Process Modelling and Applications for Aggregate Production

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Processing and Extractive Metallurgy".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1847

Special Issue Editors


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Guest Editor
Department of Industrial and Materials Science, Chalmers University of Technology, SE-412 96 Göteborg, Sweden
Interests: steady-state and dynamic process simulations; unit and process optimization; crushing and screening modelling; aggregates; data science; environmental product declarations and operator training

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Guest Editor
Department of Industrial and Materials Science, Chalmers University of Technology, SE-412 96 Göteborg, Sweden
Interests: modeling and simulation; optimization and trade-offs; optimization architectures; data-driven decision making; coarse comminution; aggregates and minerals processing; crushing and screening; digital support tools

Special Issue Information

Dear Colleagues,

Process modelling is often the foundation for decision making regarding process design, configuration, and production planning. Currently, steady-state modelling is the dominating simulation approach. However, there is more diversity in the field, including time-dependent dynamics simulations, discrete event simulation, different hybrid simulation machine learning and more. The process can be simulated as a stand-alone application or with a third party software as software-in-the-loop. Applications of process simulations can include process evaluation, debottlenecking, production planning, optimization, control and operator training, etc.

This Special Issue of Minerals aims to gather the most recent advances in research and application using process modelling and simulations for improved process understanding and performance. We would like to invite researchers in this field to submit their research papers, review papers, and communications related to Process modelling and applications in aggregate production.

Dr. Gauti Asbjörnsson
Dr. Kanishk Bhadani
Guest Editors

Manuscript Submission Information

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Keywords

  • steady-state simulation
  • dynamic simulation
  • discrete event simulation
  • hybrid simulations
  • machine learning
  • software-in-the-loop
  • operational calibration and validation
  • process control
  • process optimization
  • process evaluation
  • KPI
  • debottlenecking
  • aggregates
  • course comminution
  • classification
  • materials handling

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Published Papers (1 paper)

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Research

20 pages, 11204 KiB  
Article
Trade-Off Curves for Performance Optimization in a Crushing Plant
by Kanishk Bhadani, Gauti Asbjörnsson, Monica Soldinger Almefelt, Erik Hulthén and Magnus Evertsson
Minerals 2023, 13(10), 1242; https://doi.org/10.3390/min13101242 - 23 Sep 2023
Cited by 2 | Viewed by 1329
Abstract
Operational flexibility in an aggregate production process is required to adapt to changes in customer demands. Excessive demand for a particular product fraction can lead to operational alteration wherein re-crushing of the existing larger-sized product fraction is necessary. The choice of re-crushing existing [...] Read more.
Operational flexibility in an aggregate production process is required to adapt to changes in customer demands. Excessive demand for a particular product fraction can lead to operational alteration wherein re-crushing of the existing larger-sized product fraction is necessary. The choice of re-crushing existing product fractions results in feed condition changes to the crusher. One common approach to producing the desired product is by varying the operation settings of a crusher in a crushing plant. However, knowledge of differences in operational performance for changing feed conditions in the circuit is required. This potentially leads to a problem of performance optimization based on the desired target product, available feed material and capability of the crusher. The paper presents an application of a multi-objective optimization method to generate multiple operational settings for the dynamic change in the operation condition in a crushing plant. Controlled experimental survey data with varying feed conditions are used to calibrate the crusher model using an unconstrained optimization problem solved using a gradient-based algorithm (Quasi-Newton method). Trade-off curves between various performance indicators of the crushing plant using a dynamic simulation platform are generated using multi-objective optimization using a non-gradient-based algorithm (genetic algorithm). The results of the application can help the operators and plant managers to make proactive decisions to steer the operation of the crushing plant towards the desired needs of the operation. Full article
(This article belongs to the Special Issue Process Modelling and Applications for Aggregate Production)
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