Modeling chemical process systems via neural computation

Bhat, Naveen; Minderman, Peter A.; McAvoy, Thomas J.; Wang, N.S.

doi:10.1109/37.55120

Cited by 230 publications

(49 citation statements)

References 6 publications

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“…The exact level of "noise" handled by an ANN is likely to be a function of the system that is being modeled, as well as a function of the training algorithm and network architecture. Bhat et al 18 have obtained excellent network predictions for their system with 10% Gaussian distributed noise on the training data. The trained network makes almost instantaneous predictions due to the relatively simple computational procedure.…”

Section: Resultsmentioning

confidence: 99%

A Simple Neural Network for Estimating Emission Rates of Hydrogen Sulfide and Ammonia from Single Point Sources

Rege¹,

Tock

1996

Journal of the Air & Waste Management Association

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Neural networks have shown tremendous promise in modeling complex problems. This work describes the development and validation of a neural network for the purpose of estimating point source emission rates of hazardous gases. This neural network approach has been developed and tested using experimental data obtained for two specific air pollutants of concern in West Texas, hydrogen sulfide and ammonia. The prediction of the network is within 20% of the measured emission rates for these two gases at distances of less than 50 m. The emission rate estimations for ground level releases were derived as a function of seven variables: downwind distance, crosswind distance, wind speed, downwind concentration, atmospheric stability, ambient temperature, and relative humidity. A backpropagation algorithm was used to develop the neural network and is also discussed here. The experimental data were collected at the Wind Engineering Research Field Site located at Texas Tech University in Lubbock, Texas. Based on the results of this study, the use of neural networks provides an attractive and highly effective tool to model atmospheric dispersion, in which a large number of variables interact in a nonlinear manner. INTRODUCTIONThe modeling of atmospheric dispersion has been actively researched for many decades. However, because of the vast number of variables involved and the arbitrary variations in wind

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Section: Resultsmentioning

confidence: 99%

A Simple Neural Network for Estimating Emission Rates of Hydrogen Sulfide and Ammonia from Single Point Sources

Rege¹,

Tock

1996

Journal of the Air & Waste Management Association

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“…This matrix contains information about how the gradient changes in different directions in weight space (12) Newton Method: The Newton method weights update is processed as follows: (13) However, the Newton method is not commonly used because computing the Hessian matrix is computationally expensive. Furthermore, the Hessian matrix may not be positive definite at every point in the error surface.…”

Section: Second-order Gradient Methodsmentioning

confidence: 99%

“…It was used in a controller for turbo generators [117], digital current regulation of inverter drivers [16], and welding process modeling and control [4], [32]. The MLP was used in modeling chemical process systems [12], to produce quantitative estimation of concentration of chemical components [74], and to select powder metallurgy materials and process parameters [23]. Optimization of the gas industry was reported by [121], as well as prediction of daily natural gas consumption needed by gas utilities [65].…”

Section: A Mlpsmentioning

confidence: 99%

A comprehensive review for industrial applicability of artificial neural networks

Meireles

Almeida

Simões

2003

IEEE Trans. Ind. Electron.

390

136

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Abstract-This paper presents a comprehensive review of the industrial applications of artificial neural networks (ANNs), in the last 12 years. Common questions that arise to practitioners and control engineers while deciding how to use NNs for specific industrial tasks are answered. Workable issues regarding implementation details, training and performance evaluation of such algorithms are also discussed, based on a judiciously chronological organization of topologies and training methods effectively used in the past years. The most popular ANN topologies and training methods are listed and briefly discussed, as a reference to the application engineer. Finally, ANN industrial applications are grouped and tabulated by their main functions and what they actually performed on the referenced papers. The authors prepared this paper bearing in mind that an organized and normalized review would be suitable to help industrial managing and operational personnel decide which kind of ANN topology and training method would be adequate for their specific problems.

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“…The description is based on the papers of Bhat & McAvoy (1990), Bhat et al (1990) and Morris et al (1994).…”

Section: Development Of a Neural Network Controller For The Pilot Plamentioning

confidence: 99%

Advanced Control of a Rotary Dryer

Yliniemi¹,

Koskinen²,

Leiviskä³

1998

IFAC Proceedings Volumes

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Real knowledge is based on experience.(Chinese saying) AbstractDrying, especially rotary drying, is without doubt one of the oldest and most common unit operations in the process industries. Rotary dryers are workhorses which are easy and reliable to operate, but neither energy-efficient nor environmentally friendly. In order to conform better to the requirements of modern society concerning working conditions, safety practices and environmental aspects, the development of control systems can provide opportunities for improving dryer operation and efficiency. Our in depth understanding of rotary drying is poor, because it is a very complex process that includes the movement of solids in addition to thermal drying. Thus even today rotary dryers are controlled partly manually, based on the operator's "eye" and experience, and partly relying on conventional control methods. The control of a rotary dryer is difficult due to the long time delay, which means that accidental variations in the input variables can disturb the process for long periods of time before they are reflected in the output variables. To eliminate such disturbances at an early stage, increasing interest has been shown in more sophisticated control systems such as model-based constructs, fuzzy logic and neural nets in recent years. Although it has proved difficult and time-consuming to develop model-based control systems, due to the complexity of the process, intelligent control methods based on fuzzy logic and neural nets offer attractive solutions for improving dryer control. These methods make it possible to utilise experience, knowledge and historical data, large amounts of which are readily available.The aim of this research was to improve dryer control by developing new hybrid control systems, one consisting of a fuzzy logic controller (FLC) and PI controller and the other of a three-layer neural network (NN) and PI controller. The FLC and NN act as supervisory controllers giving set points for the PI controllers. The performance of each was examined both with simulations and in pilot plant experiments. The pilot plant dryer at the University of Oulu closely resembles a real industrial situation, so that the results are relevant. Evaluation of these results showed that the intelligent hybrid controllers are well suited for the control of a rotary dryer, giving a performance in which disturbances can be eliminated rapidly and operation of the dryer can thereby be improved, with the aim of enhancing its efficiency and environmental friendliness.

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Modeling chemical process systems via neural computation

Cited by 230 publications

References 6 publications

A Simple Neural Network for Estimating Emission Rates of Hydrogen Sulfide and Ammonia from Single Point Sources

A Simple Neural Network for Estimating Emission Rates of Hydrogen Sulfide and Ammonia from Single Point Sources

A comprehensive review for industrial applicability of artificial neural networks

Advanced Control of a Rotary Dryer

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