Process Automation and Control Program

Chris Swartz, John MacGregor



McMaster’s Chemical Engineering Department is a leader in the field of process control, and operates the highly successful McMaster Advanced Control Consortium (MACC). This group has developed empirical modelling techniques which can be applied to large process databases containing flawed information. The resulting models can help with monitoring and control, troubleshooting, or product development. MACC’s activities have already led to tangible benefits for steel processes, and the Chair focused on steel related applications of these concepts has fostered interdisciplinary problem solving. We currently have active work on control and optimization systems for electric arc furnace steelmaking.  There is potential for a wide variety of work in this field:

  • Multivariate analysis to optimize discontinuous multistage processes. Steel operations generate a wealth of data on process conditions and product attributes. Database analysis techniques can quickly pinpoint the variables responsible for quality excursions, identify operating windows and even guide product development.
  • Feedback control methods can be applied to complex processes where uncertainty in the data would defeat conventional approaches. An extension of this is the "soft sensors" idea where a property may be inferred from other data in the absence of instruments. These techniques have been used to predict and prevent slab caster breakouts at Dofasco.
  • Process scheduling is another area with major cost implications for steel operations. Schedules based on historical data have improved the throughput Dofasco’s batch annealing shop.
  • Multivariate methods are ideal for examining the output of sophisticated sensors. An obvious use in steel production is to interpret surface inspection data where multispectral imaging is emerging to overcome the drawbacks of grey-scale analysis.
  • Control and Optimization of EAFs is underway.