An Effective PEMS Alternative to CEMS for Quantifying Glass Furnace NOx Emissions

Ross, C.P.; Russell, Dick; Mino, John

doi:10.1002/9780470294598.ch11

Cited by 4 publications

(2 citation statements)

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“…Ross et al [25] tested PEMS for NO x monitoring at a glass manufacturing furnace based on periodic monitoring with a portable chemical cell gas analyzer in 2008. The predictive calculations were accepted by air emission control agencies for demonstrating regulation compliance.…”

Section: Pems Researchmentioning

confidence: 99%

Development of Predictive Emissions Monitoring System Using Open Source Machine Learning Library – Keras: A Case Study on a Cogeneration Unit

Tarnoczi

Wiens

et al. 2019

IEEE Access

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The study provides an overview of Predictive Emissions Monitoring System's (PEMS) research, application, installation, and regulatory framework as well as develops predictive models for NO x emissions from a natural gas fired cogeneration unit using an open source machine learning library, Keras, and open source programming languages, Python and R. Nine neural network based predictive models were trained with 12 086 examples and tested with 3020 examples. The neural network-based models use eight process parameters as inputs to predict NO x emissions. All models meet the regulatory requirements for precision. The best model (32-64-64-64) has four hidden layers and uses the Nadam method for optimization. The best model has a mean absolute error of 0.5982, r-value of 0.9451, and a difference of 0.14% between the measured and predicted emission values using the test dataset. The study demonstrated the feasibility of using open source machine learning library in PEMS development. It also provides guidance to facility operators to develop their own PEMS models for monitoring emissions.INDEX TERMS Air emissions monitoring, environmental monitoring, Keras, machine learning, NO x , PEMS, predictive emissions monitoring system, tensorflow.

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Section: Pems Researchmentioning

confidence: 99%

Development of Predictive Emissions Monitoring System Using Open Source Machine Learning Library – Keras: A Case Study on a Cogeneration Unit

Tarnoczi

Wiens

et al. 2019

IEEE Access

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show abstract

“…[12][13][14][15][16][17] The types of PEMS include boilers, continuous catalyst regeneration (CCR) reformers, coker heaters, crude heaters, ethylene furnaces, gas turbine-heat recovery steam generators (HRSGs), pyrolysis furnaces, etc. Some special PEMS are used in biomass-fired plants, 18 glass furnaces, 19 lime kilns, 20 passenger ferries, 12,21 and waste-fuel incinerators. 22 The monitoring targets of most PEMS focus on nitrogen oxides (NO x ), carbon monoxide (CO), and oxygen (O 2 ); however, a few PEMS have also incorporated particulate, total reduced sulfur (TRS), and…”

Section: Introductionmentioning

confidence: 99%

A Feasibility Study of a Predictive Emissions Monitoring System Applied to Taipower’s Nanpu and Hsinta Power Plants

Chien

Hsueh

Chu

et al. 2010

Journal of the Air & Waste Management Association

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The Hsinta and Nanpu Power Stations are located in southern Taiwan. The Hsinta Power Station consists of five combined-cycle gas turbines (CCGT), whereas the Nanpu Power Station consists of four. A project was undertaken to develop and deploy a predictive emissions monitoring system (PEMS) on CCGT unit 3 of Hsinta Power Station (HT-3) and CCGT unit 1 of Nanpu Power Station (NP-1) with the long-term goal of developing a universal model for this kind of power plant. After the first-year PEMS project at the Hsinta power plant, one goal of the second-year PEMS project was to set up a second PEMS at the Nanpu power plant and compare the PEM models applied the to two gas-fired combined cycle power generation units. Consequently, the second and third PEMS of Taiwan at CCGT HT-3 and NP-1 were finished. After comparing the differences among HT-1, HT-3, and NP-1 PEMS models, the pattern of model functionality indicated that this model could be applied to the other units of the same type and size. However, the PEMS function constant or parameter coefficients must be modified on a case-by-case basis. With regard to the PEMS model developed for HT-3, the relative accuracy (RA) of the 15-variable model with start-up mode is only 7.43% and met the criteria of draft PS-16. With regard to the PEMS model developed for NP-1, the RA of the 10-variable model with start-up mode was only 7.76% and also met the criteria of draft PS-16.

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Predicting Emissions from the Chemical and Energy Industries: Progress in Applying Modeling Approaches

Мешалкин

Skobelev²,

Vocciante

et al. 2021

Theor Found Chem Eng

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An Effective PEMS Alternative to CEMS for Quantifying Glass Furnace NOx Emissions

Cited by 4 publications

References 0 publications

Development of Predictive Emissions Monitoring System Using Open Source Machine Learning Library – Keras: A Case Study on a Cogeneration Unit

Development of Predictive Emissions Monitoring System Using Open Source Machine Learning Library – Keras: A Case Study on a Cogeneration Unit

A Feasibility Study of a Predictive Emissions Monitoring System Applied to Taipower’s Nanpu and Hsinta Power Plants

Predicting Emissions from the Chemical and Energy Industries: Progress in Applying Modeling Approaches

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