Continuous Emission Monitoring System Research Articles

Feasibility of Using Isokinetic Sampling Techniques to Extract a Representative Sample from Processes in the United Kingdom

The requirement to monitor and control particulate emissions from industrial processes using continuous emission monitoring systems (CEMS) has significantly increased over recent years. Under current legislation, CEMS equipment requires calibration against the standard reference method (SRM) using isokinetic sampling and gravimetric analysis under controlled conditions as detailed through BS EN 13284-1 “Stationary source emissions–Determination of low range mass concentration of dust. Manual gravimetric method”. This process includes pumping a known volume of gas through a filter, which is weighed before and after sampling, and the total mass of dust per m3 can then be calculated to output results in mg/m3. As tougher legislation is introduced and stringent emission limit values (ELVs) are imposed on emissions processes in the United Kingdom (UK), the calibration of CEMS is increasingly more difficult due to the reliability of the SRM at low concentrations. The accuracy of results from the SRM and therefore CEMS equipment must be questioned when the uncertainty of measurement is higher than process ELVs. This research analyses data taken from an industrial survey and 21 UK processes where the standard reference method, in accordance with the procedure in BS EN 13284-1 has been used for particulate measurement. Investigating the reliability of isokinetic sampling when used as a method to extract a representative sample from a stack process when used in conjunction with innovative, alternative methods of sample analysis. In processes with particulate emissions <5 mg/m3, 80.7% of the total sample was collected in the rinse, and for processes >5 mg/m3, 56.4% of the sample was collected in the rinse. The data does not suggest any correlation between any of the measured parameters and the percentage of particulate in the rinse, including the stack velocity, isokinetic percentage, sample volume, and total mass concentration.

Risk Assessment of Delay for CEMS and WHRU Installation Project on Plant Shutdown at Central Processing Gas Gundih

This paper addresses a risk assessment of delay for installation of Continuous Emission Monitoring System and Waste Heat Recovery Unit as an effort to fulfil compliance with the Regulation of the State Minister of the Environment Number 13 of 2009 concerning Quality Standards for Emissions from Immovable Sources for Oil and Gas Businesses and Activities. The installation of the unit consists of three steps, pre-shutdown, window shutdown and post shutdown. In accordance with Gas Sale Agreement, that the entire period of normal maintenance work that causes gas to not flow, should not be more than 15 (fifteen) days every year. The planning and implementation of maintenance is very important by utilizing the condition of the window shutdown to avoid material and non-material losses. Reviewing the vulnerability of this project, it is necessary to conduct research that assesses the level of risk of project delays using the ISO 31000:2018 risk management concept approach, through identification of risk variables from several literature studies, risk analysis through Group Discussion Forums, and risk management efforts to reduce risk of delays in the installation project. From the risk identification, 30 risk variables that affect project delays consist of 2 High categories, 3 Medium to High categories, 15 Medium categories, 6 Low to Medium categories and 4 Low categories. The Risk Treatment carried out in the high and Medium to high categories resulted in 9 mandatory RCOs to reduce delays and 1 RCO to speed up the implementation schedule from 19 days to 13 days and earn a profit of $615,000 because the process of selling gas to consumers can be accelerated by 2 days.

An improved hourly-resolved NOx emission inventory for power plants based on continuous emission monitoring system (CEMS) database: A case in Jiangsu, China

High-resolved emission inventories are essential to evaluate air quality and health effects. However, for one of the most polluting industries in China – power plants, emission uncertainties remain high due to the limitation of point measurements. In this study, we established an hourly NOx emission inventory based on the continuous emission monitoring system (CEMS) network of Jiangsu, China, with an integrated new method that coupled unit-level fuel consumption and emission factors. To evaluate the accuracy of our method, we compared the accuracies of our method to three other bottom-up inventories and determined that, our results (CEMS(Improved) for which the normalized mean bias (NMB) was 3.24%) exhibited higher accuracies compared to the other two methods: BASE EIs and CEMS(Traditional), for which the NMB was −38.53% and 294.1%, respectively. The total NOx emissions of power plants in Jiangsu, China, were 43701 tons in 2018, equivalent to 42.2% of that in 2017. 53% of total emissions were contributed by the southern Jiangsu area and super units (>1000 MW) exhibited the largest emissions shares (70%). The daily, monthly, and hourly average emissions demonstrated a “high in winter and summer” pattern. The ratio of the highest daily emissions in the June-July-August period (JJA) to the lowest in the September–November period (SON) was 1.20, which is relatively higher than that of fuel consumption (1.18) and power generation (1.15). The weekly and monthly emissions on hourly trends varied consistently, with peaks at 12:00,16:00,19:00, and 21:00 (except for high-discharge months). The hourly emissions on holidays and weekends were 6.4% and 1.6%, lower than those of workdays and weekends, with emission disparities being the largest in the period of 8:00–10:00. Our new hourly CEMS emission inventory can significantly reduce the uncertainties of emission estimation and better serve policy-making for mitigating power plant emissions in China.

Air pollutant emission and reduction potentials from the sintering process of the iron and steel industry in China in 2017

The iron and steel industry (ISI) is one of the most energy-intensive industries in China, which makes a substantial contribution to the emissions of air pollutants. Among the various manufacturing processes, sintering is the major emitting process, which shares over half of the emissions of sulfur dioxide (SO2), nitrogen oxide (NOx) and particulate matter (PM) for the entire industry. In this study we made a comprehensive evaluation of the air pollutant emissions from the sintering process of China's ISI in 2017 based on the Continuous Emission Monitoring System (CEMS) database and estimated the future reduction potentials. We found that there was a general decreasing trend of emission concentrations in the sintering flue gas in response to the strengthened emission control policies, but the mild increase of the oxygen content in the second half of the year flattened the decreasing trend, indicating the necessity for simultaneous control of the oxygen content in the flue gas. Despite the relative high standard-reaching rates of 90% to the emission concentration limits in GB 28662-2012, the standard-reaching rates to the ultra-low emission standards were only 12%, 40% and 27% for NOx, SO2 and PM respectively, with the lowest value mostly occurred in the western provinces. In 2017, the NOx, SO2 and PM emissions from the sintering process were 378.6 kt, 169.0 kt and 51.9 kt, respectively. If the ultra-low emission standards were met, the corresponding NOx, SO2 and PM emissions would decrease by 69.9%, 52.9%, and 56.4% respectively, illustrating large emission reducing potentials by achieving the ultra-low emission standards.

Daily Emission Patterns of Coal-Fired Power Plants in China Based on Multisource Data Fusion.

Daily emission estimates are essential for tracking the dynamic changes in emission sources. In this work, we estimate daily emissions of coal-fired power plants in China during 2017-2020 by combining information from the unit-based China coal-fired Power plant Emissions Database (CPED) and real-time measurements from continuous emission monitoring systems (CEMS). We develop a step-by-step method to screen outliers and impute missing values for data from CEMS. Then, plant-level daily profiles of flue gas volume and emissions obtained from CEMS are coupled with annual emissions from CPED to derive daily emissions. Reasonable agreement is found between emission variations and available statistics (i.e., monthly power generation and daily coal consumption). Daily power emissions are in the range of 6267-12,994, 0.4-1.3, 6.5-12.0, and 2.5-6.8 Gg for CO2, PM2.5, NO x , and SO2, respectively, with high emissions in winter and summer caused by heating and cooling demand. Our estimates can capture sudden decreases (e.g., those associated with COVID-19 lockdowns and short-term emission controls) or increases (e.g., those related to a drought) in daily power emissions during typical socioeconomic events. We also find that weekly patterns from CEMS exhibit no obvious weekend effect compared to those in previous studies. The daily power emissions will help to improve chemical transport modeling and facilitate policy formulation.

Reduction of HgCl2 to Hg0 in flue gas at high temperature. Part Ⅰ: Influences of oxidative species

The reduction of gaseous oxidized mercury Hg2+(g) to elemental mercury Hg0(g) is a key technology to obtain the total flue gas mercury in the mercury continuous emission monitoring system (Hg-CEMS). Investigation of the reduction of HgCl2(g) to Hg0(g) in the high-temperature decomposition (HTD) was conducted with/without oxidative components in a fixed bed reactor. The chemical reaction path was discussed in-depth by thermodynamic calculation and theoretical analysis. The results show that the higher the temperature, the higher the efficiency of HgCl2(g) reduction. The chlorine species produced in the HTD process inhibit the conversion rate because of re-oxidation on Hg0(g). For eliminating the negative effect of Cl radicals and promoting the reactive sites, a quartz filler with appropriate size demonstrates a good performance for enhancing the decomposition of HgCl2(g). However, the presence of O2 and HCl leads to the complete failure of HgCl2(g) HTD because O2 greatly promotes the combination of Hg0(g) with chlorine. Compared with HCl molecules, Cl atoms are more active in thermodynamic favor in speeding up re-oxidation of Hg0(g). Active Cl atoms are the most crucial factor hindering HTD reaction due to their high reaction initiative and low activation energy.

Nonlinear modeling of industrial boiler NOx emissions

ABSTRACT Pollutant emissions into the atmosphere are recognized as a significant problem in fossil fuel combustion. The pollution emission measurement in industrial boilers is difficult and expensive but fundamental for monitoring and controlling. Frequently continuous emissions monitoring (CEM) system is out of service or useless due to obsolescence, high maintenance cost, and so on or simply is not installed. When a system for measuring pollutant emissions is not available, an alternative method must be employed to get the pollutant emission value. According to the black-box model approach, this article describes the nonlinear modeling of NOx emissions from a utility boiler. Bayesian-Gaussian (BG), multilayer perceptron (MLP), and Volterra polynomial basis functions (VPBF) neural networks are developed for model benchmarking. Experimental data from a utility boiler was acquired in order to model definition and evaluation. The models process three boiler variables oxygen excess, fuel mass flow and flue gas recirculation gates for NOx emission estimation. Models with BG show better performance than models with MLP and VPBF for NOx prediction. Implications: The technology to control NOx emissions generated by combustion operates under strict regulations. In order to reduce NOx emissions, theoretical models of NOx generation have been studied extensively, including nitrogen chemistry and the dynamic flow of gas particles which is very complex. The new technology trends would require the continuous measurement of high precision NOx emissions to achieve further reductions in NOx emissions. Currently, NOx emissions are measured by a Continuous Emission Monitoring system, which turns out to be extremely expensive and difficult to maintain, so alternative low-cost solutions are desirable. Our contribution shows how algorithms based on different artificial intelligence techniques are viable and quality alternatives for the measurement of continuous NOx emissions. The NOx emissions models based on IA algorithms are viable alternatives that have versatility and self-tuning capacity due to the fact that they are based on boiler operation parameters which have valuable information few explored nowadays.

Evaluating the data quality of continuous emissions monitoring systems in China

Starting in 2013, China's key polluting firms have been required to install continuous emissions monitoring systems (CEMS) and to publish the data for real-time oversight and public scrutiny. However, the CEMS data has rarely been used in local environmental law enforcement because its quality is still of great concern. A lack of criteria to evaluate data quality is one of the causes. In this paper, we design a comprehensive analytical framework for evaluating the quality of CEMS data, which includes completeness, accuracy, and authenticity. To demonstrate the applicability of the framework, we build a CEMS dataset for key polluting firms in Henan province from 2017 to 2019 by scraping the CEMS data from a public platform. We then conduct a comprehensive evaluation using our proposed framework. Some data quality issues are identified. About one-third of the firms did not meet official guidelines for data completeness. When comparing the CEMS data with onsite measurement results, we observe statistically significant inconsistencies in about one-fifth of the firms. In addition, we find evidence that some firms might manipulate CEMS data by strategically turning down the CEMS when a pollutant's concentration is expected to exceed the limit. Our framework can be expanded by incorporating more evaluation methods and data. We suggest that government agencies should implement a comprehensive framework to enhance the quality of CEMS data, thereby facilitating its application in law enforcement.

Applying Continuous Self-monitoring Systems for Industrial Stack Gases: International and Russian Experience of Legal Regulation

The approaches to the control of pollutant emissions of industrial enterprises by means of automatic measuring systems are presented. The available international experience of normative regulation of continuous emissions control, including the regulatory policy of the United States of America, the European Union, the People's Republic of China, the Republic of India, the Republic of Korea, is summarized. It was found that almost all countries that regulate air quality on the basis of the concept of the best available techniques have formed lists of pollutants, threshold values of their emissions, lists of industries or types of units, stationary emissions sources which are to be equipped with automatic measuring systems. The Russian system regulation of the negative impact on the environment in terms of the application of continuous emission monitoring systems is considered. Technical features of the organization of automatic control of emissions at different types of stationary sources are analyzed. The cases of the absence of practical possibility of making instrumental measurements of emissions are discussed.

Emission characteristics and impact factors of air pollutants from municipal solid waste incineration in Shanghai, China

The emission of air pollutants from the municipal solid waste (MSW) incineration is one of the major concerns in air pollution. The up-to-date emission situation for Chinese MSW incineration is largely unknown. The emission factors (EFs) are the key parameters to estimate the emissions from MSW incineration. The localized EFs from MSW incineration in Shanghai, China were established using continuous emission monitoring system data from 2017 to 2019. Our results showed that the EFs were 9.80 g t−1 of PM, 46.62 g t−1 of SO2, 812.68 g t−1 of NOx, 25.84 g t−1 of CO, and 17.49 g t−1 of HCl for the period 2017–2019, nearly 1.7–24.2 times lower than those in 2010, implying that the current EFs should be updated to avoid overestimation of MSW emissions in China. Compared with 2010, the emissions of PM, SO2, CO, and HCl in 2019 were significantly reduced by 84%, 69%, 47%, and 72%, respectively, except for NOx with a 106% increase, although the corresponding MSW incineration amount increased by 356%. The current levels of air pollutants from MSW incineration have already met the current national emission standard as well as the stricter standard of the European Union (98.87%–99.91%). Our findings suggest that China should update the current standards of MSW incineration, which can be a benefit for the prevention and control of MSW incineration in the future. It is still challenging to control NOx emissions from MSW incineration for Shanghai and even greater China.

Study on emission factors of FCC flue gas pollutants in petroleum refineries.

Fluid catalytic cracking (FCC) unit is one of the means to lighten heavy oil in refineries, and its regenerated flue gas is also the main source of air pollutants from refinery. However, it is not clear about the type and amount of pollutants discharged from FCC units in China. The emissions of regenerated pollutants in the stack flue gases of three typical FCC units in China were investigated in this study, including a partial regeneration unit without a CO boiler (U1), a partial regeneration unit with a CO boiler (U2), and a full regeneration unit (U3). Different monitoring methods were used to analyze the concentration of sulfur dioxide (SO2) and nitrogen oxides (NOx), and the results showed that Fourier transform infrared spectroscopy (FTIR) monitoring results of SO2 and NOx are approximately 10 times and 5 times larger than those of the continuous emission monitoring system (CEMS) data, respectively. Also, the contents of characteristic pollutants such as NH3, C6H6, HCN, C8H8, C2H4, CH4, and CO were also monitored by FTIR, and the emission factors based on coke burn-off rate and throughput were investigated. The pollutants in U1 exhibited relatively higher contents with the NH3, HCN, and C6H6 of 116.99, 71.94, and 56.41 mg/Nm3 in flue gas, respectively. The emission of regenerated pollutants in U2 and U3 are significantly different from U1. Regeneration processes (including coke properties, operating modes, and presence or absence of CO boilers) affected pollutants' emission factors in varying degrees. At last, reasonable emission factors based on the different FCC regeneration processes contribute to the prediction, assessment, and control for the pollutant emission.

A Real-Time Output–Loss Method for Monitoring Heat Rate for Coal-Fired Power Plants

Abstract This paper describes a real-time performance monitoring method based on PTC 4-2013 for determining instantaneous heat rates for coal-fired power plants. The calculation protocol uses a modified output–loss approach applied to a control volume that closely conforms to the boiler. The largest energy balance term is the heat transfer rate to the steam, which is known accurately in real-time when the plant employs properly calibrated instrumentation. The first-law energy balance also requires a balanced combustion equation which depends on coal composition, which is not known in real-time. A periodic or alert-driven calibration utilizes an ultimate analysis of a carefully collected coal sample and historic plant data obtained during the collection time of the coal sample. This is used to calculate correction factors for the coal mass flowrate, air preheater leakage, and CO2 and SO2 concentrations at the economizer exit derived from continuous emissions monitoring systems (CEMS) measurements performed at that location. The iterative calculations required to determine the coal composition in real-time are presented. The real-time performance algorithm exhibited significant sensitivity associated with measurements of the steam heat transfer rate, which was the dominant term in the overall boiler energy balance. Other input parameters generally yielded a much lower influence on calculated heat rate. It was concluded that for optimal accuracy of the output–loss method the steam and coal mass flowrates must be measured as accurately as possible.

Data Analysis and Symbolic Regression Models for Predicting CO and NOx Emissions from Gas Turbines

Predictive emission monitoring systems (PEMS) are software solutions for the validation and supplementation of costly continuous emission monitoring systems for natural gas electrical generation turbines. The basis of PEMS is that of predictive models trained on past data to estimate emission components. The gas turbine process dataset from the University of California at Irvine open data repository has initiated a challenge of sorts to investigate the quality of models of various machine learning methods to build a model for predicting CO and NOx emissions depending on ambient variables and the parameters of the technological process. The novelty and features of this paper are: (i) a contribution to the study of the features of the open dataset on CO and NOx emissions for gas turbines, which will enable one to more objectively compare different machine learning methods for further research; (ii) for the first time for the CO and NOx emissions, a model based on symbolic regression and a genetic algorithm is presented—the advantage of this being the transparency of the influence of factors and the interpretability of the model; (iii) a new classification model based on the symbolic regression model and fuzzy inference system is proposed. The coefficients of determination of the developed models are: R2=0.83 for NOx emissions, R2=0.89 for CO emissions.

Trends in air pollutant emissions from the sintering process of the iron and steel industry in the Fenwei Plain and surrounding regions in China, 2014–2017

China is the largest iron and steel producer and consumer in the world. The iron and steel manufacture, especially the sintering process, is energy-intensive, and contributes substantially to air pollutant emissions in China. Compared with other regions, the Fenwei Plain, a coal base, has a heavy industry concentration, and high pollutant emission total amount. In addition, urban air pollution has rebounded, and the pollutants concentrations in many cities have increased rather than decreased. In this study, we investigated the inter-annual trends of particulate matter (PM), sulfur dioxide (SO2) and nitrogen oxide (NOx) from the sintering process of iron and steel industry (ISI) in the Fenwei Plain and the surrounding regions in China from 2014 to 2017 based on the Continuous Emission Monitoring System (CEMS). We found that the oxygen content of the flue gas is the key to judge whether the sintering flue gas air pollutant emission concentration can meet the standard. Therefore, we adopted the converted concentration by the reference oxygen content in the final analysis. Overall, the SO2 and PM emission concentrations exhibit a downward trend from 2014 to 2017, in response to the strengthening of the emission control policies and standards in the ISI, whereas the NOx emission concentration did not change significantly during the same period The emission factors (EFs) of PM, NOx and SO2 obtained in this study are lower than previous estimates. In 2017, the SO2 and PM emissions were 27% and 32% lower than the levels in 2014.While NOx was 22% higher than the level in 2014. Our study confirmed the effectiveness of current emission control policies and standards in the iron and steel sector. However, the levels of NOx emissions were still high until 2017, illustrating the urgent need for more advanced emission control technologies to further reduce NOx emissions from the sintering flue gas in China.

Air Pollutant Emission Abatement of the Fossil-Fuel Power Plants by Multiple Control Strategies in Taiwan

This study is an investigation of air pollutant emission abatement in the electricity generation sector from fossil-fuel power plants in Taiwan in 2014 and 2018. PM concentrations are determined by the results of regular tests, while SOx and NOx are determined by continuous emission monitoring systems (CEMS) of flue gas from power plants. The results indicate that electricity generation from fossil-fuel power plants increased by 13.8% from 2014 to 2018. However, emissions of air pollutants from fossil-fuel power plants declined during this period. The results indicate that the annual emissions of SOx, NOx, and PM were 40,826, 59,196, and 5363 tons per year (TPY), respectively, in 2014. The emissions decreased to 30,097 TPY (28% reduction) for SOx, 48,530 TPY (18% reduction) for NOx, and 4496 TPY (16% reduction) for PM in 2018. The ensemble mean values of each air pollutant emission factor also decreased significantly. SOx emissions decreased from 0.2443 to 0.1583 mg/kWh (35% reduction). NOx emissions decreased from 0.3542 to 0.2552 g/kWh (28% reduction). PM emissions decreased from 0.0321 to 0.0236 mg/kWh (26.5% reduction). The results indicated that phasing out of high-pollutant generating units and switching the fuel from coal to natural gas could abate the emissions of SOx and PM, and NOx emissions could be abated by introducing control devices. In addition, new power generation sectors will be constructed and equipped with ultra-low emission control systems to reduce air pollution and create a cleaner and healthier electricity generation system in Taiwan.

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 (VPD) 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 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.

Air quality and health benefits from ultra-low emission control policy indicated by continuous emission monitoring: a case study in the Yangtze River Delta region, China

Abstract. To evaluate the improved emission estimates from online monitoring, we applied the Models-3/CMAQ (Community Multiscale Air Quality) system to simulate the air quality of the Yangtze River Delta (YRD) region using two emission inventories with and without incorporated data from continuous emission monitoring systems (CEMSs) at coal-fired power plants (cases 1 and 2, respectively). The normalized mean biases (NMBs) between the observed and simulated hourly concentrations of SO2, NO2, O3, and PM2.5 in case 2 were −3.1 %, 56.3 %, −19.5 %, and −1.4 %, all smaller in absolute value than those in case 1 at 8.2 %, 68.9 %, −24.6 %, and 7.6 %, respectively. The results indicate that incorporation of CEMS data in the emission inventory reduced the biases between simulation and observation and could better reflect the actual sources of regional air pollution. Based on the CEMS data, the air quality changes and corresponding health impacts were quantified for different implementation levels of China's recent “ultra-low” emission policy. If the coal-fired power sector met the requirement alone (case 3), the differences in the simulated monthly SO2, NO2, O3, and PM2.5 concentrations compared to those of case 2, our base case for policy comparisons, would be less than 7 % for all pollutants. The result implies a minor benefit of ultra-low emission control if implemented in the power sector alone, which is attributed to its limited contribution to the total emissions in the YRD after years of pollution control (11 %, 7 %, and 2 % of SO2, NOX, and primary particle matter (PM) in case 2, respectively). If the ultra-low emission policy was enacted at both power plants and selected industrial sources including boilers, cement, and iron and steel factories (case 4), the simulated SO2, NO2, and PM2.5 concentrations compared to the base case would be 33 %–64 %, 16 %–23 %, and 6 %–22 % lower, respectively, depending on the month (January, April, July, and October 2015). Combining CMAQ and the Integrated Exposure Response (IER) model, we further estimated that 305 deaths and 8744 years of life loss (YLL) attributable to PM2.5 exposure could be avoided with the implementation of the ultra-low emission policy in the power sector in the YRD region. The analogous values would be much higher, at 10 651 deaths and 316 562 YLL avoided, if both power and industrial sectors met the ultra-low emission limits. In order to improve regional air quality and to reduce human health risk effectively, coordinated control of multiple sources should be implemented, and the ultra-low emission policy should be substantially expanded to major emission sources in industries other than the power industry.

Reasons analysis of the large deviation in the NOx emission data of the coal-fired power plant

The problem of the large deviation in the CEMS (Continuous Emission Monitoring System) data of NOx emission from the coal-fired power plant is analyzed. The deviation existed between the data measured at the chimney and the outlet of the denitration system. On-site inspection and testing are carried out. The results show that the uneven distribution of NOx concentration at the denitration outlet caused by the uneven ammonia injection, is the main reason for the difference in the NOx concentration monitoring value between the chimney inlet and the outlet of the denitration system. Several suggestions are given to reduce the deviation between the CEMS monitoring data and the actual average data. In the subsequent operation of the denitration device, AIG ammonia injection optimization and adjustment tests should be carried out regularly. The ammonia injection branch pipes and nozzles should be checked, especially at the shutdown time.

Moisture Removal Techniques for a Continuous Emission Monitoring System: A Review

A continuous emission monitoring system (CEMS) is a well-known tool used to analyze the concentrations of air pollutants from stationary sources. In a CEMS, the presence of a high moisture level in a sample causes a loss of analytes due to artifact formation or absorption. This issue brings about a bias in the measurement data. Thus, moisture removal is an important pretreatment step. Condensation and permeation methods have been widely employed to remove moisture from the CEMS for gaseous compounds. In terms of particulate matter, dilution methods have been applied to reduce the moisture level in the gas stream. Therefore, condensation, permeation, and dilution methods are critically reviewed in this work. The removal efficiencies and recovery rates of analytes are discussed, as well as the advantages and disadvantages of each technique. Furthermore, the suitable applications of each technique are determined. Condensation methods have not been well documented so far, while permeation and dilution methods have been continuously studied. Many types of permeation materials have been developed. The limitations of each method have been overcome over the years. However, the most reliable technique has not yet been discovered.

База данных измерительных систем для экомониторинга выбросов тепловых электрических станций

According to the Russian environmental legislation, all sources of atmospheric pollution at Category I industrial enterprises must necessarily be equipped with automatic systems for monitoring and accounting of marker pollutants emissions. Such industrial facilities include more than 100 thermal power plants. The list of marker pollutants has been established in the relevant industry information and technical reference books on the best available technologies (ITR BAT). The list of marker pollutants monitored at thermal power plants by automatic systems includes nitrogen oxides NOX, sulfur dioxide SO2, carbon monoxide CO, and solid fuel ash. The use of domestically produced measuring equipment for these purposes is set forth as a priority task. However, there is currently no systematic information about gas analysis systems available in the market. The measuring instruments are analyzed, and a database of domestic sampling and non-sampling gas analytical systems is set up, which includes the main methods and means of measuring pollutants emissions, the technical characteristics of which meet the requirements of regulatory documents, and which allows specialists to optimally choose measuring instruments for organizing a continuous emission monitoring system (CEMS) at thermal power plants, taking into account specific conditions and requirements. The Microsoft Access Relational Database Management System (DBMS), which is a flexible database management system, is used to create the database. The developed database of measuring instruments for emission monitoring includes equipment from 33 Russian companies. The DBMS enables the user to organize data in tabular form and produce requests for selecting certain specified parameters. Both basic and additional criteria for selecting measuring instruments can be included in the request form. The developed DBMS takes into account the measured parameters of medium, the measurement method and means, compliance with BAT, measurement ranges and errors, estimated and warranty service life, mass and dimensional characteristics, and cost and complexity of maintenance, all serving for the convenience of selecting and implementing CEMS at thermal power plants. An example of the choice of measuring instruments from the proposed database for monitoring the marker pollutants emissions for the MPEI thermal power plant using the optimal choice algorithm is given.