Increased emphasis on more environmentally friendly, efficient and safe processes has led companies to focus optimisation efforts across plants including refining, chemical and pulp and paper.
Plant control systems which rely on a concert of supervisory and loop-level controls to hold set points and reject disturbances, present notorious optimisation challenges.
Optimisation challenges
Multiphase flows, entrained solids, hybrid continuous-batch operations and other highly nonlinear behaviors contribute to this complexity.
Even plants with the same process for manufacturing the same product often have different capacities and layouts and require separate optimisations to maximise production and minimise operational costs.
Trial-and-error approaches to improve performance can adversely affect plant operations and safety. Setting less aggressive controller gains as a quick fix to control system instability problems often leads to sub-optimal performance.
Dynamic simulations
As an alternative to these approaches, engineers can perform dynamic simulations of the control system (controllers and process) to gain insight into the system dynamics, understand what is causing instabilities, tune controllers, design and validate a better control architecture and achieve better plant performance.
During plant design, simulation enables engineers to optimise processes, formulate the plant control system architecture and study steady-state capacity.
Once in operation, plant simulations allow engineers to identify the root cause of inefficiencies and fine-tune the process. Often, problems can be resolved by tuning isolated control loops with a single controller. Loop tuning uses an empirical model based on process data during a predefined set point change, helping calculate new gain coefficients such as in PID controllers.
Modelling plant processes must be done before running control system simulations. Approaches such as data-based and first-principles each have advantages and drawbacks so engineers should understand model types and have insight into the level of model fidelity needed to solve their problem.
Process modelling
Process models can either be linear, representing a small operating region of interest and limited input range or nonlinear, representing the dynamics of a much larger range of operating conditions and input amplitudes.
It can be difficult to develop an accurate model that provides enough confidence to reconfigure a control system. Engineers must recognise not only the principles behind the models they develop but also the capabilities and limitations of their simulation software. In the race for greater process precision and speed, simulation is a cost-effective way to validate control systems, find and eliminate problems before implementation and optimise plants already in operation.
Desktop simulation software, already fully capable of performing such validation and optimisation, continues to improve yearly as new features are added, thereby helping engineers to meet a rapidly expanding set of plant control challenges.
