Introduction ❯ Process Simulators
Process model simulators predominantly fall into one of two categories, either steady state
. Broadly, the method of constructing the model falls into one of two types, equation based
or modular based
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;
- design (what process conditions are required to produce the required product)
- equipment design (to size the equipment required to produce the required product)
- design optimisation, (to determine the optimum configuration of equipment and maximise energy recovery)
- optimisation, (to determine changes to the current operating conditions that can either reduce operating costs or increase production)
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;
- simple PID controllers are replaced by the actual process control system configuration, either emulated or by stimulation of the actual vendor's hardware
- the actual process SIS (Safety Instrumented System) configuration, either emulated or by stimulation of the actual vendor's hardware is incorporated, (dynamic process simulations for dynamic response studies either do not incorporate, or have simple Cause and Effect matrices implementing the overall effect of the ESD (Emergency Shut-Down) system, but not consider the operator's interaction with the logic
- if not included in the SIS or ESD system, the Burner Management System (BMS) will be incorporated
- key proprietary vendor controllers, such as anti-surge controllers and Gas Turbine & Steam Turbine controllers and protective logic may be emulated or stimulation of the actual vendor's hardware incorporated
- (rotating) machine monitoring (e.g. vibration, axial displacement) systems are typically not included but their inputs to the SIS can be used to simulate equipment failure
- controls that allow the instructor or process engineer to alter the environmental conditions affecting the operation of the simulated process, (e.g. ambient air temperature, cooling water temperature, fuel gas composition, feed pressures), are incorporated
- the ability for the instructor or process engineer to simulate the failure of process equipment, (e.g. motor, drive shaft or belt, transmitter, valve actuator failure; valve, heat-exchanger leak; utilities (electrical power, Instrument Air, cooling water) failure) is incorporated
- the ability to enable the instructor to simulate the failure of process equipment, (e.g. motor, drive shaft or belt, transmitter, valve actuator failure; valve, heat-exchanger leak or fouling) and utilities (electrical power, Instrument Air, cooling water) failure is incorporated
- the simulator user (instructor) interface incorporates tools to evaluate the performance of the trainee
However Fire & Gas Systems are not included.
Operator Training Simulators are used for training operators, typically;
- new operators to the process plant
- refresher courses on start-up, shutdown and trip procedures for experienced operators
- operator certification and, or qualification
- review, disection and corrective action review and training based on abnormal operating events that have occured
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;
- test the operation of the DCS control scheme with dynamic process values
- test the operation of the SIS logic scheme with dynamic process values
- verify the communication between the DCS and the SIS and vendor package PLC with the DCS and or SIS
- confirm operating procedures and the available equipment and pipelines
- confirm if start-up is possible with the available process feed
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,
- it is easier for the user to "map" the real world into the virtual one
- addition and deletion of modules to the flow sheet can be done easily without changing the solution strategy
- programming and debugging of the modules is easier than analysing sets of equations
- the equations are solved simulataneously in a matrix, so if there is no feasible solution, then the starting point for the next attempt is "garbage" and so the last good condition must be reloaded. It is also difficult to identify which is the equation causing the infeasible solution, and very easy to create an infeasible solution
However modern process simulators allow the simulation engineer to build in an eqaution based modelling engine using equipment modules, which is solving the first three problems.
Some commerical process simulators now solve the first two problems by providing the simulation engineer to build the process using process modules in a graphical model builder and the simulation software generates the equations required based on the layout.
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
- Laplace transforms can represent process dead time, which is hard to do with algebraic equations. (Process delay has a major effect on process controllability when using PID controllers)
- Because the identification of the process dynamics is done in the modelling package, the development and maintenance of the model is easier for the "casual" user than the equivalent modular based model
- The parameters of the equations that represent the process are fitted based on the process response and so the physical properties of the process are implicity included. Because the models don't use physical property calculations, the model is capable of running fast enough to predict the resulting steady state of the process and the path of the process variables to the steady state, within the execution timeframe of the controller. (Regular (PID) DCS controllers have scan rates in the region of 0.25 seconds).
Being able predict the path of the process value to the predicted optimum value enables the optimiser to find the optimal path to the optimum operating point, while ensuring the process remains within operating constraints, both maximum and minimum values and rates of change
- Equation based models handle instrument error and incorrect or errors from modelling simplifications better than modular based simulators. Modular based simulators invariably have a data reconcilation step, were the model is run against values obtained from the instrumentation system and then a least squares fit is performed to fit the model to the process
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 7 Jul 19