Breaking Through Bottlenecks: A Practical Approach to Crushing Circuit Constraint Analysis in Mining
Overview
Mining operations are consistently tasked with increasing throughput, yet this goal is frequently limited by bottlenecks within ore processing plants. While a single bottleneck can limit the entire operation, identifying the true constraint in a complex, interconnected plant is far from straightforward. Process variability and changing operating conditions can easily mask what is holding production back.
In this webinar, we present a practical, data-driven approach to constraint and bottleneck analysis using the MathWorks Process Industry Toolbox, demonstrated on a mining crushing circuit. This workflow is built around a modular, visual model of the crusher circuit, with a focus on the secondary crusher. We directly connect to real operational data via AVEVA PI Data Archive or PI Asset Framework (PI AF). This tight integration allows rapid model iteration and enables the model to operate as a digital twin, continuously driven by live plant data-driven approach to constraint and bottleneck analysis‑driven approach to constraint and bottleneck analysis.
The model is laid out to closely resemble standard plant flow diagrams, making it intuitive for engineers, metallurgists, and operations teams to engage with. This enables productive discussions around constraints, improvement options and trade-offs. Any discussion and review is clearly related to its real-life counterpart without the need to dive into codes.
About the Presenter
Branko Dijkstra
Dr. Branko Dijkstra is a Principal Technical Consultant specialising in modelling and controls in the Process Industry. He joined MathWorks after several years working in the automotive, microlithography and food manufacturing industries. As consultant he has worked with customers in a wide range of industries such as automotive, aerospace, mining and food. Central to all these engagements has been the premise of using systematic modelling and data analysis as the basis for understanding and improving processes, including deployment of automatic systems into the field.
Branko received his M.Eng. based on his work modelling a batch crystallization plant and he received his Ph.D. in control engineering (microlithography) both from Delft University of Technology, The Netherlands.
Shine Rezaei Boroujeni
Shine Boroujeni is a Senior Application Engineer at MathWorks with a background in machine learning and the Theory of Constraints (TOC). Over the past seven years, Shine worked as a Data Analyst at gold mining companies, contributing to a broad range of data-driven initiatives in both operational and technical domains. Shine holds an MPhil in Data Science, an MSc in Electrical and Computer Science, and a BSc in Biomedical Engineering.
