Flare Modeling and Optimization

PROJECT PI

Dr. Daniel Chen, Professor, Dan F. Smith Department of Chemical Engineering


SHORT DESCRIPTION

This project is currently funded by TARC and Lamar Visionary Initiative. Response surface and neural network models were developed to predict the combustion efficiency (CE), opacity, and soot emission of industrial flare operations based on controlled flare test data.

FULL DESCRIPTION

Many factors can influence the flare performance. As a result, an inferential model is indispensable to identify the steam and fuel set points of the incipient smoke point (ISP), maximize combustion efficiency (CE), and minimize fuel and steam consumption. The specification /optimization efforts will lay the foundation for dual control of opacity and CE with model predictive control (MPC).

In this project funded by TARC and Lamar Visionary Initiative, response surface and neural network models were developed to predict the combustion efficiency (CE), opacity, and soot emission of industrial flare operations based on controlled flare test data from 1983-2015. In addition, set point for steam /air assists and makeup fuel corresponding to the incipient smoke point and smokeless flaring will be performed. The required steam/air assists and fuel addition in order to maximize CE while in compliance with the regulations will be determined. Alternatively, the operations to minimize fuel and steam while in compliance with the regulations will also be explored.




john zink flare tests in tulsa oklahoma
John Zink Flare Tests (Tulsa, OK, 2010)

RESEARCH TEAM/FUNDING

Co-PIs: Helen Lou, Xiangchang Li, Christopher Martin, Peyton Richmond, and Matthew Johnson
Students: Vijaya Damodara (PhD, Spring 2013), Raj Alphones (Ph.D., Spring 2014), Ajit Patki (DE, Sp. 2013), Anan Wang (PhD, Fall 2014)
Funding: Texas Air Research Center (TARC), Texas Commission on Environmental Quality (TCEQ), and Air Quality Research Program (AQRP), & Lamar Visionary Initiative.

SELECTED PUBLICATIONS

  • Alphones, D. Chen, V. Damodara, E. Fortner, S. Evans, M. Johnson, "Response Surface and Neural Network Modeling of Flare Performance,", AIChE Spring Meeting, Apr. 10-14, 2016, Houston TX.

  • Damodara, V., Chen, D., Lou, H., Rasel, K., Richmond, P., Wang, A., Li, X., "Reduced Combustion Mechanism for C1-C4 Hydrocarbons and its Application in CFD Flare Modeling," Journal of the Air & Waste Management Association, Accepted Nov 21, 2016.

  • Singh, P. Gangadharan, D. Chen, H. Lou, X. Li & P. Richmond (2014), “Computational fluid dynamics modeling of laboratory flames and an industrial flare”, Journal of the Air & Waste Management Association, 64:11, 1328-1340, DOI: 10.1080/10962247.2014.948229.