Enhancing Efficiency in the Process Industry in Mining

Overview

The process industry faces unique challenges that demand sophisticated solutions for production forecasting, risk management, and optimization.  

This presentation will highlight the Process Industry Toolbox, which is a collection of tools focused on addressing the unique challenges of the process industry. We will cover the segmentation of these solutions and their critical role in enhancing production capabilities.

Discover how the segments of advanced forecasting, supervisory control, and foundational control techniques can optimize production, reduce risk, and improve operational efficiency, by leveraging well established techniques from many different industries. 

Several user stories will be highlighted uncovering a small section of MathWorks customer who have achieved success adopting the techniques mentioned in this presentation. Many topics will be mentioned, providing an opportunity for further discussion in more detail on the topics that are relevant to you. 

Highlights

• Segmentation of Solutions: Explore how the Process Industry Toolbox segments solutions to enhance production capabilities and optimize operations.
• Advanced Forecasting: Delve into techniques that improve predictive accuracy, enabling better planning and resource allocation.
• Supervisory Control: Learn about advanced control systems that monitor and optimize production processes, ensuring efficiency and stability.
• Fundamental Control Techniques: Understand how fundamental techniques can be applied to reduce risks and improve operational efficiency.

About the Presenter

Branko Dijkstra is a principal technical consultant specializing in model based workflows for process industry optimisation. Prior to joining MathWorks, Branko was an engineering manager for the development of automotive climate control and electric vehicle thermal management systems in the Asia Pacific region, and before that worked in the micro-lithography industry. Branko received his M.E. and his Ph.D. in control engineering from Delft University of Technology, the Netherlands based on his thesis: Iterative Learning Control applied to a wafer stage.