Convolutional Neural Network–Aided Temperature Field Reconstruction: An Innovative Method for Advanced Reactor Monitoring

Leite, Victor Coppo; Merzari, Elia; Ponciroli, Roberto; Ibarra, Lander

doi:10.1080/00295450.2022.2151822

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…Efficiency in non-isothermal continuous stirred tank reactor temperature estimation was evaluated by Apriliani et al [17], comparing the reduced rank ensemble Kalman filter to other filters and underscoring the computational superiority and robustness of the ensemble Kalman filter. Addressing reactor monitoring, Leite et al [18] proposed a physics-informed Convolutional Neural Network (CNN) to reconstruct temperature fields from constrained boundary measurements, effectively pinpointing complex localized temperature peaks.…”

Section: State Estimationmentioning

confidence: 99%

Advances in Machine Learning for Monitoring, Control, and Optimization of Temperature of Reactors

Ahmad

2023

Preprint

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Machine Learning (ML) has brought about a transformative era in reactor operations, reshaping monitoring, control, and optimization strategies. This survey comprehensively explores the diverse spectrum of ML applications in industrial reactors. From real-time sensor analysis ensuring reactor functionality to adaptive control algorithms ensuring stability, ML's impact is profound and multifaceted. The benefits of ML are equally evident in optimization, encompassing performance trend prediction, proactive maintenance, and fine-tuning of operations for enhanced efficiency. This review identifies dominant ML techniques, operations stages receptive to ML integration, core data sources, and critical input-output parameters. Aimed at both academics and practitioners, this exploration enriches reactor technologies, unlocking their full potential through insights driven by ML.

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Section: State Estimationmentioning

confidence: 99%

Advances in Machine Learning for Monitoring, Control, and Optimization of Temperature of Reactors

Ahmad

2023

Preprint

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“…The reconstruction of temperature fields or velocity fields using neural networks that are able to be constrained by physical laws is a developing field [29]. By leveraging known physical relationships (such as the heat diffusion equation), Physics Informed Neural Networks (PINNS) attempt to recreate temperature distributions from a limited number of temperature probes [30][31][32] or inversely, predict where to place a limited number of temperature probes for improved measurements [29]. However, these have not been applied to the problem of using a NN to improve accuracy by including physical laws within the network to overcome issues with noise in the temperature measurement.…”

Section: Introductionmentioning

confidence: 99%

Using Recurrent Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data for use in Bio-Microfluidics

Kullberg,

Sanchez,

Mitchell

et al. 2023

Preprint

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Many biological systems have a narrow temperature range of operation, meaning high accuracy and spatial distribution level are needed to study these systems. Most temperature sensors cannot meet both the accuracy and spatial distribution required in the microfluidic systems that are often used to study these systems in isolation. This paper introduces a neural network called the Multi-Directional Fluorescent Temperature Long Short-Term Memory Network (MFTLSTM) that can accurately calculate the temperature at every pixel in a fluorescent image to improve upon the standard fitting practice and other machine learning methods use to relate fluorescent data to temperature. This network takes advantage of the nature of heat diffusion in the image to achieve an accuracy of ±0.0199 K RMSE within the temperature range of 298K to 308 K with simulated data. When applied to experimental data from a 3D printed microfluidic device with a temperature range of 290 K to 380 K, it achieved an accuracy of ±0.0684 K RMSE. These results have the potential to allow high temperature resolution in biological systems than is available in many microfluidic devices.

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Reactor field reconstruction from sparse and movable sensors using Voronoi tessellation-assisted convolutional neural networks

Gong,

Li,

Xiao

et al. 2024

NUCL SCI TECH

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The aging of operational reactors leads to increased mechanical vibrations in the reactor interior. The vibration of the in-core sensors near their nominal locations is a new problem for neutronic field reconstruction. Current field-reconstruction methods fail to handle spatially moving sensors. In this study, we propose a Voronoi tessellation technique in combination with convolutional neural networks to handle this challenge. Observations from movable in-core sensors were projected onto the same global field structure using Voronoi tessellation, holding the magnitude and location information of the sensors. General convolutional neural networks were used to learn maps from observations to the global field. The proposed method reconstructed multi-physics fields (including fast flux, thermal flux, and power rate) using observations from a single field (such as thermal flux). Numerical tests based on the IAEA benchmark demonstrated the potential of the proposed method in practical engineering applications, particularly within an amplitude of 5 cm around the nominal locations, which led to average relative errors below 5% and 10% in the $$L_2$$ L 2 and $$L_{\infty }$$ L ∞ norms, respectively.

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Convolutional Neural Network–Aided Temperature Field Reconstruction: An Innovative Method for Advanced Reactor Monitoring

Cited by 7 publications

References 23 publications

Advances in Machine Learning for Monitoring, Control, and Optimization of Temperature of Reactors

Advances in Machine Learning for Monitoring, Control, and Optimization of Temperature of Reactors

Using Recurrent Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data for use in Bio-Microfluidics

Reactor field reconstruction from sparse and movable sensors using Voronoi tessellation-assisted convolutional neural networks

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