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

Gu, Xuan; Li, Baojie; Sun, Chao; Liao, Hong; Zhao, Yongqi; Yang, Yang

doi:10.1016/j.jclepro.2022.133176

Cited by 14 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data acquisition and handling system (DAHS) to collect and store each spot data and carry out the calculations needed to receive the perfect mass emission. Dust and smoke emissions have long been recognized as major atmospheric pollutants [8], primarily because emissions from such smokestacks are visible to observers. There has been a requirement to monitor and quantify these emissions for some time, and various instruments have been marketed worldwide.…”

Section: Introductionmentioning

confidence: 99%

Design of a continuous emission monitoring system (CEMS) for the NH3 analyzer at PT. Pusri Palembang

Sofyan,

Nugrasyah

2023

jocse

View full text Add to dashboard Cite

Green Industry award from the President of the Republic of Indonesia in 2012. PT. Pusri demonstrated success in environmental management by achieving gold proper in 2022. Under the Minister of Environment Regulation (Permen LHK) No. 13 of 2021 concerning SISPEK (Continuous Industrial Monitoring Information System),which contains regulations regarding systems that aim to obtain emission data and information correctly, accurately, and continuously in an integrated manner. Data from monitoring stationary source emissions or stack emissions using continuous measurements or CEMS must be measured and evaluated. This study

show abstract

Section: Introductionmentioning

confidence: 99%

Design of a continuous emission monitoring system (CEMS) for the NH3 analyzer at PT. Pusri Palembang

Sofyan,

Nugrasyah

2023

jocse

View full text Add to dashboard Cite

show abstract

“…In recent years, continuous emissions monitoring systems (CEMS) have been introduced in China, the USA, and other locations, as a means to detect in an integrated manner at the emissions effluent source, on an hourly and/or daily time scale, and contains well-known aspects of quality control and assurance. This platform provides reliable technical information to fundamentally quantify local emissions on a stack-by-stack basis, in order to improve both the temporal resolution and magnitude of emission inventories (Tang et al, 2019;Gu et al, 2022;Chen et al, 2019;Lange et al, 2022).…”

mentioning

confidence: 99%

Remotely Sensed and Surface Measurement Derived Mass-Conserving Inversion of Daily High-Resolution NO_x Emissions and Inferred Combustion Technologies in Energy Rich Northern China

Cohen

Qin

et al. 2023

Preprint

View full text Add to dashboard Cite

Abstract. This work presents a new model free inversion estimation framework using daily TROPOMI NO2 columns and observed fluxes from the continuous emissions monitoring systems (CEMS) to quantify three years of daily-scale emissions of NOx at 0.05°×0.05° over Shanxi Province, a major world-wide energy producing and consuming region. The NOx emissions, day-to-day variability, and uncertainty on a climatological basis are computed to be 1.83, 1.01, and 1.06 Tg per year respectively. The highest emissions are concentrated in the lower Fen River valley, which accounts for 25 % of the area, 52 % of the NOx emissions, and 72 % of CEMS sources. Two major forcing factors (10th to 90th percentile) are horizontal transport distance per day (66–666 km) and lifetime of NOx (6.7–18.4 h). Both of these values are consistent with NOx emissions to both the surface layer and the free troposphere. The third forcing factor, the ratio of NOx / NO2, on a pixel-by-pixel basis is demonstrated to have a significant correlation with the combustion temperature and energy efficiency of large energy consuming sources. Specifically, thermal power plants, cement, and iron and steel companies have a relatively high NOx / NO2 ratio, while coking, industrial boilers, and aluminium oxide show relatively low ratio. Variance maximization is applied to daily TROPOMI NO2 columns identifies three significant modes, and successfully attributes them both spatially and temporally to (a) this work’s computed emissions, (b) remotely sensed TROPOMI UVAI, and (c) computed transport based on TROPOMI NO2.

show abstract

A novel hybrid forecasting approach for NOx emission of coal‐fired boiler combined with CEEMDAN and self‐attention improved by LSTM

Yan,

Chen,

Yang

et al. 2024

Asia-Pacific J Chem Eng

View full text Add to dashboard Cite

The precise prediction of NOx generation concentration in coal‐fired boilers serves as the foundational cornerstone for the judicious optimization and control of selective catalytic reduction denitrification (SCR) systems. Owing to the intricate nature of the denitrification process within SCR, there exists a temporal delay in regulating the ammonia injection rate based on the monitored data of NOx concentration at the SCR inlet. Such delays can give rise to ammonia leakage and subsequent obstruction of the air preheater. In light of this, a predictive model, CEEMDAN‐LSTM‐SA, is proposed as an amalgamation of data decomposition and the LSTM (long short‐term memory) fusion self‐attention mechanism within a deep learning network, which is introduced to forecast the NOx emission concentration at the SCR inlet of coal‐fired units. To mitigate the impact of data outliers on the training effectiveness of the model, a clustering method coupled with a statistical testing strategy is initially applied to refine the dataset first. CEEMDAN data decomposition technology is leveraged to facilitate the breakdown of data, alleviating its non‐stationary and intricate characteristics. Subsequently, through spectral analysis, the decomposed components are grouped and aggregated to form novel data elements, which are then subjected to prediction by the constructed LSTM‐SA deep learning network. The ultimate NOx emission concentration prediction value is derived through a process of fusion. Upon scrutinizing and comparing the predictions derived from various models using coal‐fired power plant data, it is evident that the performance metrics of CEEMDAN‐LSTM‐SA predictions exhibit a mean absolute error of 7.425, mean absolute percentage error of 2.415%, root mean square error of 9.715, R‐squared (R2) value of .789, mean absolute relative error of 2.109%, and a Theil's information criterion of .016. In contrast to other models, including traditional self‐attention networks, LSTM, and LSTM‐SA combination networks, CEEMDAN‐LSTM‐SA proposed in this study demonstrates superior prediction accuracy and enhanced generalization capabilities. Consequently, this predictive model stands poised to furnish an efficacious framework for the SCR ammonia injection strategy within thermal power units.

show abstract

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

Cited by 14 publications

References 37 publications

Design of a continuous emission monitoring system (CEMS) for the NH3 analyzer at PT. Pusri Palembang

Design of a continuous emission monitoring system (CEMS) for the NH3 analyzer at PT. Pusri Palembang

Remotely Sensed and Surface Measurement Derived Mass-Conserving Inversion of Daily High-Resolution NO_x Emissions and Inferred Combustion Technologies in Energy Rich Northern China

A novel hybrid forecasting approach for NOx emission of coal‐fired boiler combined with CEEMDAN and self‐attention improved by LSTM

Contact Info

Product

Resources

About

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

Cited by 14 publications

References 37 publications

Design of a continuous emission monitoring system (CEMS) for the NH3 analyzer at PT. Pusri Palembang

Design of a continuous emission monitoring system (CEMS) for the NH3 analyzer at PT. Pusri Palembang

Remotely Sensed and Surface Measurement Derived Mass-Conserving Inversion of Daily High-Resolution NOx Emissions and Inferred Combustion Technologies in Energy Rich Northern China

A novel hybrid forecasting approach for NOx emission of coal‐fired boiler combined with CEEMDAN and self‐attention improved by LSTM

Contact Info

Product

Resources

About

Remotely Sensed and Surface Measurement Derived Mass-Conserving Inversion of Daily High-Resolution NO_x Emissions and Inferred Combustion Technologies in Energy Rich Northern China