Steady State Simulation
In a steady state simulator, the model is run until the calculated results for the current iteration do not significantly differ from the previous one. Real world conditions which cause the process to be dynamic, (i.e. the thermal mass of the equipment, equipment volumes, controllers) are excluded. The user does not care how long it takes the process to steady out, only that it does.
Steady state simulators are used for process;
Steady state simulators are typically equation based models, were the mathematical equation(s) that describe how the various process unit operations, (e.g. pumps, heat exchangers, vessels...) respond to changes in the operating conditions are coded into modules. The equations and/or correlations implemented have typically been developed based experiments. These are often generalisations and often do not scale well.
The simulation system provides a means for the simulation engineer to connect the simulation modules together so that the real process can be replicated. Because the simulation modules are developed from mathematical models which describe the on the under lying process, equation based models are often called "first principles models". An alternative to developing mathematical equations representing the process unit operations is to use regressed process data, either polynominal or neural networks.
Steady state process models do not include any form of process control, rather the simulation engineer specifies the values of key parameters, (typically model boundaries, e.g. product production rate and purity, feed flow,...). The simulator will then solve the model to determine all the intermediate process values that will result in the specified values being met. The engineer can further constrain the model by specifying limits on the intermediate process values, (e.g. maximum temperatures, pressures).
Dynamic simulators are used for process design, operator training and for optimum process control. Process design and operator training dynamic simulators are generally built using first principles models, while optimum process control models are often implemented using Laplace transform models obtained from "stepping" the actual process.
Dynamic process design simulators are the "next stage up" from steady state models, with factors such as equipment thermal mass and volumes are added. Process stream specifications are removed and key operating conditions are maintained using simple PID controllers. The simulators are used to study the response of the process to sudden changes in operating conditions, (e.g. if a boiler trips will the steam header pressure drop to the steam turbine trip point?).
The level of detail incorporated into an Operator Training Simulators is significantly higher.
The basic dynamic process model, (e.g. includes thermal mass of the system; equipment volumes; indicated liquid levels related to level tappings, equipment geometry and liquid density), is extended;
Operator Training Simulators are used for training operators, typically;
Operator Training Simulator can also be used by commissioning teams during the planning of an initial start-up of a new chemical process plant. Their use allows the teams to reduce the start-up time of the new chemical plant by enabling the team to;
This allows detection of incorrect configuration, missing control loops, equipment and process lines and the identification of the required additions or modifications to be made prior to plant start-up.
The major problem for operator training simulator implementers is the vast number of calculations that are required, and which have to be done in real time, as the simulator must have the look and feel of the real process to the trainee and respond to their changes via the control system. Therefore for large models, or models making heavy use of complex physical property calculations, i.e. flash / distillation columns, the accuracy of the physical property prediction routines must be reduced. Of course the side effect is that more "tuning" of the model is required and it may need to verified against a steady state model.
Equation vs. Modular Based Simulators
In equation based simulators, the mathematical equations that describe the physical process are entered into an equation solver which then uses appropriate techniques to solve them. In modular based process simulators the mathematical equations that describe the physical process are coded into modules which the user "flow sheets" together. Modular based process simulators prevail over equation based simulators because,
However, equation based simulators have proved highly successful in the field of optimum process control. Here, rather than using algebraic equations to develop a model of the process, the actual plant is "step tested" and the resulting process data converted into Laplace transforms. The success of equation based simulators in this field is due to,
Example Process Simulator
It is intended that a modular based process simulator OpenProcessSim that can be used for steady state (process design and optimisation) and dynamic (process / control system verification) process simulation as well as providing an introduction to the process equipment simulated and how it can be simulated, will be developed over time on this site.
Last modified 27 Oct 09