Achieve Success with Biosimulation

Biosimulation, if done right, aids in allocating equipment, managing utility requirements and optimizing processes. Here's how to apply simulation to improve purification.

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Biosimulation allows engineering
teams to size and allocate
equipment more efficiently.
Photo courtesy of Fluor Corp.




By Brian Schmidt, Process Engineer, Fluor Corp.

Process simulation is widely used in petrochemicals manufacturing and outside the process industries. The technique, which mathematically models a process to determine the impacts of change, is now becoming more popular for bioprocess development.

Editor's Note: All figures for this article are contained in a single PDF document which may be accessed by clicking the "Download Now" button at the end of the article.
The reasons for simulation’s higher profile are clear: Biosimulation allows engineering teams to size and allocate equipment more efficiently, and to determine utility requirements early in the development process.
In addition, it also permits process optimization.

But biosimulation for its own sake, without clearly defined goals, wastes time and money. Using simulation effectively requires that key variables be identified early on in the project, including throughput, “uptime” or equipment utilization, potential bottlenecks, schedules and resource allocation. It also requires that goals be stated clearly, early on, so that the right type, quantity and quality of information is fed to the model.

In general, simulation can help users:
  • Size utility generation, and even distribution and storage requirements and equipment, in order to more accurately and efficiently use peak and average utility consumption data

  • Determine Clean-in-Place (CIP) requirements, including those for skids, water and cleaning chemicals

  • Perform economic analyses to determine project feasibility, profitability and payback

  • Optimize the process, by testing the effect of various scenarios on throughput and other variables.
Using a biopurification train model as an example, this article will discuss tips on how to make any bioprocess simulation more efficient and to achieve significant increases in throughput.

Model development: goal setting

The first step in developing a process model is to identify what the goals of the effort will be. The design team must identify what it hopes to accomplish and what questions the model will address. The model may be used to:
  • Debottleneck a process
  • Size equipment
  • Size utility systems
  • Track raw materials used in the process
In each case, different types of information will be required. Goal setting establishes what information must be gathered and dictates how the model will be structured. In general, the more questions the model must answer, the more information it will require.

Information gathering

Once goals have been identified, the team must gather the data needed to develop the process model. Each modeling objective will require a different type of information, depending on the final model’s focus. Most models will require the following:
  • Process flow information and process operating parameters;

  • Process flow diagrams (PFDs), piping and instrumentation diagrams;

  • (P&IDs) and process descriptions will be required to ensure the flow and connectivity of unit operations. Information on how each of the unit operations works, including identification of each major step of each unit operation.
Duration can be expressed in time or as a set of process conditions that can be used by the software to calculate the duration of each step. For example, transfer rate and batch size can be used to calculate how long a transfer from tank to tank will take. Process descriptions may provide detail on how each of the unit operations operates depending on the level of detail. It may be necessary to obtain data by reviewing Standard Operating Procedures (SOPs) or interviewing design team members and operations staff, who can help identify such hard-to-determine factors as manual operations within the process.


Additional data such as raw material properties, utility design flow rates, and data on utility consumption, manpower and costs—and this is only a partial list.

When data are not available, as is usually the case during a new facility conceptual design project, assumptions can be made using generic data that can be replaced once real data are available.

Building a model for a purification train

Once the appropriate data have been gathered, the model can be built. We’ll apply this concept to a biopurification train. First, model the product’s path through the process. Generally, it helps to break this portion of the project down into smaller steps, and to work with one unit operation at a time.

Then, for each operation, enter its process information, its scheduling links and its equipment data. After data for the first unit operation has been entered, make sure that the model accepts and works with these data. This extra step will save time later on by eliminating the need to debug multiple units individually.

Some suggestions:
  • Model directly whenever possible; don’t model operations that reference operating parameters from other areas of the process.

  • In the case of purification, start with the chromatography, viral filtration and ultrafiltration systems, rather than from the buffer preparation and buffer hold systems, which reference data from other areas of the process. For example, buffer hold tanks that feed chromatography systems get their pull-out durations and volumes of buffer fed to the chromatography columns from the chromatography system itself.
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