How a Quantum Computer Could Quantify Uncertainty in Microkinetic Models

Becerra, Alejandro; Prabhu, Anand; Rongali, Mary Sharmila; Velpur, Sri Charan Simha; Debusschere, Bert; Walker, Eric A.

doi:10.1021/acs.jpclett.1c01917

Cited by 11 publications

(10 citation statements)

References 33 publications

(55 reference statements)

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“…Reiher et al [Reiher et al, 2017] propose to calculate the reaction mechanisms by implementing a multi-configurational wave function as a sub-model to be solved on a quantum computer to improve/enhance classical methods such as density functional theory (DFT). Apart from enzyme design Cheng et al [2020], these kind of computational studies of reaction mechanisms and kinetics can be found for a wide area of high impact applications such as green catalysis of the Mannich reaction Stevens [2017], biochemical redox reactions [Jinich et al, 2019] and calculating uncertainties in microkinetic models Becerra et al [2021].…”

Section: Biochemical Applicationsmentioning

confidence: 99%

Quantum Computing: Fundamentals, Trends and Perspectives for Chemical and Biochemical Engineers

Nourbakhsh¹,

Jones²,

Kristjuhan³

et al. 2022

Preprint

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We use the benefits and components of classical computers every day. However, there are many types of problems which, as they grow in size, their computational complexity grows larger than classical computers will ever be able to solve. Quantum computing (QC) is a computation model that uses quantum physical properties to solve such problems. QC is at the early stage of large-scale adoption in various industry domains to take advantage of the algorithmic speed-ups it has to offer. It can be applied in a variety of areas, such as computer science, mathematics, chemical and biochemical engineering, and the financial industry. The main goal of this paper is to give an overview to chemical and biochemical researchers and engineers who may not be familiar with quantum computation. Thus, the paper begins by explaining the fundamental concepts of QC. The second contribution this publication tries to tackle is the fact that the chemical engineering literature still lacks a comprehensive review of the recent advances of QC. Therefore, this article reviews and summarizes the state of the art to gain insight into how quantum computation can benefit and optimize chemical engineering issues.A bibliography analysis covers the comprehensive literature in QC and analyzes quantum computing research in chemical engineering on various publication topics, using Clarivate analytics covering the years 1990 to 2020. After the bibliographic analysis, relevant applications of QC in chemical and biochemical engineering are highlighted and a conclusion offers an outlook of future directions within the field.

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Section: Biochemical Applicationsmentioning

confidence: 99%

Quantum Computing: Fundamentals, Trends and Perspectives for Chemical and Biochemical Engineers

Nourbakhsh¹,

Jones²,

Kristjuhan³

et al. 2022

Preprint

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“…This scenario above is known as a reduced kinetic model. 19,31 In eqn (7a) and (7b), two reacting species and one inert species are modeled. H 2 association is connected with the rate constant k 5 , and O 2 dissociation is connected with the rate constant k À6 .…”

Section: Jacobian Linearized Ode Modelmentioning

confidence: 99%

How a quantum computer could accurately solve a hydrogen-air combustion model

et al. 2022

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“…Each MK model integration takes a few minutes to complete. Additionally, large MK models are often sensitive to the initial operating conditions (Becerra et al, 2021), i.e., small perturbations in initial conditions can cause large changes in the output. Therefore, we anticipate a MK model embedded in process flowsheet with recycle loops would need to be integrated tens to hundreds of times to converge the flowsheet in sequential modular simulation environments (e.g., AspenPlus) which would make process simulation or optimization cumbersome and computationally intensive.…”

Section: Mk Model Reductionmentioning

confidence: 99%

Nonlinear Reactor Design Optimization With Embedded Microkinetic Model Information

2022

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Despite the success of multiscale modeling in science and engineering, embedding molecular-level information into nonlinear reactor design and control optimization problems remains challenging. In this work, we propose a computationally tractable scale-bridging approach that incorporates information from multi-product microkinetic (MK) models with thousands of rates and chemical species into nonlinear reactor design optimization problems. We demonstrate reduced-order kinetic (ROK) modeling approaches for catalytic oligomerization in shale gas processing. We assemble a library of six candidate ROK models based on literature and MK model structure. We find that three metrics—quality of fit (e.g., mean squared logarithmic error), thermodynamic consistency (e.g., low conversion of exothermic reactions at high temperatures), and model identifiability—are all necessary to train and select ROK models. The ROK models that closely mimic the structure of the MK model offer the best compromise to emulate the product distribution. Using the four best ROK models, we optimize the temperature profiles in staged reactors to maximize conversions to heavier oligomerization products. The optimal temperature starts at 630–900K and monotonically decreases to approximately 560 K in the final stage, depending on the choice of ROK model. For all models, staging increases heavier olefin production by 2.5% and there is minimal benefit to more than four stages. The choice of ROK model, i.e., model-form uncertainty, results in a 22% difference in the objective function, which is twice the impact of parametric uncertainty; we demonstrate sequential eigendecomposition of the Fisher information matrix to identify and fix sloppy model parameters, which allows for more reliable estimation of the covariance of the identifiable calibrated model parameters. First-order uncertainty propagation determines this parametric uncertainty induces less than a 10% variability in the reactor optimization objective function. This result highlights the importance of quantifying model-form uncertainty, in addition to parametric uncertainty, in multi-scale reactor and process design and optimization. Moreover, the fast dynamic optimization solution times suggest the ROK strategy is suitable for incorporating molecular information in sequential modular or equation-oriented process simulation and optimization frameworks.

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How a Quantum Computer Could Quantify Uncertainty in Microkinetic Models

Cited by 11 publications

References 33 publications

Quantum Computing: Fundamentals, Trends and Perspectives for Chemical and Biochemical Engineers

Quantum Computing: Fundamentals, Trends and Perspectives for Chemical and Biochemical Engineers

How a quantum computer could accurately solve a hydrogen-air combustion model

Nonlinear Reactor Design Optimization With Embedded Microkinetic Model Information

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