Elsevier

Neurocomputing

Volume 20, Issues 1–3, 31 August 1998, Pages 67-82
Neurocomputing

Application of radial basis function and feedforward artificial neural networks to the Escherichia coli fermentation process

https://doi.org/10.1016/S0925-2312(98)00025-3Get rights and content

Abstract

Radial basis function and feedforward neural networks are considered for modelling of the recombinant Escherichia coli fermentation process. The models use industrial on-line data from the process as input variables in order to estimate the concentrations of biomass and recombinant protein, normally only available from off-line laboratory analysis. The models performances are compared by prediction error and graphical fit using results obtained from a common testing set of fermentation data.

Introduction

Current practice in bioprocess industries is for the physicochemical variables to be monitored regularly on-line during a fermentation. However, significant indicators of bioprocess behaviour, such as biomass and recombinant protein concentrations (in the case of a recombinant process) are usually measured off-line in the laboratory, providing delayed and relatively infrequent information. It is therefore very difficult to recognise the early signs of an undesirable fermentation, hindering on-line control actions and ultimately leading to a significant waste of time and resource. This problem has led to the development of a range of “software sensors”. These sensors utilise mathematical models (ranging from structured to data-based) and algorithms, together with available on-line information, to estimate the key bioprocess parameters.

There are a wide number of data-based modelling techniques available to formulate process models, each varying in complexity and ease of development. They range from well-established statistical techniques, such as multiple linear regression (MLR), principal component regression (PCR), partial least squares (PLS) [19], to non-linear techniques, such as non-linear principal component analysis (NLPCA), non-linear autoregressive moving average with exogenous input (NARMAX) model [2]and artificial neural networks (ANN). In recent years, there has been a resurgence of interest in the potential of artificial neural networks (ANNs) as a modelling tool in bioprocesses. Their ability to learn complex non-linear relationships without prior knowledge of the model structure makes them a very attractive alternative to other non-linear modelling techniques. Numerous successful applications have been made to simulated and actual fermentation data 5, 7, 8, 13, 18, 19, 22, 23. Most of these used feedforward neural network architecture.

The purpose of this study is to compare the effectiveness and accuracy of radial basis function and feedforward neural networks in bioprocess modelling and estimation. The models are developed and applied to industrial on-line data from recombinant Escherichia coli fermentations and their suitability assessed from their performances on a common testing set.

The paper is organised as follows. Section 2describes the two techniques used and the bioprocess to which these techniques are applied. Section 3presents the results obtained and finally Section 4summarises the work.

Section snippets

Feedforward artificial neural networks

A standard feed-forward ANN (FANN), shown in Fig. 1, has been used in this study. Although the figure demonstrates only an FANN with one hidden layer, this study allowed a selection of topologies with two hidden layers depending upon the FANN performance on the process data. The non-linear processing function of the hidden layer neurons used was a sigmoid f(z)=1/(1+e−z),where z is the sum of the weighted inputs and bias term.

Once the topology of the neural network is selected, the weights for

Model topology

Twelve of the data sets were chosen to form a training set, to be used to train the different models, i.e. determine the model parameters for a range of input variable combinations. These sets were then subdivided into three training subsets, each containing 4, 8 and 12 data sets from the original set, respectively, allowing for model training with different amounts of process information to assess the influence of this factor upon the model performance. The remaining twelve sets formed the

Conclusion

This study has compared the performance of conjugate gradient and chemotaxis trained FANN and RBFN models in the estimation of fermentation performance parameters. It has been shown that conjugate gradient FANN and RBFN provide similar quality predictions for both biomass and recombinant protein concentrations, provided their structures (especially the nearest-neighbour constant in RBFN) are optimised. The chemotaxis FANN was the slowest and least accurate of the compared modelling techniques.

Further reading

6, 9, 14, 20, 24.

Acknowledgements

The authors gratefully acknowledge the support of the Department of Chemical and Process Engineering, University of Newcastle upon Tyne, Zeneca Pharmaceuticals, and the Biotechnology and Biological Sciences Research Council in the UK. The comments of reviewers are also gratefully acknowledged.

Mark R. Warnes originally studied as a Theoretical Physics undergraduate at the University of Newcastle upon Tyne, UK from 1989 to 1992. After graduation, he remained at Newcastle to embark upon the degree of Doctor of Philosophy with the Department of Chemical & Process Engineering. These postgraduate studies included the consideration of feedforward and radial basis function neural networks as modelling tools for recombinant fermentation processes. Having finished these studies in September

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    Mark R. Warnes originally studied as a Theoretical Physics undergraduate at the University of Newcastle upon Tyne, UK from 1989 to 1992. After graduation, he remained at Newcastle to embark upon the degree of Doctor of Philosophy with the Department of Chemical & Process Engineering. These postgraduate studies included the consideration of feedforward and radial basis function neural networks as modelling tools for recombinant fermentation processes. Having finished these studies in September 1995 (submitting his thesis in May 1996) Mark currently works in Coventry, UK as the Web Editor for the University of Warwick (http://www.warwick.ac.uk).

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    Jarmila Glassey graduated from the Faculty of Chemical Technology, Slovak Technical University in Bratislava, Slovak Republic in 1990. After graduation she worked as a research associate in the Department of Chemical and Process Engineering at the University of Newcastle where she was awarded the degree of Doctor of Philosophy in 1995. Jarka became a lecturer in this Department in 1994 and is leading the biochemical engineering modules within the Department. Her research interests are in the area of bioprocess supervision, modelling, optimisation and control using databased modelling techniques.

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    Gary A. Montague graduated in chemical engineering from the University of Newcastle. After studying at the University of Sheffield for a year, he returned to Newcastle to undertake his Ph.D. research. Following a year's secondment to ICI, Gary took up his lecturing commitments and is currently Professor of Bioprocess Control in the Department of Chemical and Process Engineering at the University of Newcastle. Gary has undertaken research predominantly in the areas of process monitoring, control and optimisation. He collaborates closely with industry and has worked extensively with many major pharmaceutical companies.

    Bo Kara's background is in microbial physiology and biochemical engineering gained during graduate and postgraduate studies at the University of Manchester Institute of Science and Technology (UMIST). Bo joined Zeneca Pharmaceuticals (then ICI Pharmaceuticals) in 1987 after a brief period working in the brewing industry (BRF International). Current interests are in microbial physiology, expression system development, fermentation process optimisation and scale up. Other interests include process modelling and control, particularly the application of artificial neural networks to bioprocess modelling.

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