An adaptive sampling surrogate model building framework for the optimization of reaction systems

Franzoi, Robert E.; Kelly, Jeffrey D.; Menezes, Brenno C.; Swartz, Christopher L.E.

doi:10.1016/j.compchemeng.2021.107371

Cited by 13 publications

(4 citation statements)

References 42 publications

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“…Moreover, the experimental determination process is usually time-consuming, and leveraging big data further brings a great challenge. ML is flexible and efficient to recommend the most suitable solution based on reaction data. − So far, there may have been three general types of applications of ML for optimization of reaction conditions to maximize the reaction yield/selectivity/conversion: (i) coupling ML/DL with physical models; (ii) coupling ML/DL with experiments; (iii) coupling ML/DL with robotic platforms as an optimizer. In particular, multiobjective optimization is most often applied for the identification of the trade-offs between the criteria of conflicting performance.…”

Section: Current Status and Challengesmentioning

confidence: 99%

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Zhu

Chen

Ouyang

et al. 2022

Ind. Eng. Chem. Res.

116

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Artificial intelligence (AI), machine learning (ML), and data science are leading to a promising transformative paradigm. ML, especially deep learning and physics-informed ML, is a valuable toolkit that complements incomplete domain-specific knowledge in conventional experimental and computational methods. ML can provide flexible techniques to facilitate the conceptual development of new robust predictive models for multiphase flows and reactors by finding hidden pattern/information/mechanism in a data set. Due to such emergence, we thereby comprehensively survey, explore, analyze, and discuss key advancements of recent ML applications to hydrodynamics, heat and mass transfer, and reactions in single-phase and multiphase flow systems from different aspects: (1) development of multiphase closure models of drag force, turbulence stresses and heat/mass transfer to improve the accuracy and efficiency of typical CFD simulations; (2) image reconstruction, regime identification, key parameter predictions, and optimization of multiphase flow and transport fields; (3) reaction kinetics modeling (e.g., predictions of reaction networks, kinetic parameters, and species production) and reaction condition optimization. These sections also discuss and analyze the key advantages and weakness of ML for solving the problems in the domain of multiphase flows and reactors. Finally, we summarize the under-solving challenges and opportunities in order to identify future directions that would be useful for the research community. Future development and study of multiphase flows and reactors are envisaged to be accelerated by ML and data science.

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Section: Current Status and Challengesmentioning

confidence: 99%

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Zhu

Chen

Ouyang

et al. 2022

Ind. Eng. Chem. Res.

116

View full text Add to dashboard Cite

show abstract

“…Trust region methods are one way to overcome this limitation (Eason and Biegler, 2016). For example, Franzoi et al (2021) use a trust region based adaptive sampling strategy to develop highly accurate multipleinput single-output (MISO) surrogate models to replace Arrhenius-form kinetics in the Williams-Otto optimization problem (Williams and Otto, 1960). Using this approach, they iteratively refined the surrogate model and performed optimization, ultimately obtaining solutions within 0.016% of the best known objective value.…”

Section: Machine Learning Emulatorsmentioning

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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“…T HE rapid growth of the Industry 4.0 (I4) segments classified here as information and communication technologies (ICT), modeling and solving algorithms (MSA), highperforming computing (HPC), and mechatronics (MEC) has introduced enhanced decision-making tools, computational power, management of information, operational efficiency, visualization, control, monitoring, among others [1]- [3]. Among these, recent technological advances in (a) image processing with deep learning algorithms and (b) decisionmaking capabilities with novel flowsheet framework, provide the resources required for handling complex problems in a wide variety of applications such as in online scheduling strategies [4], in which systematic and autonomous systems have been increasingly employed for industrial processes, permitting the integration of design and control environments [5].…”

Section: Introductionmentioning

confidence: 99%

Cyber-Physical System Demonstration of an Automated Shuttle-Conveyor-Belt Operation for Inventory Control of Multiple Stockpiles: A Proof of Concept

et al. 2022

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Smart manufacturing in the so-called Industry 4.0 age pushes the research and development of laboratory-scale proof of concepts before its deployment in pilots and real-size equipment. As such, we present a cyber-physical system (CPS) demonstration in the mining industry field engineered to autonomously manage the handling of solids flowing in a conveyor-belt that drops materials in containers, forming multiple stockpiles per belt. The CPS operates to control multiple stockpiles' inventories using mixed-integer optimization that minimizes the square deviation of the measured inventory to their targets (heights). Within the sensing-optimizing-actuating (SOA) cycle, the CPS demonstration is performed as follows. First, the sensing (data measurement, data processing, and system evaluation) uses a deep neural network in real-time to assess the level of materials stored in transparent containers. Second, the optimizing (mathematical programming, optimization techniques, and decision-making capabilities) is performed using a flowsheet network formulation called unit-operation-port-state superstructure (UOPSS) that permits a fast solution for the position-idle-time-varying discrete manipulated variables as operational schedules. Third, the actuating (cyber-physical integration) implements a physical actuation solution through an integrated CPS environment. According to the findings of our experimentation, stockpiling process control in a smart manufacturing context has enormous potentials to control multiple stockpiles' inventory autonomously.

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An adaptive sampling surrogate model building framework for the optimization of reaction systems

Cited by 13 publications

References 42 publications

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Nonlinear Reactor Design Optimization With Embedded Microkinetic Model Information

Cyber-Physical System Demonstration of an Automated Shuttle-Conveyor-Belt Operation for Inventory Control of Multiple Stockpiles: A Proof of Concept

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