There are many challenges that come with operating a process plant. Using a dynamic simulator for operator training and automation improvement is a demonstrated solution to reduce risks and increase business results of the plant. However, not all dynamic simulators are the same, or even provide the same benefits. Mimic was built to address the life cycle needs of process plant operations, with the lowest life cycle costs available. This provides the greatest return on investment of any dynamic simulator.
Mimic provides life cycle business benefits
Dynamic, first principle, process modeling objects in Mimic simulate accurate, real-time plant behaviors. Mimic supports selective application of simulation fidelity, from low to high. Every Mimic release is designed to be fast, easy, and flexible, consistently reducing the time and cost require to develop a dynamic simulation. Life cycle operations results extend to all control system platforms. Mimic is designed to work with control system simulators. It’s built for operator training and plant operations improvement.
Virtual plant: control system simulator and Mimic
Training operators or changing control strategies on a real plant can be dangerous or costly. In the virtual plant, the control system is replicated in the control system simulator. Mimic works with the control system simulator, providing real-time IO signals and dynamic process models that respond to the control system just like the real plant. Mimic Operator Training Manager allows effective, measurable training of operators, and Mimic Test Bench automates testing of the control system.
The Mtell Previse condition monitoring solution uses machine learning to prevent breakdowns, increase asset lifecycle, reduce maintenance costs, and increase production output for any industrial process.
Contemporary condition monitoring applications use techniques to “trap” anomalies or changes in operational behavior of a machine that might indicate a problem. Such methods are complex, limited to certain equipment, prone to error, and ALWAYS require further expert investigation and validation; producing high levels of false positive
Mtell Previse uses Conscious Monitoring Agents™ to learn operational behavioral patterns using actual data from sensors on and around a machine or manufacturing process. Mtell Previse recognizes diverse patterns in the sensor signals that indicate degradation, failure and root cause.