Biomedical subjects

H Christopher Frey

Publications and source records attributed to H Christopher Frey.

At least 19 recordsLinked to original sources

PubMed2006

Improved system integration for integrated gasification combined cycle (IGCC) systems.

Integrated gasification combined cycle (IGCC) systems are a promising technology for power generation. They include an air separation unit (ASU), a gasification system, and a gas turbine combined cycle power block, and feature competitive efficiency and lower emissions compared to conventional power generation technology. IGCC systems are not yet in widespread commercial use and opportunities remain to improve system feasibility via improved process integration. A process simulation model was developed for IGCC systems with alternative types of ASU and gas turbine integration. The model is applied to evaluate integration schemes involving nitrogen injection, air extraction, and combinations of both, as well as different ASU pressure levels. The optimal nitrogen injection only case in combination with an elevated pressure ASU had the highest efficiency and power output and approximately the lowest emissions per unit output of all cases considered, and thus is a recommended design option. The optimal combination of air extraction coupled with nitrogen injection had slightly worse efficiency, power output, and emissions than the optimal nitrogen injection only case. Air extraction alone typically produced lower efficiency, lower power output, and higher emissions than all other cases. The recommended nitrogen injection only case is estimated to provide annualized cost savings compared to a nonintegrated design. Process simulation modeling is shown to be a useful tool for evaluation and screening of technology options.

Gases↗

PubMed2006

Evaluation and recommendation of sensitivity analysis methods for application to Stochastic Human Exposure and Dose Simulation models.

Sensitivity analyses of exposure or risk models can help identify the most significant factors to aid in risk management or to prioritize additional research to reduce uncertainty in the estimates. However, sensitivity analysis is challenged by non-linearity, interactions between inputs, and multiple days or time scales. Selected sensitivity analysis methods are evaluated with respect to their applicability to human exposure models with such features using a testbed. The testbed is a simplified version of a US Environmental Protection Agency's Stochastic Human Exposure and Dose Simulation (SHEDS) model. The methods evaluated include the Pearson and Spearman correlation, sample and rank regression, analysis of variance, Fourier amplitude sensitivity test (FAST), and Sobol's method. The first five methods are known as "sampling-based" techniques, wheras the latter two methods are known as "variance-based" techniques. The main objective of the test cases was to identify the main and total contributions of individual inputs to the output variance. Sobol's method and FAST directly quantified these measures of sensitivity. Results show that sensitivity of an input typically changed when evaluated under different time scales (e.g., daily versus monthly). All methods provided similar insights regarding less important inputs; however, Sobol's method and FAST provided more robust insights with respect to sensitivity of important inputs compared to the sampling-based techniques. Thus, the sampling-based methods can be used in a screening step to identify unimportant inputs, followed by application of more computationally intensive refined methods to a smaller set of inputs. The implications of time variation in sensitivity results for risk management are briefly discussed.

Analysis of Variance↗

PubMed2006

Road grade estimation for on-road vehicle emissions modeling using light detection and ranging data.

Vehicle-specific power (VSP) is useful for explaining a substantial portion of variability in real-world vehicle emissions, such as those measured with portable emissions monitoring systems (PEMS). VSP is a function of vehicle speed, acceleration, and road grade. Road grade is shown to significantly affect estimates of both VSP and of real-world emissions via sensitivity analysis and analysis of empirical data. However, road grade is difficult to measure reliably using PEMS. Therefore, alternative methods for estimating road grade were identified and compared. A preferred method for estimating road grade was explored in more detail based on light detection and ranging (LIDAR) data. The method includes buffering LIDAR data onto roadway maps using a geographic information system tool, defining segments of roadway based on criteria pertaining to vertical curvature, quantification of roadway elevations within the buffered segments, and estimation of road grade and banking by fitting a plane to each segment. Factors influencing errors in road grade estimates are discussed. The method was evaluated by application to selected interstate highways and comparison to design drawing data. The development and application of LIDAR-based road grade data are demonstrated via a case study using PEMS data collected in the Research Triangle Park, NC, area. LIDAR data are shown to be reliable and accurate for road grade estimation for vehicle emissions modeling.

Air Pollutants↗

PubMed2006

Air emission inventories in North America: a critical assessment.

Although emission inventories are the foundation of air quality management and have supported substantial improvements in North American air quality, they have a number of shortcomings that can potentially lead to ineffective air quality management strategies. Major reductions in the largest emissions sources have made accurate inventories of previously minor sources much more important to the understanding and improvement of local air quality. Changes in manufacturing processes, industry types, vehicle technologies, and metropolitan infrastructure are occurring at an increasingly rapid pace, emphasizing the importance of inventories that reflect current conditions. New technologies for measuring source emissions and ambient pollutant concentrations, both at the point of emissions and from remote platforms, are providing novel approaches to collecting data for inventory developers. Advances in information technologies are allowing data to be shared more quickly, more easily, and processed and compared in novel ways that can speed the development of emission inventories. Approaches to improving quantitative measures of inventory uncertainty allow air quality management decisions to take into account the uncertainties associated with emissions estimates, providing more accurate projections of how well alternative strategies may work. This paper discusses applications of these technologies and techniques to improve the accuracy, timeliness, and completeness of emission inventories across North America and outlines a series of eight recommendations aimed at inventory developers and air quality management decision-makers to improve emission inventories and enable them to support effective air quality management decisions for the foreseeable future.

Air Pollutants, Occupational↗

PubMed2006

Uncertainty analysis of integrated gasification combined cycle systems based on Frame 7H versus 7F gas turbines.

Integrated gasification combined cycle (IGCC) technology is a promising alternative for clean generation of power and coproduction of chemicals from coal and other feedstocks. Advanced concepts for IGCC systems that incorporate state-of-the-art gas turbine systems, however, are not commercially demonstrated. Therefore, there is uncertainty regarding the future commercial-scale performance, emissions, and cost of such technologies. The Frame 7F gas turbine represents current state-of-practice, whereas the Frame 7H is the most recently introduced advanced commercial gas turbine. The objective of this study was to evaluate the risks and potential payoffs of IGCC technology based on different gas turbine combined cycle designs. Models of entrained-flow gasifier-based IGCC systems with Frame 7F (IGCC-7F) and 7H gas turbine combined cycles (IGCC-7H) were developed in ASPEN Plus. An uncertainty analysis was conducted. Gasifier carbon conversion and project cost uncertainty are identified as the most important uncertain inputs with respect to system performance and cost. The uncertainties in the difference of the efficiencies and costs for the two systems are characterized. Despite uncertainty, the IGCC-7H system is robustly preferred to the IGCC-7F system. Advances in gas turbine design will improve the performance, emissions, and cost of IGCC systems. The implications of this study for decision-making regarding technology selection, research planning, and plant operation are discussed.

Coal↗

PubMed2006

Application of classification and regression trees for sensitivity analysis of the Escherichia coli O157:H7 food safety process risk model.

Microbial food safety process risk models are simplifications of the real world that help risk managers in their efforts to mitigate food safety risks. An important tool in these risk assessment endeavors is sensitivity analysis, a systematic method used to quantify the effect of changes in input variables on model outputs. In this study, a novel sensitivity analysis method called classification and regression trees was applied to safety risk assessment with the use of portions of the Slaughter Module and Preparation Module of the E. coli O157:H7 microbial food safety process risk as an example. Specifically, the classification and regression trees sensitivity analysis method was evaluated on the basis of its ability to address typical characteristics of microbial food safety process risk models such as nonlinearities, interaction, thresholds, and categorical inputs. Moreover, this method was evaluated with respect to identification of high exposure scenarios and corresponding key inputs and critical limits. The results from the classification and regression trees analysis applied to the Slaughter Module confirmed that the process of chilling carcasses is a critical control point. The method identified a cutoff value of a 2.2-log increase in the number of organisms during chilling as a critical value above which high levels of contamination would be expected. When classification and regression trees analysis was applied to the cooking effects part of the Preparation Module, cooking temperature was found to be the most sensitive input, with precooking treatment (i.e., raw product storage conditions) ranked second in importance. This case study demonstrates the capabilities of classification and regression trees analysis as an alternative to other statistically based sensitivity analysis methods, and one that can readily address specific characteristics that are common in microbial food safety process risk models.

Consumer Product Safety↗

PubMed2005

Quantitative analysis of variability and uncertainty with known measurement error: methodology and case study.

The appearance of measurement error in exposure and risk factor data potentially affects any inferences regarding variability and uncertainty because the distribution representing the observed data set deviates from the distribution that represents an error-free data set. A methodology for improving the characterization of variability and uncertainty with known measurement errors in data is demonstrated in this article based on an observed data set, known measurement error, and a measurement-error model. A practical method for constructing an error-free data set is presented and a numerical method based upon bootstrap pairs, incorporating two-dimensional Monte Carlo simulation, is introduced to address uncertainty arising from measurement error in selected statistics. When measurement error is a large source of uncertainty, substantial differences between the distribution representing variability of the observed data set and the distribution representing variability of the error-free data set will occur. Furthermore, the shape and range of the probability bands for uncertainty differ between the observed and error-free data set. Failure to separately characterize contributions from random sampling error and measurement error will lead to bias in the variability and uncertainty estimates. However, a key finding is that total uncertainty in mean can be properly quantified even if measurement and random sampling errors cannot be separated. An empirical case study is used to illustrate the application of the methodology.

Journal Article↗

PubMed2005

Sensitivity analysis of a two-dimensional probabilistic risk assessment model using analysis of variance.

This article demonstrates application of sensitivity analysis to risk assessment models with two-dimensional probabilistic frameworks that distinguish between variability and uncertainty. A microbial food safety process risk (MFSPR) model is used as a test bed. The process of identifying key controllable inputs and key sources of uncertainty using sensitivity analysis is challenged by typical characteristics of MFSPR models such as nonlinearity, thresholds, interactions, and categorical inputs. Among many available sensitivity analysis methods, analysis of variance (ANOVA) is evaluated in comparison to commonly used methods based on correlation coefficients. In a two-dimensional risk model, the identification of key controllable inputs that can be prioritized with respect to risk management is confounded by uncertainty. However, as shown here, ANOVA provided robust insights regarding controllable inputs most likely to lead to effective risk reduction despite uncertainty. ANOVA appropriately selected the top six important inputs, while correlation-based methods provided misleading insights. Bootstrap simulation is used to quantify uncertainty in ranks of inputs due to sampling error. For the selected sample size, differences in F values of 60% or more were associated with clear differences in rank order between inputs. Sensitivity analysis results identified inputs related to the storage of ground beef servings at home as the most important. Risk management recommendations are suggested in the form of a consumer advisory for better handling and storage practices.

Analysis of Variance↗

PubMed2004

Optimization under variability and uncertainty: a case study for NOx emissions control for a gasification system.

Methods for optimization of process technologies considering the distinction between variability and uncertainty are developed and applied to case studies of NOx control for Integrated Gasification Combined Cycle systems. Existing methods of stochastic optimization (SO) and stochastic programming (SP) are demonstrated. A comparison of SO and SP results provides the value of collecting additional information to reduce uncertainty. For example, an expected annual benefit of 240,000 dollars is estimated if uncertainty can be reduced before a final design is chosen. SO and SP are typically applied to uncertainty. However, when applied to variability, the benefit of dynamic process control is obtained. For example, an annual savings of 1 million dollars could be achieved if the system is adjusted to changes in process conditions. When variability and uncertainty are treated distinctively, a coupled stochastic optimization and programming method and a two-dimensional stochastic programming method are demonstrated via a case study. For the case study, the mean annual benefit of dynamic process control is estimated to be 700,000 dollars, with a 95% confidence range of 500,000 dollars to 940,000 dollars. These methods are expected to be of greatest utility for problems involving a large commitment of resources, for which small differences in designs can produce large cost savings.

Air Pollution↗

PubMed2004

Quantification of variability and uncertainty for air toxic emission inventories with censored emission factor data.

Probabilistic emission inventories were developed for urban air toxic emissions of benzene, formaldehyde, chromium, and arsenic for the example of Houston. Variability and uncertainty in emission factors were quantified for 71-97% of total emissions, depending upon the pollutant and data availability. Parametric distributions for interunit variability were fit using maximum likelihood estimation (MLE), and uncertainty in mean emission factors was estimated using parametric bootstrap simulation. For data sets containing one or more nondetected values, empirical bootstrap simulation was used to randomly sample detection limits for nondetected values and observations for sample values, and parametric distributions for variability were fit using MLE estimators for censored data. The goodness-of-fit for censored data was evaluated by comparison of cumulative distributions of bootstrap confidence intervals and empirical data. The emission inventory 95% uncertainty ranges are as small as -25% to +42% for chromium to as large as -75% to +224% for arsenic with correlated surrogates. Uncertainty was dominated by only a few source categories. Recommendations are made for future improvements to the analysis.

Air Pollutants↗

PubMed2004

Propagation of uncertainty in hourly utility NOx emissions through a photochemical grid air quality model: a case study for the Charlotte, NC, modeling domain.

One of the major hypothesized sources of uncertainties in air quality model inputs is the emission inventory. A probabilistic hourly NOx emission inventory for 32 units of nine coal-fired power plants in the Charlotte domain for the year 1995 was propagated through the Multiscale Air Quality Simulation Platform (MAQSIP). The inventory was developed using time series techniques. Time series for a 4-d episode were simulated and propagated through the air quality model 50 times in order to represent the ranges of uncertainty in hourly emissions and predicted ozone levels. Intra-unit autocorrelation in emissions and inter-unit dependence were accounted for. The range of uncertainty in predicted ozone was greater when inter-unit dependence was included as compared to when units were treated as statistically independent. Uncertainties in maximum ozone hourly or 8-h concentrations at a specific location could be attributed to a specific power plant based upon regression analysis. Out of 3969 grid cells in the modeling domain, there were 43 and 1654 grid cells with a probability greater than 0.9 of exceeding a 1-h 120 ppb standard and an 8-h 80 ppb standard, respectively. The time series of predicted ozone values had similar autocorrelation as compared to monitored data. The implications of these results for air quality management are addressed.

Air Pollutants↗

PubMed2004

Quantification of highway vehicle emissions hot spots based upon on-board measurements.

The purpose of this study is to demonstrate a methodology for quantification of high emissions hot spots along roadways based upon real-world, on-road vehicle emissions measurements. An emissions hot spot is defined as a fixed location along a corridor in which the peak emissions are statistically significantly greater by more than a factor of 2 than the average emissions for free-flow or near free-flow conditions on the corridor. A portable instrument was used to measure on-road tailpipe emissions of carbon monoxide, nitric oxide, hydrocarbons, and carbon dioxide on a second-by-second basis during actual driving. Measurements were made for seven vehicles deployed on two primary arterial corridors. The ratio of average emissions at hot spots to the average emissions observed during a trip was as high as 25 for carbon monoxide, 5 for nitric oxide, and 3 for hydrocarbons. The relationships between hot spots and explanatory variables were investigated using graphical and statistical methods. Average speed, average acceleration, standard deviation of speed, percent of time spent in cruise mode, minimum speed, maximum acceleration, and maximum power have statistically significant associations with vehicle emissions and influence emissions hot spots. For example, stop-and-go traffic conditions that result in sudden changes in speed, and traffic patterns with high accelerations, are shown to generate hot spots. The implications of this work for future model development and applications to environmental management are discussed.

Air Pollutants↗

PubMed2004

Development of probabilistic emission inventories of air toxics for Jacksonville, Florida, USA.

Probabilistic emission inventories were developed for 1,3-butadiene, mercury (Hg), arsenic (As), benzene, formaldehyde, and lead for Jacksonville, FL. To quantify inter-unit variability in empirical emission factor data, the Maximum Likelihood Estimation (MLE) method or the Method of Matching Moments was used to fit parametric distributions. For data sets that contain nondetected measurements, a method based upon MLE was used for parameter estimation. To quantify the uncertainty in urban air toxic emission factors, parametric bootstrap simulation and empirical bootstrap simulation were applied to uncensored and censored data, respectively. The probabilistic emission inventories were developed based on the product of the uncertainties in the emission factors and in the activity factors. The uncertainties in the urban air toxics emission inventories range from as small as -25 to +30% for Hg to as large as -83 to +243% for As. The key sources of uncertainty in the emission inventory for each toxic are identified based upon sensitivity analysis. Typically, uncertainty in the inventory of a given pollutant can be attributed primarily to a small number of source categories. Priorities for improving the inventories and for refining the probabilistic analysis are discussed.

Air Pollutants↗

PubMed2004

Quantification of variability and uncertainty using mixture distributions: evaluation of sample size, mixing weights, and separation between components.

Variability is the heterogeneity of values within a population. Uncertainty refers to lack of knowledge regarding the true value of a quantity. Mixture distributions have the potential to improve the goodness of fit to data sets not adequately described by a single parametric distribution. Uncertainty due to random sampling error in statistics of interests can be estimated based upon bootstrap simulation. In order to evaluate the robustness of using mixture distribution as a basis for estimating both variability and uncertainty, 108 synthetic data sets generated from selected population mixture log-normal distributions were investigated, and properties of variability and uncertainty estimates were evaluated with respect to variation in sample size, mixing weight, and separation between components of mixtures. Furthermore, mixture distributions were compared with single-component distributions. Findings include: (1). mixing weight influences the stability of variability and uncertainty estimates; (2). bootstrap simulation results tend to be more stable for larger sample sizes; (3). when two components are well separated, the stability of bootstrap simulation is improved; however, a larger degree of uncertainty arises regarding the percentiles coinciding with the separated region; (4). when two components are not well separated, a single distribution may often be a better choice because it has fewer parameters and better numerical stability; and (5). dependencies exist in sampling distributions of parameters of mixtures and are influenced by the amount of separation between the components. An emission factor case study based upon NO(x) emissions from coal-fired tangential boilers is used to illustrate the application of the approach.

Journal Article↗

PubMed2004

Comparison of sensitivity analysis methods based on applications to a food safety risk assessment model.

Sensitivity analysis (SA) methods are a valuable tool for identifying critical control points (CCPs), which is one of the important steps in the hazard analysis and CCP approach that is used to ensure safe food. There are many SA methods used across various disciplines. Furthermore, food safety process risk models pose challenges because they often are highly nonlinear, contain thresholds, and have discrete inputs. Therefore, it is useful to compare and evaluate SA methods based upon applications to an example food safety risk model. Ten SA methods were applied to a draft Vibrio parahaemolyticus (Vp) risk assessment model developed by the Food and Drug Administration. The model was modified so that all inputs were independent. Rankings of key inputs from different methods were compared. Inputs such as water temperature, number of oysters per meal, and the distributional assumption for the unrefrigerated time were the most important inputs, whereas time on water, fraction of pathogenic Vp, and the distributional assumption for the weight of oysters were the least important inputs. Most of the methods gave a similar ranking of key inputs even though the methods differed in terms of being graphical, mathematical, or statistical, accounting for individual effects or joint effect of inputs, and being model dependent or model independent. A key recommendation is that methods be further compared by application on different and more complex food safety models. Model independent methods, such as ANOVA, mutual information index, and scatter plots, are expected to be more robust than others evaluated.

Analysis of Variance↗

PubMed2004

Quantification of variability and uncertainty for censored data sets and application to air toxic emission factors.

Many environmental data sets, such as for air toxic emission factors, contain several values reported only as below detection limit. Such data sets are referred to as "censored." Typical approaches to dealing with the censored data sets include replacing censored values with arbitrary values of zero, one-half of the detection limit, or the detection limit. Here, an approach to quantification of the variability and uncertainty of censored data sets is demonstrated. Empirical bootstrap simulation is used to simulate censored bootstrap samples from the original data. Maximum likelihood estimation (MLE) is used to fit parametric probability distributions to each bootstrap sample, thereby specifying alternative estimates of the unknown population distribution of the censored data sets. Sampling distributions for uncertainty in statistics such as the mean, median, and percentile are calculated. The robustness of the method was tested by application to different degrees of censoring, sample sizes, coefficients of variation, and numbers of detection limits. Lognormal, gamma, and Weibull distributions were evaluated. The reliability of using this method to estimate the mean is evaluated by averaging the best estimated means of 20 cases for small sample size of 20. The confidence intervals for distribution percentiles estimated with bootstrap/MLE method compared favorably to results obtained with the nonparametric Kaplan-Meier method. The bootstrap/MLE method is illustrated via an application to an empirical air toxic emission factor data set.

Air↗

PubMed2003

On-road measurement of vehicle tailpipe emissions using a portable instrument.

A study design procedure was developed and demonstrated for the deployment of portable onboard tailpipe emissions measurement systems for selected highway vehicles fueled by gasoline and E85 (a blend of 85% ethanol and 15% gasoline). Data collection, screening, processing, and analysis protocols were developed to assure data quality and to provide insights regarding quantification of real-world intravehicle variability in hot-stabilized emissions. Onboard systems provide representative real-world emissions measurements; however, onboard field studies are challenged by the observable but uncontrollable nature of traffic flow and ambient conditions. By characterizing intravehicle variability based on repeated data collection runs with the same driver/vehicle/route combinations, this study establishes the ability to develop stable modal emissions rates for idle, acceleration, cruise, and deceleration even in the face of uncontrollable external factors. For example, a consistent finding is that average emissions during acceleration are typically 5 times greater than during idle for hydrocarbons and carbon dioxide and 10 times greater for nitric oxide and carbon monoxide. A statistical method for comparing on-road emissions of different drivers is presented. Onboard data demonstrate the importance of accounting for the episodic nature of real-world emissions to help develop appropriate traffic and air quality management strategies.

Air Pollutants↗

PubMed2003

Quantification of hourly variability in NO(x) emissions for baseload coal-fired power plants.

The objectives of this paper are to (1) quantify variability in hourly utility oxides of nitrogen (NO(x)) emission factors, activity factors, and total emissions; (2) investigate the autocorrelation structure and evaluate cyclic effects at short and long scales of the time series of total hourly emissions; (3) compare emissions for the ozone (O3) season versus the entire year to identify seasonal differences, if any; and (4) evaluate interannual variability. Continuous emissions monitoring data were analyzed for 1995 and 1998 for 32 units from nine baseload power plants in the Charlotte, NC, airshed. Unit emissions have a strong 24-hr cycle attributable primarily to the capacity factor. Typical ranges of the coefficient of variation for emissions at a given hour of the day were from 0.2 to 0.45. Little difference was found when comparing weekend emissions with the entire week or when comparing the O3 season with the entire year. There were substantial differences in the mean and standard deviation of emissions when comparing 1995 and 1998 data, indicative of the effect of retrofits of control technology during the intervening time. The wide range of variability and its autocorrelation should be accounted for when developing probabilistic utility emission inventories for analysis of near-term future episodes.

Air Pollutants↗