Journal Articles

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Riley, B. P., Daoutidis, P., & Zhang, Q. (2025). Integer L-shaped method with non-supporting no-good optimality cuts. [preprint]

70. Kong, B., Daoutidis, P., & Zhang, Q. (2026). Design of microgrids with ammonia-based energy storage via Bayesian optimization. Energy Conversion and Management, 359, 121529. [link] [preprint]

69. Srinivasan, P., Kuo, H.-J., Lin, Y.-C., Lu, Y.-A., Hu, W.-S., & Zhang, Q. (2026). A mechanistic model of rAAV production in synthetic cell lines. Biotechnology & Bioengineering, 123(6), 1684-1694. [link] [preprint]

68. Kankani, K., Palys, M. J., Daoutidis, P., & Zhang, Q. (2026). Assessing the techno-economic impact of process flexibility on methanol- and syngas-based e-kerosene production. Industrial & Engineering Chemistry Research, 65(11), 6077-6091. Invited article for Special Issue: Honoring Professor Costas Kiparissides – New Frontiers in Reaction Engineering for Polyolefin Processes. [link] [preprint]

67. Jagana, J. S., Rajagopalan, S., Amaran, S., & Zhang, Q. (2026). Design for flexibility: An adjustable robust optimization approach with decision-dependent uncertainty. AIChE Journal, 72(4), e70222. [link] [preprint] [code]

66. Sahu, V. K. & Zhang, Q. (2026). Advanced available-to-promise in online chemical production scheduling. Industrial & Engineering Chemistry Research, 65(3), 1734-1750. Invited article for Special Issue: Advances in the Optimization of Process Operations - In Memory of Pedro Castro. [link] [preprint] [code]

65. Rathi, T., Riley, B. P., Flores-Quiroz, A., & Zhang, Q. (2025). Column generation for multistage stochastic mixed-integer nonlinear programs with discrete state variables. Journal of Global Optimization, 94, 95-126. [link] [preprint] [code]

64. Baldea, M., Endler, E. E., Hale, E., Maravelias, C. T., Barolo, M., Harjunkoski, I., Mercangoz, M., Shah, S. L., Soroush, M., Young, B. R., & Zhang, Q. (2025). Transforming the process industries through electrification: Challenges and opportunities. Industrial & Engineering Chemistry Research, 64(34), 16466-16478. ACS Editors' Choice. [link]

63. Jeong, Y. & Zhang, Q. (2025). Transition toward a decarbonized steel industry: A supply chain analysis for the United States. Industrial & Engineering Chemistry Research, 64(33), 16263-16280. Invited article for Special Issue: Net Zero Technologies. [link] [preprint]

62. Ge, C., Yuan, Z., & Zhang, Q. (2025). Robust design of flexible supply chains with mobile and modular facilities. AIChE Journal, e70011. [link] [preprint] [code]

61. Kuo, H.-J., Srinivasan, P., Lin, Y.-C., Lu, M., Rungkittikhun, C., Zhang, Q., & Hu, W.-S. (2025). Transcriptomic functional characterization of recombinant adeno-associated virus producing cell line adapted to suspension-growth. Biotechnology Process, 41(5), e70042. [link]

60. Lu, Y.-A., Fukae, Y., Hu, W.-S., & Zhang, Q. (2025). Recurrent neural networks for forecasting time-varying process behavior in mammalian cell culture. Industrial & Engineering Chemistry Research, 64(18), 9048–9058. Invited article for Special Issue: AI/ML in Chemical Engineering. [link] [preprint] [code]

59. Dixit, S., Gupta, R., & Zhang, Q. (2025). Decision-focused surrogate modeling for mixed-integer linear optimization. Transactions on Machine Learning Research. [link] [preprint] [code]

58. Rathi, T., Brzakala, C., Wang, H., & Zhang, Q. (2025). Assessing the impact of chain of custody models on the long-term planning of low-carbon ammonia supply chains. Industrial & Engineering Chemistry Research, 64(3), 1680-1699. Invited article for Special Issue: 2024 Class of Influential Researchers. [link] [preprint]

57. Jagana, J. S., Amaran, S., & Zhang, Q. (2025). Multistage robust mixed-integer optimization for industrial demand response with interruptible load. Computers & Chemical Engineering, 194, 108974. [link] [preprint] [code] [talk]

56. Lu, Y.-A., Hu, W.-S., Paulson, J. A., & Zhang, Q. (2025). BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification. Computers & Chemical Engineering, 192, 108859. [link] [preprint] [code] [talk]

55. Andrés-Martínez, O., Malmali, M., Zhang, Q., & Daoutidis, P. (2024). Optimal design of an absorbent-enhanced ammonia synthesis process for solar thermochemical energy storage. ACS Sustainable Chemistry & Engineering, 12(25), 9446-9460. [link] [preprint] [talk]

54. Lu, Y.-A., McCann, M. G., Hu, W.-S., & Zhang, Q. (2024). Multi-cell-line learning for the data-driven construction of mechanistic metabolic models. Biotechnology & Bioengineering, 121, 2833-2847. [link] [preprint] [code]

53. Kong, B., Zhang, Q., & Daoutidis, P. (2024). Nonlinear model predictive control of flexible ammonia production. Control Engineering Practice, 148, 105946. [link] [preprint] [talk]

52. Stinchfield, G, Morgan, J. C., Naik, S., Biegler, L. T., Eslick, J. C., Jacobson, C., Miller, D. C., Siirola, J. D., Zamarripa, M., Zhang, C., Zhang, Q., & Laird, C. D. (2024). A mixed integer linear programming approach for the design of chemical process families. Computers & Chemical Engineering, 183, 108620. [link]

51. Kong, B., Zhang, Q., & Daoutidis, P. (2024). Real-time operation of a stand-alone microgrid with green ammonia storage. IEEE Transactions on Control Systems Technology, 32(4), 1463-1470. [link] [preprint] [talk]

50. Gupta, R. & Zhang, Q. (2024). Data-driven decision-focused surrogate modeling. AIChE Journal, 70(4), e18338. [link] [preprint] [code]

49. Tian, H., Jagana, J. S., Zhang, Q., & Ierapetritou, M. G. (2024). Feasibility/flexibility-based optimization for process design and operations. Computers & Chemical Engineering, 180, 108461. [link] [preprint]

48. Rathi, T., Gupta, R., Pinto, J. M., & Zhang, Q. (2024). Enhancing explainability of stochastic programming solutions via scenario and recourse reduction. Optimization & Engineering, 25, 795-820. [link] [preprint]

47. Riley, B. P., Daoutidis, P., & Zhang, Q. (2023). Multi-scenario design of ammonia-based energy storage systems for use as non-wires alternatives. Journal of Energy Storage, 73, 108795. [link] [preprint] [talk]

46. Andrés-Martínez, O., Zhang, Q., & Daoutidis, P. (2023). Optimal operation of a reaction-absorption process for ammonia production at low pressure. Industrial & Engineering Chemistry Research, 62, 14456-14466. [link] [preprint]

45. Wang, H., Daoutidis, P., & Zhang, Q. (2023). Ammonia-based green corridors for sustainable maritime transportation. Digital Chemical Engineering, 6, 100082. Invited article for Special Issue: Emerging Stars in Digital Chemical Engineering. [link] [corrigendum] [preprint]

44. Gupta, R. & Zhang, Q. (2023). Efficient learning of decision-making models: A penalty block coordinate descent algorithm for data-driven inverse optimization. Computers & Chemical Engineering, 170, 108123. [link] [preprint]

43. Lu, Y.-A., O’Brien, C. M., Mashek, D. G., Hu, W.-S., & Zhang, Q. (2023). Kinetic-model‐based pathway optimization with application to reverse glycolysis in mammalian cells. Biotechnology & Bioengineering, 120, 216-229. [link] [preprint]

42. Daoutidis, P. & Zhang, Q. (2022). From Amundson, Aris, and Sargent to the future of process systems engineering. Chemical Engineering Research & Design, 188, 704-713. Invited article for Special Issue: 100 Years of IChemE. [link] [preprint]

41. Zhang, Q. & Pinto, J. M. (2022). Energy-aware enterprise-wide optimization and clean energy in the industrial gas industry. Computers & Chemical Engineering, 165, 107927. [link] [preprint]

40. Rathi, T. & Zhang, Q. (2022). Capacity planning with uncertain endogenous technology learning. Computers & Chemical Engineering, 164, 107868. [link] [preprint] [talk]

39. Gupta, R. & Zhang, Q. (2022). Decomposition and adaptive sampling for data-driven inverse linear optimization. INFORMS Journal on Computing, 34(5), 2720-2735. [link] [preprint] [talk] [code]

38. Allman, A. & Zhang, Q. (2022). Distributed fairness-guided optimization for coordinated demand response in multi-stakeholder process networks. Computers & Chemical Engineering, 161, 107777. [link] [preprint]

37. Sánchez, A., Zhang, Q., Martín, M., & Vega, P. (2022). Towards a new renewable power system using energy storage: An economic and social analysis. Energy Conversion and Management, 252, 115056. [link]

36. Wang, H., Daoutidis, P., & Zhang, Q. (2021). Harnessing the wind power of the ocean with green offshore ammonia. ACS Sustainable Chemistry & Engineering, 9, 14605-14617. [link] [preprint]

35. Wei, J., Zhang, Q., & Yuan, Z. (2021). A unified approach to multi-scenario sensor network design for data reconciliation. AIChE Journal, 68(1), e17404. [link] [preprint]

34. Sánchez, A., Martín, M., & Zhang, Q. (2021). Optimal design of sustainable power-to-fuels supply chains for seasonal energy storage. Energy, 234, 121300. [link] [preprint]

33. Feng, W., Feng, Y., & Zhang, Q. (2021). Multistage distributionally robust optimization for integrated production and maintenance scheduling. AIChE Journal, 67(9), e17329. [link] [preprint] [talk]

32. Allman, A. & Zhang, Q. (2021). Branch-and-price for a class of nonconvex mixed-integer nonlinear programs. Journal of Global Optimization, 81, 861-880. [link] [preprint] [talk]

31. Allman, A., Lee, C., Martín, M., & Zhang, Q. (2021). Biomass waste-to-energy supply chain optimization with mobile production modules. Computers & Chemical Engineering, 150, 107326. [link] [preprint] [talk]

30. O’Brien, C. M., Zhang, Q., Daoutidis, P., & Hu, W.-S. (2021). A hybrid mechanistic-empirical model for in silico mammalian cell bioprocess simulation. Metabolic Engineering, 66, 31-40. [link]

29. Liu, B., Zhang, Q., & Yuan, Z. (2021). Two-stage distributionally robust optimization for maritime inventory routing. Computers & Chemical Engineering, 149, 107307. [link]

28. Feng, W., Feng, Y., & Zhang, Q. (2021). Multistage robust mixed-integer optimization under endogenous uncertainty. European Journal of Operational Research, 294, 460-475. [link] [preprint] [talk]

27. Wang, H., Palys, M. J., Daoutidis, P., & Zhang, Q. (2021). Optimal design of sustainable ammonia-based food-energy-water systems with nitrogen management. ACS Sustainable Chemistry & Engineering, 9, 2816-2834. [link] [preprint] [talk]

26. Palys, M. J., Wang, H., Zhang, Q., & Daoutidis, P. (2021). Renewable ammonia for sustainable energy and agriculture: vision and systems engineering opportunities. Current Opinion in Chemical Engineering, 31, 100667. [link] [preprint]

25. Zhang, Q. & Feng, W. (2020). A unified framework for adjustable robust optimization with endogenous uncertainty. AIChE Journal, 66(12), e17047. Invited article for Futures Issue. [link] [preprint] [talk]

24. Shetty, M., Walton, A., Gathmann, S. R., Ardagh, M. A., Gepeesingh, J., Resasco, J., Birol, T., Zhang, Q., Tsapatsis, M., Vlachos, D. G., Christopher, P., Frisbie, C. D., Abdelrahman, O. A., & Dauenhauer, P. J. (2020). The catalytic mechanics of dynamic surfaces: stimulating methods for promoting catalytic resonance. ACS Catalysis, 10, 12666-12695. [link]

23. Liu, B., Zhang, Q., Ge, X., & Yuan, Z. (2020). CVaR-based approximations of Wasserstein distributionally robust chance constraints with application to process scheduling. Industrial & Engineering Chemistry Research, 59, 9562-9574. [link]

22. Allman, A. & Zhang, Q. (2020). Dynamic location of modular manufacturing facilities with relocation of individual modules. European Journal of Operational Research, 286, 494-507. [link] [preprint]

21. Ardagh, M. A., Shetty, M., Kuznetsov, A., Zhang, Q., Christopher, P., Vlachos, D., Abdelrahman, O. A., & Dauenhauer, P. J. (2020). Catalytic resonance theory: parallel reaction pathway control. Chemical Science, 11, 3501-3510. [link]

20. Kuznetsov, A., Kumar, G., Ardagh, M. A., Tsapatsis, M., Zhang, Q., & Dauenhauer, P. J. (2020). On the economics and process design of renewable butadiene from biomass-derived furfural. ACS Sustainable Chemistry & Engineering, 8, 3273-3282. [link]

19. Allman, A. & Zhang, Q. (2020). Distributed cooperative industrial demand response. Journal of Process Control, 86, 81-93. [link] [preprint]

18. Ardagh, M. A., Birol, T., Zhang, Q., Abdelrahman, O. A., & Dauenhauer, P. J. (2019). Catalytic resonance theory: superVolcanoes, catalytic molecular pumps, and oscillatory steady state. Catalysis Science & Technology, 9, 5058-5076. [link]

17. Flores-Quiroz, A., Pinto, J. M., & Zhang, Q. (2019). A column generation approach to multiscale capacity planning for power-intensive process networks. Optimization & Engineering, 20(4), 1001-1027. Invited article for Special Issue: Enterprise-wide Optimization. [link] [preprint]

16. Zhang, Q., Martín, M., & Grossmann, I. E. (2019). Integrated design and operation of renewables-based fuels and power production networks. Computers & Chemical Engineering, 122, 80-92. [link]

15. Zhang, Q., Bremen, A. M., Grossmann, I. E., & Pinto, J. M. (2018). Long-term electricity procurement for large industrial consumers under uncertainty. Industrial & Engineering Chemistry Research, 57, 3333-3347. [link]

14. Castro, P. M., Grossmann, I. E., & Zhang, Q. (2018). Expanding scope and computational challenges in process scheduling. Computers & Chemical Engineering, 114, 14-42. [link]

13. Luo, Y., Zhang, Q., Zhu, L., & Chen, X. (2018). Optimal operation of parallel distillation systems with multiple product grades: An industrial case study. Computers & Chemical Engineering, 111, 210-224. [link]

12. Curcio, E., Amorim, P., Zhang, Q., & Almada-Lobo, B. (2018). Adaptation and approximate strategies for solving the lot-sizing and scheduling problem under multistage demand uncertainty. International Journal of Production Economics, 202, 81-96. [link]

11. Grossmann, I. E., Apap, R. M., Calfa, B. A., Garcia-Herreros, P., & Zhang, Q. (2017). Mathematical programming techniques for optimization under uncertainty and their application in process systems engineering. Theoretical Foundations of Chemical Engineering, 51(6), 893-909. [link]

10. Zhang, Q., Sundaramoorthy, A., Grossmann, I. E., & Pinto, J. M. (2017). Multiscale production routing in multicommodity supply chains with complex production facilities. Computers & Operations Research, 79, 207-222. [link]

9. Zhang, Q. & Grossmann, I. E. (2016). Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives. Chemical Engineering Research & Design, 116, 114-131. Invited article for Roger Sargent Special Issue. [link]

8. Zhang, Q., Lima, R. M., & Grossmann, I. E. (2016). On the relation between flexibility analysis and robust optimization for linear systems. AIChE Journal, 62(9), 3109-3123. Invited article for Tribute to Founders: Roger Sargent. [link]

7. Grossmann, I. E., Apap, R. M., Calfa, B. A., Garcia-Herreros, P., & Zhang, Q. (2016). Recent advances in mathematical programming techniques for the optimization of process systems under uncertainty. Computers & Chemical Engineering, 91, 3-14. [link]

6. Zhang, Q., Morari, M. F., Grossmann, I. E., Sundaramoorthy, A., & Pinto, J. M. (2016). An adjustable robust optimization approach to scheduling of continuous industrial processes providing interruptible load. Computers & Chemical Engineering, 86, 106-119. [link]

5. Zhang, Q., Cremer, J. L., Grossmann, I. E., Sundaramoorthy, A., & Pinto, J. M. (2016). Risk-based integrated production scheduling and electricity procurement for power-intensive continuous processes. Computers & Chemical Engineering, 86, 90-105. [link]

4. Zhang, Q., Sundaramoorthy, A., Grossmann, I. E., & Pinto, J. M. (2016). A discrete-time scheduling model for continuous power-intensive process networks with various power contracts. Computers & Chemical Engineering, 84, 382-393. [link]

3. Zhang, Q., Grossmann, I. E., Sundaramoorthy, A., & Pinto, J. M. (2016). Data-driven construction of Convex Region Surrogate models. Optimization & Engineering, 17(2), 289-332. [link]

2. Zhang, Q., Grossmann, I. E., Heuberger, C. F., Sundaramoorthy, A., & Pinto, J. M. (2015). Air separation with cryogenic energy storage: Optimal scheduling considering electric energy and reserve markets. AIChE Journal, 61(5), 1547-1558. [link]

1. Zhang, Q., Shah, N., Wassick, J., Helling, R., & Van Egerschot, P. (2014). Sustainable supply chain optimisation: An industrial case study. Computers & Industrial Engineering, 74, 68-83. [link]