On the Optimization of the Cooling Profile in Crystallization Using the Design of Dynamic Experiments Approach

Comprehensive Quality by Design in Pharmaceutical Development and Manufacture
2009 AIChE Annual Meeting
AIChE Annual Meeting
November 11, 2009 - 7:00pm
To control the size and shape of crystals during batch crystallization, one must either have an accurate crystal growth and nucleation model or run a series of experiments to find the optimal operating conditions. Since the former is a very intricate and time-consuming task, especially for new compounds, the later will be applied using a novel technique, Design of Dynamic Experiments (DoDE)(Georgakis, 2008, 2009), to determine the optimal operating conditions for a batch crystallizer that yield the desired size and shape distributions of the product crystals. The novelty of DoDE is that it is a model-free approach. Instead of using a fundamental model, the optimal operating conditions are found by performing a number of experiments in which the time dependent decision variable(s) is (are) varied according to a systematic set of dynamic signatures, often constrained between minimum and maximum allowed values. The results of the experiments are then used to calculate the optimal profile of the time-dependent decision variable(s). This method determines the optimum processing conditions while utilizing a minimum number of resources. In crystallization the most prevalent time dependent decision variables are the cooling rate and the antisolvent addition. In this presentation, we will discuss results showing the usefulness of DoDE to optimize the cooling profile of a two-liter batch crystallization unit equipped with a model predictive temperature controller to obtain the desired cooling profile(s) in each of the DoDE experiments as well as in the optimal one producing the desired distribution of crystal population characteristics. While the methodology presented here is applicable to any crystallization, the experimental data reported relate to the crystallization of potassium dihydrogen phosphate (KH2PO4), or KDP.

____________________________
Georgakis, C. (2008). Dynamic Design of Experiments for the Modeling and Optimization of Batch Process.

Georgakis, C. (2009, July 12-15). A Model-Free Methodology for the Optimization of Batch Processes: Design of Dynamic Experiments. Paper presented at the IFAC Symposium on Advanced Control of Chemical Processes 2009, Istanbul, Turkey.&'
Professional Development Hours
0.4 PDHs
You will be able to download and print a certificate for these PDH credits once the content has been viewed. If you have already viewed this content, please click here to login.
Presenter(s): 

Would you like to access this content?

No problem. You just have to complete the following steps.

You have completed 0 of 2 steps.

  1. Log in

    You must be logged in to view this content. Log in now.

  2. Purchase Technical Presentation

    You must purchase this technical presentation using one of the options below.
    If you already purchased this content recently, please click here to refresh the system's record of ownerships.

Pricing

Credits 0.5 Use credits
List Price $25.00 Buy now
AIChE Members $15.00 Buy now
AIChE Undergraduate Student Members Free Free access
AIChE Graduate Student Members Free Free access
Skill Level: