Theoretical and Experimental Study of Gaussian Plume Model in Small Scale System

Brusca, S.; Famoso, Fabio; Lanzafame, R.; Mauro, Stefano; Garrano, A. Marino Cugno; Monforte, Pietro

doi:10.1016/j.egypro.2016.11.008

Cited by 31 publications

(11 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the study and application of the Gaussian model [17][18][19], some focus is done on the issue of gas diffusion with the known emission source. The default coordinate system is set up taking the emission source as the origin, and the wind direction, and its vertical relations as axes.…”

Section: Gas Spatial Diffusion Modelmentioning

confidence: 99%

Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis

Bai

Wang

et al. 2020

IJERPH

View full text Add to dashboard Cite

Pollutant analysis and pollution source tracing are critical issues in air quality management, in which correlation analysis is important for pollutant relation modeling. A dynamic correlation analysis method was proposed to meet the real-time requirement in atmospheric management. Firstly, the spatio-temporal analysis framework was designed, in which the process of data monitoring, correlation calculation, and result presentation were defined. Secondly, the core correlation calculation method was improved with an adaptive data truncation and grey relational analysis. Thirdly, based on the general framework and correlation calculation, the whole algorithm was proposed for various analysis tasks in time and space, providing the data basis for ranking and decision on pollutant effects. Finally, experiments were conducted with the practical data monitored in an industrial park of Hebei Province, China. The different pollutants in multiple monitoring stations were analyzed crosswise. The dynamic features of the results were obtained to present the variational correlation degrees from the proposed and contrast methods. The results proved that the proposed dynamic correlation analysis could quickly acquire atmospheric pollution information. Moreover, it can help to deduce the influence relation of pollutants in multiple locations.

show abstract

Section: Gas Spatial Diffusion Modelmentioning

confidence: 99%

Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis

Bai

Wang

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…The average mixing ratio measured in each flight (Figure 2) was fed into a Gaussian plume model to retrieve the mixing ratio inside the stack prior to diffusion. The Gaussian plume model was chosen to estimate the concentration inside the stack based on measurements taken by the sUAS ~1 m above the stack exit [32][33][34][35]. The model was chosen due to its proven performance over small distances.…”

Section: Gaussian Modeling Of the Point Sourcementioning

confidence: 99%

Application of a Small Unmanned Aerial System to Measure Ammonia Emissions from a Pilot Amine-CO2 Capture System

Schuyler

Irvin

Abad

et al. 2020

Sensors

View full text Add to dashboard Cite

The quantification of atmospheric gases with small unmanned aerial systems (sUAS) is expanding the ability to safely perform environmental monitoring tasks and quickly evaluate the impact of technologies. In this work, a calibrated sUAS is used to quantify the emissions of ammonia (NH3) gas from the exit stack a 0.1 MWth pilot-scale carbon capture system (CCS) employing a 5 M monoethanolamine (MEA) solvent to scrub CO2 from coal combustion flue gas. A comparison of the results using the sUAS against the ion chromatography technique with the EPA CTM-027 method for the standard emission sampling of NH3 shows good agreement. Therefore, the work demonstrates the usefulness of sUAS as an alternative method of emission measurement, supporting its application in lieu of traditional sampling techniques to collect real time emission data.

show abstract

“…The results reveal that the error of the Gaussian plume model is consistently below 7% in all configurations, showing a positive compliance amongst the measured and modelled data. Therefore, it can be concluded that the near real-time nature of the Gaussian plume model makes it a powerful tool to analyse and predict the dispersion of pollutants for regulatory purposes [26,27].…”

Section: Tshi Approaches Utst Ctstmentioning

confidence: 99%

Heat Integration of a Boiler and Its Corresponding Environmental Study in an Oleochemical Production Plant: An Industry Case Study in Malaysia

et al. 2019

View full text Add to dashboard Cite

The growing demands for oleochemical products are expected to reach approximately RM 157.59 billion by 2026 due to an increased drive from the food and beverages, chemicals, and pharmaceutical industries. However, this will lead to an increase in energy consumption and subsequent flue gas emission. Proper utilization of waste gas recovery systems is thus a major research area, focusing on reducing fuel consumption and emissions of greenhouse gases without affecting process performance. In this paper, a palm oil-based oleochemical plant is studied. The fuel consumption and emission of flue gas from a thermal oil boiler were measured and the feasibility of implementation of a waste heat recovery system and its environmental impact study. The results show that the implementation of such a system can reduce natural fuel gas consumption by 17.29% and approximately 149.29 t per annum of carbon dioxide gas (CO2). Moreover, the concentration of CO2 released into highly-populated communities is estimated through a Gaussian Plume Model at different wind speed conditions. The preliminary results show that the CO2 concentration at two locations—an apartment and a local school located within 1.5 km of the plant—is well below the concentration limit of 1.938 g/m3 recommended by the Wisconsin Department of Health and Services.

show abstract

Theoretical and Experimental Study of Gaussian Plume Model in Small Scale System

Cited by 31 publications

References 16 publications

Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis

Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis

Application of a Small Unmanned Aerial System to Measure Ammonia Emissions from a Pilot Amine-CO2 Capture System

Heat Integration of a Boiler and Its Corresponding Environmental Study in an Oleochemical Production Plant: An Industry Case Study in Malaysia

Contact Info

Product

Resources

About