Predicting power plant emissions using public data and machine learning
Author:
Affiliation:
1. Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA
Abstract
Funder
New York State Energy Research and Development Authority
National Science Foundation
Publisher
Royal Society of Chemistry (RSC)
Subject
Pollution,Water Science and Technology,Environmental Chemistry,Environmental Engineering
Link
http://pubs.rsc.org/en/content/articlepdf/2023/VA/D3VA00191A
Reference24 articles.
1. Regulation by Simulation: The Role of Production Cost Models in Electricity Planning and Pricing
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3. Dynamic modeling and optimization of a coal-fired utility boiler to forecast and minimize NOx and CO emissions simultaneously
4. A globally enhanced general regression neural network for on-line multiple emissions prediction of utility boiler
5. Prediction of NOX emission for coal-fired boilers based on deep belief network
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