GENERAL CONTROL MODULE
StarCS is a open, plant-wide control platform with a wide-ranging and versatile set of tools aimed at process stabilisation and optimisation. Using the latest technology in both software and advanced process control, Mintek has developed an adaptable and operator-friendly control system that integrates all Mintek’s control systems (MillStar, FloatStar etc.)
Although StarCS was developed specifically for the metallurgical process industry, it can be used to control any industrial process.
StarCS incorporates a layered approach, which enables implementation of just about any controller. The versatility of this control platform stems from the use of OPC communication, an International standard procedure for the process industry. Mintek is continually developing new modules for use on StarCS that offers process-specific solutions to our clients.
The StarCS Autotuner is a module that was developed to ensure continual optimal plant performance. It provides the plant with a tool to tune their controllers whenever necessary. The Autotuner automatically adjusts controller parameters of the control loops on a plant online. This ensures enhanced plant performance in the short and long term.
The Autotuner is also used by the Mintek engineers to efficiently commission our control systems, shortening the time required for the commissioning.
ADVANCED CONTROL PACKAGE
A number of tools are included in the Advanced Control Package. Among them are the advanced algorithms (containing a ratio controller, advanced PID controller, soft limiter and an MVSplitter), the time delay compensator, feedforward controller, advanced filters, safety limit controllers and nonlinear compensators.
These algorithms have been refined over an extended period of time in order to make sure they operate optimally and robustly within the harsh environment of the minerals processing industry. Although they have all been specifically developed to aid in solving problems encountered in the minerals processing industry, they can be applied to other processing industries as well.
The StarCS Model Predictive Control (MPC) was specifically developed for problems associated with the minerals processing industry. Its particular characteristics enable the handling of integrators, noise, model errors, limits, interaction, widely differing dynamics, and an unequal number of manipulated and controlled variables.
The MPC uses an objective function and multivariable control technique to calculate the future movement of all available manipulated variables to eradicate future deviations from setpoints in the controlled variables. The MPC is very capable in handling multi-input-multi-output (MIMO systems), since it uses an optimisation function that does not need equal numbers of manipulated and controlled variables to calculate the best possible movement of the manipulated variables. This feature also makes the MPC very efficient in handling sensor or equipment failures.
Many metallurgical plants are hampered by slow communication networks and long deadtimes, with communication loop times of up to 5 seconds, but at the same time having fast process and filter dynamics. PID Control is generally ineffective in controlling such plants.
The StarCS Deadbeat Controller implements an algorithm that overcomes this problem and at the same time is robust to plant noise and disturbances as well as model uncertainties. It is based on advanced discrete controller design techniques.
The Simulators module has an extensive suite of simulators that can be used to easily simulate almost any plant. The linear models can be specified as either Laplace or discrete transfer functions. Models of any order can be simulated for both single and multiple inputs/output simulators. Other features such as noise, limits, drift rates, actuators with slack, local PI-loops on inputs and additional transfer functions on outputs are also supported. Separate algorithms are included for a non-linear tank simulator and a random noise and disturbances generator.
Fuzzy Logic provides a framework allowing the effortless incorporation of process know-how into a comprehensive control strategy. Its main strengths are shown in the absence of reliable measurements or models, and where the art of controlling a process is defined better using ill-described concepts rather that crisp rules. Rules and functions are set up in a logical and intuitive fashion, allowing rapid controller development and integration.