Online Non-Destructive Moisture Content Estimation of Filter Media During Drying Using Artificial Neural Networks

• URL: http://arxiv.org/abs/2303.15570v1
• Date: Mon, 27 Mar 2023 19:37:53 GMT
• Title: Online Non-Destructive Moisture Content Estimation of Filter Media During Drying Using Artificial Neural Networks
• Authors: Christian Remi Wewer and Alexandros Iosifidis
• Abstract summary: Moisture content (MC) estimation is important in the manufacturing process of drying bulky filter media products. An artificial neural network (ANN) based method is compared to state-of-the-art MC estimation methods reported in the literature. Experimental results show that ANNs combined with oven settings data, drying time and product temperature can be used to reliably estimate the MC of bulky filter media products.
• Score: 95.42181254494287
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Moisture content (MC) estimation is important in the manufacturing process of drying bulky filter media products as it is the prerequisite for drying optimization. In this study, a dataset collected by performing 161 drying industrial experiments is described and a methodology for MC estimation in an non-destructive and online manner during industrial drying is presented. An artificial neural network (ANN) based method is compared to state-of-the-art MC estimation methods reported in the literature. Results of model fitting and training show that a three-layer Perceptron achieves the lowest error. Experimental results show that ANNs combined with oven settings data, drying time and product temperature can be used to reliably estimate the MC of bulky filter media products.

Related papers

• MoistNet: Machine Vision-based Deep Learning Models for Wood Chip Moisture Content Measurement [11.65689410574879]
  This study explores the use of deep learning and machine vision to predict moisture content classes from RGB images of wood chips. Two high-performing neural networks, MoistNetLite and MoistNetMax, have been developed. Results demonstrate that MoistNetLite achieves 87% accuracy with minimal computational overhead, while MoistNetMax exhibits exceptional precision with a 91% accuracy in wood chip moisture content class prediction.
  arXiv  Detail & Related papers  (2024-09-07T22:03:13Z)
• EM Distillation for One-step Diffusion Models [65.57766773137068]
  We propose a maximum likelihood-based approach that distills a diffusion model to a one-step generator model with minimal loss of quality. We develop a reparametrized sampling scheme and a noise cancellation technique that together stabilizes the distillation process.
  arXiv  Detail & Related papers  (2024-05-27T05:55:22Z)
• DeepForge: Leveraging AI for Microstructural Control in Metal Forming via Model Predictive Control [0.0]
  This study presents a novel method for microstructure control in closed die hot forging. It combines Model PredictiveCMP with a machine called DeepForge.
  arXiv  Detail & Related papers  (2024-02-25T15:37:14Z)
• Noise-Free Score Distillation [78.79226724549456]
  Noise-Free Score Distillation (NFSD) process requires minimal modifications to the original SDS framework. We achieve more effective distillation of pre-trained text-to-image diffusion models while using a nominal CFG scale.
  arXiv  Detail & Related papers  (2023-10-26T17:12:26Z)
• Application of Artificial Neural Networks for Investigation of Pressure Filtration Performance, a Zinc Leaching Filter Cake Moisture Modeling [0.0]
  This study developed an ANN model to predict the cake moisture of the pressure filtration process of zinc production. The ANN model was evaluated by the Coefficient of determination (R2), the Mean Square Error (MSE), and the Mean Absolute Error (MAE) metrics for both datasets.
  arXiv  Detail & Related papers  (2023-08-11T13:58:42Z)
• Grain and Grain Boundary Segmentation using Machine Learning with Real and Generated Datasets [0.0]
  Grain boundary segmentation using Convolutional Neural Networks (CNN) trained on a combination of real and generated data. A Voronoi tessellation pattern combined with random synthetic noise and simulated defects is developed to create a novel artificial grain image fabrication method.
  arXiv  Detail & Related papers  (2023-07-12T04:38:44Z)
• A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content [0.0]
  We propose a deep learning (DL)-based model for investigating the cyclic viscoelastic-viscoplastic-damage behavior of nanoparticles with moisture content. A long short-term memory network is trained using a combined framework of a sampling technique and a perturbation method. The training framework, along with the training data generated by an experimentally validated viscoelastic-viscoplastic model, enables the DL model to accurately capture the rate-dependent stress-strain relationship.
  arXiv  Detail & Related papers  (2023-05-14T08:33:11Z)
• Recognition of Defective Mineral Wool Using Pruned ResNet Models [88.24021148516319]
  We developed a visual quality control system for mineral wool. X-ray images of wool specimens were collected to create a training set of defective and non-defective samples. We obtained a model with more than 98% accuracy, which in comparison to the current procedure used at the company, it can recognize 20% more defective products.
  arXiv  Detail & Related papers  (2022-11-01T13:58:02Z)
• Efficient training of lightweight neural networks using Online Self-Acquired Knowledge Distillation [51.66271681532262]
  Online Self-Acquired Knowledge Distillation (OSAKD) is proposed, aiming to improve the performance of any deep neural model in an online manner. We utilize k-nn non-parametric density estimation technique for estimating the unknown probability distributions of the data samples in the output feature space.
  arXiv  Detail & Related papers  (2021-08-26T14:01:04Z)
• VAE-LIME: Deep Generative Model Based Approach for Local Data-Driven Model Interpretability Applied to the Ironmaking Industry [70.10343492784465]
  It is necessary to expose to the process engineer, not solely the model predictions, but also their interpretability. Model-agnostic local interpretability solutions based on LIME have recently emerged to improve the original method. We present in this paper a novel approach, VAE-LIME, for local interpretability of data-driven models forecasting the temperature of the hot metal produced by a blast furnace.
  arXiv  Detail & Related papers  (2020-07-15T07:07:07Z)

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.