Optimization Capabilities for Crushing Plants
Doctoral thesis, 2022

Responsible production and minimal consumption of resources are becoming competitive factors in the industry. The aggregates and minerals processing industries consist of multiple heavy mechanized industrial processes handling large volumes of materials and are energy-intensive. One such process is a crushing plant operation consisting of rock size reduction (comminution) and particle size separation (classification) processes. The objective of the crushing plant operation for the aggregates industry is to supply specific size fractions of rock material for infrastructure development, while the objective in minerals processing is to maximize material ore throughput below a target size fraction for the subsequent process. The operation of a crushing plant is complex and suffers variabilities during the process operation, resulting in a drive for optimization functionality development. Process knowledge and understanding are needed to make proactive decisions to enable operations to maintain and elevate performance levels.

To examine the complex relationships and interdependencies of the physical processes of crushing plants, a simulation platform can be used at the design stage. Process simulation for crushing plants can be classified as either steady-state simulation or dynamic simulation. The steady-state simulation models are based on instantaneous mass balancing while the dynamic simulation models can capture the process change over time due to non-ideal operating conditions. Both simulation types can replicate the process performance at different fidelities for industrial applications but are limited in application for everyday operation. Most companies operating crushing plants are equipped with digital data-collection systems capturing continuous production data such as mass flow and power draw. The use of the production data for the daily decision-making process is still not utilized to its full potential. There are opportunities to integrate optimization functions with the simulation platform and digital data platforms to create decision-making functionality for everyday operation in a crushing plant. This thesis presents a multi-layered modular framework for the development of the optimization capabilities in a crushing plant aimed at achieving process optimization and process improvements. The optimization capabilities for crushing plants comprise a system solution with the two-fold application of 1) Utilizing the simulation platform for identification and exploration of operational settings based on the stakeholder’s need to generate knowledge about the process operation, 2) Assuring the reliability of the equipment model and production data to create validated process simulations that can be utilized for process optimization and performance improvements.

During the iterative development work, multiple optimization methods such as multi-objective optimization (MOO) and multi-disciplinary optimization (MDO) are applied for process optimization. An adaptation of the ISO 22400 standard for the aggregates production process is performed and applied in dynamic simulations of crushing plants. A detailed optimization method for calibration and validation of process simulation and production data, especially for mass flow data, is presented. Standard optimization problem formulations for each of the applications are demonstrated, which is essential for the replicability of the application. The proposed framework poses a challenge in the future development of a large-scale integrated digital solution for realizing the potential of production data, simulation, and optimization. In conclusion, optimization capabilities are essential for the modernization of the decision-making process in crushing plant operations.

Key Performance Indicators (KPIs)

Crushing

Digital Twin

Modelling

Calibration

Multi-Disciplinary Optimization (MDO)

Dynamic Simulations

Multi-Objective Optimization (MOO)

Screening

Process Optimization

Process Improvement

Production Data

Virtual Development Laboratory (VDL), Chalmers Tvärgata 4C, Göteborg
Opponent: Professor Jan Rosenkranz, Luleå University of Technology, Sweden (Password: 791088)

Author

Kanishk Bhadani

Chalmers, Industrial and Materials Science, Product Development

State of the Art in Application of Optimization Theory in Minerals Processing

European Symposium on Comminution and Classification, Izmir, Turkey,; (2017)

Other conference contribution

Application of multi-disciplinary optimization architectures in mineral processing simulations

Minerals Engineering,; Vol. 128(2018)p. 27-35

Journal article

Comparative Study of Optimization Schemes in Mineral Processing Simulations

IMPC 2018 - 29th International Mineral Processing Congress,; Vol. 2019(2019)p. 464-473

Paper in proceeding

In modern times, infrastructure development is one of the cornerstones of human success and facilitates economic growth and mobility. Modern infrastructure developments such as roads, railways, housing, and commercial buildings are directly dependent on the supply of a range of crushed rock materials. The rock materials used are produced in a crushing plant. The purpose of a crushing plant is to reduce (crushing), and separate (screening) rock material from a quarry to provide a range of rock products (known as aggregates) that can be used in infrastructure development. The production and quality of different aggregates products depend on how various equipment present in a crushing plant is operated. The aggregates production industry faces challenges in decision-making in the production processes as complex interdependencies exist within the process operation. For example, changing the setting on one equipment can affect performance that can be easily accounted for, but changing multiple equipment settings simultaneously becomes cognitively challenging to understand. The question that here arises is how can one operate and optimize a crushing plant to meet the varying demands of the different aggregates products in a modern state of digital tools?

To address this challenge, multiple methods to facilitate process performance improvements and optimization for a crushing plant operation are presented. A multi-layered modular framework for the development of the optimization capabilities in a crushing plant is presented. The framework demonstrates the use of a parallel simulation system to the physical system for creating a decision-making tool for a crushing plant operation. The development is striving towards creating a digital twin of physical processes to create operational guidance for crushing plants.

The optimization capabilities for crushing plants comprise a system solution with a two-fold application. The first is to utilize the simulation platform for identification and exploration of operational settings based on the stakeholder’s need to generate knowledge about the process operation. The second is to assure the reliability of the equipment model and production data to create validated process simulations that can be utilized for process optimization and performance improvements. The presented methods can enable integration of optimization functions with the simulation platform and digital data platforms to create useful decision-making functionality for everyday operation in a crushing plant. Optimization capabilities are essential for the modernization of the decision-making process in crushing plant operations.

Optimization of real processes for rock material production

Development Fund of the Swedish Construction Industry (SBUF) (13753), 2019-11-01 -- 2022-06-30.

Subject Categories

Mineral and Mine Engineering

Mechanical Engineering

Production Engineering, Human Work Science and Ergonomics

Driving Forces

Sustainable development

Areas of Advance

Production

ISBN

978-91-7905-635-3

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5101

Publisher

Chalmers

Virtual Development Laboratory (VDL), Chalmers Tvärgata 4C, Göteborg

Online

Opponent: Professor Jan Rosenkranz, Luleå University of Technology, Sweden (Password: 791088)

More information

Latest update

3/18/2022