Using Modeling Simulation to Optimize Plant Control Systems
The majority of professionals involved in industrial engineering understand the challenges that control system tuning present in process industry power plants, including refining, power and mechanical processes. These challenges make trial-and-error loop tuning of plant control systems unfeasible and hazardous. Therefore, you need to implement an alternative method that would safeguard your safety and ensure cost efficiency. Forget about a plant upgrade, as it could surpass the original design specifications of your process operations and demand different control architecture. Instead, you should consider using modeling and simulation to help you optimize your plant control systems while staying true to the best practices for plant operations.
The best practices involve adopting safe, efficient and eco-friendly processes when optimizing refining, paper, pulp and chemical plants. You need to ensure proper coordination exists between loop level and supervisory controls to eliminate disturbances and subsequent optimization problems. Many problems will emerge if you permit nonlinear behaviors, such as hybrid continuous-batch operations, entrained solids and multiphase flows to thrive. Modeling and simulation will allow you the liberty to optimize and experiment with different layouts to maximize production at the lowest operational costs.
Many engineers make the mistake of applying quick fixes, such as making controller gains less aggressive, to solve system instability. Such trial-and-error approaches often lead to costly performance issues. You would rather carry out dynamic control system simulations to understand the system dynamics. In this way, you will be in a better position to understand the source of instabilities and consequently eliminate plant inefficiencies. Additionally, you be able to build efficient control architecture and fine-tune controllers.
Through simulation, you will be able to study steady-state capacity, optimize processes and create an effective architecture for the control system architecture. You can eradicate inefficiencies by using one controller to tune separate control loops.
Before you run any simulation, you must perform modeling plant processes. You should understand model types and level of fidelity to help you decide the modeling approaches to adopt, including first principles and data-based models. You will need to decide whether to apply linear or nonlinear process models depending on the size and design of your plant. Linear process models represent an operating region that is limited in size and input range, while nonlinear models involve a larger range of input amplitudes and operating conditions.
To avoid challenges associated with developing an accurate confidence-boosting model for reconfiguration, you must be familiar with the principles, limitations and the capabilities of your model and simulation software.
Simulation will be useful in reproducing the problems in a real life process of a control system. Therefore, simulation represents a model of the real operation of a plant, and it provides a basis for an official investigation of the inefficiencies and their solutions.
Software simulation will enable you to utilize optimization methods and help you fine tune configurations in the model. You will be able to use actual data measured from the real system.
A process model will make your control system analysis more systematic and allow easy analysis of frequency-domain and time-domain to be conducted. You will be able to avoid costly and hard-to-fix problems in the future when you use simulation to validate the controller.
If you desire greater process speed and precision, you should perform modeling and simulation rather than applying a trial-and-error approach. You will discover that simulation is cost-effective and the most effective way of validating control systems. Do not opt for a system that will leave you with regrets once you have implemented your plant.