Other goals for simulation include maintaining operational competence:
- With increased intervals between scheduled shutdowns, namely achieving two to three years of continuous operation;
- During increased use of advanced control that has resulted in reduced opportunities for operational “hands-on;”
- During periods of staff restructuring for greater flexibility, such as responsibility for unfamiliar plants; and
- During periods of demographic change to allow for staff retirements.
|FIGURE 2: SYNCRUDE CANADA COKER|
|Maintenance work on a coker unit at Syncrude’s oil sands plant in Alberta, Canada.|
BP uses the latest dynamic simulation models to conduct engineering studies or help troubleshoot and investigate process improvements. During one recent startup, the plant’s model was used to establish improved procedures. To ensure that simulators match plant reality, BP engineers test them regularly by putting them through start up, shut down, and various other situations. The simulator center is used almost continually for training new operators and for retraining experienced staff to maintaining skill levels and capturing knowledge, so expertise isn’t lost as engineers and operators retire.
Rather than waiting for government enforcement, BP reports that the simulator trains operators and engineers in how to respond during plant upset conditions. Personnel are put under pressure in realistic scenarios, such as pump failure, instrument failure and electrical outage. The system allows engineers to design, test, and train operators on new processes offline before they’re implemented. Simulation gives BP ongoing cost savings, enabling well-trained personnel to react quickly to process situations before they become incidents, reducing maintenance costs, increasing plant reliability and safety, improving production rates, and avoiding ESD shutdowns by catching “trips” early.
Improving Refinery Operations
During oil processing, heavy products are broken down by high temperatures into lighter products in cokers. This cracking process strips off lighter liquid hydrocarbon products such as naphtha and gas oils, leaving the heavier coke behind. Dr. Peter Witt, a research scientist at CSIRO Minerals, reports, “Our company is using simulation software to help Syncrude Canada Ltd. reduce the coke deposits that build up in its fluid coker stripper (Figure 3), while maintaining or improving hydrocarbon stripping.” CSIRO is part of Australia’s Commonwealth Scientific and Industrial Research Organization. Syncrude is the world’s largest producer of crude oil from oil sands and the largest single-source producer in Canada.
FIGURE 3: HIGH FIDELITY SIMULATOR
Invensys’ SimSci-Esscor DynSim scenario checks refinery steam pressure distribution system, consisting of high-pressure (HP) steam at 600 psig, MP steam at 300 psig, and LP steam at 150 psig. Inset shows the HP steam pressure drops to 440 psig when GTG B is tripped. Supplemental firing is ramped up by automatic pressure control over 10 minutes after the event.
A long-time user of Ansys’ CFX simulation software, CSIRO collaborated with Clean Power from Lignite CRC to develop the fluidized bed model in CFX-4. Because of its multiphase capability and its ability to be extended into new application areas, CFX is used by CSIRO to perform complex computational fluid dynamics (CFD) modeling of multiphase, combustion and reacting processes in the mineral processing, chemical, and petrochemical industries.
In the past, physical modeling was used to understand the flow of solids and gas in the stripper. This modeling is performed at ambient conditions, so scaling of the physical size and materials is required to approximate actual high temperature and pressure in the stripper. The scaling process can introduce some uncertainty in understanding the actual stripper operation.
Using CFD modeling (Figure 4) to compliment the physical modeling programs allowed CSIRO to eliminate scaling and also use the facility’s actual dimensions and operating conditions. Also, CFX simulation provides much greater detail of the flows and forces in the stripper than can be obtained from physical models or from the plant. This is due to the difficulty of making measurements and visualizing the flow in complex multiphase systems.
|FIGURE 4: 3-D FLUIDIZED BED MODEL OF COKER|
Three dimensional fluidized model of the Syncrude fluid coker stripper. The model predicts the motion of bubbles (in purple) rising from injectors in the lower part of the bed and the complex flow behavior of coke particles. Flow simulations provide insights into the stripper operation, which are used to improve the design.
CSIRO previously used cold-flow modeling (but not CFD modeling) to investigate the fluid bed coker stripper’s operation and behavior of gas and solids in the unit. The project has produced detailed, high-quality reports that provide a better understanding of the fluid coker stripper operation. It’s anticipated that design changes will be identified to improve stripping efficiency, reduce shed fouling, and optimize stripper operation. Assisting Polymer Production
As the world’s largest privately held chemical company, Huntsman
annually manufactures more than 30 billion pounds of highly integrated products for a variety of global industries, including chemicals, plastics, automotive, paints, coatings and packaging. Huntsman’s polypropylene production capabilities encompass PP homopolymers, PP impact copolymers, PP random copolymers, and PP specialty grades. The company’s plant in Longview, Tex., uses two Unipol polypropylene lines to produce about 260 KTA.
The Longview facility specifically wanted an advanced process control solution that would maximize production of it impact copolymer line while minimizing off-specification product and related costs. Huntsman chose Pavilion Technologies
to provide a modeling and simulation solution for its impact copolymer line. Specific project objectives included improving product quality and consistency, eliminating existing process control problems/reject upsets, reducing transition times, and improving reactor stability through controlling condensed and reactive propylene and bad weight. The project was completed in less than three months, which was two months ahead of schedule.
The implementation included Pavilion’s Process Perfecter advanced process control solution, which combines proprietary nonlinear steady-state optimization and model predictive control to deliver a solution capable of controlling quality targets, performing automatic transitions, changing bad weight, and rejecting disturbances. Key benefits to this implementation included a 40% flexible production rate increase (without affecting resin properties), 25-70% reduction of product quality variance (depending on the product), reduced transition times, improved catalyst activity, and an annual polypropylene production value estimated in excess of $1 million annually.
It is highly likely that with the rate of improvement of simulators, even in the chemical processing industry (see “The Light at the End of the Tunnel is a Train
,” CONTROL’s August cover story) that not only will one engineer be able to design an entire plant using simulation, but also be able to train operators, prototype changes, and operate the plant with one operator per shift.