Qi Chen, IE Grossmann. 2019. “Effective GDP optimization models for modular process synthesis.” Ind. Eng. Chem. Res. Available online. (accepted) (Task 3.1)

Chen, Q. and Grossmann, I.E. “Effective GDP optimization models for modular process synthesis”, Current Opinion in Chemical Engineering. Accepted (2018) (Task 2.7)

ES Rawlings, Qi Chen, IE Grossmann, JA Caballero. 2019. “Kaibel column: Modeling, optimization, and conceptual design of multi-product dividing wall columns.” Comp. Chem. Eng. 125, 31-39. (Task 3.1)

C.O. Okoli, A. Ostace, S. Nadgouda, A. Lee, A. Tong, A.P. Burgard, D. Bhattacharyya, D.C. Miller. 2019. “A framework for the optimization of chemical looping combustion processes.” Powder Technology, https://doi.org/10.1016/j.powtec.2019.04.035 (Task 3.2)

Thierry, D. and Biegler, L. T. (2019), Dynamic real‐time optimization for a CO2 capture process. AIChE J. doi:10.1002/aic.16511 (Task 2.3)

Hanselman, C.L., Tafen, D.Y., Alfonso, D.R., Lekse, J.W., Matranga, C., Miller, D.C., Gounaris, C.E. “Tuning Oxygen Desorption in a Doped BaFe1-xInxO3 Perovskite Oxygen Carrier”, Computers & Chemical Engineering. submitted. (Task 2.4)

David L. Woodruff, Andrea Staid, Bethany Nicholson, and Katherine Klise, “PARMEST: PARAMETER ESTIMATION VIA PYOMO”, submitted to Foundations of Computer Aided Process Design 2019. (Task 3.2.3)


Sitter, S., Chen, Q., and Grossmann, I.E. “An Overview of Process Intensification Methods”, Submitted for publication (2018) (Task 2.7)

Lara, C.L., Mallapragada, D., Papageorgiou D., Venkatesh, A., Grossmann, I.E. “Electric Power Infrastructure Planning: Mixed-Integer Programming Model and Nested Decomposition Algorithm”, European Journal of Operational Research, (2018), In press. (Task 2.8)

Sauk, B., Ploskas, N., Sahinidis, N. (2018). “GPU parameter tuning for tall and skinny dense linear least squares problems,” Optimization Methods and Software. doi: 10.1080/10556788.2018.1527331. (Task 3.2.1)

Rodriguez, J.S., Nicholson, B., Laird, C.D., and Zavala, V.M. “Benchmarking ADMM in Nonconvex NLPs”, Computers & Chemical Engineering, accepted (2018) (Task 3.2.3)

Yu, M., D.W. Griffith, and L.T. Biegler, Nonlinear Programming Formulations for Nonlinear and Economic Model Predictive Control, In Handbook of Model Predictive Control, S. Rackovic and W. Levine (eds.), to appear. (in preparation)

D. Miller, J. Siirola, D. Agarwal, A. Burgard, A. Lee, J. Eslick, B. Nicholson, C. Laird, L. Biegler, D. Bhattacharyya, N. Sahinidis, I. Grossmann, C. Gounaris & D. Gunter, “Next Generation Multi-Scale Process Systems Engineering Framework”, Computer Aided Chemical Engineering, 44, 2209-2214 (2018)


Wan, W., J.P. Eason, B. Nicholson, and L.T. Biegler, Parallel Cyclic Reduction Decomposition for DynamicOptimization Problems, Computers and Chemical Engineering, accepted for publication. (2017)

Nicholson, B.L. and J.D. Siirola, A Framework for Modeling and Optimizing Dynamic Systems Under Uncertainty. In press, special FOCAPO/CPC issue of Computers & Chemical Engineering (2017)

Nicholson, B.L., J.D. Siirola, J.-P. Watson, V.M. Zavala, and L.T. Biegler. pyomo.dae: A Modeling and Automatic Discretization Framework for Optimization with Differential and Algebraic Equations, Math Programming Computation (2017)


C.L. Hanselman and C.E. Gounaris. A Mathematical Optimization Framework for the Design of Nanopatterned Surfaces. AIChE Journal, 62(9):3250-3263. (2016)