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Dynamic Process Simulators and Training Systems

Immersive Virtual Reality Plant - A Comprehensive Plant-Crew Training Solution Improves Process Reliability and Safety - Capital-intensive industries face the challenge of replacing an aging workforce with a computer-savvy, gaming generation over the next five years. Industries such as oil and gas, refining and power companies must institutionalize their workforce knowledge in efficient and effective ways. Leveraging virtual reality (VR) models to improve time-to-competency in critical areas such as safety and environment protection systems, knowledge and performance training, and reliability provides a vehicle to rapidly train this new workforce in ways that align with their interests and skills. Today, with continuing advances in hardware and software techniques, VR is viewed as the best aid to improving multimedia training, process design, maintenance and safety, which are currently based around conventional 2-dimensional (2-D) equipment views - from Invensys.

Simulation Using Foundation Fieldbus Function Blocks - Terry Blevins - From

The Following Papers are from Hyperion

Case Studies
Motor Oil Hellas (MOH) - Dynamic Simulation for Desulphurisation Plants

The Following Technical Papers are compliments of SimSci-Esscor

The Following Technical Papers are from Sim-Serv

Dynamic Simulators for Process Control and Optimization as well as for Operator Training in Pulp and Paper Industry - Erik Dahlquist and Fredrik Wallin, Malardalen University ,Vasteras, Sweden Hakan Ekwall, ABB Industry,Vasteras, Sweden -  By using a dynamic physical model, that is adapted to real process data, robust mathematical process models can be created. By doing this it is possible to build in process know how from many different sources, and also to include factors, that are not easy to measure. From the dynamic model a training simulator can be made. From the dynamic model it may also be possible to do a model reduction to get an MPC, a Model Predictive Control. Data reconciliation is needed, to keep control of the measurements of all kind. A decision support system keeps control over the process status, to support operators. The production is also optimized at several levels. These functions may also be achieved by using principally the samemathematical models and algorithms.

Modelling and Simulation in Advanced Control - Esko K. Juuso - Operating conditions are often changing so strongly that the changes in nonlinearities must be taken into account. Various approaches exist for handling nonlinearities in changing operating environment: nonlinear control is extended with adaptation approaches, model-based methodologies, intelligent analysers and expertise. Linguistic equation (LE) controllers combine various control strategies in a compact matrix-based environment. Importance of modelling and simulation is increasing with integration of the control approaches as the increasing number of adjustable parameters requires efficient comparisons of alternatives. Predefined adaptation models and mechanisms obtained by tuning with modelling and simulation facilitate fast operation in changing process conditions. The performance of these systems consisting of practical and interactive small scale intelligent systems has been demonstrated in several applications. This paper has been prepared for the Sim-Serv roadmap of continuous and hybrid simulation.

Simulation Aids In The Automation Of Industrial Processes - Juan Atanasio Carrasco, Matti Paljakka - The use of simulation aids in the automation of industrial processes is not a new idea. Simulation facilitates the realization of engineering activities related with the installation and the optimization of those control systems in real plants. Nowadays the use of simulation aids is a simpler issue because of the characteristics of current process control systems and current commercial process simulation tools. It will be easier in a near future, when the majority of the control systems will be more based on the use of software controllers. This paper has two main objectives: first a study of the state of the art of simulation for process control and second a research on the use of simulators for automation testing due to the fact that this issue is one of the main objectives of the Sim-Serv community.

Other Links
 Using Simulation to Optimise Results of Automation Projects -Tom Fiske and MYNAH Technologies. This paper explores how the use of a simulation system for testing and training reduces time-to-market and increases business results of process automation projects.