Continuous Emission Monitoring System Research Articles

Benefits of current and future policies on emissions of China's coal-fired power sector indicated by continuous emission monitoring

Emission inventories are critical to understanding the sources of air pollutants, but have high uncertainties in China due in part to insufficient on-site measurements. In this study, we developed a method of examining, screening and applying online data from the country's improving continuous emission monitoring systems (CEMS) to reevaluate a “bottom-up” emission inventory of China's coal-fired power sector. The benefits of China's current national emission standards and ultra-low emission policy for the sector were quantified assuming their full implementation. The derived national average emission factors of SO2, NOx and particulate matter (PM) were 1.00, 1.00 and 0.25 kg/t-coal respectively for 2015 based on CEMS data, smaller than those of previous studies that may not fully recognize improved emission controls in recent years. The annual emissions of SO2, NOx and PM from the sector were recalculated at 1321, 1430 and 334 Gg respectively, 75%, 63% and 76% smaller than our estimates based on a previous approach without the benefit of CEMS data. The results imply that online measurement with proper data screening can better track the recent progress of emission controls. The emission intensity (the ratio of emissions to economic output) of Northwest China was larger than that of other regions, attributed mainly to its less intensive economy and industry. Transmission of electricity to more-developed eastern provinces raised the energy consumption and emissions of less-developed regions. Judged by 95 percentiles of flue-gas concentrations measured by CEMS, most power plants met the current national emission standards in 2015 except for those in Northwest and Northeast China, while plants that met the ultra-low emission policy were much scarcer. National SO2, NOx and PM emissions would further decline by 68%, 55% and 81% respectively if the ultra-low emission policy can be strictly implemented, implying the great potential of the policy for emission abatement.

Reliability analysis of continuous emission monitoring system with common cause failure based on fuzzy FMECA and Bayesian networks

Continuous emission monitoring system (CEMS) has been widely used in many engineering fields. Common cause failures (CCFs) have remarkable effects on the system reliability of CEMS, because of shared work conditions and dependent failures for different components. A method for reliability evaluation of CEMS with CCFs is proposed based on fuzzy Failure Mode Effects and Criticality Analysis (FMECA) as well as Bayesian network (BN). By introducing the system composition and function principles of CEMS, the CEMS failure mode is clearly defined and the weak components of the system are identified. According to the hazard ranking of the CEMS failure modes, the places where reliability improvement or preventive maintenance should be implemented are found out. Then, BN-based reliability model of the sampling system, which is the weakest subsystem of CEMS, is constructed according to the results of a fault tree analysis. The behavior of CCF is further incorporated, and the α-factor model is used to evaluate the probability of CCF. Lastly, a numerical example is used to illustrate the proposed method. A comparison between the proposed method and the one without considering CCF is carried out. The result demonstrates that the proposed method has better reliability assessment accuracy for the CEMS with CCF than the one without considering CCF.

Dehumidification performance of the Nafion tube for SO2 at low concentration

Under the new situation of low emission standard for coal-fired power plants, improving sample dehumidification technology is the key to accurate monitoring of ultra-low concentration SO2 flue gas. As a new dehumidification technology, Nafion membrane had been considered as a promising alternative to pretreatment for Continuous Emission Monitoring System (CEMS). The factors affecting the dehumidification efficiency of Nafion tube and the SO2 preservation rate were investigated by simulating the flue gas experiment. The results showed that the gas flow rate and SO2 concentration had no significant effect on Nafion dehumidification efficiency and preservation rate. The humidity increased correspondingly to the dehumidification rate, which increased by 0.3%, but when the humidity increased from 15% to 30%, the preservation rate decreased by 1%, and when the SO2 concentration increases, the effect decreased.

The impact of geometric parameters of a S-type Pitot tube on the flow velocity measurements for greenhouse gas emission monitoring

In the monitoring of greenhouse gas emission from industrial smoke-stacks, the most common device used to measure the stack gas velocity is the S-type Pitot tube in South Korea, which is used to estimate the volumetric flow rate by what is termed the Continuous Emission Monitoring System (CEMS). The S-type Pitot tube installed in the stack is inevitably affected during velocity measurements by velocity changes, yaw and pitch angle misalignments due to the harsh environments. Various geometries of the S-type Pitot tube can affect the characteristics of the S-type Pitot tube coefficients, including the degree of sensitivity to velocity changes and yaw and pitch yaw angle misalignments. Nevertheless, there are no detailed guidelines pertaining to the S-type Pitot tube geometry considering accurate and reliable measurements in the ISO, EPA and ASTM international standards. In the present study, S-type Pitot tubes with various geometric parameters, in this case the distance between the impact and wake orifices and the bending angle of the orifices, were manufactured by a 3D printer. Wind tunnel experiments were conducted in the Korea Research Institute of Standards and Science (KRISS) air speed standard system to determine the optimal geometry of an S-type Pitot tube for the accuracy velocity measurements in actual smokestacks which undergo velocity changes and yaw and pitch angle misalignments. Particle image velocimetry was also used to understand the flow phenomena around an S-type Pitot tube under various geometric and misalignment conditions by means of qualitative visualization. The results indicate that S-type Pitot tubes with a long effective length have more constant distributions of the S-type Pitot tube coefficients when the velocity changes from 2 m/s to 15 m/s. The error indexes for yaw angle misalignments show that S-type Pitot tube models with large effective lengths are less affected by yaw angle misalignments. The S-type Pitot tube coefficients were mostly insensitive to the both positive and negative pitch angle misalignments regardless of the velocity and geometry of the various models tested.

Abnormal Data Screening Method of Thermal Power Based on BP Neural Network Algorithm

As China has been the biggest carbon dioxide emissions country in the world and taken electric power industry as a breakthrough to build carbon market, it is necessary to promote the use of carbon continuous emission monitoring system to improve the accuracy of carbon emissions accounting. Carbon emission model is built to convert CO2 flow rate and concentration to mass. The CO2 emissions per gas inflow is a relatively stable value with a random fluctuation and will be affected by gas inflow, which is called per CO2 emissions. Based on the BP neural network algorithm, we take per CO2 emissions, gas inflow, gas turbine load, steam turbine load as input layers to obtain a real value prediction interval of per CO2 emission, and conduct carbon emission abnormal data screening. In the case study, it is proved that the neural network algorithm give an efficient way to screen big CO2 emission abnormal data.

Impact of Control Measures on Nitrogen Oxides, Sulfur Dioxide and Particulate Matter Emissions from Coal-Fired Power Plants in Anhui Province, China

Anhui is one of the highest provincial emitters of air pollutants in China due to its large coal consumption in coal-fired plants. In this study, the total emissions of nitrogen oxides (NOx), sulfur dioxide (SO2) and particulate matter (PM) from coal-fired power plants in Anhui were investigated to assess the impact of control measures on the atmospheric emissions based upon continuous emission monitoring systems (CEMS). The total NOx, SO2 and PM emissions significantly decreased from 2013 to 2017 and they were estimated at 24.5 kt, 14.8 kt and 3.0 kt in 2017, respectively. The emission reductions of approximately 79.0%, 70.1% and 81.2% were achieved in 2017 compared with a 2013 baseline, respectively, due to the application of high-efficiency emission control measures, including the desulfurization, denitration and dust-removing devices and selective catalytic reduction (SCR). The NOx, SO2 and PM emission intensities were 0.125 g kWh−1, 0.076 g kWh−1 and 0.015 g kWh−1 in 2017, respectively, which were lower than the average of national coal-fired units. The coal-fired units with ≥600 MW generated 80.6% of the total electricity amount while they were estimated to account for 70.5% of total NOx, 70.1% of total SO2 and 71.9% of total PM. Their seasonal emissions showed a significant correlation to the power generation with the maximum correlation found in summer (July and August) and winter (January and December). The major regional contributors are the cities along the Huai River Basin and Yangtze River Basin, such as Huainan, Huaibei, Tongling, Maanshan and Wuhu, and the highest emission occurred in Huainan, accounting for approximately 26–40% of total emission from all the power plants. Our results indicated that the application of desulfurization, denitration and dust-removing devices has played an important role in controlling air pollutant emissions from coal-fired power plants.

A Dynamic Dust Emission Allocation Method and Holiday Profiles Applied to Emission Processing for Improving Air Quality Model Performance

ABSTRACT An accurate depiction of temporal and spatial variations in emissions is critical in simulating air quality with atmospheric chemical transport models. Most emission processing systems typically use prescribed profiles to allocate anthropogenic emissions based on the assumptions that the temporal variance is periodical and spatial variance is time-independent. However, these assumptions are not applicable to emission sources heavily influenced by meteorology and holiday activity. In this study, we improved the temporal and spatial allocation of anthropogenic emissions by, first of all, developing a dynamic allocation method for fugitive dust that uses the negative correlation between dust emissions and precipitation, based on hourly rainfall data generated by the Weather Research and Forecasting model. Second, we employed holiday-specific profiles that were established using continuous emission monitoring system and traffic flow monitoring data to allocate power plant and on-road mobile emissions during the Spring Festival period, when human activity differs considerably from that of non-holiday periods. The new dynamic allocation method and holiday-specific profiles were applied to emissions in the Pearl River Delta region as a demonstration. Validated using a chemical transport model, this method obviously improved the model performance for periods with rainfall, with the normalized mean bias (NMB) decreasing by 6.27% for PM10 (particulate matter with a diameter of ≤ 10 µm) and 4.33% for PM2.5 (particulate matter with a diameter of ≤ 2.5 µm). The holiday simulations revealed that the holiday-specific profiles mitigated overestimations of NO2, SO2, and PM10 for the Spring Festival period, with the NMBs decreasing by 37.95%, 18.56%, and 20.83%, respectively. Hence, refining the allocation of emissions improved model simulation and air quality forecasting.

Efficient Detection of Environmental Violators: A Big Data Approach

The detection of environmental violators is critical to the long-term adoption of sustainability in supply chain management. However, there exist manufacturing facilities that report false environmental monitoring data, thereby seriously hampering governments’ efforts to identify true offenders and to properly intervene. We integrate waste gas data from the world’s largest Continuous Emission Monitoring System (CEMS) with a publicly available Violation and Punishment Dataset to build prediction models for the identification of environmental violators. We utilize and create innovative machine learning approaches to overcome analytical challenges associated with empirical data. First, we use a feature engineering approach to generate features from the raw, and possibly fraudulent, reporting data. This overcomes the challenges associated with low fidelity, irregularity, and the presence of extreme values in the raw dataset. Second, while building prediction models, we develop new approaches to positive and unlabeled learning to overcome the challenges posed by sparsity and mislabeled data. Our prediction model achieves satisfactory results in a related field test. Our study develops new techniques for big data analytics, which greatly improve the efficiency and effectiveness in the detection of environmental violators and enhance operational outcomes of environmental protection agencies. This research is a joint effort between academia and practitioners, as evidenced by the participation of the Ministry of Ecology and Environment of People’s Republic of China. The Ministry kindly granted us direct data access, as well as opportunities to interview Subject Matter Experts at the Ministry, which led to research insights incorporated in this manuscript. Our research findings have global implications, as CEMS devices are universally adopted to monitor waste gas emissions.

Investigation of aerosol and gas emissions from a coal-fired power plant under various operating conditions

ABSTRACTThe concentrations of fine particles and selected gas pollutants in the flue gas entering the stack were measured under several common operation modes in an operating coal power plant producing electricity. Particle size distributions in a diameter range from 10 nm to 20 μm were measured by a scanning mobility particle sizer (SMPS), and the flue gas temperature and concentrations of CO2 and SO2 were monitored by a continuous emission monitoring system (CEMS). During the test campaign, five plant operating modes were studied: soot blowing, bypass of flue-gas desulfurization (FGD), reheat burner operating at 0% (turned off), 27%, and 42% (normal condition) of its full capacity. For wet and dry aerosols, the measured mode sizes were both around 40 nm, but remarkable differences were observed in the number concentrations (#/cm3, count per square centimeter). A prototype photoionizer enhanced electrostatic precipitator (ESP) showed improved removal efficiency of wet particles at voltages above +11.0 kV. Soot blowing and FGD bypass both increased the total particle number concentration in the flue gas. The temperature was slightly increased by the FGD bypass mode and varied significantly as the rating of reheat burner changed. The variations of CO2 and SO2 emissions showed correlations with the trend of total particle number concentration possibly due to the transitions between gas and particle phases. The results are useful in developing coal-fired power plant operation strategies to control fine particle emissions and developing amine-based CO2 capture technologies without operating and environmental concerns associated with volatile amine emissions.Implications: The measurement of the fine particle size distributions in the exhaust gas under several common operating conditions of a coal-fired power plant revealed different response relations between aerosol number concentration and the operating condition. A photo-ionizer enhanced ESP was demonstrated to capture fine particles with higher efficiency compared to conventional ESPs, and the removal efficiency increased with the applied voltage. The characteristic information of aerosols and main gaseous pollutants in the exhaust gas is extremely important for developing and deploying CO2 scrubbers, whose amine emissions and operating effectiveness depends greatly on the upstream concentrations of fine particles, SO2, from the power plant.

Enhanced PEMS Performance and Regulatory Compliance through Machine Learning

<p><em>Modeling technologies</em><em> can pro</em><em>vide strong support to existing emission management systems, by means of what is known as a Predictive Emission Monitoring System (PEMS). These systems do not measure emissions through any hardware device, but use computer models to predict emission concentrations on the ground of process data (e.g., fuel flow, load) and ambient parameters (e.g., air temperature, relative humidity). They actually represent a relevant application arena for the so-called Inferential Sensor technology which has quickly proved to be invaluable in modern process automation and optimization strategies (Qin et al., 1997; Kadlec et al., 2009). While lots of applications demonstrate that software systems provide accuracy comparable to that of hardware-based Continuous Emission Monitoring Systems (CEMS), virtual analyzers are able to offer additional features and capabilities which are often not properly considered by end-users. Depending on local regulations and constraints, PEMS can be exploited either as primary source of emission monitoring or as a back-up of hardware-based CEMS able to validate analyzers’ readings and extend their service factor. PEMS consistency (and therefore its acceptance from environmental authorities) is directly linked to the accuracy and reliability of each parameter used as input of the models. While environmental authorities are steadily opening to PEMS, it is easy to foresee that major recognition and acceptance will be driven by extending PEMS robustness in front of possible sensor failures. Providing reliable instrument fail-over procedures is the main objective of Sensor Validation (SV) strategies. In this work, the capabilities of a class of machine learning algorithms will be presented, showing the results based on tests performed actual field data gathered at a fluid catalytic cracking unit.</em></p>

Measuring of industrial emission parameters

The current system for estimating an air pollution level in the Russian Federation is based on the calculation of the maximum concentrations of pollutants reduced to a 20-minute time interval. The maximum ground level concentration of a pollutant, Cmax, mg·m−3, is calculated by the formula, which includes both reference values and parameters determined by the results of instrumental measurements. Measuring off-gas parameters from the air pollution source requires simultaneous monitoring of all parameters for 20 minutes. Deviation from this requirement and carrying out measurements not simultaneously can lead to errors in the estimation of emission efficiency. Continuous Emission Monitoring System can cope with such a task the significance of which increases after approval of the Federal Law 7-FZ “On Environmental Protection”. These systems allow getting the real picture of pollutant emissions into the atmosphere, and in case of exceeding parameters, immediately make decisions in order to reduce the negative impact on the environment.

A comparison of plume rise algorithms to stack plume measurements in the Athabasca oil sands

Abstract. Plume rise parameterizations calculate the rise of pollutant plumes due to effluent buoyancy and exit momentum. Some form of these parameterizations is used by most air quality models. In this paper, the performance of the commonly used Briggs plume rise algorithm was extensively evaluated, through a comparison of the algorithm's results when driven by meteorological observations with direct observations of plume heights in the Athabasca oil sands region. The observations were carried out as part of the Canada-Alberta Joint Oil Sands Monitoring Plan in August and September of 2013. Wind and temperature data used to drive the algorithm were measured in the region of emissions from various platforms, including two meteorological towers, a radio-acoustic profiler, and a research aircraft. Other meteorological variables used to drive the algorithm include friction velocity, boundary-layer height, and the Obukhov length. Stack emissions and flow parameter information reported by continuous emissions monitoring systems (CEMSs) were used to drive the plume rise algorithm. The calculated plume heights were then compared to interpolated aircraft SO2 measurements, in order to evaluate the algorithm's prediction for plume rise. We demonstrate that the Briggs algorithm, when driven by ambient observations, significantly underestimated plume rise for these sources, with more than 50 % of the predicted plume heights falling below half the observed values from this analysis. With the inclusion of the effects of effluent momentum, the choice of different forms of parameterizations, and the use of different stability classification systems, this essential finding remains unchanged. In all cases, approximately 50 % or more of the predicted plume heights fall below half the observed values. These results are in contrast to numerous plume rise measurement studies published between 1968 and 1993. We note that the observations used to drive the algorithms imply the potential presence of significant spatial heterogeneity in meteorological conditions; we examine the potential impact of this heterogeneity in our companion paper (Akingunola et al., 2018). It is suggested that further study using long-term in situ measurements with currently available technologies is warranted to investigate this discrepancy, and that wherever possible, meteorological input variables are observed in the immediate vicinity of the emitting stacks.

Experimental research on selective adsorption of gaseous mercury (II) over SiO2, TiO2 and γ-Al2O3

Solid selective adsorption to partition mercury speciation (Hg0, Hg2+) in flue gas is known as the innovative technology in developing solid agents application in online measurement of mercury speciation by continuous emission monitoring system (Hg-CEMS). Porous materials SiO2, TiO2 and γ-Al2O3 were chosen as the selective adsorbents and the systematical experiments were conducted in a fixed-bed reactor. Adsorption tests indicated that Hg0 was scarcely adsorbed by these three porous adsorbents while Hg2+ exhibited an apparent adsorption behavior. γ-Al2O3 was verified to be efficient in Hg2+/Hg0 separation because of its high HgCl2 adsorption capacity and SO2 anti-interference ability. The intrinsic mechanisms of HgCl2 selective adsorption and SO2 competition over γ-Al2O3 surface sites were further investigated. Temperature programmed desorption (TPD) analyses demonstrated that the weak interaction between Hg0 and γ-Al2O3 surface was ascribed to a physisorption manner while the adsorption of HgCl2 was characterized chemical bonding. Hydroxyl (Al-OH), Lewis Aln+-Bronsted On− pairs and the coordinatively unsaturated oxygen (O2− ions) were deduced to be effective basic sites for the chemisorption of HgCl2 on γ-Al2O3. Simultaneously, the competitive adsorption between SO2 and HgCl2 in O2− ions was confirmed to be responsible for the increased breakthrough of HgCl2 over γ-Al2O3 layer with the existence of SO2.

Emissions preparation and analysis for multiscale air quality modeling over the Athabasca Oil Sands Region of Alberta, Canada

Abstract. The oil sands (OS) of Alberta, Canada, which are classified as unconventional oil, are the third-largest oil reserves in the world. We describe here a 6-year effort to improve the emissions data used for air quality (AQ) modeling of the roughly 100 km × 100 km oil extraction and processing industrial complex operating in the Athabasca Oil Sands Region (AOSR) of northeastern Alberta. This paper reviews the national, provincial, and sub-provincial emissions inventories that were available during the three phases of the study, supplemented by hourly SO2 and NOx emissions and stack characteristics for larger point sources measured by a continuous emission monitoring system (CEMS), as well as daily reports of SO2 from one AOSR facility for a 1-week period during a 2013 field campaign when the facility experienced upset conditions. Next it describes the creation of several detailed hybrid emissions inventories and the generation of model-ready emissions input files for the Global Environmental Multiscale–Modelling Air quality and CHemistry (GEM-MACH) AQ modeling system that were used during the 2013 field study and for various post-campaign GEM-MACH sensitivity studies, in particular for a high-resolution model domain with 2.5 km grid spacing covering much of western Canada and centered over the AOSR. Lastly, it compares inventory-based bottom-up emissions with aircraft-observation-based top-down emissions estimates. Results show that emissions values obtained from different data sources can differ significantly, such as a possible 10-fold difference in PM2.5 emissions and approximately 40 and 20 % differences for total VOC (volatile organic compound) and SO2 emissions. A novel emissions-processing approach was also employed to allocate emissions spatially within six large AOSR mining facilities in order to address the urban-scale spatial extent of the facilities and the high-resolution 2.5 km model grid. Gridded facility- and process-specific spatial surrogate fields that were generated using spatial information from GIS (geographic information system) shapefiles and satellite images were used to allocate non-smokestack emissions for each facility to multiple grid cells instead of treating these emissions as point sources and allocating them to a single grid cell as is normally done. Facility- and process-specific temporal profiles and VOC speciation profiles were also developed. The pre-2013 vegetation and land-use databases normally used to estimate biogenic emissions and meteorological surface properties were modified to account for the rapid change in land use in the study area due to marked, year-by-year changes in surface mining activities, including the 2013 opening of a new mine. Lastly, mercury emissions data were also processed in addition to the seven criteria-air-contaminant (CAC) species (NOx, VOC, SO2, NH3, CO, PM2.5, and PM10) to support AOSR mercury modeling activities. Six GEM-MACH modeling papers in this special issue used some of these new sets of emissions and land-use input files.

Quantifying coal power plant responses to tighter SO2 emissions standards in China

We evaluate the impact of China's new air pollution standards on sulfur dioxide (SO2) emissions by comparing newly available data from Continuous Emissions Monitoring Systems (CEMS) at coal power plants with satellite measures. First, we show that following the July 2014 deadline for implementing tighter emissions standards, stack concentrations of SO2 reported by CEMS declined by 13.9%. Second, on average the ratios of the declines of SO2 measures in the satellite data and the CEMS data are about 0.5. However, the degree of correspondence between the two data sources varies by policy stringency, with weak correspondence found in key regions facing the toughest new limits. Third, large plants achieved compliance earlier than small (typically) power and heat cogeneration plants. To achieve continued air quality improvement, our results suggest a need for increased scrutiny of emissions data quality and monitoring practices and clear long-term targets.

Deposition Loss of Particles in the Sampling Lines of Continuous Emission Monitoring System (CEMS) in Coal-fired Power Plants

Due to the severe air pollution situation, more stringent regulations on pollutant emissions are being promulgated in China, necessitating more accurate and reliable monitoring of particulate matter (PM) emission in coal-fired power plants. In this work, we study the sampling loss of continuous emission monitoring system (CEMS) under different conditions by numerically solving the particle transport equation in the sampling line. With a high Reynolds number, the particle deposition loss is higher than in conventional laminar sampling and increases with the Reynolds number when the plant load changes. The temperature difference between the hot sampling gas and the pipe wall has a great effect on the sampling loss of PM10. A small temperature difference of 2 K, which is very likely to exist even with thick thermal insulation, will increase the deposition velocity of PM1-2.5 by ten times. The surface roughness, from either the pipe itself or deposited particles, also enhances the deposition loss by partly shifting the capture boundary to a higher diffusivity region. Combining all the possible factors, the loss ratio of 10-µm particles can reach 69% after 0.2 s and 95% after 0.5 s. The loss ratios of 2.5- and 1-µm particles are much lower but also reach 4.1% and 7.9%, respectively, after 1 s, which cannot be neglected when high accuracy monitoring is needed.

Status and Needs Research for On-line Monitoring of VOCs Emissions from Stationary Sources

Based on atmospheric volatile organic compounds (VOCs) pollution control requirements during the twelfth-five year plan and the current status of monitoring and management at home and abroad, instrumental architecture and technical characteristics of continuous emission monitoring systems (CEMS) for VOCs emission from stationary sources are investigated and researched. Technological development needs of VOCs emission on-line monitoring techniques for stationary sources in china are proposed from the system sampling pretreatment technology and analytical measurement techniques.

Ispitivanje ispravnosti automatskih mernih sistema za kontinualno merenje emisije u TE Nikola Tesla A na bloku A6 u periodu od 2012. do 2016. godine

U termoelektrani „Nikola Tesla A“, u okviru tzv. CEMS (Continuous Emission Monitoring Systems) projekta, tokom 2011. i 2012. godine u potpunosti je uvedeno kontinualno merenje emisije zagađujućih materija u vazduh. Nakon ugradnje opreme, izvršene su inicijalne kalibracije i validacije automatskih mernih sistema (AMS) – obezbeđenje poverenja nivoa 2 tj. QAL2 procedura (Quality Assurance Level 2) prema zahtevima standarda SRPS EN 14181. Tokom narednih godina sprovođeni su godišnji kontrolni testovi (Annual Surveillance Test, AST), radi provere varijabilnosti i validnosti kalibracionih funkcija. U radu je prikazana prva kalibracija i validacija automatskih mernih sistema (QAL2) u TE „Nikola Tesla A“ na bloku A6 za parametre CO, NOX, SO2 i praškste materije, sa osvrtom na godišnje kontrolne testove (AST) u periodu od 2012. do 2016. godine. Ispitivanja su sprovedena od strane Laboratorije za zaštitu životne i radne sredine Rudarskog instituta d.o.o. Beograd.

SO2 Concentration retrieval algorithm using EMD and PCA with application in CEMS based on UV-DOAS

A concentration retrieval algorithm is presented, which is applied to in-situ measurement of the concentration of SO2 based on Ultraviolet-visible Differential Optical Absorption Spectroscopy (UV-DOAS). The algorithm uses the standard deviation of the differential absorbance to represents the gas concentration. Empirical Mode Decomposition (EMD) and Principle Component Analysis (PCA) are used to process the differential absorbance spectrum to improve the SNR. In the algorithm, the basis data for the concentration retrieval of SO2 is the combination of the EMD & PCA processing result and the standard deviation of the differential absorbance to improve the SNR. The algorithm is applied for a Continuous Emission Monitoring System (CEMS) whose optical path length is 0.3 m. The nonlinear calibration for the system was executed. The relative error of the retrieving concentration is less than 4.68%. The measuring result is −2.05 mg/m3 during the concentration of SO2 is zero. For testing the effect of in-situ application, the contrast experiment was implemented in one coil-fired power plant. And the result proof that the algorithm has high stability and durability.

Actual issues of introduction of continuous emission monitoring systems for control of negative impact of TPP to atmospheric air

Over the past 3 years there have been significant changes in Russian environmental legislation related to the transition to technological regulation based on the principles of the best available technologies (BAT). These changes also imply control and accounting of the harmful impact of industrial enterprises on the environment. Therefore, a mandatory requirement for equipping automatic continuous emission monitoring systems (ACEMS) is established for all large TPPs. For a successful practical solution of the problem of introducing such systems in the whole country there is an urgent need to develop the governing regulatory document for the design and operation of systems for continuous monitoring of TPP emissions into the air, allowing within reasonable limits to unify these systems for their work with the state data fund of state environmental monitoring and make easier the process of their implementation at operating facilities for industrial enterprises. Based on the large amount of research in the field of creation of ACEMS, which conducted in National Research University “MPEI”, a draft guidance document was developed, which includes the following regulatory provisions: goals and objectives of ACEMS, the stages of their introduction rules of carrying out preliminary inspection of energy facilities, requirements to develop technical specifications, general requirements for the operation of ACEMS, requirements to the structure and elements of ACEMS, recommendations on selection of places of measuring equipment installation, rules for execution, commissioning and acceptance testing, continuous measurement method, method for determination of the current gross and specific emissions. The draft guidance document, developed by the National Research University “MPEI”, formed the basis of the Preliminary national standards PNST 187-2017 “Automatic systems for continuous control and metering of contaminants emissions from thermal electric power stations into the atmospheric air. General requirements”. [1]