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The objective of this work was to explain the efficient NOx removal from simulated flue gas observed in the hybrid corona discharge-catalyst hybrid systems with either V2O5/TiO2catalyst or zeolite, both at room temperature. The simulated flue gas was a mixture of N2(80%):O2(5%):CO2(15%):NO (200 ppm). After analysing the results of several experiments on NOx removal in the corona discharge-catalyst (or zeolite) systems, two different mechanisms of NOx conversion in the hybrid systems are proposed. In the positive corona discharge-catalyst system, NOx molecules are removed due to the activation of the catalyst surface by the corona discharge (TiO2+ ε (≥ 3.2 eV) → hole++ e), which is followed by: (a) the catalyst surface driven formation of NH2radicals (which reduce NOx into N2in gas phase) and H+(or NH4 +) ions, (b) the oxidation of NOx to surface-trapped NO3-. The NO3-ions trapped on the catalyst surface are precursors of the formation of NH4NO3aerosols depositing on the catalyst surface. In the corona discharge-zeolite system, the conversion of NOx into NH4NO3, N2and H2O occurs due to the reactions involving zeolite-fixed NH4+ions.

The objective of this work was to investigate NOxremoval with a hybrid system consisting of a DC corona discharge and a molecular sieve at a room temperature (22 °C). In the hybrid reactor a positive DC corona discharge was generated between a hollow needle and a flat mesh. A gas mixture of N2(80%): O2(5%):CO2(15%):NO(200 ppm), simulating a flue gas, flowed through the tube with a flow rate of 1 l/min. The obtained results showed that due to adsorption of NO2, resulted from NO oxidation, by molecular sieve placed in the corona discharge region, NOxremoval increased significantly. It was also found that the number of NO molecules oxidised into NO2does not depend on initial NO concentration. Thus, in the presence of the molecular sieve in the discharge region, the removal of NO is not controlled by gas phase chemical kinetics but by surface processes.

The main objective of on‐line quality control in fermentation is to perform the production processes as reproducible as possible. Since temperature is the main control parameter in the fermentation process of beer breweries, it is of primary interest to keep it close to the predefined set point. Here, we report on a model‐supported temperature controller for large production‐scale beer fermenters. The dynamic response of the temperature in the tank on temperature changes in the cooling elements has been modeled by means of a difference equation. The heat production within the tank is taken into account by means of a model for the substrate degradation. Any optimization requires a model to predict the consequences of actions. Instead of using a conventional mathematical model of the fermentation kinetics, an artificial neural network approach has been used. The set point profiles for the temperature control have been dynamically optimized in order to minimize the production cost while meeting the constraints posed by the product quality requirements.

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.

Hybrid process models, which simultaneously utilize artificial neural networks, fuzzy rule systems, and mathematical balance equations, are shown to improve industrial-scale biotechnical production processes. As an example the application of hybrid models to the repeated fed-batch production of a recombinant therapeutical protein from mammalian cells will be discussed. State estimation, prediction and on-line determination of optimal feeds will be considered.

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.

The objective of this work was to investigate NOxremoval with a hybrid system consisting of a DC corona discharge and a NH3-saturated molecular sieve at a room temperature (22 °C). In the hybrid reactor a positive DC corona discharge was generated between a hollow needle and a flat mesh. A gas mixture of N2(80%):O2(5%):CO2(15%):NO(200 ppm), simulating a flue gas, flowed through the tube with a flow rate of 1 1/min. The obtained results showed that the presence of NH3as adsorbed on the molecular sieve increased NOxremoval from 60 % to 75 %. Due to NH4+ions formed on the molecular sieve surface NOxare converted into N2and H2O, i.e. the same harmless products as in the classical Selective Catalytic Reduction method which requires high temperature.