The virtual plant offers a break through in training and knowledge discovery but its potential depends upon the ability to develop dynamic simulations that capture the process relationships and response important for process understanding and control.
The best practice of practical real time simulation could easily fill a book but I need to wind this up and move on to other opportunities so here are a few ideas on how to make a process model more flexible in terms of cost and performance and maintainability. It is important to realize the art of simulation is simplification to what is essential.
A significant portion of the time is spent trying to decipher the intricacies of a plant’s DCS configuration and displays. If there is an accurate P&D with the relative location of every pump, fan, valve, and measurement noted along with the complete DCS tag name, and there is browser access to each tag name to assign DCS outputs as process inputs and process outputs as DCS inputs in the model, the need to dig into the configuration is vastly reduced. Note that special DCS I/O such as pulse counts must still be identified and separately addressed.
The computational requirements, numerical hazards, and data requirements on the piping system and fluid flow of a pressure-flow solver are considerable. If there are flow loops for every throttle valve, then the complexity and cost of a pressure-flow solver may be avoided. Of course, this simplification will not identify improperly sized pumps, valves, and pipes. I propose it would be better to add imbedded flow loops in the process simulation rather then venturing into a pressure-flow solver. This simplified approach uses a combination of flow loops and a pathway methodology where the 1 or 0 status of on-off valves and pumps determine an open piping path. The total flow coming out of a piping tee can be written back as the flow going into the tee. The use of flow loops reduces but does not eliminate the need to simulate valve backlash and stick-slip. If a pressure-flow solver is deemed valuable, than I suggest a sequential modular method to avoid ill conditioned matrices and numerical problems during batch operations and the startup and shutdown of equipment.
If the model starts out with initialized but settable molecular weights, densities, and heat capacities, then levels, temperatures, blending, and temperature can be simulated. If the dissociation constants for bases and pH are added, then pH can be added. For the modeling of vaporizers and evaporators, it may be sufficient to add vapor pressures and boiling points of selected components as a function of composition. For reactors, the standard form of Arrhenius and Michaelis-Menten kinetics may be sufficient. Neural networks may be able identify kinetic rates to provide a simpler and higher fidelity hybrid model. The complexity of a full blown physical property package could be reserved for more complex vapor equilibrium problems such as distillation.
Finally, it is most important to get the dynamics right. The process models from on-demand and on-line tuning packages such as DeltaV Insight and model predictive controllers such as DeltaV Predict can be used to supplement or replace first principle models for specific parts of the process.
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