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Artificial neural networks are powerful in representing nonlinear processes, but they may not be reliable when they are applied beyond their range of experience. In order to avoid suspect predictions, appropriate extensions must be installed which monitor their reliability. When their performance becomes unsatisfactory, a sufficient safety measure must be provided. A software tool is described, in which these aspects of improving artificial neural nets are implemented. Application examples are presented from a production scale beer brewery fermenter.

Process models are used to formulate knowledge about process behaviour. They are applied, e.g., to predict the process' future behaviour and for state estimation when reliable on-line measuring techniques to monitor the key variables of the process are not available. There are different sources of information available for modelling, which provide process knowledge in different representations. Some elements or aspects may be described by physically based mathematical models and others by heuristically obtained rules of thumb, while some information may still be hidden in the process data recorded during previous runs of the process. Heuristic rules are conveniently processed with fuzzy expert systems, while artificial neural networks present themselves as a powerful tool for uncovering the information within the process data without the need to transform the information into one of the other representations. Artificial neural networks and fuzzy technology are increasingly being employed for modelling biotechnological processes, thus extending the traditional way of process modelling by mathematical equations. However, a sufficiently comprehensive combination of all these techniques has not yet been put forward. Here, we present a simple way of combining all the available knowledge relating to a given process. In a case study, we demonstrate the development of a hybrid model for state estimation and prediction on the example of a yeast production process. The model was validated during a cultivation performed in a standard pilot-scale fermenter.

In this paper atmospheric pressure microwave discharge methods and devices used for producing the non-thermal plasmas for processing of gases are presented. The main part of the paper concerns the microwave plasma sources (MPSs) for environmental protection applications. A few types of the MPSs, i.e. waveguide-based surface wave sustained MPS, coaxial-line-based and waveguide-based nozzle-type MPSs, waveguide-based nozzleless cylinder-type MPS and MPS for microdischarges are presented. Also, results of the laboratory experiments on the plasma processing of several highly-concentrated (up to several tens percent) volatile organic compounds (VOCs), including Freon-type refrigerants, in the moderate (200–400 W) waveguide-based nozzle-type MPS (2.45 GHz) are presented. The results showed that the microwave discharge plasma fully decomposed the VOCs at relatively low energy cost. The energy efficiency of VOCs decomposition reached 1000 g/kWh. This suggests that the microwave discharge plasma can be a useful tool for environmental protection applications. In this paper also results of the use of the waveguide-based nozzleless cylinder-type MPS to methane reforming into hydrogen are presented.

The influence of stirrer speed in the third preculture on the performance of penicillin V production by Penicillium chrysogenum in complex medium in a 100-l air-lift tower loop reactor was investigated. The process performance in the main culture was improved by increasing the stirrer speed from 500 to 750 rpm: the pellet size was reduced to half, the cell growth was influenced only slightly, but the production phase was extended considerably, and the final penicillin concentration was increased from 5.1 g 1-1 to 10.4 g 1-1.