Academy Discounts

Get a 10% discount on all eLearning courses until November 29, 2020. Use code ELEARN10 at checkout.

(132d) Data-Driven Approach to Systematic Process Intensification Using Building Block Superstructure

Kim, S. H., Georgia Institute of Technology
Demirel, S. E., Artie McFerrin Department of Chemical Engineering, Texas A&M University
Li, J., Artie McFerrin Department of Chemical Engineering, Texas A&M University
Hasan, M. M. F., Artie McFerrin Department of Chemical Engineering, Texas A&M University
Realff, M., Georgia Institute of Technology
Boukouvala, F., Georgia Institute of Technology
Process intensification (PI) involves improvements in process performance that can lead to substantially smaller, cleaner, safer, and more energy-efficient technologies [1, 2]. However, identification of intensified solutions at the conceptual design stage is a challenge as there can be a plethora of possible process configurations. Also known as superstructure optimization, optimization-based process synthesis can be used to identify the optimal process configuration. However, traditional optimization-based approach requires all process configurations and alternatives to be specified beforehand with fixed connectivity and equipment types. Such pre-postulated superstructure may not exist in some cases; thus, the optimal intensified process cannot be identified [3].

A building block superstructure has been proposed recently to overcome the aforementioned challenge [4]. Unlike the conventional superstructure approach, the building block superstructure does not require a priori postulation of process alternatives. Instead, each building block is used to represent a fundamental constituent of a unit operation, which can then be combined together to represent physicochemical phenomena. These building blocks can be positioned on a two-dimensional grid, and the optimal intensified flowsheet can be automatically generated by solving a mixed-integer nonlinear programming (MINLP) model [5]. However, as these building blocks are represented using a set of algebraic equations describing complex thermodynamic and material and energy transfer, it contains several non-convex equations, which increases the complexity of the final optimization model. Therefore, locating an optimal solution still remains a challenge [6].

In this work, we propose the use of data-driven approach for the design of a building block superstructure. Specifically, instead of relying on a complicated set of algebraic equations describing physical phenomena, we construct accurate and tractable machine learning-based process models to reduce the complexity of the building block superstructure model [7]. In particular, we use an iterative machine learning-based modeling and optimization algorithm, which involves: 1) constructing an efficient design of experiments, 2) constructing machine learning-based process models, and 3) formulating and solving the machine learning-based MINLP problem. A case study on a reactive separation system with non-ideal thermodynamics will be presented, and known process information and constraints will be considered during the design stage to identify a realistic intensified design.

  1. Stankiewicz, A. and J.A. Moulijn, Process Intensification: Transforming Chemical Engineering. Vol. 96. 2000. 22-33.
  2. Tian, Y., et al., An overview of process systems engineering approaches for process intensification: State of the art. Chemical Engineering and Processing - Process Intensification, 2018. 133: p. 160-210.
  3. Demirel, S.E., J. Li, and M.M.F. Hasan, A General Framework for Process Synthesis, Integration, and Intensification. Industrial and Engineering Chemistry Research, 2019. 58(15): p. 5950-5967.
  4. Li, J., S.E. Demirel, and M.M.F. Hasan, Process synthesis using block superstructure with automated flowsheet generation and optimization. AIChE Journal, 2018. 64(8): p. 3082-3100.
  5. Demirel, S.E., J. Li, and M.M.F. Hasan, Systematic process intensification using building blocks. Computers & Chemical Engineering, 2017. 105: p. 2-38.
  6. Boukouvala, F., M.M.F. Hasan, and C.A. Floudas, Global optimization of general constrained grey-box models: new method and its application to constrained PDEs for pressure swing adsorption. Journal of Global Optimization, 2017. 67(1): p. 3-42.
  7. Kim, S.H. and F. Boukouvala, Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques. Optimization Letters, 2019.