Physics-Informed Machine Learning for Grade Prediction in Froth Flotation

• URL: http://arxiv.org/abs/2408.15267v1
• Date: Mon, 12 Aug 2024 12:10:36 GMT
• Title: Physics-Informed Machine Learning for Grade Prediction in Froth Flotation
• Authors: Mahdi Nasiri, Sahel Iqbal, Simo Särkkä,
• Abstract summary: This paper develops a physics-informed neural network model to predict the concentrate gold grade in froth flotation cells. The complexity of models based on first-principles restricts their direct use, while purely data-driven models often fail in dynamic industrial environments.
• Score: 8.271361104403802
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, physics-informed neural network models are developed to predict the concentrate gold grade in froth flotation cells. Accurate prediction of concentrate grades is important for the automatic control and optimization of mineral processing. Both first-principles and data-driven machine learning methods have been used to model the flotation process. The complexity of models based on first-principles restricts their direct use, while purely data-driven models often fail in dynamic industrial environments, leading to poor generalization. To address these limitations, this study integrates classical mathematical models of froth flotation processes with conventional deep learning methods to construct physics-informed neural networks. These models demonstrated superior generalization and predictive performance compared to purely data-driven models, on simulated data from two flotation cells, in terms of mean squared error and mean relative error.

Related papers

• Replication Study: Enhancing Hydrological Modeling with Physics-Guided Machine Learning [0.0]
  Current hydrological modeling methods combine data-driven Machine Learning algorithms and traditional physics-based models. Despite the accuracy of ML in outcome prediction, the integration of scientific knowledge is crucial for reliable predictions. This study introduces a Physics Informed Machine Learning model, which merges the process understanding of conceptual hydrological models with the predictive efficiency of ML algorithms.
  arXiv  Detail & Related papers  (2024-02-21T16:26:59Z)
• MINN: Learning the dynamics of differential-algebraic equations and application to battery modeling [3.900623554490941]
  We propose a novel architecture for generating model-integrated neural networks (MINN) MINN allows integration on the level of learning physics-based dynamics of the system. We apply the proposed neural network architecture to model the electrochemical dynamics of lithium-ion batteries.
  arXiv  Detail & Related papers  (2023-04-27T09:11:40Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator [57.57321628587564]
  We introduce Hybrid Graph Network Simulator (HGNS) for learning reservoir simulations of 3D subsurface fluid flows. HGNS consists of a subsurface graph neural network (SGNN) to model the evolution of fluid flows, and a 3D-U-Net to model the evolution of pressure. Using an industry-standard subsurface flow dataset (SPE-10) with 1.1 million cells, we demonstrate that HGNS is able to reduce the inference time up to 18 times compared to standard subsurface simulators.
  arXiv  Detail & Related papers  (2022-06-15T17:29:57Z)
• On the Generalization and Adaption Performance of Causal Models [99.64022680811281]
  Differentiable causal discovery has proposed to factorize the data generating process into a set of modules. We study the generalization and adaption performance of such modular neural causal models. Our analysis shows that the modular neural causal models outperform other models on both zero and few-shot adaptation in low data regimes.
  arXiv  Detail & Related papers  (2022-06-09T17:12:32Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
  The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
  arXiv  Detail & Related papers  (2021-10-18T14:43:50Z)
• Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components [52.77024349608834]
  We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements. We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
  arXiv  Detail & Related papers  (2021-02-15T19:56:48Z)
• Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: Generalized Formulations [5.827236278192557]
  This study presents a new modeling framework, named physics regularized Gaussian process (PRGP) This novel approach can encode physics models, i.e., classical traffic flow models, into the Gaussian process architecture and so as to regularize the Machine Learning training process. To prove the effectiveness of the proposed model, this paper conducts empirical studies on a real-world dataset that is collected from a stretch of I-15 freeway, Utah.
  arXiv  Detail & Related papers  (2020-07-14T17:27:23Z)
• Modeling System Dynamics with Physics-Informed Neural Networks Based on Lagrangian Mechanics [3.214927790437842]
  Two main modeling approaches often fail to meet requirements: first principles methods suffer from high bias, whereas data-driven modeling tends to have high variance. We present physics-informed neural ordinary differential equations (PINODE), a hybrid model that combines the two modeling techniques to overcome the aforementioned problems. Our findings are of interest for model-based control and system identification of mechanical systems.
  arXiv  Detail & Related papers  (2020-05-29T15:10:43Z)
• Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications [14.164058812512371]
  This study presents a new modeling framework, named physics regularized machine learning (PRML), to encode classical traffic flow models into the machine learning architecture. To prove the effectiveness of the proposed model, this paper conducts empirical studies on a real-world dataset which is collected from a stretch of I-15 freeway, Utah.
  arXiv  Detail & Related papers  (2020-02-06T17:22:20Z)

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.