Application of Zone Method based Physics-Informed Neural Networks in Reheating Furnaces

• URL: http://arxiv.org/abs/2308.16089v2
• Date: Wed, 28 Feb 2024 19:26:34 GMT
• Title: Application of Zone Method based Physics-Informed Neural Networks in Reheating Furnaces
• Authors: Ujjal Kr Dutta, Aldo Lipani, Chuan Wang, Yukun Hu
• Abstract summary: Foundation Industries (FIs) constitute glass, metals, cement, ceramics, bulk chemicals, paper, steel, etc. Reheating furnaces within the manufacturing chain of FIs are energy-intensive. Accurate and real-time prediction of underlying temperatures in reheating furnaces has the potential to reduce the overall heating time. We propose a Physics-Informed Neural Network (PINN) by incorporating prior physical knowledge using a set of novel Energy-Balance regularizers.
• Score: 25.031487600209346
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Foundation Industries (FIs) constitute glass, metals, cement, ceramics, bulk chemicals, paper, steel, etc. and provide crucial, foundational materials for a diverse set of economically relevant industries: automobiles, machinery, construction, household appliances, chemicals, etc. Reheating furnaces within the manufacturing chain of FIs are energy-intensive. Accurate and real-time prediction of underlying temperatures in reheating furnaces has the potential to reduce the overall heating time, thereby controlling the energy consumption for achieving the Net-Zero goals in FIs. In this paper, we cast this prediction as a regression task and explore neural networks due to their inherent capability of being effective and efficient, given adequate data. However, due to the infeasibility of achieving good-quality real data in scenarios like reheating furnaces, classical Hottel's zone method based computational model has been used to generate data for model training. To further enhance the Out-Of-Distribution generalization capability of the trained model, we propose a Physics-Informed Neural Network (PINN) by incorporating prior physical knowledge using a set of novel Energy-Balance regularizers.

Related papers

• Predicting ionic conductivity in solids from the machine-learned potential energy landscape [68.25662704255433]
  Superionic materials are essential for advancing solid-state batteries, which offer improved energy density and safety. Conventional computational methods for identifying such materials are resource-intensive and not easily scalable. We propose an approach for the quick and reliable evaluation of ionic conductivity through the analysis of a universal interatomic potential.
  arXiv  Detail & Related papers  (2024-11-11T09:01:36Z)
• Investigation of Energy-efficient AI Model Architectures and Compression Techniques for "Green" Fetal Brain Segmentation [42.52549987351643]
  Fetal brain segmentation in medical imaging is challenging due to the small size of the fetal brain and the limited image quality of fast 2D sequences. Deep neural networks are a promising method to overcome this challenge. Our study aims to explore model architectures and compression techniques that promote energy efficiency.
  arXiv  Detail & Related papers  (2024-04-03T15:11:53Z)
• Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settings [6.611985866622974]
  This paper presents a finetuning approach to adapt standard object detection models to downstream tasks. Case study and evaluation of the energy demands of the developed models are presented. Finally, this paper introduces a novel way to quantify this trade-off through a customized Efficiency Factor metric.
  arXiv  Detail & Related papers  (2024-03-11T11:41:30Z)
• Data-driven building energy efficiency prediction using physics-informed neural networks [2.572906392867547]
  We introduce a physics-informed neural network model for predicting energy performance of residential buildings. A function, based on physics equations, calculates the energy consumption of the building based on heat losses and enhances the loss function of the deep learning model. This methodology is tested on a real case study for 256 buildings located in Riga, Latvia.
  arXiv  Detail & Related papers  (2023-11-14T09:55:03Z)
• Energy Efficiency of Training Neural Network Architectures: An Empirical Study [11.325530936177493]
  The evaluation of Deep Learning models has traditionally focused on criteria such as accuracy, F1 score, and related measures. The computations needed to train such models entail a large carbon footprint. We study the relations between DL model architectures and their environmental impact in terms of energy consumed and CO$$ emissions produced during training.
  arXiv  Detail & Related papers  (2023-02-02T09:20:54Z)
• An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design [55.62660894625669]
  Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable. Recent developments in machine learning have provided alternative solutions in replacing the time-consuming lithography simulations with deep neural networks. We propose a litho-aware data augmentation framework to resolve the dilemma of limited data and improve the machine learning model performance.
  arXiv  Detail & Related papers  (2022-10-27T20:53:39Z)
• Physics Informed Neural Networks for Control Oriented Thermal Modeling of Buildings [3.1132272756008375]
  This paper presents a data-driven modeling approach for developing control-oriented thermal models of buildings. Along with measured data and building parameters, we encode the neural networks with the underlying physics that governs the thermal behavior of these buildings.
  arXiv  Detail & Related papers  (2021-11-23T18:27:54Z)
• On Energy-Based Models with Overparametrized Shallow Neural Networks [44.74000986284978]
  Energy-based models (EBMs) are a powerful framework for generative modeling. In this work we focus on shallow neural networks. We show that models trained in the so-called "active" regime provide a statistical advantage over their associated "lazy" or kernel regime.
  arXiv  Detail & Related papers  (2021-04-15T15:34:58Z)
• A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning [48.63610479916003]
  This article breaks down and analyzes the main factors that influence the environmental footprint of distributed learning policies. It models both vanilla and decentralized FL policies driven by consensus. Results show that FL allows remarkable end-to-end energy savings (30%-40%) for wireless systems characterized by low bit/Joule efficiency.
  arXiv  Detail & Related papers  (2021-03-18T16:04:42Z)
• Thermodynamics-based Artificial Neural Networks for constitutive modeling [0.0]
  We propose a new class of data-driven, physics-based, neural networks for modeling of strain rate independent processes at the material point level. The two basic principles of thermodynamics are encoded in the network's architecture by taking advantage of automatic differentiation. We demonstrate the wide applicability of TANNs for modeling elasto-plastic materials, with strain hardening and softening strain.
  arXiv  Detail & Related papers  (2020-05-25T15:56:34Z)
• NeurOpt: Neural network based optimization for building energy management and climate control [58.06411999767069]
  We propose a data-driven control algorithm based on neural networks to reduce this cost of model identification. We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy.
  arXiv  Detail & Related papers  (2020-01-22T00:51:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.