A Real-Time Method for Determining the Composition and Heating Value of Opportunity Fuel Blends

2012 ◽  
Author(s):  
Vilas Jangale ◽  
Alexei Saveliev ◽  
Serguei Zelepouga ◽  
Vitaly Gnatenko ◽  
John Pratapas
Keyword(s):  
Real Time ◽  
Laminar Flame ◽  
Energy Content ◽  
The United States ◽  
Least Squares Regression ◽  
Heating Value ◽  
Regression Methods ◽  
Fuel Blends ◽  
Pyrolysis Of Biomass ◽  
Methane Number

Engine manufacturers and researchers in the United States are finding growing interest among customers in the use of opportunity fuels such as syngas from the gasification and pyrolysis of biomass and biogas from anaerobic digestion of biomass. Once adequately cleaned, the most challenging issue in utilizing these opportunity fuels in engines is that their compositions can vary from site to site and with time depending on feedstock and process parameters. At present, there are no identified methods that can measure the composition and heating value in real-time. Key fuel properties of interest to the engine designer/researcher such as heating value, laminar flame speed, stoichiometric air to fuel ratio and Methane Number can then be determined. This paper reports on research aimed at developing a real-time method for determining the composition of a variety of opportunity fuels and blends with natural gas. Interfering signals from multiple measurement sources are processed collectively using multivariate regression methods, such as, the principal components regression and partial least squares regression to predict the composition and energy content of the fuel blends. The accuracy of the method is comparable to gas chromatography.

Phenomenological Analysis of the Combustion of Gaseous Fuels to Measure the Fuel's Energy Quality and Availability to Produce Work in Si Engines, Part 2

2021 ◽  
Author(s):  
Juan Pablo Gomez Montoya ◽  
Andres Amell
Keyword(s):  
Compression Ratio ◽  
Laminar Flame ◽  
Flame Temperature ◽  
Electrical Energy ◽  
Gaseous Fuels ◽  
Diesel Fuels ◽  
Fuel Blends ◽  
Si Engines ◽  
High Turbulence ◽  
Methane Number

Abstract A novel methodology is proposed to evaluate fuel´s performance in spark ignition (SI) engines based on the fuel´s energy quality and availability to produce work. Experiments used a diesel engine with a high compression ratio (CR), modified by SI operation, and using interchangeable pistons. The interchangeable pistons allowed for the generation of varying degrees of turbulence during combustion, ranging from middle to high turbulence. The generating efficiency (ηq), and the maximum electrical energy (EEmax) were measured at the knocking threshold (KT). A cooperative fuel research (CFR) engine operating at the KT was also used to measure the methane number (MN), and critical compression ratio (CCR) for gaseous fuels. Fuels with MNs ranging from 37 to 140 were used: two biogases, methane, propane, and five fuel blends of biogas with methane/propane and hydrogen. Results from both engines are linked at the KT to determine correlations between fuel´s physicochemical properties and the knocking phenomenon. Certain correlations between knocking and fuel properties were experimentally determined: energy density (ED), laminar flame speed (SL), adiabatic flame temperature (Tad), heat capacity ratio (γ), and hydrogen/carbon (H/C) ratio. Based on the results, a mathematical methodology for estimating EEmax and ηq in terms of ED, SL, Tad, γ, H/C, and MN is presented. These equations were derived from the classical maximum thermal efficiency for SI engines given by the Otto cycle efficiency (ηOtto). Fuels with MN > 97 got higher EEmax, and ηq than propane, and diesel fuels.

An Alternative Calculation for Methane Number for Lean Burn Spark Ignited Engines Operating on Low Energy Content Gaseous Fuels

2017 ◽  
Author(s):  
Hui Xu ◽  
Axel O. zur Loye ◽  
Robin J. Bremmer
Keyword(s):  
Laminar Flame ◽  
Energy Content ◽  
Flame Temperature ◽  
Relative Difference ◽  
Low Energy ◽  
Si Engine ◽  
Lean Burn ◽  
Combustion Properties ◽  
Si Engines ◽  
Methane Number

Low energy content fuels such as landfill gas can contain a significant amount of diluents like CO2. Critical fuel properties including the lower heating value (LHV) and an anti-knock property, in particular the methane number (MN), should be considered to optimize operation of a spark ignited (SI) engine. The MN has been shown to be a good indicator of knock propensity in stoichiometric SI engines. However, this approach is not always as effective for lean burn SI engines. Two fuels with the same methane number, but with different compositions, may exhibit a different propensity to knocking in an advanced lean burn SI engine. This effect is particularly pronounced when comparing fuels that have different amounts of diluents. In this paper we propose an alternative calculation of the MN, which compensates for the effect of diluents. More specifically, we define a lean burn methane index (LBMI), which is calculated without the diluents. This approach was validated using chemical kinetics modeling. The analysis considered fundamental combustion properties, including laminar flame speed (LFS), adiabatic flame temperature (AFT) and the autoignition interval (AI). For this study, a baseline fuel was selected based on a typical US pipeline natural gas composition. CO2 was then added as a diluent to the baseline fuel to simulate low energy density fuel compositions. Lambda values were selected to provide the same AFT or engine-out NOx. Low energy content fuel were found to have very similar AI values (less than 2% relative difference) to the baseline fuel at the target lambda values. A key result of this study is that the LBMI is a much better predictor of knock propensity than the traditional MN, when comparing fuels with widely varying levels of dilution for advanced lean burn SI engines.

Demonstrating Nutrient Cost Gradients: A Brooklyn Case Study

2014 ◽  
Vol 84 (5-6) ◽  
pp. 244-251 ◽  
Author(s):  
Robert J. Karp ◽  
Gary Wong ◽  
Marguerite Orsi
Keyword(s):  
Low Income ◽  
Energy Content ◽  
Food Selection ◽  
The United States ◽  
United States Department ◽  
Micronutrient Deficiencies ◽  
Caloric Density ◽  
Low Income Families ◽  
The Cost ◽  
The Impact

Abstract. Introduction: Foods dense in micronutrients are generally more expensive than those with higher energy content. These cost-differentials may put low-income families at risk of diminished micronutrient intake. Objectives: We sought to determine differences in the cost for iron, folate, and choline in foods available for purchase in a low-income community when assessed for energy content and serving size. Methods: Sixty-nine foods listed in the menu plans provided by the United States Department of Agriculture (USDA) for low-income families were considered, in 10 domains. The cost and micronutrient content for-energy and per-serving of these foods were determined for the three micronutrients. Exact Kruskal-Wallis tests were used for comparisons of energy costs; Spearman rho tests for comparisons of micronutrient content. Ninety families were interviewed in a pediatric clinic to assess the impact of food cost on food selection. Results: Significant differences between domains were shown for energy density with both cost-for-energy (p < 0.001) and cost-per-serving (p < 0.05) comparisons. All three micronutrient contents were significantly correlated with cost-for-energy (p < 0.01). Both iron and choline contents were significantly correlated with cost-per-serving (p < 0.05). Of the 90 families, 38 (42 %) worried about food costs; 40 (44 %) had chosen foods of high caloric density in response to that fear, and 29 of 40 families experiencing both worry and making such food selection. Conclusion: Adjustments to USDA meal plans using cost-for-energy analysis showed differentials for both energy and micronutrients. These differentials were reduced using cost-per-serving analysis, but were not eliminated. A substantial proportion of low-income families are vulnerable to micronutrient deficiencies.

Potential Urban Rainfall Prediction Measurement System

1984 ◽  
Vol 16 (8-9) ◽  
pp. 349-362 ◽  
Author(s):  
John L Vogel
Keyword(s):  
Real Time ◽  
The United States ◽  
Monitoring Systems ◽  
Climatic Region ◽  
Convective Storm ◽  
Rainfall Prediction ◽  
Water Quality Regulations ◽  
General Data ◽  
Real Time Information ◽  
Time Information

Continued growth of urban regions and more stringent water quality regulations have resulted in an increased need for more real-time information about past, present, and future patterns and intensities of precipitation. Detailed, real-time information about precipitation can be obtained using radar and raingages for monitoring and prediction of precipitation amounts. The philosophy and the requirements for the development of real-time radar prediction-monitoring systems are described for climatic region similar to the Midwest of the united States. General data analysis and interpretation techniques associated with rainfall from convective storm systems are presented.

Detection by real-time quaking-induced conversion (RT-QuIC), ELISA, and IHC of chronic wasting disease prion in lymph nodes from Pennsylvania white-tailed deer with specific PRNP genotypes

2021 ◽  
pp. 104063872110214
Author(s):  
Deepanker Tewari ◽  
David Steward ◽  
Melinda Fasnacht ◽  
Julia Livengood
Keyword(s):  
Real Time ◽  
Disease Susceptibility ◽  
Chronic Wasting Disease ◽  
Low Frequency ◽  
The United States ◽  
Detection Methods ◽  
Spongiform Encephalopathy ◽  
Wasting Disease ◽  
Diagnostic Laboratory ◽  
Highly Sensitive

Chronic wasting disease (CWD) is a prion-mediated, transmissible disease of cervids, including deer ( Odocoileus spp.), which is characterized by spongiform encephalopathy and death of the prion-infected animals. Official surveillance in the United States using immunohistochemistry (IHC) and ELISA entails the laborious collection of lymphoid and/or brainstem tissue after death. New, highly sensitive prion detection methods, such as real-time quaking-induced conversion (RT-QuIC), have shown promise in detecting abnormal prions from both antemortem and postmortem specimens. We compared RT-QuIC with ELISA and IHC for CWD detection utilizing deer retropharyngeal lymph node (RLN) tissues in a diagnostic laboratory setting. The RLNs were collected postmortem from hunter-harvested animals. RT-QuIC showed 100% sensitivity and specificity for 50 deer RLN (35 positive by both IHC and ELISA, 15 negative) included in our study. All deer were also genotyped for PRNP polymorphism. Most deer were homozygous at codons 95, 96, 116, and 226 (QQ/GG/AA/QQ genotype, with frequency 0.86), which are the codons implicated in disease susceptibility. Heterozygosity was noticed in Pennsylvania deer, albeit at a very low frequency, for codons 95GS (0.06) and 96QH (0.08), but deer with these genotypes were still found to be CWD prion-infected.

Real-Time Moisture Ratio Study of Drying Date Fruit Chips Based on On-Line Image Attributes Using kNN and Random Forest Regression Methods

Measurement ◽  
2020 ◽  
pp. 108899
Author(s):  
Madi Keramat-Jahromi ◽  
Seyed Saeid Mohtasebi ◽  
Hossein Mousazadeh ◽  
Mahdi Ghasemi-Varnamkhasri ◽  
Maryam Rahimi-Movassagh
Keyword(s):  
Random Forest ◽  
Real Time ◽  
Moisture Ratio ◽  
Random Forest Regression ◽  
Regression Methods ◽  
Line Image ◽  
Date Fruit ◽  
On Line

scholarly journals Measuring the Effectiveness of Adaptive Random Forest for Handling Concept Drift in Big Data Streams

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 859
Author(s):  
Abdulaziz O. AlQabbany ◽  
Aqil M. Azmi
Keyword(s):  
Big Data ◽  
Random Forest ◽  
Real Time ◽  
Data Streams ◽  
Learning Algorithm ◽  
Concept Drift ◽  
The United States ◽  
Careful Consideration ◽  
Data Sets ◽  
Stream Data

We are living in the age of big data, a majority of which is stream data. The real-time processing of this data requires careful consideration from different perspectives. Concept drift is a change in the data’s underlying distribution, a significant issue, especially when learning from data streams. It requires learners to be adaptive to dynamic changes. Random forest is an ensemble approach that is widely used in classical non-streaming settings of machine learning applications. At the same time, the Adaptive Random Forest (ARF) is a stream learning algorithm that showed promising results in terms of its accuracy and ability to deal with various types of drift. The incoming instances’ continuity allows for their binomial distribution to be approximated to a Poisson(1) distribution. In this study, we propose a mechanism to increase such streaming algorithms’ efficiency by focusing on resampling. Our measure, resampling effectiveness (ρ), fuses the two most essential aspects in online learning; accuracy and execution time. We use six different synthetic data sets, each having a different type of drift, to empirically select the parameter λ of the Poisson distribution that yields the best value for ρ. By comparing the standard ARF with its tuned variations, we show that ARF performance can be enhanced by tackling this important aspect. Finally, we present three case studies from different contexts to test our proposed enhancement method and demonstrate its effectiveness in processing large data sets: (a) Amazon customer reviews (written in English), (b) hotel reviews (in Arabic), and (c) real-time aspect-based sentiment analysis of COVID-19-related tweets in the United States during April 2020. Results indicate that our proposed method of enhancement exhibited considerable improvement in most of the situations.

Abstract 11: mActive: A Blinded, Randomized mHealth Trial Supporting Digital Tracking and Smart Texting for Promotion of Physical Activity

Circulation ◽  
2015 ◽  
Vol 131 (suppl_1) ◽  
Author(s):  
Seth S Martin ◽  
David I Feldman ◽  
Roger S Blumenthal ◽  
Steven R Jones ◽  
Wendy S Post ◽  
...  
Keyword(s):  
Physical Activity ◽  
Phase I ◽  
Real Time ◽  
Phase Ii ◽  
The United States ◽  
Step Count ◽  
Daily Step ◽  
Baseline Activity ◽  
Daily Step Count ◽  
Digital Tracking

Introduction: The recent advent of smartphone-linked wearable pedometers offers a novel opportunity to promote physical activity using mobile health (mHealth) technology. Hypothesis: We hypothesized that digital activity tracking and smart (automated, real-time, personalized) texting would increase physical activity. Methods: mActive (NCT01917812) was a 5-week, blinded, sequentially-randomized, parallel group trial that enrolled patients at an academic preventive cardiovascular center in Baltimore, MD, USA from January 17 th to May 20 th , 2014. Eligible patients were 18-69 year old smartphone users who reported low leisure-time physical activity by a standardized survey. After establishing baseline activity during a 1-week blinded run-in, we randomized 2:1 to unblinded or blinded tracking in phase I (2 weeks), then randomized unblinded participants 1:1 to receive or not receive smart texts in phase II (2 weeks). Smart texts provided automated, personalized, real-time coaching 3 times/day towards a daily goal of 10,000 steps. The primary outcome was change in daily step count. Results: Forty-eight patients (22 women, 26 men) enrolled with a mean (SD) age of 58 (8) years, body mass index of 31 (6), and baseline daily step count of 9670 (4350). The phase I change in activity was non-significantly higher in unblinded participants versus blinded controls by 1024 steps/day (95% CI -580-2628, p=0.21). In phase II, smart text receiving participants increased their daily steps over those not receiving texts by 2534 (1318-3750, p<0.001) and over blinded controls by 3376 (1951-4801, p<0.001). The unblinded-texts group had the highest proportion attaining the 10,000 steps/day goal (p=0.02) (Figure). Conclusions: In present-day adult smartphone users receiving preventive cardiovascular care in the United States, a technologically-integrated mHealth strategy combining digital tracking with automated, personalized, real-time text message coaching resulted in a large short-term increase in physical activity.

scholarly journals Metropolitan governance structure and growth–inequality dynamics in the United States

2018 ◽  
Vol 51 (3) ◽  
pp. 598-616 ◽  
Author(s):  
Jaewoo Cho ◽  
Jae Hong Kim ◽  
Yonsu Kim
Keyword(s):  
Economic Growth ◽  
Metropolitan Areas ◽  
Governance Structure ◽  
The United States ◽  
Governance Structures ◽  
Least Squares Regression ◽  
Metropolitan Governance ◽  
Trade Off ◽  
Using Data ◽  
Nonlinear Fashion

While much scholarly attention has been paid to ways in which metropolitan areas are politically structured and operated to achieve a dual goal, economic growth, and equality, relatively less is known about the complex relationship between metropolitan governance structures and growth–inequality dynamics. This study investigates how and to what extent metropolitan governance structures shape regional economic growth and inequality trajectories using data for 267 US metropolitan areas from 1990 to 2010. Findings from a two-stage least squares regression analysis suggest that economic growth is associated with governance structures in a nonlinear fashion, with relatively more rapid growth rates in both highly centralized and decentralized metropolitan areas. However, these regions are also found to experience a larger increase in income inequality, indicating an important trade-off to be considered carefully in exploring ways to reform existing governance settings. These findings further suggest that the so-called growth–inequality trade-off may exist not only in their direct interactions but through their connections via governance or other variables.

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