Simple feedback logic, genetic algorithms and artificial neural networks for real-time control of a collection system

1998 ◽  
Vol 38 (3) ◽  
pp. 187-195
Author(s):  
Pavel Hajda ◽  
Vladimir Novotny ◽  
Xin Feng ◽  
Ruoli Yang
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Real Time Control ◽  
Flow Routing ◽  
Time Control ◽  
On Line ◽  
Artificial Neural ◽  
Simple Feedback

This paper describes a pilot-scale implementation of a simple, real-time control (RTC) algorithm based on feedback and also outlines the development and simulation testing of a new RTC methodology that combines genetic algorithms (GAs) and artificial neural networks (ANNs). Computer simulations indicated that the simple feedback logic could reduce pumping by 50 to 80 percent if used to replace the existing RTC system in the test area. Experience with the algorithm after its implementation has confirmed the potential of the algorithm to reduce pumping. Additional simulations with an emerging approach to control (based on GAs) indicated possibilities of reducing pumping still further. Although relatively simple flow routing was used in the GAs, these algorithms do not restrict flow routing to any particular method. If highly accurate flow routing is incorporated, GAs are likely to be rendered too slow for on-line applications. Nevertheless, GAs can still be used, because they can be combined with fast executing on-line algorithms, such as ANNs. This possibility was demonstrated by training a multi-layer ANN to approximate one of the GAs developed. In verification runs the trained ANN provided virtually the same control decisions as did the GA used as the source of the training data.

Control of a robot manipulator and pendubot system using artificial neural networks

Robotica ◽  
2005 ◽  
Vol 23 (6) ◽  
pp. 781-784 ◽  
Author(s):  
Joseph Constantin ◽  
Chaïban Nasr ◽  
Denis Hamad
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Robot Manipulator ◽  
Recursive Algorithm ◽  
Dynamic Control ◽  
Real Time Control ◽  
Time Control ◽  
The Neural Network ◽  
Artificial Neural ◽  
Pendubot System

The paper introduces artificial neural networks for the conventional control of robotic systems for better tracking performance. Different advanced dynamic control techniques are explained and a new second order recursive algorithm has been developed to tune the weights of the neural network. The problem of real-time control of a Pendubot system in difficult situations has been addressed. Examples, such as positioning and balancing structures, are presented and performances are compared to a conventional PD controller.

scholarly journals A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1845
Author(s):  
Annalisa Santolamazza ◽  
Daniele Dadi ◽  
Vito Introna
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Machine Learning Techniques ◽  
Real Time Control ◽  
Time Control ◽  
Artificial Neural ◽  
Using Data

Wind energy has shown significant growth in terms of installed power in the last decade. However, one of the most critical problems for a wind farm is represented by Operation and Maintenance (O&M) costs, which can represent 20–30% of the total costs related to power generation. Various monitoring methodologies targeted to the identification of faults, such as vibration analysis or analysis of oils, are often used. However, they have the main disadvantage of involving additional costs as they usually entail the installation of other sensors to provide real-time control of the system. In this paper, we propose a methodology based on machine learning techniques using data from SCADA systems (Supervisory Control and Data Acquisition). Since these systems are generally already implemented on most wind turbines, they provide a large amount of data without requiring extra sensors. In particular, we developed models using Artificial Neural Networks (ANN) to characterize the behavior of some of the main components of the wind turbine, such as gearbox and generator, and predict operating anomalies. The proposed method is tested on real wind turbines in Italy to verify its effectiveness and applicability, and it was demonstrated to be able to provide significant help for the maintenance of a wind farm.

Nitrogen Removal in a Real-Time Controlled Sequencing Batch Membrane Bioreactor

2010 ◽  
Vol 5 (3) ◽  
Author(s):  
Cheng-Nan Chang ◽  
Li-Ling Lee ◽  
Han-Hsien Huang ◽  
Ying-Chih Chiu
Keyword(s):  
Real Time ◽  
Nitrogen Removal ◽  
Membrane Bioreactor ◽  
Synthetic Wastewater ◽  
Real Time Control ◽  
Time Control ◽  
Cake Layer ◽  
On Line ◽  
Solid Liquid ◽  
Solid Liquid Separation

The performance of a real-time controlled Sequencing Batch Membrane Bioreactor (SBMBR) for removing organic matter and nitrogen from synthetic wastewater has been investigated in this study under two specific ammonia loadings of 0.0086 and 0.0045g NH4+-N gVSS−1 day−1. Laboratory results indicate that both COD and DOC removal are greater than 97.5% (w/w) but the major benefit of using membrane for solid-liquid separation is that the effluent can be decanted through the membrane while aeration is continued during the draw stage. With a continued aeration, the sludge cake layer is prevented from forming thus alleviating the membrane clogging problem in addition to significant nitrification activities observed in the draw stage. With adequate aeration in the oxic stage, the nitrogen removal efficiency exceeding 99% can be achieved with the SBMBR system. Furthermore, the SBMBR system has also been used to study the occurrence of ammonia valley and nitrate knee that can be used for real-time control of the biological process. Under appropriate ammonia loading rates, applicable ammonia valley and nitrate knee are detected. The real-time control of the SBMBR can be performed based on on-line ORP and pH measurements.

Modifications of rainfall runoff and decision finding models for on-line simulation in real time control

1999 ◽  
Vol 39 (9) ◽  
pp. 201-207
Author(s):  
Andreas Cassar ◽  
Hans-Reinhard Verworn
Keyword(s):  
Real Time ◽  
Urban Drainage ◽  
Rain Event ◽  
Rainfall Runoff ◽  
Real Time Control ◽  
Design Storm ◽  
Time Control ◽  
Timing Data ◽  
On Line ◽  
Rainfall Runoff Model

Most of the existing rainfall runoff models for urban drainage systems have been designed for off-line calculations. With a design storm or a historical rain event and the model system the rainfall runoff processes are simulated, the faster the better. Since very recently, hydrodynamic models have been considered to be much too slow for real time applications. However, with the computing power of today - and even more so of tomorrow - very complex and detailed models may be run on-line and in real time. While the algorithms basically remain the same as for off-line simulations, problems concerning timing, data management and inter process communication have to be identified and solved. This paper describes the upgrading of the existing hydrodynamic rainfall runoff model HYSTEM/EXTRAN and the decision finding model INTL for real time performance, their implementation on a network of UNIX stations and the experiences from running them within an urban drainage real time control project. The main focus is not on what the models do but how they are put into action and made to run smoothly embedded in all the processes necessary in operational real time control.

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