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Data-Driven Model Reduction, Scientific Frontiers, and Applications ()

Akhil Arora, Artie McFerrin Department of Chemical Engineering
Reduced Order Model-Based Process Synthesis, Optimization, and Intensification

Authors

  • Akhil Arora
  • M.M. Faruque Hasan

Abstract

A majority of chemical systems with complex physicochemical phenomena can be studied through physics-based first-principles models. The resulting mathematical models typically consist of ordinary differential equation, and/or partial differential equation systems for computing spatial and/or temporal variations. However, such detailed models are inappropriate for optimization as discretization of the representative differential equations in space and/or time often result in large-scale nonlinear problems. In this presentation, through a variety of chemical systems, we demonstrate how effective data-driven strategies can be conceptualized and implemented instead to yield tractable optimization problems with lesser computational intensiveness. The case studies studied are prevalent in Oil and Gas industry for methanol, ammonia, and hydrogen production via natural gas utilization. Firstly, for steady-state methanol and ammonia synthesis, accurate surrogate models are developed using ALAMO which then replace the underlying ODE-based models in the overall optimization formulation [1, 2, 3]. Secondly, to study more complex chemically-intensified systems, an in-house simulation-based grey-box optimization algorithm is presented where surrogate models are developed on-the-fly for approximating black-box objective and constraints [4]. The developed algorithm is applied to optimize several process intensification case studies including sorption-enhanced steam methane reforming and sorption-enhanced methanol synthesis [5, 6].

References

  1. A. Arora, J. Li, M.S. Zantye, and M.M.F. Hasan, Process Design Frameworks for Economic Utilization of Small-Scale and Unconventional Feedstocks, Found. Comput. Process Des. (), Accepted .
  2. A. Arora, J. Li, M.S. Zantye, and M.M.F. Hasan, Functionality-Based Design Framework for Reducing Capital Intensity of Small-Scale, Modular Processes (), Submitted.
  3. Z.T. Wilson, N.V. Sahinidis, The {ALAMO} Approach to Machine Learning, Comput. Chem. Eng. 106 (), 785–795.
  4. I. Bajaj, S.S. Iyer, and M.M.F. Hasan, A Trust Region-Based Two Phase Algorithm for Constrained Black-Box and Grey-Box Optimization With Infeasible Initial Point, Comput. Chem. Eng. 116 (), 306–321.
  5. A. Arora, I. Bajaj, S.S. Iyer, and M.M.F. Hasan, Optimal Synthesis of Periodic Sorption Enhanced Reaction Processes with Application to Hydrogen Production, Comput. Chem. Eng. 115 (), 89–111.
  6. A. Arora, S.S. Iyer, I. Bajaj, and M.M.F. Hasan, Optimal Methanol Production Via Sorption Enhanced Reaction Process, Ind. Eng. Chem. Res. 57 (), 14143–14161.