Emission Monitoring System Research Articles

The Study of Carbon Emissions Monitoring System for Inland River Ships

In order to study the carbon emission characteristics of inland ships, a carbon emission monitoring system is designed. By real-time monitoring of oil consumption and the method of tree of decision in ship discharge, it achieves the monitor of carbon emission. The recursive least square method and Wilcoxon symbols average rank test are used to improve the precision of the carbon emissions monitoring. Finally, through carbon testing experiment, it demonstrates the feasibility of the carbon emission monitoring system, and finds that the main factor affecting the accuracy of the system is carbon oxidation rate.

Reduced emissions of CO2, NOx, and SO2 from U.S. power plants owing to switch from coal to natural gas with combined cycle technology

AbstractSince 1997, an increasing fraction of electric power has been generated from natural gas in the United States. Here we use data from continuous emission monitoring systems (CEMS), which measure emissions at the stack of most U.S. electric power generation units, to investigate how this switch affected the emissions of CO2, NOx, and SO2. Per unit of energy produced, natural gas power plants equipped with combined cycle technology emit on an average 44% of the CO2 compared with coal power plants. As a result of the increased use of natural gas, CO2 emissions from U.S. fossil‐fuel power plants were 23% lower in 2012 than they would have been if coal had continued to provide the same fraction of electric power as in 1997. In addition, natural gas power plants with combined cycle technology emit less NOx and far less SO2 per unit of energy produced than coal power plants. Therefore, the increased use of natural gas has led to emission reductions of NOx (40%) and SO2 (44%), in addition to those obtained from the implementation of emission control systems on coal power plants. These benefits to air quality and climate should be weighed against the increase in emissions of methane, volatile organic compounds, and other trace gases that are associated with the production, processing, storage, and transport of natural gas.

Development of acoustic emission monitoring system for the safety of geotechnical structures

미소파괴음과 미소진동을 이용한 지반구조물 계측은 구조물 내부의 미시적 변형이나 파괴거동을 음향과 진동으로 계측하는 방법으로, 계측대상의 대규모 파괴에 앞서 이들의 발생량과 발생빈도가 급격히 증가하는 특성을 활용하여 파괴의 사전징후를 파악할 수 있다. 이 방법의 실제 적용을 위해서는 고사양의 센서, 고속의 신호획득장치 그리고 운영프로그램으로 구성된 계측시스템이 요구된다. 근래 국내기술에 의한 현장계측용 미소파괴음과 미소진동 계측시스템이 개발되었다. 특히 계측된 신호를 분석하고 해석할 수 있는 다양한 기능의 운영프로그램을 개발하고 선진외국 제품과의 상호비교를 통해 효용성을 평가하였다. 본 보고에서는 개발된 미소파괴음 계측시스템의 구성과 특징 등에 대해 소개하고, 향후 해결과제와 나아갈 방향에 대하여 논의하였다. The monitoring method of geotechnical structures using acoustic emission(AE) and microseismicity(MS) is to detect the microscopic deformation and fracture behavior in the inner structures by measuring induced acoustics and vibrations. It can identify a pre-indication of failure by taking advantage of the characteristics that the amount and occurrence rates of AE and MS increase rapidly prior to large scale destruction of the target structures. The monitoring system consisting of high-quality sensors, high-speed data acquisition device and the operation program is required for the practical application of this method. Recently, the AE and MS monitoring systems have been localized. In particular, the developed operation software which can analyze and interpret the measured signals was demonstrated through a number of applications to domestic fields. This report introduces the configuration and features of developed monitoring system, then the challenges and future direction of AE monitoring in geotechnical structures are discussed.

Linking Data and Converging Systems for Smarter Urban Services: Two Cases of U-City Service in Korea

To overcome many difficulties in sustaining the satisfying quality of life for a large population1, urban spaces are evolving toward more efficient spaces by combination with ICTs (information and communication technologies) and raising the possibility to provide improved urban services which can enrich the quality of life of the citizen. In many cities over the world, a good number of public and private services in transportation, environment, urban safety from crime or disaster, health and other fields are designed and implemented with IT infrastructure. In spite of the expectation of cost-effective urban services based on the linkage between data and systems, obstructions in administrative and technical domains have made it difficult to be implemented. In this study, two advanced ICTs based urban services, which were developed by linkage and convergence of urban information and systems of Korea were introduced and analysed. The structural design of system convergence and data sharing scheme of Carbon Emission Monitoring System (CEMS) in Sejong City uses UIS (Urban Information System for local authorities), GIS data and other data provided by the public agencies, such as Korea Meteorological Administration for the monitoring and analysing the characteristics of the energy consumption of household2. Another system, Urban Integrated CCTV Control System (ICCS) in Anyang City, shows integrated CCTV networks for crime prevention, traffic control and public facility management to provide extended urban services, such as disaster prevention, police investigation and others. Qualitative and Quantitative effects analyses with technical and policy directions were suggested for the development and improvement of future urban services for a liveable city.

가구 탄소모니터링 시스템에 의한 탄소배출특성 - 세종시 첫마을을 대상으로 -

Korean Government has developed Sejong City as a new administration city. This city of future was planned and designed toward one of the most eco-friendly city on the basis of ICTs. To attain this object, a carbon emission monitoring system (CEMS) was designed and installed as a part of u-city service which provides various information anytime and anywhere to enrich the people's quality of life. In this paper, at first, the structure and functions of CEMS are introduced. This system is consist of 5 parts - data collection from user and linked public DBs, transforming data into meaningful information for the policy makers, system-user interfacing via statistical tables and graphs, and system maintenance. This system can be operated by the citizen participation through whole the process. With the help of GIS map and graphic interface, statistics of monitored data for both citizen and decision maker provided and after feed-back, they have affected on the behaviour of citizen's energy consumption and related policy as well. By the CEMS, energy consumption data of 124 agreed households were collected during 9 months in 2012. Electricity, gas and water consumption were remote-metered automatically by the system and analysed. This showed that more than 85% of CO2 emission is rely on electricity usage. Furthermore, number of family members and size of house influences on the emission of CO2 by each household together with the life-style of the occupants. Electricity and water consumption showed the seasonal factor while gas consumption represents the number of family members. Even this paper has limitations caused by 9 months of data collection, it shows the policy directions to reduce the emission of CO2 focusing on the house size and number of family members of each households. With the result of this research, life-style of the generation of dwellers should be investigated and the CO2 emission characteristics of other housing type as well for the data building for future policy making.

Technical Study on Flue Gas Continuous Emission Monitoring Systems for Power Plant

Monitoring emissions of air pollution sources has become a complex and contentious issue. Regulations for continuous monitoring of these sources have expanded dramatically in scope over the past years. To demonstrate compliance with emission limits, and as required by Pollution Control Board, the power plant shall have a Continuous Emission Monitoring (CEM) System installed in each flue of the stack for each unit. This paper will mainly design a set of CEM system for flue gas from power plant. The measurement shall conform to EPA regulations.

Research of Method for Multi-Component Gas Monitoring Based on Non-Dispersive Infrared Technology

The methods for Multi-component gas monitoring combination with multi-windows infrared sensor can be to detect and analyze conventional gas such as CO2 in the atmosphere based on the non-dispersive infrared (NDIR) detection technology, and can adapt to multi-task and multi-parameter measurement, convert the monitoring object to complete the different monitoring tasks at any time. First, the principle of the NDIR technology was described in this paper, and proposed a targeted circuit design and key technology solutions. The preliminary experimental analysis was shown that the test result is valid. NDIR infrared gas detection method as rapid and accurate gas analysis techniques can be generally used in the detection of continuous emission monitoring system as well as motor vehicle exhaust.

Research on Continuous Emission Monitoring System in Thermal Power Plant

The system aims to collect reliable emission data in real time, provide the operator with real-time emission parameters, and guide the optimization of unit operation and control of pollutants in flue gas emissions. One kind of continuous emission monitoring system is designed based on PROFIBUS-DP protocol in power plant, the architecture of system is presented and the principle is described in detail.

Performance Analysis of the Domestic and Imported Portable SO<sub>2</sub> Gas Analyzers for Stationary Pollution Source

As an air pollutant, the emission and monitoring of SO2 gas have been widely concerned. In the paper, the characteristics of domestic and imported portable SO2 gas analyzers based on nondispersive infrared (NDIR) absorption technique have been tested. The performance of the analyzers has been discussed detailedly. The results reveal that domestic portable SO2 gas analyzer can provide excellent performance like imported instrument. In additon, all the analyzers have been in comparison with the continuous emission monitoring system (CEMS) for monitoring the SO2 emission of a power plant. The results demonstrate that domestic portable SO2 gas analyzers can be used to evaluate and supervise CEMS operating situation. Based on the comparison results and domestic demand, we also give some suggestion for the future technique research and instrument development.

Research on Particulate Material Continuous Emission Monitoring Systems Calibration

According to the current national standard and guidelines, the paper evaluates the quality assurance procedures and requirements for the calibration of particulate matter continuous emission monitoring systems (PM-CEMS). The effect of the experimental variables such as flue gas conditions, concentrations range, additional data consistency, correlation coefficient, calibration model, confidence intervals and tolerance interval on the reliability of CEMS are presented.

Research on Performance of a Mercury Continuous Emission Monitoring System

The environmental pollution caused by coal-fired mercury emission has received worldwide attention, because of hazards for human health. Its significant to monitor mercury emission accurately in flue gas for mercury pollution control. In this paper, a detailed description of a mercury continuous emission monitoring system (Hg CEMS) built for the detection of mercury in flue gas is presented. The key performance parameters of the Hg CEMS were discussed in detail. The Hg CEMS was installed in a power plant to monitor mercury emission in flue gas continuously. Results from a series of laboratory performance tests and field application prove that the Hg CEMS is reliable and capable for mercury detection in flue gas. The Hg CEMS can meet the requirement of mercury monitoring in China well.

Design of Acoustic Emission Monitoring System for Defects of Hull Plate

An acoustic emission (AE) monitoring system has been developed for micro cracks of hull plate, which has a human-machine interface (HMI) designed by Labview, with functions of signal acquisition, storage and replay, key parameters extraction. With the powerful signal processing capabilities of MATLAB, defects can be rapidly and accurately located.

Influence of Unloading Rate on the Strainburst Characteristics of Beishan Granite Under True-Triaxial Unloading Conditions

Rockburst is a sudden and violent failure of rocks and it often occurs in hard rocks in highly stressed ground. Strainburst is classified as one type of rockburst and it often occurs in rocks near or at the excavation boundary. Deep insight into the strainburst phenomenon is essential for safe underground construction at depth. In this paper, an experimental laboratory study on the strainburst behavior of Beishan granite is presented. Based on in-situ stress measurement data from the Beishan area in China, a series of tests under different unloading rates were performed to investigate the strainburst process using a true-triaxial strainburst test system which was equipped with an acoustic emission (AE) monitoring system. In addition, a high-speed video camera was used to record and visualize the initiation and ejection of rock fragments as well as the sudden dynamic failure (strainburst) of the test samples. AE characteristics associated with the cumulative energy and frequency–amplitude distributions were analyzed. Characteristics of the microscopic structure of a fragment generated from one test were observed using a scanning electron microscope. The experimental results indicate that the degree of violence during failure and the associated AE energy release in the strainburst process are dependent on the unloading rate. When the unloading rate is high, the rock is prone to strainburst. On the other hand, as the unloading rate decreases, the failure mode changes from strainburst to spalling. In addition, the cumulative AE energy is not sensitive to unloading rates greater than 0.05 MPa/s. When the unloading rate is less than 0.05 MPa/s, the cumulative AE energy shows a marked decreasing trend during rock failure.

Influence of SO3 in flue gas on electrostatic precipitability of high-alumina coal fly ash from a power plant in China

Abstract In China, particles in flue gasses emitted by coal-fired power plants are mainly cleaned using electrostatic precipitators (ESPs). However, some power plants have experienced decrease in the collection efficiency of ESPs after burning low-sulfur and high-alumina coals. In order to make ESPs collect effectively the PM 2.5 in the flue gas before and after SO 3 -flue gas conditioning (FGC), the resistivity, size distribution, chemical composition, and specific surface of the fly ash sampled from Tuoketuo Power Plant ESPs were analyzed using the portable dust electrical resistivity test instrument, Bahco centrifuge, constant law, and the COULTERTM SA 3100TM specific surface area analyzer. Simultaneously, the SO 2 emission was monitored by a continuous emission monitoring system. The results showed that, when the SO 3 content increased by 34.3 mg/m 3 (12 × 10 − 6 volume fraction), the resistivity of the fly ash in the flue gas decreased by approximately two orders of magnitude. In addition, the surface tension of the fly ash diminished, the adhesive force and size increased, and the specific surface area decreased. The efficiency of the ESPs increased from 96.7% to 99.8%. The SO 2 emissions and the content of SO 3 in the fly ash have not changed appreciably before and after SO 3 -FGC. Therefore, SO 3 -FGC is an effective method of improving the efficiency of ESPs in collecting high-alumina coal fly ash.

RBF neural network inferential sensor for process emission monitoring

Inferential sensing, or soft sensing, gained popularity in recent years as an alternative to continuous emission monitoring systems because of its simplicity, reliability, and cost effectiveness as compared to analogous hardware sensors. In this paper we address the problem of NOx emission using a model of furnace of an industrial boiler, and propose a neural network structure for high performance prediction of NOx as well as O2. The studied boiler is 160MW, gas fired with natural gas, water-tube boiler, having two vertically aligned burners. The boiler model is a 3D problem that involves turbulence, combustion, radiation in addition to NOx modeling. The 3D computational fluid dynamic model is developed using Fluent simulation package. The model provides calculations of the 3D temperature distribution as well as the rate of formation of the NOx pollutant, enabling a better understanding on how and where NOx are produced. The boiler was simulated under various operating conditions. The generated data is then used for initial development and assessment of neural network soft sensors for emission prediction based on the conventional process variable measurements. The performance of the proposed soft sensor is then evaluated using actual data from an industrial boiler. The developed soft sensor achieves comparable accuracy to the continuous emission monitor analyzer, however, with substantial reduction in the cost of equipment and maintenance.

Application of the selected physical methods in biological research

This paper deals with the application of acoustic emission (AE), which is a part of the non-destructive methods, currently having an extensive application. This method is used for measuring the internal defects of materials. AE has a high potential in further research and development to extend the application of this method even in the field of process engineering. For that matter, it is the most elaborate acoustic emission monitoring in laboratory conditions with regard to external stimuli. The aim of the project is to apply the acoustic emission recording the activity of bees in different seasons. The mission is to apply a new perspective on the behavior of colonies by means of acoustic emission, which collects a sound propagation in the material. Vibration is one of the integral part of communication in the community. Sensing colonies with the support of this method is used for understanding of colonies biological behavior to stimuli clutches, colony development etc. Simulating conditions supported by acoustic emission monitoring system the illustrate colonies activity. Collected information will be used to represent a comprehensive view of the life cycle and behavior of honey bees (Apis mellifera). Use of information about the activities of bees gives a comprehensive perspective on using of acoustic emission in the field of biological research.

An Application of Emission Monitoring System Based on Real-time Traffic Monitoring

An Application of Emission Monitoring System Based on Real-time Traffic Monitoring

Dielectric properties of MSWI bottom ash for non-invasive monitoring of moisture

The dielectric procperties of MSWI bottom ash as a function of volumetric water content (VWC) are reported in this paper. The objective was to aid the development of microwave based non-invasive emission monitoring and control system for various bottom ash applications. The dielectric measurements were made, on a 1.5-year-old bottom ash, with an electrical network analyzer in microwave range (300MHz-1.5GHz). The VWC of the samples ranged between 0.05 and 0.40m(3) m(-3). The relationship between the dielectric permittivity and the VWC was modeled with an empirical model and a physically based Birchak model (BM). The results showed that a linear relationship existed between the permittivity and the VWC at higher water contents (>0.25m(3) m(-3)). However, at lower water contents (<0.25m(3) m(-3)), the relationship between the permittivity and the WVC was affected by the composition of the bottom ash. The permittivity measurement, with the current method, was not affected by high salt concentrations (10 and 20 dS/m). The empirical model, as compared to BM, provided the best fit between the actual and the predicted water content. The root mean square error (RMSE) values were 0.008-0.010 and 0.06-0.09m(3) m(-3) for the empirical and the Birchak model, respectively.

A custom acoustic emission monitoring system for harsh environments: application to freezing-induced damage in alpine rock walls

Abstract. We present a custom acoustic emission (AE) monitoring system designed to perform long-term measurements on high-alpine rock walls. AE monitoring is a common technique for characterizing damage evolution in solid materials. The system is based on a two-channel AE sensor node (AE-node) integrated into a wireless sensor network (WSN) customized for operation in harsh environments. This wireless architecture offers flexibility in the deployment of AE-nodes at any position of the rock wall that needs to be monitored, within a range of a few hundred meters from a core station connected to the internet. The system achieves near real-time data delivery and allows the user to remotely control the AE detection threshold. In order to protect AE sensors and capture acoustic signals from specific depths of the rock wall, a special casing was developed. The monitoring system is completed by two probes that measure rock temperature and liquid water content, both probes being also integrated into the WSN. We report a first deployment of the monitoring system on a rock wall at Jungfraujoch, 3500 m a.s.l., Switzerland. While this first deployment of the monitoring system aims to support fundamental research on processes that damage rock under cold climate, the system could serve a number of other applications, including rock fall hazard surveillance or structural monitoring of concrete structures.

Comparative analysis of artificial neural networks and dynamic models as virtual sensors

This paper presents a comparison of predictive models for the estimation of engine power and tailpipe emissions for a 4kW gasoline scooter. This study forms a benchmark toward establishing an online emissions control and monitoring system to bring the emissions to within specific limits. Three emissions predictive models were investigated in this study; direct and series artificial neural network (ANN) models and a MATLAB dynamic model. The direct models takes variables lambda, throttle position, engine and vehicle speed to predict the engine power and the emissions CO, CO2 and HC. The series model first takes the mentioned input to predict the engine power and consequently using the engine power as the fifth input to predict the emissions. For the ANN models, two multilayered networks were compared and analyzed; the backpropagation (BP) and optimization layer-by-layer (OLL) algorithms. The predictive accuracy for each algorithm were compared and it was found that the OLL network is the most accurate with a maximum mean relative error (MRE) of 1.78% and 1.38% for the direct and series predictive model respectively. Comparative results showed that the series neural network model gives the most accurate predictions, with MRE of 0.63% and 0.47% for the engine power and emissions respectively. The series neural network model can be seen as generic virtual power and emissions sensors, substituting costly and cumbersome hardware. Simple obtainable process parameters together with the series neural network will contribute immensely in control and tuning of emissions for real-time vehicular applications.