Real Time Monitoring of Environmental Efficiency of Power Plants

Majanne, Yrjö; Korpela, Timo; Judl, Jáchym; Koskela, Sirkka; Laukkanen, Ville; Häyrinen, Anna

doi:10.1016/j.ifacol.2015.12.428

Cited by 5 publications

(8 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, we can assume that sensors used for relevant data collection are more widely adopted than in other sectors. Nonetheless, case studies in this category include power plants [32], a generic enterprise case study [20], and solar photovoltaic systems [33]. Of these 10, eight are conference proceedings and only two are journal articles [30,31].…”

Section: Goal and Scopementioning

confidence: 99%

“…This is understandable, given the need to understand potential unforeseen consequences of decisions aimed at reducing environmental impacts. Still, some publications propose strategies, based on attributional modeling, to reduce environmental impacts based on hotspot identification [30], fuel purchasing and operational strategies [32], discrete-event simulation of manufacturing systems [59,64], decision support for material and process innovation [40], as well as shifting production towards hours that are forecast to have a less impacting electricity mix [73,74,76]. Considering consequential LCA publications, a trade-off between different optimization objectives, such as water consumption and the impact on climate change, is quantified in the case of an industrial wastewater treatment system [24].…”

Section: Goal and Scopementioning

confidence: 99%

“…Two thirds of the publications in the goal 2 class deal with a single optimization objective: reduction in energy consumption [75] or reduction in climate change impacts [32,43,49,56,64,73,74,76,77]. By doing so, these publications ignore the risk of potential burden shifting, which happens when impacts are reduced in one impact category but increased in one or more others.…”

Section: Goal and Scopementioning

confidence: 99%

See 2 more Smart Citations

Systematic Literature Review on Dynamic Life Cycle Inventory: Towards Industry 4.0 Applications

Cornago

Tan

Brondi³

et al. 2022

Sustainability

View full text Add to dashboard Cite

Life cycle assessment (LCA) is a well-established methodology to quantify the environmental impacts of products, processes, and services. An advanced branch of this methodology, dynamic LCA, is increasingly used to reflect the variation in such potential impacts over time. The most common form of dynamic LCA focuses on the dynamism of the life cycle inventory (LCI) phase, which can be enabled by digital models or sensors for a continuous data collection. We adopt a systematic literature review with the aim to support practitioners looking to apply dynamic LCI, particularly in Industry 4.0 applications. We select 67 publications related to dynamic LCI studies to analyze their goal and scope phase and how the dynamic element is integrated in the studies. We describe and discuss methods and applications for dynamic LCI, particularly those involving continuous data collection. Electricity consumption and/or electricity technology mixes are the most used dynamic components in the LCI, with 39 publications in total. This interest can be explained by variability over time and the relevance of electricity consumption as a driver of environmental impacts. Finally, we highlight eight research gaps that, when successfully addressed, could benefit the diffusion and development of sound dynamic LCI studies.

show abstract

Section: Goal and Scopementioning

confidence: 99%

Section: Goal and Scopementioning

confidence: 99%

Section: Goal and Scopementioning

confidence: 99%

See 1 more Smart Citation

Systematic Literature Review on Dynamic Life Cycle Inventory: Towards Industry 4.0 Applications

Cornago

Tan

Brondi³

et al. 2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…2.1. Matimba uses subbituminous coal with a calorific value of ∼ 20 MJ kg −1 (Makgato and Chirwa, 2017). We apply the ratio ranging from 2005 ratio CEMS regressed to 2005 ratio CEMS regressed + standard deviation to Matimba, following the methodology in Fig.…”

Section: Application To Matimba Power Plant In South Africamentioning

confidence: 99%

A methodology to constrain carbon dioxide emissions from coal-fired power plants using satellite observations of co-emitted nitrogen dioxide

et al. 2020

View full text Add to dashboard Cite

Abstract. We present a method to infer CO2 emissions from individual power plants based on satellite observations of co-emitted nitrogen dioxide (NO2), which could serve as complementary verification of bottom-up inventories or be used to supplement these inventories. We demonstrate its utility on eight large and isolated US power plants, where accurate stack emission estimates of both gases are available for comparison. In the first step of our methodology, we infer nitrogen oxides (NOx) emissions from US power plants using Ozone Monitoring Instrument (OMI) NO2 tropospheric vertical column densities (VCDs) averaged over the ozone season (May–September) and a “top-down” approach that we previously developed. Second, we determine the relationship between NOx and CO2 emissions based on the direct stack emissions measurements reported by continuous emissions monitoring system (CEMS) programs, accounting for coal quality, boiler firing technology, NOx emission control device type, and any change in operating conditions. Third, we estimate CO2 emissions for power plants using the OMI-estimated NOx emissions and the CEMS NOx∕CO2 emission ratio. We find that the CO2 emissions estimated by our satellite-based method during 2005–2017 are in reasonable agreement with the US CEMS measurements, with a relative difference of 8 %±41 % (mean ± standard deviation). The broader implication of our methodology is that it has the potential to provide an additional constraint on CO2 emissions from power plants in regions of the world without reliable emissions accounting. We explore the feasibility by comparing the derived NOx∕CO2 emission ratios for the US with those from a bottom-up emission inventory for other countries and applying our methodology to a power plant in South Africa, where the satellite-based emission estimates show reasonable consistency with other independent estimates. Though our analysis is limited to a few power plants, we expect to be able to apply our method to more US (and world) power plants when multi-year data records become available from new OMI-like sensors with improved capabilities, such as the TROPOspheric Monitoring Instrument (TROPOMI), and upcoming geostationary satellites, such as the Tropospheric Emissions: Monitoring Pollution (TEMPO) instrument.

show abstract

“…with an error of less than 5% (e.g., Glenn et al, 2003). 2 may have larger uncertainties than fuel-based emissions 23 estimates due to uncertainties in the calculation of flue gas flow (Majanne et al, 2015). Nevertheless, we use 2 to relate 24 NO x emissions to CO 2 emissions, since the primary uncertainty of and 2 arises from the calculation of the flue 25 gas flow, which will cancel in .…”

Section: Estimating No X To Co 2 Emission Ratios Using Cems Data ( ) 14mentioning

confidence: 99%

A methodology to constrain carbon dioxide emissions from coal-fired power plants using satellite observations of co-emitted nitrogen dioxide

Liu¹,

Duncan²,

Krotkov³

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. We present a novel method to infer CO2 emissions from individual power plants based on satellite observations of co-emitted nitrogen dioxide (NO2) and demonstrate its utility on US power plants, where accurate stack emission estimates of both gases are available for comparison. In the first step of our methodology, we infer nitrogen oxides (NOx) emissions from isolated power plants using Ozone Monitoring Instrument (OMI) NO2 tropospheric vertical column densities (VCDs) averaged over the ozone season (May&#8211;September) and a &quot;top-down&quot; approach that we previously developed. Second, we determine the relationship between NOx and CO2 emissions based on the direct stack emissions measurements reported by continuous emissions monitoring system (CEMS) programs, accounting for coal type, boiler firing type, NOx emission control device type, and changes in operating conditions. Third, we estimate CO2 emissions of the ozone season for a plant using the OMI-estimated NOx emissions and the CEMS NOx&#8201;/&#8201;CO2 emission ratio. We find that the CO2 emissions estimated by our satellite-based method during 2005&#8211;2017 are in reasonable agreement with the CEMS measurements, with a relative difference of 8&#8201;%&#8201;&#177;&#8201;41&#8201;% (mean&#8201;&#177;&#8201;standard deviation) for the selected US power plants in our analysis. Total uncertainty in the inferred CO2 estimates is partly associated with the uncertainty associated with the OMI NO2 VCD data, so we expect that it will decrease when our method is applied to OMI-like sensors with improved capabilities, such as TROPOspheric Monitoring Instrument (TROPOMI) and geostationary Tropospheric Emissions: Monitoring Pollution (TEMPO). The broader implication of our methodology is that it has the potential to provide an additional constraint on CO2 emissions from power plants in regions of the world without reliable emissions accounting. We explore the feasibility by applying our methodology to a power plant in South Africa, where the satellite-based emission estimates show reasonable consistency with other estimates.

show abstract

Real Time Monitoring of Environmental Efficiency of Power Plants

Cited by 5 publications

References 7 publications

Systematic Literature Review on Dynamic Life Cycle Inventory: Towards Industry 4.0 Applications

Systematic Literature Review on Dynamic Life Cycle Inventory: Towards Industry 4.0 Applications

A methodology to constrain carbon dioxide emissions from coal-fired power plants using satellite observations of co-emitted nitrogen dioxide

A methodology to constrain carbon dioxide emissions from coal-fired power plants using satellite observations of co-emitted nitrogen dioxide

Contact Info

Product

Resources

About