AI-Driven Optimization under Uncertainty for Mineral Processing Operations

• URL: http://arxiv.org/abs/2512.01977v1
• Date: Mon, 01 Dec 2025 18:35:54 GMT
• Title: AI-Driven Optimization under Uncertainty for Mineral Processing Operations
• Authors: William Xu, Amir Eskanlou, Mansur Arief, David Zhen Yin, Jef K. Caers,
• Abstract summary: We introduce an AI-driven approach that formulates mineral processing as a Partially Observable Markov Decision Process (POMDP)<n>We show that this approach has the potential to consistently perform better than traditional approaches at maximizing an overall objective, such as net present value (NPV)<n>Our methodological demonstration of this optimization-under-uncertainty approach for a synthetic case provides a mathematical and computational framework for later real-world application.
• Score: 0.7340017786387767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The global capacity for mineral processing must expand rapidly to meet the demand for critical minerals, which are essential for building the clean energy technologies necessary to mitigate climate change. However, the efficiency of mineral processing is severely limited by uncertainty, which arises from both the variability of feedstock and the complexity of process dynamics. To optimize mineral processing circuits under uncertainty, we introduce an AI-driven approach that formulates mineral processing as a Partially Observable Markov Decision Process (POMDP). We demonstrate the capabilities of this approach in handling both feedstock uncertainty and process model uncertainty to optimize the operation of a simulated, simplified flotation cell as an example. We show that by integrating the process of information gathering (i.e., uncertainty reduction) and process optimization, this approach has the potential to consistently perform better than traditional approaches at maximizing an overall objective, such as net present value (NPV). Our methodological demonstration of this optimization-under-uncertainty approach for a synthetic case provides a mathematical and computational framework for later real-world application, with the potential to improve both the laboratory-scale design of experiments and industrial-scale operation of mineral processing circuits without any additional hardware.

Related papers

• PeroMAS: A Multi-agent System of Perovskite Material Discovery [51.859972927223936]
  Perovskite Solar Cells (PSCs) are renowned for their superior optoelectronic performance and cost potential.<n>Existing AI approaches focus predominantly on discrete models, including material design, process optimization, and property prediction.<n>We propose a multi-agent system for perovskite material discovery, named PeroMAS.
  arXiv  Detail & Related papers  (2026-02-10T09:33:06Z)
• When AI Bends Metal: AI-Assisted Optimization of Design Parameters in Sheet Metal Forming [0.4347560796121297]
  This paper presents an AI-assisted workflow that reduces expert involvement in parameter optimization through the use of Bayesian optimization.<n>A deep learning model provides an initial parameter estimate, from which the optimization cycle iteratively refines the design until a termination condition is met.<n>We demonstrate our approach, based on a sheet metal forming process, and show how it enables us to accelerate the exploration of the design space while reducing the need for expert involvement.
  arXiv  Detail & Related papers  (2025-11-27T10:31:24Z)
• Holistic Bioprocess Development Across Scales Using Multi-Fidelity Batch Bayesian Optimization [37.69303106863453]
  We propose a multi-fidelity batch Bayesian optimization framework to accelerate bioprocess development and reduce experimental costs.<n>A custom simulation of a Chinese Hamster Ovary bioprocess, capturing non-linear and coupled scale-up dynamics, is used for benchmarking.<n>Case studies show how the proposed workflow can achieve a reduction in experimental costs and increased yield.
  arXiv  Detail & Related papers  (2025-08-14T16:29:34Z)
• LLM-guided Chemical Process Optimization with a Multi-Agent Approach [8.714038047141202]
  We present a multi-agent LLM framework that autonomously infers operating constraints from minimal process descriptions.<n>Our AutoGen-based framework employs OpenAI's o3 model with specialized agents for constraint generation, parameter validation, simulation, and optimization guidance.
  arXiv  Detail & Related papers  (2025-06-26T01:03:44Z)
• Accelerating Battery Material Optimization through iterative Machine Learning [0.8189259726221193]
  We introduce an iterative machine learning (ML) framework that integrates active learning to guide targeted experimentation and facilitate incremental model refinement.<n>Our results demonstrate that active-learning-driven experimentation markedly reduces the total number of experimental cycles necessary.
  arXiv  Detail & Related papers  (2025-05-12T11:45:02Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing [53.77822620185878]
  We propose a synergistic methodology to concurrently optimize perovskite memristor fabrication and develop robust analog DNNs.<n>We develop "BayesMulti", a training strategy utilizing BO-guided noise injection to improve the resistance of analog DNNs to memristor imperfections.<n>Our integrated approach enables use of analog computing in much deeper and wider networks, achieving up to 100-fold improvements.
  arXiv  Detail & Related papers  (2024-12-03T19:20:08Z)
• Sparse Attention-driven Quality Prediction for Production Process Optimization in Digital Twins [53.70191138561039]
  We propose to deploy a digital twin of the production line by encoding its operational logic in a data-driven approach. We adopt a quality prediction model for production process based on self-attention-enabled temporal convolutional neural networks. Our operation experiments on a specific tobacco shredding line demonstrate that the proposed digital twin-based production process optimization method fosters seamless integration between virtual and real production lines.
  arXiv  Detail & Related papers  (2024-05-20T09:28:23Z)
• A Reinforcement Learning Approach for Process Parameter Optimization in Additive Manufacturing [0.0]
  The article introduces a Reinforcement Learning (RL) methodology transformed into an optimization problem in the realm of metal additive manufacturing. An experimentally validated Eagar-Tsai formulation is used to emulate the Laser-Directed Energy Deposition environment. The framework, therefore, provides a model-free approach to learning without any prior observations.
  arXiv  Detail & Related papers  (2022-11-17T14:05:51Z)
• Generating Hidden Markov Models from Process Models Through Nonnegative Tensor Factorization [0.0]
  We introduce a novel mathematically sound method that integrates theoretical process models with interrelated minimal Hidden Markov Models. Our method consolidates: (a) theoretical process models, (b) HMMs, (c) coupled nonnegative matrix-tensor factorizations, and (d) custom model selection.
  arXiv  Detail & Related papers  (2022-10-03T16:19:27Z)
• Extending Process Discovery with Model Complexity Optimization and Cyclic States Identification: Application to Healthcare Processes [62.997667081978825]
  The paper presents an approach to process mining providing semi-automatic support to model optimization. A model simplification approach is proposed, which essentially abstracts the raw model at the desired granularity. We aim to demonstrate the capabilities of the technological solution using three datasets from different applications in the healthcare domain.
  arXiv  Detail & Related papers  (2022-06-10T16:20:59Z)
• Constrained multi-objective optimization of process design parameters in settings with scarce data: an application to adhesive bonding [48.7576911714538]
  Finding the optimal process parameters for an adhesive bonding process is challenging. Traditional evolutionary approaches (such as genetic algorithms) are then ill-suited to solve the problem. In this research, we successfully applied specific machine learning techniques to emulate the objective and constraint functions.
  arXiv  Detail & Related papers  (2021-12-16T10:14:39Z)
• AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes [56.65379135797867]
  We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes. We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm.
  arXiv  Detail & Related papers  (2020-08-29T14:57:53Z)

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.