English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The main aim of this paper is to establish a reliable model of a process behavior under the normal operating conditions. The use of this model should reflect the true behavior of the process in the whole way and thus distinguish a normal mode from the abnormal modes. In order to obtain a reliable model for the process dynamics, the black-box identification by means of a NARMAX model has been chosen in this paper. It is based on the neural networks approach. The main advantage of the proposed approach consists in the natural ability of neural networks in modeling non-linear dynamics in a fast and simple way and in the possibility to address the process to be modeled as an input-output black-box, with little or no mathematical information on the system. This paper will show the choice and the performance of the neural network in the training and the test phases. A study is related to the number of inputs, and of hidden neurons used and their influence on the behavior of the neural predictor. Three statistical criterions, Aikeke’s information criterion (AIC), Rissanen’s Minimum Description Length (MDL), and Bayesian information criteria (BIC), are used for the validation of the experimental data. In order to illustrate the ideas proposed concerning the dynamics modelling, a heat exchanger is used. The outlet temperature is modeled according to the inlet temperature. The model is implemented by training a Multilayer Perceptron artificial neural network with input-output experimental data. Satisfactory agreement between identified and experimental data is found and results show that the model successfully predicts the evolution of the outlet temperature of the process.

Model Order Reduction and the Heat Transfer Modelling of a Heat Exchanger by Using Artificial Neural Approach