When process simulation first became available to chemical engineers in the early 1970s, the general thought was it eventually would replace the need for experienced engineers. Instead, what actually came out of the process simulator revolution was the need not only for experienced engineers but also for experienced process simulation engineers.
Process simulators play a role in virtually every aspect of design and operation today. Calculations that once required weeks now take seconds. With that broad capability and power comes the responsibility to thoroughly understand your process simulator. However, this skill isn’t easy to master. Even with years of experience, a typical user constantly faces new challenges. Many issues cause unexpected and unnecessary errors, and wasted time and effort. Here, we’ll discuss the five most common pitfalls in building a process simulation model as well as the techniques and methods to avoid them.
This top five list includes:
1. Data entry;
2. Simulation defaults;
3. Thermodynamic method selection;
4. Recycle loop design; and
5. Model complexity.
The most common pitfall is incorrect data entry. Such mistakes are the easiest ones to make. They typically involve errors in units of measurement (UOM), data reliability and data reconciliation.
Incorrect units of measurement are by far the most prevalent source of these errors. The most famous example of a UOM mistake was NASA’s loss of the Mars Climate Orbiter in 1998. A mismatch between English and SI units resulted in an orbit trajectory that brought the spacecraft too close to the planet, causing it to pass through the upper atmosphere and disintegrate. Unfortunately, the system of checks and balances at the Jet Propulsion Laboratory in California never uncovered and corrected the problem. The mistake doomed the over-$650-million project.
UOM errors typically are very difficult to catch but are easily prevented. Careful data entry followed by a meticulous UOM audit will prevent costly mistakes. Such an audit underpins a successful process simulation.
Data reliability also presents a difficult challenge for the simulation engineer. Typically for a given unit operation, many different operating conditions can be specified in a process model. Carefully assess which data are the most reliable. Knowledge of the process is key to making the best selection — operations personnel often can provide invaluable insights.
Data reconciliation involves using process data along with mathematical methods to correct for measurement errors. Steady-state process models will converge to a perfect heat and material balance. However, real plants don’t operate at steady state. In addition, process data suffer from random errors due to measurement noise and gross errors due to faulty equipment and miscalibration. Data reconciliation can turn your process measurements into consistent, reliable information that can bolster efforts to improve and optimize plant operation and management.
Several software programs can help with data validation and reconciliation. Some connect the model to a data historian to constantly pull real-time measurements into the process model and then reconcile those measurements to determine when problems arise within the plant and from where those problems most likely originate. After running data reconciliation, you may have the option of manually transferring and using the reconciled plant data as feed information to other software.
Error mitigation. Consider a number of steps:
• Conduct data audits. Double-check entries to ensure they match what is intended, especially UOM.
• Spend time getting organized. Have all information ready for entry and in the required UOM.
• Minimize distractions. Try not to perform other tasks while doing data entry. Stay focused.
• Use “alternative forms” of data input. Copy/paste information from one source to another, import data directly from the source or create dynamic links between parameters within the model.
• Don’t ignore warning messages. While most warnings are innocuous, others (especially error messages) indicate major problems in the model.
• Ensure data reliability. Rely on information you’re confident about and, when given the option, let the program calculate questionable parameters.
• Question measurements. All measurement devices are only accurate to a degree. Use data reconciliation to minimize errors.
The second largest pitfall comes from the use of simulator defaults. Process simulators are designed to run with a minimum of data input. To accomplish this, they employ an extensive list of calculational defaults. These default values appear throughout the program — from convergence criteria to thermodynamic method selection and characterization options. The default values typically provide a good starting point for most simulations. However, the efficacy of each requires checking.