Enhancing Efficiency in the Process Industry with Advanced Forecasting and Control Solutions

The process industry faces unique challenges that demand sophisticated solutions for production forecasting, risk management, and optimisation. Our 3-part webinar series will highlight the importance of addressing these solutions to maintain competitiveness and drive innovation.

These webinars will highlight the Process Industry Support Package, 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 optimise production, reduce risk, and improve operational efficiency.

Production Forecasting: Gain insights into production forecasting over various time horizons—from weeks to years. Learn about large scope studies and the tools that facilitate optimisation, including parallel computing, cloud deployment, and database management. We will cover key aspects such as production line forecasting, risk assessment, optimisation, and planning. These concepts will be demonstrated using a model of a mining operation, showcasing practical applications of these forecasting tools.

Supervisory Control: Explore the realm of supervisory control, focusing on timeframes from minutes to days. Understand the integration of a typical Distributed Control System (DCS) used to operate the plant and Advanced Process Control (APC) unit running within the Operational Technology (OT) network as well as discover the capabilities of OPC-UA integration and physical plant modelling. These concepts will be demonstrated using a model of mineral processing operations, highlighting the realisation of higher-level algorithms such as predictive maintenance strategies, techno-economic optimisation, and controller calibration.

Foundational Control: Deep-dive into foundational control techniques that operate on a second-to-minute basis. Learn about Model-Based Design (MBD) and code generation for embedded and PLC platforms. Modelling and simulation are central to this style of development, allowing algorithms to be developed and tested prior to deployment into production. Bring part of your commissioning earlier in your design process using virtual commissioning, with a special focus on formal verification, validation, and testing capabilities which are essential for ensuring reliable operation. These concepts will be demonstrated using a model of a series of tanks highlighting the techniques used to model this system, then the development of foundational control algorithms including PID and Model Predictive Control (MPC). Formal techniques for ensuring code quality. Finally, production code will be generated from the algorithm.

Join us to explore these transformative technologies and see firsthand how they can enhance your process industry operations. Whether you're focused on forecasting, control, or optimisation, this webinar series offers valuable insights and practical demonstrations to help you stay ahead in a competitive landscape.

About the Presenter 

Samuel Oliver is a technical consultant from MathWorks who has dedicated the last 16 years to working closely with customers from the finance, mining, energy, defence, and automotive industries to help execute their most important projects delivering millions of dollars of value. Solutions leverage his broad range of skills in AI, statistics, optimisation, control systems, physical system modelling, big data, large-scale computing, the cloud, and embedded systems. Prior to joining MathWorks, Sam worked in the automotive industry developing advanced dynamic safety-critical control systems. He received an M.Eng.Sc. in mechatronics, a B.Eng. in mechanical and manufacturing engineering, and a B.Sc. in computer science from the University of Melbourne, Australia.

Branko Dijkstra is a principal technical consultant specialising in Model-Based Design 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. Before that, he worked in the microlithography industry. Branko received his M.E. based on his work modelling a batch crystallization plant. He received his Ph.D. in control engineering (microlithography) from Delft University of Technology, The Netherlands, based on his thesis Iterative Learning Control, with Applications to a Wafer-Stage.