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Dynamic Process Simulators
and Training Systems
Simulation
Using Foundation Fieldbus Function Blocks - Terry Blevins
- From modelingandcontrol.com
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
-
Integration
of a Field Surface & Production Network with a Reservoir Simulator Gokhan
Hepguler, Santanu Barua, Wade Bard
-
Integration
of Refinery Reactors into Flowsheet Simulation Dave Bluck, Richard
Yu, Lee Turpin, Robert Powell
-
DCS
Upgrades for Nuclear Power Plants: Saving Money and Reducing Risk through
Virtual-Stimulation Control System Checkout - Gregory McKim,
SimSci-Esscor; Mike Yeagerand Clint Weirich, FENOC Perry
- Simulator
Project Profile: Reduce Control System Upgrade Risk through Simulator
Controls Checkout - Joe Ciancio
- United
Taconite’s Iron Ore Pelletizing Production Performance Improvement Project
- Lewis M. Gordon and Robert A. Medower
- Operator
Training Boosts Productivity at South African Power Plant - Todd Thayer,
VP Operator Training Systems & Services - SimSci-Esscor
- Automated
Rigorous Performance Monitoring (ARPM): Online Model Based Performance
Monitoring
- The
Benefits of Using Dynamic Simulation & Training Systems for Expanding
Operator Knowledge and Understanding - Dr. Paul W. Seccombe
-
How
much money are you losing by not doing Online Optimization?
-
Validated
Dynamic Model Confirms Crude Column Relief Design (November 2001) Uwe
Nagel, OMV Deutschland GmbH; and HowardJemison, Ralph-Uwe Dietrich, and Cal
Depew of SimSci-Esscor
-
Selection
of Equations of State Models for Process Simulator Chorng Twu, John
Coon, Melinda Kusch, Allan Harvey
-
Simulation
Software and Engineering Expertise: A Marriage of Necessity John Coon,
John Cunningham, Melinda Kusch, Mike Rowland
-
Crude
Unit Optimization Using Rigorous On-line Models, a Case Study Jerry
Platt, Paul Brice, Mike Hill
-
Estimation
of Aromatic Hydrocarbon Emissions from Glycol Dehydration Units using
Process Simulation John Cunningham, John Coon, Chorng Twu
The Following Technical Papers are from Sim-Serv
8.09
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.
8.09
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.
8.09
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.