Certification Experience with Extractive Emission Monitoring Systems

2009 ◽  
pp. 96-96-11
Author(s):  
WL Bonam ◽  
WF Fuller
Keyword(s):  
Monitoring Systems ◽  
Emission Monitoring

Development of Рredictive Еmission Мonitoring Systems

2020 ◽  
Vol 24 (10) ◽  
pp. 43-49
Author(s):  
V.A. Grachev ◽  
D.O. Skobelev ◽  
A.Yu. Popov
Keyword(s):  
Mathematical Models ◽  
Digital Technologies ◽  
Monitoring Systems ◽  
Advantages And Disadvantages ◽  
Industrial Enterprises ◽  
Production Processes ◽  
Emission Monitoring

An overview of world experience in the development and implementation of emission monitoring systems for industrial enterprises, based on mathematical models is presented. The main problems of such systems have been analyzed, their advantages and disadvantages have been revealed. The authors have demonstrated that at Russian enterprises, the introduction of predictive emission monitoring systems was possible at the initial stages of transition to BAT as part of the digital technologies’ integration in production processes.

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

2017 ◽  
Vol 29 (2) ◽  
pp. 18
Author(s):  
Marko Pavlović ◽  
Mihajlo Gigov ◽  
Sandra Petković ◽  
Miroslav Sofrenić
Keyword(s):  
Quality Assurance ◽  
Monitoring Systems ◽  
Continuous Emission ◽  
Emission Monitoring ◽  
Nikola Tesla ◽  
Level 2 ◽  
Continuous Emission Monitoring

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.

scholarly journals Air pollution emissions from Chinese power plants based on the continuous emission monitoring systems network

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ling Tang ◽  
Xiaoda Xue ◽  
Jiabao Qu ◽  
Zhifu Mi ◽  
Xin Bo ◽  
...  
Keyword(s):  
Power Plants ◽  
Thermal Power ◽  
Estimation Method ◽  
Emission Factors ◽  
Monitoring Systems ◽  
Continuous Emission ◽  
Emission Monitoring ◽  
Level Information ◽  
Plant Emissions ◽  
Continuous Emission Monitoring

Abstract To meet the growing electricity demand, China’s power generation sector has become an increasingly large source of air pollutants. Specific control policymaking needs an inventory reflecting the overall, heterogeneous, time-varying features of power plant emissions. Due to the lack of comprehensive real measurements, existing inventories rely on average emission factors that suffer from many assumptions and high uncertainty. This study is the first to develop an inventory of particulate matter (PM), SO2 and NOX emissions from power plants using systematic actual measurements monitored by China’s continuous emission monitoring systems (CEMS) network over 96–98% of the total thermal power capacity. With nationwide, source-level, real-time CEMS-monitored data, this study directly estimates emission factors and absolute emissions, avoiding the use of indirect average emission factors, thereby reducing the level of uncertainty. This dataset provides plant-level information on absolute emissions, fuel uses, generating capacities, geographic locations, etc. The dataset facilitates power emission characterization and clean air policy-making, and the CEMS-based estimation method can be employed by other countries seeking to regulate their power emissions.

Experiences in Long-Term Evaluation of Mercury Emission Monitoring Systems

2008 ◽  
Vol 22 (5) ◽  
pp. 3040-3049 ◽  
Author(s):  
Chin-Min Cheng ◽  
Hung-Ta Lin ◽  
Qiang Wang ◽  
Chien-Wei Chen ◽  
Chia-Wei Wang ◽  
...  
Keyword(s):  
Monitoring Systems ◽  
Mercury Emission ◽  
Emission Monitoring ◽  
Term Evaluation

Research on Particulate Material Continuous Emission Monitoring Systems Calibration

2013 ◽  
Vol 807-809 ◽  
pp. 139-143
Author(s):  
Qiang Wang ◽  
Gang Zhou ◽  
Qi Zhang ◽  
Yang Zhang ◽  
Kai Yang
Keyword(s):  
Flue Gas ◽  
Particulate Material ◽  
Data Consistency ◽  
National Standard ◽  
Monitoring Systems ◽  
Calibration Model ◽  
Tolerance Interval ◽  
Continuous Emission ◽  
Emission Monitoring ◽  
Continuous Emission Monitoring

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.

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

Computation ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 139
Author(s):  
Olga Kochueva ◽  
Kirill Nikolskii
Keyword(s):  
Machine Learning ◽  
Gas Turbines ◽  
Symbolic Regression ◽  
Monitoring Systems ◽  
Nox Emissions ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Model Based ◽  
Emission Monitoring ◽  
Electrical Generation

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.

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