Publication

Characterizing bubbles in bioreactors using light or ultrasound probes: data analysis by classical means and by neural networks

Urs von Stockar
1991
Journal Articles

Abstract

The applicability of neural networks to a typical biochem. engineering problem was investigated. The data treatment required for a novel technique for measuring the mean bubble size and the sp. interfacial area in aerobic bioreactors was chosen as an appropriate example in case. The new measuring technique is based on either light or ultrasound transmission measurements through the dispersion. The function relating the results of such measurements to bubble size cannot be explicitly modelled. It was therefore approximated by a purely math. function fitted to numerically simulated examples. By training neural networks, the same unknown relation could be mapped with similar or better precision without any prior knowledge of its structure or form and without any phys. understanding of the problem. [on SciFinder (R)]

Official source

https://infoscience.epfl.ch/entities/publication/260464e1-ad4c-41e6-98b0-0b2abcba2e28

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Urs von Stockar
1991
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/260464e1-ad4c-41e6-98b0-0b2abcba2e28

About this result

Related concepts (32)

Types of artificial neural networks

There are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Recurrent neural network

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

Related publications (32)

Characterizing bubbles in bioreactors using light or ultrasound probes: data analysis by classical means and by neural networks

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