Case Studies in Modelling, Control in Food Processes.

This chapter discusses the importance of modelling and control in increasing food process efficiency and ensuring product quality. Various approaches to both modelling and control in food processing are set in the context of the specific challenges in this industrial sector and latest developments in each area are discussed. Three industrial case studies are used to demonstrate the benefits of advanced measurement, modelling and control in food processes. The first case study illustrates the use of knowledge elicitation from expert operators in the process for the manufacture of potato chips (French fries) and the consequent improvements in process control to increase the consistency of the resulting product. The second case study highlights the economic benefits of tighter control of an important process parameter, moisture content, in potato crisp (chips) manufacture. The final case study describes the use of NIR spectroscopy in ensuring effective mixing of dry multicomponent mixtures and pastes. Practical implementation tips and infrastructure requirements are also discussed.

Bioprocess intensification of antibiotic production by Streptomyces coelicolor A3(2) in micro-porous culture.

A novel functionalized micro-porous matrix was developed with well-controlled physicochemical proprieties such as pore size and surface chemistry. The matrix was used as a solid support in the growth of "Streptomyces coelicolor" A3(2) to enhance the production of antibiotics. The results shown support a higher production of prodigiosin and actinorhodin with overall production increase of 2-5 and 6-17, respectively, compared to conventional submerged liquid culture, offering a potential improvement in volumetric productivity. Scanning Electron Microscopy was used to evaluate pore size as well as bacterial adhesion, penetration, proliferation and migration within the micro-porous matrix.

Physiological state-specific models in estimation of recombinant Escherichia coli fermentation performance.

Implementation of advanced control strategies in bioprocesses is often hindered by the lack of on-line measurements reflecting the physiological state of the culture. Although a number of techniques have been used to estimate key variables from data monitored on-line, these often do not explicitly take into account changes in physiological state and information on many aspects of physiological state that may not be present in on-line data. Here we demonstrate that data obtained from chemical fingerprinting methods, such as pyrolysis mass spectrometry, can be used to identify changes in the physiological state during cultivation. This information can be utilized for the estimation of the physiological state and can enable physiological state-specific-model development for on-line bioprocess control.

Issues in the development of an industrial bioprocess advisory system.

The background and motivation for the construction of a fault detection and advisory system for an industrial fermentation process plant are described. Here, the knowledge extracted from the operators (implemented in the form of production rules) is integrated with multivariate data-based methods for fault detection. The industrial benefits arising from this integrated system include: (1) reduced variability, (2) increased mean performance levels, (3) reduced operator-training time and (4) knowledge management in the broader organization.

Fermentation seed quality analysis with self-organising neural networks

Industrial fermentation processes operate under well defined operating conditions to attempt to minimise production variability. Variability occurs for many reasons but a long held belief is that variation in the state of the seed is highly influential. In this paper a seed stage (a batch process) of an industrial antibiotic fermentation is considered and the performance of the main production fermentations is correlated with the quality of the seed using an unsupervised Kohonen self-organising feature map (SOM). It is shown that using only seed information poor performance in the final stage fermentations can be predicted. Data from industrial penicillin G fermenters is used to demonstrate the procedure. Copyright 1999 John Wiley & Sons, Inc.

Bioprocess supervision: neural networks and knowledge based systems.

Supervision of highly non-linear and time variant bioprocesses is of considerable importance to bioindustries as a means of achieving improved productivity and reduced process variability. Artificial intelligence methodologies, including artificial neural networks and knowledge based systems, can contribute significantly to the achievement of these objectives.

Artificial neural network based experimental design procedure for enhancing fermentation development.

Conventional experimental design techniques are available to assist in the optimization of fermentation processes, but due to the nonlinearities in the bioprocess, they are limited in their effectiveness. This problem is further complicated with recombinant systems as a result of the additional complexities of the process. This article describes a general strategy using artificial neural networks as an alternative approach to fermentation process development laboratory are presented for the neural network based procedures. (c) 1994 John Wiley & Sons, Inc.