Simulation Optimization
Simulation Optimization - Related Videos
14:33
Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations
0 Views •
Cited by 5 •
2013
Flue gas from power plants is a cheap CO2 source for algal growth. We have built prototype "flue gas to algal cultivation" systems and described how to scale up the algal cultivation process. We have demonstrated the use of a mass-transfer bio-reaction model to simulate and to design the optimal operation of flue gas for the growth of Chlorella sp. in algal photo-bioreactors.
03:37
Optimal Foraging - Concepts
0 Views •
2019
Optimal Foraging Organisms must acquire and use resources in their environment to survive. While food is one of the primary resources organisms must search for, individuals also need to seek habitats, shelter, and mates. This process of searching for resources is known as foraging, which involves a series of costs and benefits. More specifically, acquiring a resource provides the organism with a benefit, however, searching and capturing the resource requires expenditure of time and energy.
05:04
Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound
0 Views •
2024
The present protocol describes an ideal solution to train novices in the use of point-of-care ultrasound devices for the practical clinical skill of visually assessing distinct individual anatomical vascular conditions prior to and during an intended venous vascular cannulation using point-of-care ultrasound in a patient.
13:44
Simulation, Fabrication and Characterization of THz Metamaterial Absorbers
0 Views •
Cited by 6 •
2012
This protocol outlines the simulation, fabrication and characterization of THz metamaterial absorbers. Such absorbers, when coupled with an appropriate sensor, have applications in THz imaging and spectroscopy.
08:45
Study of Siphon Breaker Experiment and Simulation for a Research Reactor
0 Views •
Cited by 2 •
2017
The siphon breaking phenomenon was investigated experimentally and a theoretical model was proposed. A simulation program based on the theoretical model was developed and the results of the simulation program were compared with experimental results. It was concluded that the results of the simulation program matched the experimental results well.
FAQs
The objective function specifies what the computational search should improve, such as performance, cost, reliability, or resource use. Constraints restrict decisions to acceptable conditions rather than allowing the model to pursue an impractical optimum. Together, they determine which simulated outcomes count as preferable and make the selected solution reflect real design or operational priorities.
Uncertainty matters because biological systems may produce different outcomes under conditions that appear similar in the model inputs. Accounting for uncertainty helps evaluate whether a promising decision remains useful beyond one predicted result. This is especially important when selecting bioprocess conditions, treatment strategies, or designs whose performance must be judged under variable system behavior.
Instead of relying on exhaustive trial-and-error experiments, the approach evaluates candidate decisions through repeated model-based simulations. This can narrow attention to more promising conditions before physical testing occurs. The resulting search is valuable when experiments are costly or when many combinations of design variables, process conditions, or treatment choices would be impractical to test individually.
A decision or design input is first selected and supplied to the simulation model. The resulting system behavior is then evaluated against the chosen objective, while constraints and uncertainty remain part of the assessment. The process repeats with varied inputs, allowing comparisons among candidate decisions and revealing trade-offs rather than relying on a single trial.
In bioengineering, the framework can guide bioprocess conditions, biomaterial properties, medical device designs, and treatment strategies. Each application uses simulated outcomes to compare decisions against relevant goals, such as improved performance or reduced resource use. This supports development and evaluation of biological or biomedical systems without requiring every alternative to be tested experimentally.
Simulation Optimization is particularly useful when a system is complex, competing objectives must be balanced, or experimental trial and error would consume substantial resources. It can expose relationships between performance, cost, reliability, and resource use that may be difficult to compare directly. The resulting analysis supports more efficient design, evaluation, and selection of bioengineering strategies.