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ANID awards a UAI student for a project that will apply AI and machine learning to monitor atmospheric emissions from thermoelectric plants

ANID awards a UAI student for a project that will apply AI and machine learning to monitor atmospheric emissions from thermoelectric plants


Mateo Concha, a student of the PhD in Complex Systems Engineering, from the Faculty of Engineering and Sciences of the Adolfo Ibáñez University, was chosen as the winner of the thesis project contest in the productive sector of the National Agency for Research and Development (ANID). The doctoral thesis project is carried out jointly with the company ENGIE Chile.

With a thesis in which he will develop a model of machine learning To study the movement of particles in the atmosphere, Mateo Concha, a doctoral student in Complex Systems Engineering (DISC) from the Faculty of Engineering and Sciences of the Adolfo Ibáñez University, was awarded the national competition for the ANID SUBDIRECTORATE OF APPLIED RESEARCH – COMPETITION 2022 THESIS IN THE PRODUCTIVE SECTOR Folio TDP220017 and was recognized as the best evaluated project of the call. The project is carried out jointly with the company ENGIE Chile in the Mejillones Thermal Complex (CTM), Antofagasta region.

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The fund was awarded to Mateo Concha, among 12 other selected projects in the productive sector category. The financing grants more than 30 million pesos to develop his thesis project during 2 years of research. The project seeks to create an atmospheric emissions monitoring model in one of the generating units of the CTM, which will analyze, among other factors, how environmental conditions affect the dispersion of sulfur dioxide, nitrogen dioxide and particulate matter from power generating units.

The environmental regulations in Chile for thermoelectric plants are regulated by the Emission Standard DS No. 13/2011. Additionally, the Environmental Impact Assessment System establishes that projects of these characteristics must have environmental approval. This environmental assessment instrument has been in force since the 1990s and usually replicates zonal data from previous studies.

However, for the researcher, “the monitored data is not always accurate, since it depends on the measurement conditions, the instrument, the information channel or medium, and the actors involved. This mathematical model that I will develop will take advantage of artificial intelligence and machine learning to improve the data monitoring mechanism and its processing, using the isolation conditions typical of the Mejillones thermoelectric plants, not interfered with by other sources of emission or chimneys from various industries polluting the atmosphere”, stated Mateo Concha.foto MCG 2

This model will allow, through a system or software, to select the recorded data and estimate future emissions when there is no continuous emission monitoring equipment installed in the chimney of the generating units.

“For us at ENGIE, participating in this research and development initiative is key. Currently, we have continuous monitoring equipment to quantify emissions in the stack and to validate this initiative we will be able to use a mathematical model which will be fed by operational variables and historical emissions data.

It should be remembered that the Superintendence of the Environment has defined two mechanisms for the quantification of emissions: measurements with continuous emission monitoring systems (CEMS) and alternative methods with emission factors. The proposed mathematical model seeks to validate an intermediate solution between these two current methodologies”, comments Daniel Horta, Operational Environment Manager at ENGIE Chile.

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The fund awarded to Mateo Concha will allow him to access financing to carry out field experiments, have real-time information on pollutant concentrations, as well as air quality modeling software validated by the EPA (Environmental Protection Agency USA).

“Possibly the research can be protected with one of the alternatives that the law allows, and I hope that this methodology is presented to the Superintendence of the Environment as an alternative emissions monitoring methodology to continuous monitoring systems. This award is a source of pride, since it signified the validation of the importance of this research for our country”, concluded the researcher.

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