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ALAMO publications

The following publications describe ALAMO, its methodology, and various related algorithms and applications:

  1. Cozad, A., N. V. Sahinidis and D. C. Miller, Learning surrogate models for simulation-based optimization, AIChE Journal, 60, 2211-2227, 2014.
  2. Miller, D. C., M. Syamlal, D. S. Mebane, C. B. Storlie, D. Bhattacharyya, N. V. Sahinidis, D. Agarwal, C. Tong, S. E. Zitney, A. Sarkar, X. Sun, S. Sundaresan, E. M. Ryan, D. Engel and C. Dale, Carbon capture simulation initiative: A case study in multi-scale modeling and new challenges, Annual Reviews of Chemical and Biomolecular Engineering, 5, 301-323, 2014.
  3. Cozad, A., N. V. Sahinidis and D. C. Miller, A combined first-principles and data-driven approach to model building, Computers & Chemical Engineering, 73, 116-127, 2015.
  4. Miller, D. C., D. Agarwal, D. Bhattacharyya, J. Boverhof, Y.-W. Cheah, Y. Chen, J. Eslick, J. Leek, J. Mae, P. Mahapatra, B. Ng, N. V. Sahinidis, C. Tong, S. E. Zitney, Innovative computational tools and models for the design, optimization and control of carbon capture processes, Computer Aided Chemical Engineering, 38, 2391-2396, 2016.
  5. Wilson, Z. T and N. V. Sahinidis, The ALAMO approach to machine learning, Computers & Chemical Engineering, 106, 785-795, 2017.
  6. Lindqvist, K., Z. T. Wilson, E. Næss and N. V. Sahinidis, A machine learning approach to correlation development applied to fin-tube bundle heat exchangers, Energies, 11(12), 3450, 2018.
  7. Cozad, A. and N. V. Sahinidis, A global MINLP approach to symbolic regression, Mathematical Programming, 170, 97-119, 2018.
  8. Wilson, Z. and N. V. Sahinidis, Automated learning of chemical reaction networks, Computers & Chemical Engineering, 127, 88-98, 2019.
  9. Sarwar, O., B. Sauk and N. V. Sahinidis, A discussion on practical considerations with sparse regression methodologies, Statistical Science, 35, 593-601, 2020.
  10. Sauk, B. and N. V. Sahinidis, Backward stepwise elimination: Approximation guarantee, a batched GPU algorithm, and empirical investigation, SN Computer Science, 2:396, 2021.
  11. Na, J., J. H. Bak and N. V. Sahinidis, Efficient Bayesian inference using adversarial machine learning and low-complexity surrogate models, Computers & Chemical Engineering, 151, 107322, 2021.
  12. Engle, M. and N. V. Sahinidis, Deterministic symbolic regression with derivative information: General methodology and application to equations of state, AIChE Journal, 68, e17457, 2022.
  13. Ma, K., N. V. Sahinidis, S. Amaran, R. Bindlish, S. J. Bury, D. Griffith and S. Rajagopalan, Data-driven strategies for optimization of integrated chemical plants, Computers and Chemical Engineering, accepted, 2022.
  14. Ma, K., N. V. Sahinidis, R. Bindlish, S. J. Bury, R. Haghpanah and S. Rajagopalan, Data-driven strategies for extractive distillation unit optimization, Computers and Chemical Engineering, accepted, 2022.