Fugitive Emission Monitoring System Using Land-Based Sensors for Industrial Applications

2021 ◽  
Author(s):  
Arjun Roy ◽  
Senthilkumar Datchanamoorthy ◽  
Sangeeta Nundy ◽  
Bhaskerrao Keely ◽  
Okja Kim ◽  
...  
Keyword(s):  
Time Series Data ◽  
Field Tests ◽  
Weather Conditions ◽  
Analytical Framework ◽  
Industrial Applications ◽  
Optimal Number ◽  
Series Data ◽  
Concentration Data ◽  
Emission Data ◽  
Fugitive Emission

Abstract Metal-oxide based emission detection sensors are typically used for point measurements of hydrocarbon emissions. They are low-cost sensors and can be used for continuous monitoring of emissions. This paper describes an analytical framework that uses time series data from a collection of such sensors deployed at a customer site, along with weather conditions, to detect anomalies in emission data, identify possible emission sources and estimate the leak rate from fugitive emissions. The analytical framework also comprises an optimization module that helps in determining the optimal number of sensors required and their potential location at a customer site. The paper discusses results of the different steps in the analytical framework obtained using concentration data generated using numerical simulations and obtained through controlled leak field tests.

Americans' Social Policy Preferences in the Era of Rising Inequality

2009 ◽  
Vol 7 (3) ◽  
pp. 459-484 ◽  
Author(s):  
Leslie McCall ◽  
Lane Kenworthy
Keyword(s):  
Social Policy ◽  
Income Inequality ◽  
Equal Opportunity ◽  
Time Series Data ◽  
General Social Survey ◽  
Analytical Framework ◽  
Policy Formation ◽  
Policy Preferences ◽  
Alternative Possibilities ◽  
Series Data

Rising income inequality has been a defining trend of the past generation, yet we know little about its impact on social policy formation. We evaluate two dominant views about public opinion on rising inequality: that Americans do not care much about inequality of outcomes, and that a rise in inequality will lead to an increase in demand for government redistribution. Using time series data on views about income inequality and social policy preferences in the 1980s and 1990s from the General Social Survey, we find little support for these views. Instead, Americans do tend to object to inequality and increasingly believe government should act to redress it, but not via traditional redistributive programs. We examine several alternative possibilities and provide a broad analytical framework for reinterpreting social policy preferences in the era of rising inequality. Our evidence suggests that Americans may be unsure or uninformed about how to address rising inequality and thus swayed by contemporaneous debates. However, we also find that Americans favor expanding education spending in response to their increasing concerns about inequality. This suggests that equal opportunity may be more germane than income redistribution to our understanding of the politics of inequality.

scholarly journals Biogeochemical variations at the Porcupine Abyssal Plain Sustained Observatory (PAP-SO) in the northeast Atlantic Ocean, from weekly to inter-annual time scales

2014 ◽  
Vol 11 (8) ◽  
pp. 12415-12439
Author(s):  
S. E. Hartman ◽  
Z.-P. Jiang ◽  
D. Turk ◽  
R. S. Lampitt ◽  
H. Frigstad ◽  
...  
Keyword(s):  
Wind Speed ◽  
Time Series Data ◽  
Mixed Layer Depth ◽  
Co2 Flux ◽  
Abyssal Plain ◽  
Carbon Dioxide Partial Pressure ◽  
Series Data ◽  
Concentration Data ◽  
Northeast Atlantic

Abstract. We present high-resolution autonomous measurements of carbon dioxide partial pressure p(CO2) taken in situ at the Porcupine Abyssal Plain sustained observatory (PAP-SO) in the Northeast Atlantic (49° N, 16.5° W; water depth of 4850 m) for the period 2010 to 2012. Measurements of p(CO2) made at 30 m depth on a sensor frame are compared with other autonomous biogeochemical measurements at that depth (including chlorophyll a-fluorescence and nitrate concentration data) to analyse weekly to seasonal controls on p(CO2) flux in the inter-gyre region of the North Atlantic. Comparisons are also made with in situ regional time-series data from a ship of opportunity and mixed layer depth (MLD) measurements from profiling Argo floats. There is a persistent under saturation of CO2 in surface waters throughout the year which gives rise to a perennial CO2 sink. Comparison with an earlier dataset collected at the site (2003 to 2005) confirms seasonal and inter-annual changes in surface seawater chemistry. There is year-to-year variability in the timing of stratification and deep winter mixing. The 2010 to 2012 period shows an overall increase in p(CO2) values when compared to the 2003–2005 period. This is despite similar surface temperature, wind speed and MLD measurements between the two periods of time. Future work should incorporate daily CO2 flux measurements made using CO2 sensors at 1 m depth and the in situ wind speed data now available from the UK Met Office Buoy.

scholarly journals Construction of Complex Network with Multiple Time Series Relevance

Information ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 202
Author(s):  
Zongwen Huang ◽  
Lingyu Xu ◽  
Lei Wang ◽  
Gaowei Zhang ◽  
Yaya Liu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Weather Conditions ◽  
Series Data ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Optimal Network ◽  
Multiple Variables ◽  
Threshold Network

Multivariate time series data, which comprise a set of ordered observations for multiple variables, are pervasively generated in weather conditions, traffic, financial stocks, etc. Therefore, it is of great significance to analyze the correlation between multiple time series. Financial stocks generate a significant amount of multivariate time series data that can be used to build networks that reflect market behavior. However, traditional commercial complex networks cannot fully utilize the multiple attributes of stocks and redundant filter relationships and reveal a more authentic financial stock market. We propose a fusion similarity of multiple time series and construct a threshold network with similarity. Furthermore, we define the connectivity efficiency to choose the best threshold, establishing a high connectivity efficiency network with the optimal network threshold. By searching the central node in the threshold network, we have found that the network center nodes constructed by our proposed method have a more comprehensive industry coverage than the traditional techniques to build the systems, and this also proves the superiority of this method.

scholarly journals Correlation networks of air particulate matter ($$\hbox {PM}_{2.5}$$): a comparative study

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Dimitrios M. Vlachogiannis ◽  
Yanyan Xu ◽  
Ling Jin ◽  
Marta C. González
Keyword(s):  
Air Quality ◽  
Time Series Data ◽  
Series Data ◽  
Pollution Transport ◽  
Concentration Data ◽  
Significant Drop ◽  
Pm 2.5 ◽  
Correlation Networks ◽  
Spatial Coverage ◽  
Haze Pollution

AbstractOver the last decades, severe haze pollution constitutes a major source of far-reaching environmental and human health problems. The formation, accumulation and diffusion of pollution particles occurs under complex temporal scales and expands throughout a wide spatial coverage. Seeking to understand the transport patterns of haze pollutants in China, we review a proposed framework of time-evolving directed and weighted air quality correlation networks. In this work, we evaluate monitoring stations’ time-series data from China and California, to test the sensitivity of the framework to region size, climate and pollution magnitude across multiple years (2014–2020). We learn that the use of hourly $$\hbox {PM}_{2.5}$$ PM 2.5 concentration data is needed to detect periodicities in the positive and negative correlations of the concentrations. In addition, we show that the standardization of the correlation function method is required to obtain networks with more meaningful links when evaluating the dispersion of a severe haze event at the North China Plain or a wildfire event in California during December 2017. Post COVID-19 outbreak in China, we observe a significant drop in the magnitude of the assigned weights, indicating the improved air quality and the slowed transport of $$\hbox {PM}_{2.5}$$ PM 2.5 due to the lockdown. To identify regions where pollution transport is persistent, we extend the framework, partitioning the dynamic networks and reducing the networks’ complexity through node subsampling. The end result separates the temporal series of $$\hbox {PM}_{2.5}$$ PM 2.5 in set of regions that are similarly affected through the year.

Forecasting Daily Tourism Demand for Tourist Attractions with Big Data: An Ensemble Deep Learning Method

2021 ◽  
pp. 004728752110405
Author(s):  
Jian-Wu Bi ◽  
Chunxiao Li ◽  
Hong Xu ◽  
Hui Li
Keyword(s):  
Big Data ◽  
Web Search ◽  
Time Series Data ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Weather Conditions ◽  
Series Data ◽  
Tourism Demand ◽  
Mountain Area ◽  
Tourist Attractions

Accurate forecasting of daily tourism demand is a meaningful and challenging task, but studies on this issue are scarce. To address this issue, multisource time series data, relating to past tourist volumes, web search information, daily weather conditions, and the dates of public holidays, are selected as the forecasting variables. To fully capture the relationship between these forecasting variables and actual tourism demand automatically, an ensemble of long short-term memory (LSTM) networks is proposed with a correlation-based predictor selection (CPS) algorithm. The effectiveness of the proposed method is verified in daily tourism demand forecasting for the Huangshan Mountain Area, benchmarked against 11 forecasting methods. This study contributes to the literature by (1) introducing the use of big data in daily tourism demand forecasting, (2) proposing an ensemble of LSTM networks for daily tourism demand forecasting, and (3) providing an effective predictor selection algorithm in ensemble learning.

Short-Term Measurements of Household Electricity Demand During Hot Weather in Kuala Lumpur

2017 ◽  
Vol 7 (3) ◽  
pp. 1436
Author(s):  
Nassir Ranjbar ◽  
Sheikh Ahmad Zaki ◽  
Nelidya Md Yusoff ◽  
Fitri Yakub ◽  
Aya Hagishima
Keyword(s):  
Time Series Data ◽  
Electricity Consumption ◽  
Weather Conditions ◽  
Electricity Demand ◽  
Series Data ◽  
Air Conditioner ◽  
Kuala Lumpur ◽  
Short Term ◽  
Minute Interval ◽  
Hot Weather

The aim of this study was to conduct short-term measurements on household electricity demand under hot weather conditions in a residential area in Kuala Lumpur. The measurements included total and air conditioner (AC) electricity consumption of 10 households in an apartment building as well as outdoor air temperatures, which were collected from March to May 2016. Results indicated that the average AC electricity consumption contributed to a major portion of total household electricity consumption, which ranged from 19.4 to 52.3% during the measurement period. Additionally, 1-minute interval time series data indicated household energy consumption more accurately than 30- or 60-minute interval.

scholarly journals Price Setters and Price Takers in the EU Electricity Market, a Comparative Analysis of Household Consumer Prices

2017 ◽  
Vol 6 (1) ◽  
pp. 174
Author(s):  
Aranit Shkurti ◽  
Macit Koc
Keyword(s):  
Electricity Market ◽  
Power Plants ◽  
Time Series Data ◽  
Weather Conditions ◽  
Series Data ◽  
Important Indicator ◽  
Spot Prices ◽  
Consumer Prices ◽  
Yearly Average ◽  
The Eu

The article is concerned with the analysis of the electric power prices at the European spot exchanges, taking in consideration 27 Countries of the Union (excluding UK). The time series data are considering the half yearly average of the countries, as reported by the Eurostat database. The article examines the way spot prices are influenced by power exchanges, based on the overall installed power of more healthier economies. In recent years a growing capacity from renewable sources is pouring in the system, anyway the implementation of renewable energies do not guarantee constant supply to the network as they depend on weather conditions and therefore must still have recourse to conventional generation types - such as gas and coal - which generally have higher operating costs than renewable. An increasing number of Member States have adjusted mechanisms to promote investment in power plants or provided incentives to keep them standing. These public measures may be justified in certain situations but according to recent guidelines, the European Commission has established that the adjustment mechanisms can be in contrast with the legislation on state aid. The identification of these discrepancies is studied in this article through the key characteristics of the price differential for the EU spot markets. The inflation generated from the price adjustments within the EU members can be considered an important indicator of market inefficiency.Key words: electricity spot exchanges, subsidies, price setter, price taker, household consumers.

scholarly journals Domestic Investment and Economic Growth Nexus in Nigeria: A Post Recession View

2019 ◽  
pp. 1-12
Author(s):  
Onochie, Stanley Nwabuisi ◽  
Ozegbe, Azuka Elvis ◽  
Nwani, Stanley Emife
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Capital Expenditure ◽  
Unit Root Test ◽  
Analytical Framework ◽  
Series Data ◽  
Domestic Investment ◽  
Distributed Lag ◽  
Integration Test ◽  
The Impact

This study investigates the impact of domestic investment on economic growth in Nigeria, using annual secondary time series data spanning 37 years from 1981 to 2017 extracted from the CBN statistical bulletin. Real GDP was used to proxy economic growth, while the key explanatory variable is domestic investment with other control variables as capital expenditure, oil export earnings, exchange rate and inflation rate. The study embarked on pre-estimation test such as unit root test and the bounds co-integration test which informed our methodological choice of Autoregressive Distributed Lag (ARDL). The short run and long run estimates show that domestic investment has positive but insignificant impact on economic growth in Nigeria. This finding departs from those of previous writers due to the improved analytical framework employed in this study. On the basis of our findings, the study recommends a compulsory individual and national savings to boost the level of domestic investment in the country so as to achieve the much desired economic growth and development.

scholarly journals A Decomposition-Ensemble Approach with Denoising Strategy for PM2.5 Concentration Forecasting

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Guangyuan Xing ◽  
Er-long Zhao ◽  
Chengyuan Zhang ◽  
Jing Wu
Keyword(s):  
Time Series Data ◽  
Learning Algorithm ◽  
Hybrid Approach ◽  
Original Data ◽  
Series Data ◽  
Noise Elimination ◽  
Concentration Data ◽  
Ensemble Approach ◽  
Novel Approach ◽  
Pm2.5 Concentration

To enhance the forecasting accuracy for PM2.5 concentrations, a novel decomposition-ensemble approach with denoising strategy is proposed in this study. This novel approach is an improved approach under the effective “denoising, decomposition, and ensemble” framework, especially for nonlinear and nonstationary features of PM2.5 concentration data. In our proposed approach, wavelet denoising approach, as a noise elimination tool, is applied to remove the noise from the original data. Then, variational mode decomposition (VMD) is implemented to decompose the denoised data for producing the components. Next, kernel extreme learning machine (KELM) as a popular machine learning algorithm is employed to forecast all extracted components individually. Finally, these forecasted results are aggregated into an ensemble result as the final forecasting. With hourly PM2.5 concentration data in Xi’an as sample data, the empirical results demonstrate that our proposed hybrid approach significantly performs better than all benchmarks (including single forecasting techniques and similar approaches with other decomposition) in terms of the accuracy. Consequently, the robustness results also indicate that our proposed hybrid approach can be recommended as a promising forecasting tool for capturing and exploring the complicated time series data.

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