Inferential For Industrial Plants Based On Neural Network And Extended Kalman Filter
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Inferential for Industrial Plants Based on Neural Network and Extended Kalman Filter
Industrial plants have stringent requirements on product quality, therefore real time measurements such as online analyzers and laboratory analysis are used as process quality indicators. However, online analyzers are very costly and maintenance intensive. These concerns motivate the development of prediction models. Yet, the highly nonlinear relationships between the process variables (inputs) and the product (outputs) have limited the chances to come up with reliable mathematical models. The implementation of intelligent control technology based on artificial neural networks has shown significant results, especially in highly nonlinear applications. This project discusses the methodology and implementation of inferential model based on artificial neural networks using various backpropagation learning algorithms such as gradient descent, scaled conjugate gradient, Bayesian regularization and Lavenberg-Marquardt. The objective is to enhance the online prediction and reduce the necessity of costly online analyzers. This project also discusses alternative approaches to model an inferential where artificial neural networks, extended Kalman filter and process noise estimation model are used in conjunction to solve learning problems. The project addresses the drawback of backpropagation learning algorithms and proposes different learning approach. The results show significant potential for this algorithm to be used in industrial applications.
Statistics and Neural Networks
Recent years have seen a growing awareness of the interface between statistical research and recent advances in neural computing and artifical neural networks. This book covers various aspects of current work in the area, drawing together contributions from authors who are leading researchers in the two fields. Their contributions show a strong awareness of the common ground and of the advantages to be gained by taking the wider perspective. Topics covered include: nonlinear approaches to discriminant analysis; information-theoretic neural networks for unsupervised learning; Radial Basis Function networks; techniques for optimizing predictions; approaches to the analysis of latent structure, including probabalistic principal component analysis, density networks and the use of multiple latent variables; and a substantial chapter outlining techniques and their application in industrial case-studies. This research interface is currently extremely active and this volume gives an authoritative overview of the area, its current status and directions for future research.
Nonlinear Model-based Process Control
Author: Rashid M. Ansari
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. The last decade has seen considerable interest in reviving the fortunes of non linear control. In contrast to the approaches of the 60S, 70S and 80S a very pragmatic agenda for non-linear control is being pursued using the model-based predictive control paradigm. This text by R. Ansari and M. Tade gives an excellent synthesis of this new direction. Two strengths emphasized by the text are: (i) four applications found in refinery processes are used to give the text a firm practical continuity; (ii) a non-linear model-based control architecture is used to give the method a coherent theoretical framework.