Tuesday, November 29, 2011

10 Most Common Pitfalls in Process Simulation #7: Model Complexity

There are 2 ways to designing a process simulation model –

  1. Make it so simple that there are obviously no deficiencies anywhere in the model.
  2. Make it so complex that there are no obvious deficiencies anywhere in the model. 

Tuesday, November 22, 2011

10 Most Common Pitfalls in Process Simulation #6: Recycles

By definition, recycles in process simulation are any time when you are returning to a previously calculated unit operation or operations to iteratively recalculate the unit due to a change made elsewhere in the model.  They are very common and can often be difficult parts of a model to converge.  When working with a model, it is important to identify and fully understand the recycles that are in your process.  Recycles can come in many forms within process simulation.  The most common form is the compositional recycle.   

Tuesday, November 15, 2011

10 Most Common Pitfalls in Process Simulation #5: Tolerances

Many of the calculations in process simulation are iterative.  When these calculations iterate, the program needs to compare the results calculated from one iteration to the results calculated in the next iteration in order to determine whether it is converged to a solution and can stop calculating or if it needs to keep going.  This way of comparing results is the basic formula for tolerance values, and simulation programs use tolerance to determine this stopping point.  The diagram below shows how a typical tolerance calculation is performed, where N = the value of the current iteration number, N-1 = the value of the previous iteration number, and Ep is the tolerance value.  This example is for a pressure calculation. 

Saturday, November 12, 2011

10 Most Common Pitfalls in Process Simulation #4: Thermodynamic Method Selection

Proper selection of thermodynamic methods for your simulation is the most important decision that you make when modeling a process.  The method you end up choosing could have a drastic change on your results.  In the example below, using Peng-Robinson instead of Grayson-Streed for your column would result in a higher duty for your column’s condenser and an increased reflux ratio.  If you did not know that this was wrong, you could end up designing and purchasing a column much larger than you needed.  

Friday, November 11, 2011

EYESIM - Practical Applications

EYESIM Virtual Reality Training System will soon be utilized by the National Energy Technology Laboratory to model Integrated Gasification Combined Cycle (IGCC) processes. The process converts coal to energy and minimizes harmful emissions by sequestering carbon products. The process is leading the way for clean coal processing and is an integral step toward the future. 

Tuesday, November 8, 2011

New application for Connoisseur (APC software) shows impressive initial results

Invensys Operations Management is offering a new solution to aid in achieving the difficult task of maximizing the heat rate of power plant facilities while minimizing key emissions.  Smart Firing Control (SFC) offers the first fully automated closed loops enhancement of this requirement to the industry and could truly revolutionize the way a power plant is controlled. 

Saturday, November 5, 2011

10 Most Common Pitfalls in Process Simulation #3: Bad Plant Data

Plant data changes over time.  What you enter into a simulation program when you initially build a model will not be the same information that is valid in the future.  Your feed information may be different, especially in the case of assay data which always changes over time.  Your unit operation’s process conditions may also be different now than they were when you built the model.  Not only does plant data change over time, but measurement information is often very inaccurate.  This could be due to faulty or poorly calibrated measurement equipment, leaks in lines, sensor failures, and other issues.  Because of these changes and inaccuracies over time, you always have to validate the information that is entered in your model to ensure that you are obtaining the proper results.  A simple mass balance around the plant below would show that something does not add up properly!

There are programs out there that can help with this data validation and reconciliation.  ROMeo is SimSci-Esscor’s online optimization program.  With it, you can connect your model to your data historian to constantly pull up to date measurements into your model and then reconcile those measurements to determine when problems arise within your plant and where those problems are most likely occurring.  After running data reconciliation, you can manually transfer and use your reconciled plant data as feed information to our other software, like PRO/II and PIPEPHASE.

If you do not have a data reconciliation program to determine the quality of the plant data that you are given, you can use your engineering judgment to determine if the data is valid.  For example, in the process below, depending on the mass balanced performed, we have 3 different flowrates for stream S4.  If you look at just the measurement value, you will get 30.6.  If you perform a mass balance around node C using the measured values for streams S3 and S5, you obtain a flowrate of 29.3.  If you perform an overall mass balance for the process using the measured values given (all streams with MS# shown; S1, S2, and S3 provide enough information to determine S7 = 59 and S8 = 37.9, which then allow you to measure S6 = 21.1), you get a flowrate of 28.1 for S4.  Three different mass balances each provide a different flowrate result for S4.  Which is correct?  As an engineer, it is up to you to decide, but knowing that this is a possibility and checking for this is how you help ensure good plant data instead of bad.