Publications

Group highlights

(For a full list see below or go to Google Scholar, ResearchGate.

Physics-informed neural networks with hard linear equality constraints

Physics-informed neural network (PINN) leverages known physical constraints present in the data, but it cannot strictly satisfy them in the predictions. We propose an architecture to rigorously guarantee hard linear equality constraints through projection layers derived from KKT conditions.

Chen, H., Constante-Flores, G., & Li, C.

Computers & Chemical Engineering(2024)

Paper Bibtex

Diagnosing Infeasible Optimization Problems Using Large Language Models

One of the primary barriers to deploying optimization or machine learning models in practice is the challenge of helping practitioners understand and interpret such models. We propose a first-of-its-kind system that enables natural language-based diagnosis and troubleshooting for infeasible optimization models using LLM.

Chen, H., Constante-Flores, G., & Li, C.

INFOR: Information Systems and Operational Research(2024)

Paper Preprint Bibtex

Distributed manufacturing for electrified chemical processes in a microgrid

Electrification is a potential solution to alleviate the greenhouse gas emissions of chemical industries. However, spatial and temporal variations complicate the adoption of renewable energy. We propose a multi-scale MILP model for locating modular electrified plants, renewable-based generating units, and power lines in a microgrid.

Ramanujam, A., Constante-Flores, G., & Li, C.

AIChE Journal, 69(12), e18265.(2023)

Paper Preprint Bibtex

 

Full List

Physics-informed neural networks with hard linear equality constraints
Chen, H., Constante-Flores, G., & Li, C.
Computers & Chemical Engineering(2024) Paper Bibtex

Diagnosing Infeasible Optimization Problems Using Large Language Models
Chen, H., Constante-Flores, G., & Li, C.
INFOR: Information Systems and Operational Research(2024) Paper Preprint Bibtex

PAMSO: Parametric Autotuning Multi-time Scale Optimization Algorithm
Ramanujam, A. & Li, C.
ARXIV(2024) Paper Bibtex

Solution polishing via path relinking for continuous black-box optimization
Papageorgiou, D.J., Kronqvist, J., Ramanujam, A., Kor, J., Kim, Y., Li, C.
Optimization Letters(2024) Paper Bibtex

Distributed manufacturing for electrified chemical processes in a microgrid
Ramanujam, A., Constante-Flores, G., & Li, C.
AIChE Journal, 69(12), e18265.(2023) Paper Preprint Bibtex

Recent advances and challenges in optimization models for expansion planning of power systems and reliability optimization
Cho, S., Li, C., & Grossmann, I. E.
Computers & Chemical Engineering, 107924.(2022) Paper Preprint Bibtex

A Review on the Performance of Linear and Mixed Integer Two-Stage Stochastic Programming Software
Torres, J. J., Li, C., Apap, R. M., & Grossmann, I. E.
Algorithms, 15(4)(2022) Paper Bibtex

Mixed-integer linear programming models and algorithms for generation and transmission expansion planning of power systems
Li, C., A.J. Conejo, P. Liu, B.P. Omell, J.D. Siirola, I.E. Grossmann.
European Journal of Operations Research(2021) Paper Preprint Bibtex Slides

On representative day selection for capacity expansion planning of power systems under extreme operating conditions
Li, C., A.J. Conejo, J.D. Siirola, I.E. Grossmann
International Journal of Electrical Power & Energy Systems(2021) Paper Preprint Bibtex Slides

Shale gas field development planning under production profile uncertainty
Peng, Z., Li, C., Grossmann, I. E., Kwon, K., Ko, S., Shin, J., & Feng, Y.
AIChE Journal, e17439.(2021) Paper Bibtex

A Review of Stochastic Programming Methods for Optimization of Process Systems Under Uncertainty
Li, C., & Grossmann, I. E.
Frontiers in Chemical Engineering, 2, 34(2021) Paper Preprint Bibtex

Algorithmic Approaches to Inventory Management Optimization
Perez, H. D., Hubbs, C. D., Li, C., & Grossmann, I. E.
Processes, 9(1), 102(2021) Paper Bibtex

Multi-period Design and Planning Model of Shale Gas Field Development.
Peng, Z., Li, C., Grossmann, I. E., Kwon, K., Ko, S., Shin, J., & Feng, Y.
AIChE Journal, e17195(2021) Paper Bibtex

A deep reinforcement learning approach for chemical production scheduling
Hubbs, C. D., Li, C., Sahinidis, N. V., Grossmann, I. E., & Wassick, J. M.
Computers & Chemical Engineering, 141, 106982(2020) Paper Bibtex

Sample average approximation for stochastic nonconvex mixed integer nonlinear programming via outer-approximation
Li, C., Bernal, D. E., Furman, K. C., Duran, M. A., & Grossmann, I. E.
Optimization and Engineering, 1-29(2020) Paper Bibtex

Shale gas pad development planning under price uncertainty
Li, C., Eason, J. P., Drouven, M. G., & Grossmann, I. E.
AIChE Journal, 66(6), e16933(2020) Paper Preprint Bibtex Slides

A generalized Benders decomposition-based branch and cut algorithm for two-stage stochastic programs with nonconvex constraints and mixed-binary first and second stage variables
Li, C., & Grossmann, I. E.
Journal of Global Optimization, 75, 247–272(2019) Paper Preprint Bibtex Slides

A finite ϵ-convergence algorithm for two-stage stochastic convex nonlinear programs with mixed-binary first and second-stage variables
Li, C., & Grossmann, I. E.
Journal of Global Optimization, 75(4), 921-947(2019) Paper Preprint Bibtex Slides

Global Optimization Algorithm for Multi-period Design and Planning of Centralized and Distributed Manufacturing Networks
Lara, C. L., Bernal, D. E., Li, C., & Grossmann, I. E.
Computers & Chemical Engineering, 127, 295-310(2019) Paper Preprint Bibtex

An improved L-shaped method for two-stage convex 0–1 mixed integer nonlinear stochastic programs
Li, C., & Grossmann, I. E.
Computers & Chemical Engineering, 112, 165-179(2018) Paper Preprint Bibtex

Sequence-Based Prediction of Cysteine Reactivity Using Machine Learning
Wang, H., Chen, X., Li, C., Liu, Y., Yang, F., & Wang, C.
Biochemistry, 57(4), 451-460(2018) Paper Bibtex