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An H2O2 sensor for the application in industrial sterilisation processes has been developed. Therefore, automated sterilisation equipment at laboratory scale has been constructed using parts from industrial sterilisation facilities. In addition, a software tool has been developed for the control of the sterilisation equipment at laboratory scale. First measurements with the developed sensor set-up as part of the sterilisation equipment have been performed and the sensor has been physically characterised by optical microscopy and SEM.
Realisation of a calorimetric gas sensor on polyimide foil for applications in aseptic food industry
(2012)
A calorimetric gas sensor is presented for the monitoring of vapour-phase H2O2 at elevated temperature during sterilisation processes in aseptic food industry. The sensor was built up on a flexible polyimide foil (thickness: 25 μm) that has been chosen due to its thermal stability and low thermal conductivity. The sensor set-up consists of two temperature-sensitive platinum thin-film resistances passivated by a layer of SU-8 photo resist and catalytically activated by manganese(IV) oxide. Instead of an active heating structure, the calorimetric sensor utilises the elevated temperature of the evaporated H2O2 aerosol. In an experimental test rig, the sensor has shown a sensitivity of 4.78 °C/(%, v/v) in a H2O2 concentration range of 0%, v/v to 8%, v/v. Furthermore, the sensor possesses the same, unchanged sensor signal even at varied medium temperatures between 210 °C and 270 °C of the gas stream. At flow rates of the gas stream from 8 m3/h to 12 m3/h, the sensor has shown only a slightly reduced sensitivity at a low flow rate of 8 m3/h. The sensor characterisation demonstrates the suitability of the calorimetric gas sensor for monitoring the efficiency of industrial sterilisation processes.
Aim of the AXON2 project (Adaptive Expert System for Object Recogniton using Neuml Networks) is the development of an object recognition system (ORS) capable of recognizing isolated 3d objects from arbitrary views. Commonly, classification is based on a single feature extracted from the original image. Here we present an architecture adapted from the Mixtures of Eaqerts algorithm which uses multiple neuml networks to integmte different features. During tmining each neural network specializes in a subset of objects or object views appropriate to the properties of the corresponding feature space. In recognition mode the system dynamically chooses the most relevant features and combines them with maximum eficiency. The remaining less relevant features arz not computed and do therefore not decelerate the-recognition process. Thus, the algorithm is well suited for ml-time applications.
