Crystallization methods for proteins have been the subject of decades of development yet protein crystallization remains the limiting step in structural studies. We present here a new method for protein crystallization--based on the use of high pressure--that enabled us to accelerate dramatically the growth of glucose isomerase crystals. We think this method may have a more general utility.

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In this work a method for the simulation and optimization of a pressure swing adsorption process for the separation of nitrogen from air by using neural networks was developed. The model is used to obtain a prediction for the process performance, namely, the specific product and yield, over a wide range of operating conditions. These results are compared with the predictions from a mass tranfer model, and a very good agreement is found. The network developed is also used to minimize a cost objective function, and it is shown that it can easily be used in process optimization and/or control.

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