mesothelioma. Exposure data were assembled from four independent exposure assessments performed to elucidate the airborne asbestos levels generated during the removal and replacement of brakes, gaskets, clutches and seam sealants containing asbestos. The phase contrast microscopy (PCM) and phase contrast microscopy equivalent (PCME) fiber concentrations for personal samples and air sampled identified to contain asbestos fibers through Transmission Electron Microscopy (TEM) analysis were applied to calculate the cumulative lifetime asbestos exposures. This index of exposure was compared to no-effect exposure thresholds identified through an extensive literature review for the selected pulmonary diseases. The results of this study indicate that mechanics encounter PCM fiber concentrations approximately 10 to 100 times lower than the current Occupational Safety Health Administration (OSHA) Permissible Limit Exposure (PEL) of 0.1 fibers per cubic centimeter (f/cc). Additionally, the cumulative lifetime asbestos exposures ranged from <1 fiber-year/cubic centimeter of air (f-yr/cc) to 2.0 f-yr/cc, and did not exceed the no-effect exposure thresholds for asbestosis, lung cancer and mesothelioma. The findings of this study provide additional support to previously published epidemiologic investigations and exposure assessments against an increased risk of asbestos-related disease within mechanics historically employed to service vehicles containing asbestos fibers.

A tsunami is a series of large amplitude, shallow water waves generated by an event capable of displacing a massive volume of water. The displaced water propagates at speeds in excess of 800 kph until it dissipates or impacts a shoreline where it slows to 30 -- 50 kph [NOAA and USGS Fact Sheet, 2005]. Earthquakes are the predominant tsunamigenic event, however, landslides, avalanches, submarine slumps or slides, volcanic eruptions, volcano flank failure, and meteor impact into an ocean can also cause a tsunami [McCann, 2004; O'Loughlin and Lander, 2003; Pararas-Carayannis, 2004]. This study includes past Caribbean tsunamigenic events assumed to be regionally destructive and generated by earthquakes and/or massive submarine slides/slumps. The approximate study area is from 7°N, 59°W to 36°N, 98° W. Caribbean tsunami data suggests that a tsunami will occur in this region once every three years, and destructively once every 21 years [O'Loughlin and Lander, 2003]. Excluding the December 2004 Indian Ocean tsunami, approximately 13.8% of all tsunamis and 83% of all tsunami fatalities worldwide have occurred in the Caribbean [O'Loughlin and Lander, 2003]. In the past 150 years, 2,590 victims died from tsunamis in the Caribbean. As a result of these recorded fatalities and the rise of Caribbean population by almost 300% from 1950 to 2000 [CIAT et al., 2005], protection of human life is a primary reason for establishing a tsunami warning system in this region. The goal of this study is to identify the minimum number of sea level gauge locations to aid in tsunami detection in order to provide the most warning time to the largest number of people. This study defines which historical tsunamis were likely to have been regionally destructive, analyzes the tsunamigenic potential and population distribution of the Intra-Americas Sea (IAS), models 42 historical tsunamis with the United States Navy Coastal Ocean Model (NCOM), and recommends 12 prioritized locations for coastal sea level gauge installation. The results of this systematic approach to assess priority locations for coastal sea level gauges will assist in developing a tsunami warning system for the IAS and are currently being used by NOAA and IOCARIBE-GOOS.