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Experimental validation of Optimization results
Optimization > Optimization module > Experimental validation of Optimization results

The end result of an Optimization exercise in Dynochem is often a suggested set of experimental or operating conditions for the process. Since the accuracy of the prediction will be dependent on many variables (the values of which may be difficult to determine accurately), the new set of conditions should be tested experimentally.

The benefit of using Dynochem is the time saving in finding optimal (or near-optimal) conditions without having to do extensive experimental studies, e.g. short-cutting the time taken to plan and complete a design of experiments (DoE) study.

The data from experimental validation can be recycled as new Scenario(s) in DynoChem and used to improve the robustness of the dynamic model for future use.