Semester

Fall

Date of Graduation

2003

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Chemical and Biomedical Engineering

Committee Chair

Ray Y. K. Yang.

Abstract

Artificial neural networks (ANN) have become an established discipline and have gained extensive interest within chemical engineering. In recent years, research effort has focused on the use of "hybrid artificial neural networks" (HANN) models that combine both the deterministic and the ANN elements. Several methods have been proposed for combining ANN with first principle relations. In this thesis, a new hybrid scheme, which is similar to that developed by Kasprow for a space-independent and time-dependent fed-batch microbial reactor, was developed for a space-dependent steady-state enzymatic reactor. This scheme combines ANN with mass balances and assumed rate expressions. It was shown that this new hybrid scheme performed significantly better than both black-box ANN model and the hybrid ANN with only mass balance equations. An enzymatic tubular membrane reactor (TMR) was selected as a case study due to the availability of a reliable deterministic/computational model, which can provide simulated process data as needed, as well as its potential industrial importance. Also, two modeling schemes were developed, a fully 'black box' model (BANN), based on ANN technique only, and a simple hybrid model, combining ANN with mass balances (HANN1). Qualitative and quantitative comparisons of the predicted profiles of the above three modeling schemes indicated that the new hybrid scheme (HANN2) performed better than the other two schemes. As a result of adding biochemical knowledge, in the form of mass balances and simplified rate expressions, the new hybrid scheme allowed the process data to be interpolated and extrapolated more accurately.

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