publication . Preprint . 2017

A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks

Harirchi, Farshad; Khalil, Omar A.; Liu, Sijia; Elvati, Paolo; Violi, Angela; Hero, Alfred O.;
Open Access English
  • Published: 12 Dec 2017
In this paper, we propose an optimization-based sparse learning approach to identify the set of most influential reactions in a chemical reaction network. This reduced set of reactions is then employed to construct a reduced chemical reaction mechanism, which is relevant to chemical interaction network modeling. The problem of identifying influential reactions is first formulated as a mixed-integer quadratic program, and then a relaxation method is leveraged to reduce the computational complexity of our approach. Qualitative and quantitative validation of the sparse encoding approach demonstrates that the model captures important network structural properties wi...
free text keywords: Mathematics - Optimization and Control, Computer Science - Learning, Mathematics - Dynamical Systems
Download from

[1] Feinberg, M., (1987) Chemical reaction network structure and the stability of complex isothermal reactors-I: The deficiency zero and deficiency one theorems. Chemical Engineering Science 42, pp. 2229-2268. [OpenAIRE]

[2] Strehlow, R.A., (1984) Combustion fundamentals, McGraw-Hill College.

[3] Tomlin, A.S. & Turanyi, T. & Pilling, M.J., (1997) Mathematical Tools for the Construction, Investigation and Reduction of Combustion Mechanisms. Elsevier Comprehensive Chemical Kinetics, pp. 293-437. [OpenAIRE]

[4] Lu, T. & Law, C.K., (2005) A directed relation graph method for mechanism reduction. Elsevier Proceedings of the Combustion Institute 30, pp. 1333-1341.

[5] Lu, T. & Law, C.K., (2006) On the applicability of directed relation graphs to the reduction of reaction mechanisms. Elsevier Combustion and Flame 146, pp. 472-483.

[6] Tomlin, A.S. & Pilling, M.J. & Turanyi, T. & Merkin, J.H. & Brindley, J., (1992). Mechanism Elsevier Combustion and Flame 91, pp. 107-130.

[7] Pepiot-Desjardins, P. & Pitsch, H., (2008) An efficient error-propagation-based reduction method for large chemical kinetic mechanisms. Elsevier Combustion and Flame 154, pp. 67-81. [OpenAIRE]

[8] Candes, E.J. & Wakin, M.B., (2010). An Introduction To Compressive Sampling. IEEE Signal Processing Magazine, pp. 21-30.

[9] Inc. Gurobi Optimization, (2015) Gurobi optimizer reference manual.

[10] Joshi, S. & Boyd, S., (2009) Sensor selection via convex optimization. IEEE Transactions on Signal Processing 57. pp. 451-462.

[11] Liu, S. & Chepuri, S.P. & Fardad M., (2016) Sensor selection for estimation with correlated measurement noise. IEEE Transactions on Signal Processing 64. pp. 3509-3522.

[12] Löfberg J., (2004). Yalmip : A toolbox for modeling and optimization in MATLAB. In

Any information missing or wrong?Report an Issue