Focuses on the use of a neural network-based soft-sensing concept for process variables
which cannot be measured online. An approach is proposed which performs particularly …

The last decade has seen the development of many approaches for estimating those
variables which are difficult to measure online in industrial process situations. In this paper …

Soft sensing or inferential estimation has long been considered a potent tool to deal with the
conflict between small control interval and large sampling interval existing in a wide variety …

This chapter focuses on online estimation and application of artificial neural networks
(ANNs). The step known as parameter estimation aims at choosing the unknown model …

In the paper the Soft Sensor design strategy for an industrial process, via neural NMA model,
is described. In details, the hydrogen sulphide (H 2 S percentage) in the tail stream of a …

This paper presents two soft-sensing models for predicting the product yields profile and the
cracking degree of an ethylene pyrolysis furnace. The model based on single neural …

Current soft sensing algorithms assume the availability of a large amount of training data.
The collection of the historical data often takes a lot of time and can be expensive. At the …

For successful monitoring and controlling chemical process, an accurate on-line
measurement of important quality variables is essential. However, these variables usually …

Soft Sensors are predictors for measurements that are difficult or impossible to obtain by
means of a physical sensor. A soft sensor delivers virtual measurements based on several …

The online determination of a tool's wear in order to exchange it just in time makes high
demands on a sophisticated tool monitoring systems. Research has shown that it is possible …