A Perspective on the Impact of Process Systems Engineering on Reaction Engineering

Sivaramakrishnan, Kaushik; Puliyanda, Anjana; Tefera, Dereje Tamiru; Ganesh, Ajay; Thirumalaivasan, Sushmitha; Prasad, Vinay

doi:10.1021/acs.iecr.9b00280

Cited by 12 publications

(8 citation statements)

References 277 publications

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“…Further details can be found in a range of reviews – from older work summarising the important considerations for polymerisation process control 202–205,212 to more general process control work which discuss polymerisation processes in some detail. 213…”

Section: Automationmentioning

confidence: 99%

“…Further details can be found in a range of reviewsfrom older work summarising the important considerations for polymerisation process control [202][203][204][205]212 to more general process control work which discuss polymerisation processes in some detail. 213 The ACOMP system clearly in and of itself involves automationand is an ideal basis for further innovation. Recent work using the platform has shown not only automated analysis, but integration of optimisation systems.…”

Section: Automationmentioning

confidence: 99%

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Enabling technologies in polymer synthesis: accessing a new design space for advanced polymer materials

Knox

Warren

2020

React. Chem. Eng.

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Section: Automationmentioning

confidence: 99%

Section: Automationmentioning

confidence: 99%

Enabling technologies in polymer synthesis: accessing a new design space for advanced polymer materials

Knox

Warren

2020

React. Chem. Eng.

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“…Advancement in reaction engineering using the principles of process systems engineering suggests using data fusion algorithms as soft sensors to obtain interpretable latent factors by imposing physically meaningful constraints . This spans two broad areas of chemometrics which are preliminary to state and parameter estimation, and control of product composition: (1) curve resolution (chemical signatures) and (2) calibration (compositions) .…”

Section: Introductionmentioning

confidence: 99%

Structure-Preserving Joint Non-negative Tensor Factorization to Identify Reaction Pathways Using Bayesian Networks

Puliyanda

Sivaramakrishnan

et al. 2021

J. Chem. Inf. Model.

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Extracting meaningful information from spectroscopic data is key to species identification as a first step to monitoring chemical reactions in unknown complex mixtures. Spectroscopic data obtained over multiple process modes (temperature, residence time) from different sensors [Fourier transform infrared (FTIR), proton nuclear magnetic resonance ( 1 H NMR)] comprise hidden complementary information of the underlying chemical system. This work proposes an approach to jointly capture these hidden patterns in a structure-preserving and interpretable manner using coupled non-negative tensor factorization to achieve uniqueness in decomposition. Projections onto the modes of spectral channels, specific to each sensor, are interpreted as pseudo-component spectra, while projections onto the shared process modes are interpreted as the corresponding pseudo-component concentrations across temperature and residence times. Causal structure inference among these pseudo-component spectra (using Bayesian networks) is then used to identify plausible reaction pathways among the identified species representing each pseudo-component. Tensor decomposition of the FTIR data enables the development of reaction sequences based on the identified functional groups, while that of 1 H NMR by itself is lacking in mechanism development as it solely reveals the proton environments in a pseudocomponent. However, jointly parsing spectra from both the sensors is seen to capture complementary information, wherein insights into the proton environment from 1 H NMR disambiguate pseudo-components that have similar FTIR peaks. A scalable method of parallelizing tensor decomposition to handle high-dimensional modes in process data by using grid tensor factorization, while being robust to process data artifacts like outliers, noise, and missing data, has been developed.

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“…Model predictive control (MPC) has become an attractive control strategy for a wide range of applications, ranging from traditional process control applications to more recent applications for autonomous systems 2 . Nonlinear MPC is the most popular and powerful nonlinear control method for nonlinear reactor control and chemical process systems 3 . MPC uses an accurate process model to predict its future behavior and compute the optimum trajectory for manipulated variables, by minimizing a predicted performance index while satisfying operating constraints.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear model predictive control for distributed parameter systems by time–space‐coupled model reduction

et al. 2021

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Nonlinear high‐dimensional distributed parameter systems (DPSs) described by sets of parabolic partial different equations (PDEs) exhibit a dominant, low‐dimensional slow behavior that can be captured using model reduction. A time–space‐coupled model reduction architecture combining encoder–decoder networks with recurrent neural networks (RNNs) was presented in our previous work, for modeling the spatiotemporal dynamics of DPSs without recourse to the governing equations. In this work, we further understand the stability of the training dynamics of the deep architecture by using the Lyapunov exponents (LEs). Subsequently, we construct nonlinear model predictive control (MPC) formulations for the DPS based on the learned, dimensional‐reduced model. We use a path‐integral optimal control algorithm for MPC implementation to avoid any analytic derivatives of the dynamics. The effectiveness of integration of a deep neural network‐based model with MPC is demonstrated in a tubular reactor with recycle cases. The results of the simulation also show that the LE can serve as a readout of training stability for the learned dynamical model.

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A Perspective on the Impact of Process Systems Engineering on Reaction Engineering

Cited by 12 publications

References 277 publications

Enabling technologies in polymer synthesis: accessing a new design space for advanced polymer materials

Enabling technologies in polymer synthesis: accessing a new design space for advanced polymer materials

Structure-Preserving Joint Non-negative Tensor Factorization to Identify Reaction Pathways Using Bayesian Networks

Nonlinear model predictive control for distributed parameter systems by time–space‐coupled model reduction

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