An Integration Framework for CO
            <sub>2</sub>
            Capture Processes

Sahraei, M. Hossein; Ricardez‐Sandoval, Luis A.

doi:10.1002/9781119106418.ch20

Cited by 1 publication

(1 citation statement)

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“…Advantages of MATLAB over other programming languages are: reliable optimization and machine-learning toolboxes, which have been well-established for commercial use. The automation of MATLAB to process simulators has previously been reported in process design [37][38][39] and control. 40 As shown in Figure 4, the inputs are sampled by LHS in MATLAB and passed to simulators, for example, Dymola for dynamic simulations and Aspen Plus for steady-state process simulations.…”

Section: Workflow For Surrogate Constructionmentioning

confidence: 99%

Efficient surrogates construction of chemical processes: Case studies on pressure swing adsorption and gas‐to‐liquids

2022

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We propose a sequential sampling approach to training statistical digital twins. This approach is relevant for real-world engineering problems with expensive data generation. Prerequisite for building surrogates is sufficient data; however, oversampling does not improve regression accuracy. The time for data generation may be reduced by: (a) applying a classifier to improve data quality and avoid evaluation of infeasible inputs, and (b) employing dynamic sampling linked to regression quality. In dynamic sampling, the initial sampling rate is large to generate enough data for surrogate regression in a few iterations; the sampling rate gradually slows down with the improvement of the iteratively refined surrogate. A dynamic process and a steadystate process from the field of carbon capture and utilization are used as case studies: pressure swing adsorption (PSA) and gas-to-liquids (GTL). The computational costs for surrogates generation are reduced by 86% for PSA and 51% for GTL, compared with employing a static sampling rate.

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