An efficient model development strategy for bioprocesses based on neural networks in macroscopic balances: Part II.

There is a need for efficient modeling strategies which quickly lead to reliable mathematical models that can be applied for design and optimization of (bio)-chemical processes. The serial gray box modeling strategy is potentially very efficient because no detailed knowledge is needed to construct the white box part of the model and because covenient black box modeling techniques like neural networks can be used for the black box part of the model. This paper shows for a typical biochemical conversion how the serial gray box modeling strategy can be applied efficiently to obtain a model with good frequency extrapolation properties.

An efficient model development strategy for bioprocesses based on neural networks in macroscopic balances.

In the serial gray box modeling strategy, generally available knowledge, represented in the macroscopic balance, is combined naturally with neural networks, which are powerful and convenient tools to model the inaccurately known terms in the macroscopic balance. This article shows, for a typical biochemical conversion, that in the serial gray box modeling strategy the identification data only have to cover the input-output space of the inaccurately known term in the macroscopic balances and that the accurately known terms can be used to achieve reliable extrapolation. The strategy is demonstrated successfully on the modeling of the enzymatic (repeated) batch conversion of penicillin G, for which real-time results are presented.

Hydrodynamics and mixing in a multiple air-lift loop reactor.

A new bioreactor, in which a series of air-lift reactors with an internal loop is incorporated into one vessel, is introduced. With this multiple air-lift loop reactor (MAL) and approximation of an aerated plug-flow fermentor is strived for. Mixing, liquid velocity, and gas hold-up were measured as a function of the gas flow rate in this new internal-loop reactor geometry.