Dynamic prediction and control of heat exchangers using artificial neural networks

2001 ◽  
Vol 44 (9) ◽  
pp. 1671-1679 ◽  
Author(s):  
Gerardo Dı́az ◽  
Mihir Sen ◽  
K.T Yang ◽  
Rodney L McClain
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Heat Exchangers ◽  
Dynamic Prediction ◽  
Prediction And Control ◽  
Artificial Neural ◽  
And Control

Artificial Neural Networks and Its Application in Various Fields of Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Wan Nur Azah binti Wan Nahar ◽  
Rahimah binti Mohamed Yunos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Conventional Procedure ◽  
Fields Of Study ◽  
Prediction And Control ◽  
Artificial Neural ◽  
Business Engineering ◽  
Using Data ◽  
Prediction And Forecasting ◽  
And Control

Artificial Neural Networks (ANN) approach is an alternate way to classical methods. As a computation and learning paradigm, the approach is used to solve complicated practical problems in numerous fields, such as accounting and business, engineering, medical and healthcare, geological and energy. The application varies from modelling, identification, prediction, and forecasting. In contrast to conventional procedure, ANN is trained using data exemplifying the behaviour of a system. This paper presents applications of ANN in various fields of study. The applications are in the form of designing and modelling, identification and evaluation, and prediction and control. Published literature presented in this study serves as evidence that ANN is a useful tool in various disciplines across many industries. This paper will encourage researchers and professionals to explore ANN.

Adaptive Neurocontrol and Minimization of Energy Consumption of a Heat Exchanger Test Facility

2000 ◽  
Author(s):  
Gerardo Díaz ◽  
Mihir Sen ◽  
Rodney L. McClain
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Heat Exchanger ◽  
Heat Exchangers ◽  
Test Facility ◽  
The Neural Network ◽  
On Line ◽  
Artificial Neural ◽  
And Control

Abstract It has been shown that artificial neural networks (ANNs) can be used to simulate and control thermal systems such as heat exchangers. It is known that the characteristics of thermal components such as heat exchangers vary with respect to time mainly due to fouling effects. There is a need of a model that can adapt to the new characteristics of the thermal system. In this work adaptive artificial neural networks are used to control the outlet air temperature of a heat exchanger test facility. The neurocontrollers are adapted on-line on the basis of different criteria. The parameters of the ANNs are modified considering target error and stability conditions of the closed loop system analyzed as a nonlinear iterative map. We also implement a minimization of a performance index that quantifies the energy consumption. It is shown numerically and experimentally that the neural network is able to control the thermal facility, and is also able to adapt to different disturbances applied to the system, while minimizing the amount of energy used.

Artificial Neural Networks vs. Fuzzy Logic: Simple Tools to Predict and Control Complex Processes—Application to Plasma Spray Processes

2008 ◽  
Vol 17 (3) ◽  
pp. 365-376 ◽  
Author(s):  
Abdoul-Fatah Kanta ◽  
Ghislain Montavon ◽  
Michel Vardelle ◽  
Marie-Pierre Planche ◽  
Christopher C. Berndt ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Plasma Spray ◽  
Complex Processes ◽  
Artificial Neural ◽  
And Control ◽  
Control Complex

Einsatz von künstlichen neuronalen Netzwerken an Scannersystemen*/Application of artificial neural networks on scanner systems

2017 ◽  
Vol 107 (07-08) ◽  
pp. 536-540
Author(s):  
S. J. Pieczona ◽  
F. Muratore ◽  
M. F. Prof. Zäh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural ◽  
And Control ◽  
Dynamic Enhancement

Zur Dynamiksteigerung von Scannersystemen werden verschiedene Arten von Modellierungs- und Regelungsmethoden in der Forschung genutzt. Jedoch sind Nichtlinearitäten, welche das Systemverhalten nachweisbar beeinflussen, in aller Regel nicht Teil der Untersuchung. Mit der Anwendung künstlicher neuronaler Netzwerke (KNN) wird das gesamte dynamische Systemverhalten sowohl für ein geregeltes als auch für ein ungeregeltes Scannersystem abgebildet. So wird geklärt, ob sich diese Art der Modellbildung für eine zukünftige Dynamiksteigerung eignet.   To enhance the dynamics of a scanner system, different methods of modelling and control are utilized. Nonlinearities, which have a certain impact on the system’s behavior, are generally ignored, though. By applying artificial neural networks, the overall dynamics of a controlled and an uncontrolled scanner could be represented. Thus, it will be clarified whether this kind of modelling is appropriate for a future dynamic enhancement.

New void fraction equations for two-phase flow in helical heat exchangers using artificial neural networks

2018 ◽  
Vol 130 ◽  
pp. 149-160 ◽  
Author(s):  
A. Parrales ◽  
D. Colorado ◽  
J.A. Díaz-Gómez ◽  
A. Huicochea ◽  
A. Álvarez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Void Fraction ◽  
Heat Exchangers ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Artificial Neural

A Neural Network-Based Approach for Pest Detection and Control in Modern Agriculture Using Internet of Things

2021 ◽  
pp. 1-31
Author(s):  
Pankaj Dadheech ◽  
Ankit Kumar ◽  
Vijander Singh ◽  
Linesh Raja ◽  
Ramesh C. Poonia
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Neural Structure ◽  
Modern Agriculture ◽  
Different Types ◽  
Pest Detection ◽  
Real Time Applications ◽  
Artificial Neural ◽  
And Control ◽  
The Brain

The networks acquire an altered move towards the difficulty solving skills rather than that of conventional computers. Artificial neural networks are comparatively crude electronic designs based on the neural structure of the brain. The chapter describes two different types of approaches to training, supervised and unsupervised, as well as the real-time applications of artificial neural networks. Based on the character of the application and the power of the internal data patterns we can normally foresee a network to train quite well. ANNs offers an analytical solution to conventional techniques that are often restricted by severe presumptions of normality, linearity, variable independence, etc. The chapter describes the necessities of items required for pest management through pheromones such as different types of pest are explained and also focused on use of pest control pheromones.

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