4438
Ind. Eng. Chem. Res. 2000, 39, 4438
Response to Comments from Larachi and Grandjean on “Neural Network Modeling of Structured Packing Height Equivalent to a Theoretical Plate” and“HETP and Pressure Drop Prediction for Structured Packing Distillation Columns Using a Neural Network” R. Bruce Eldridge Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712
Sir: The papers present the initial application of a neural network to the prediction of packed tower masstransfer performance. The intent of both publications was to show the readership of Ind. Eng. Chem. Res. that the method had the potential to be an improvement over the semiempirical models historically used to correlate mass-transfer performance. The database used in the study was fairly large, but it did not contain commercialscale data. Although many semiempirical models have been developed on such limited data sets, we would not feel comfortable releasing the model to the engineering community until further validation is conducted. We do not feel that the two papers in question represent a premature publication of our results. The initial results
were very promising and warranted publication. Should the method prove to be an effective modeling approach, significant effort will be required to produce a general, easily used, nonsoftware-specific model. Several approaches have been suggested, including the use of an elaborate multivariable equation derived from the neural network parameters. This approach has been used with some success for process control. In summary, we believe that the method has potential, and further work will ultimately determine if it can replace traditional modeling methods. IE000737X
10.1021/ie000737x CCC: $19.00 © 2000 American Chemical Society Published on Web 09/14/2000
