(732i) Modelling for Optimal Operation of Modular Integrated Methane Dehydroaromatization Process

"Natural gas is a viable alternative to crude oil-based feedstock to meet energy and chemical manufacturing demands. However, many of these deposits are in remote locations, motivating the development of on-site manufacturing approaches, known as modular technologies, for effective process intensification and economical production of desired products [1, 2]. Herein, a novel modular methane dehydroaromatization process integrating dehydroaromatization (DHA), chemical looping (CL), and temperature swing adsorption (TSA), proposed by Brady et.al [3] is considered. Experiments have proven the process to be a promising technology in terms of yield and energy consumption compared to conventional process technologies [4, 5]. However, the dynamics of the modular process operated in semi-batch units, involving interactions due to material re-circulation and non-linear rate kinetics, are yet to be understood from an operations perspective. Understanding the process operation is challenging because process variables are not easily measured, limiting the amount of process data needed to verify the process of interest.

This work aims to develop a dynamic model for the modular integrated dehydroaromatization process
that can describe the operating space by elucidating the process dynamics using a restricted set of process information. A novel iterative modeling framework is proposed that integrates active learning principles with state estimation techniques [6, 7, 8]. The iterative modeling framework consists of an integrated model development step for optimal dynamic experiment design and a state estimator to estimate the restricted information in the process of interest [9, 10]. This approach will allow us to learn the integrated process model iteratively through experimentation, even when the availability of process data is restricted. These models will be used to develop safe and optimal process operation strategies.

As a first step, an integrated model is being developed for the process of interest to design optimal
experiments and develop a state estimator. A stand-alone process model developed for DHA, CL, and TSA, as advection-diffusion-reaction partial differential equations, were numerically simulated using the method of lines. The verified stand-alone process models are being integrated, considering material re-circulation in semi-batch operation, using a simultaneous modeling approach incorporating a singular perturbation technique to obtain the solution [9]. Later, state estimators will be developed to extract information and update the integrated process model to design the experiments.

References

[1] Baldea Michael, Edgar Thomas F, Stanley Bill L, Kiss Anton A. Modular manufacturing processes:
Status, challenges, and opportunities AIChE journal. 2017;63:4262-4272.

[2] Bielenberg James, Palou-Rivera Ignasi. The RAPID Manufacturing Institute - Reenergizing US efforts
in process intensification and modular chemical processing Chemical Engineering and Processing.
2019;138.

[3] Brady Casper, Debruyne Quinten, Majumder Ankita, et al. An integrated methane dehydroaromatization and chemical looping process Chemical Engineering Journal. 2021;406:127168.

[4] Zichittella Guido, P ́erez-Ram ́ırez Javier. Status and prospects of the decentralised valorisation of
natural gas into energy and energy carriers Chemical Society Reviews. 2021;50:2984-3012.

[5] Ren Tao, Patel Martin, Blok Kornelis. Olefins from conventional and heavy feedstocks: Energy use in
steam cracking and alternative processes Energy. 2006;31:425-451.

[6] Bonvin D, Georgakis C, Pantelides CC, et al. Linking models and experiments Industrial & Engineering
Chemistry Research. 2016;55:6891-6903.

[7] Settles Burr. Active learning Synthesis lectures on artificial intelligence and machine learning.
2012;6:1-114.

[8] Dochain Denis. State and parameter estimation in chemical and biochemical processes: a tutorial
Journal of process control. 2003;13:801-818.

[9] Baldea Michael. From process integration to process intensification Computers & Chemical Engineer-
ing. 2015;81:104-114.

[10] Franceschini Gaia, Macchietto Sandro. Model-based design of experiments for parameter precision:
State of the art Chemical Engineering Science. 2008;63:4846-4872.

"