Neural Network Based Modelling

The purpose of this case study was to develop a simple neural network based model with the ability to predict the solvent activity in different polymer systems. The solvent activities were predicted by an ANN as a function of the binary type and the polymer volume frac-... 

Artificial-Neural-Network-Based Modeling Example of Fluid Catalytic Cracker... 

I.M. Mujtaba, N. Aziz, and M.A. Hussain. Neural network based modelling and control in batch reactor. Chemical Engineering Research and Design, 84 635-644, 2006. 

To overcome these drawbacks of the controller based on first principles model, various identification techniques were applied. Neural network based model predictive control was used for the dynamic control of SMB unit [1 ], however, its implementation to actual process can be very difficult because of the complexity of identified neural net model. 

Neural network based model of the kinetics of catalytic hydrogenation reactions... 

The application of ANN for a representation of reaction kinetics can be a very promising method to solve modelling problems. Besides intrinsic kinetics also internal diffusion resistances can be included into the neural network based model. This approach significantly reduces the time required for experimental studies. Despite that neural networks do not help to understand and develop a real reaction mechanism, they make the prediction of the reactor behaviour possible. This approach can be essential in the case of complex or uncertain kinetics - e.g. for polymerization reactions. In this study the neural network approach has been tested for a batch reactor. A trained network can be successfully implemented into any type reactor model. 

Neural Network-Based Model Reference Adaptive Control... 

A neural network-based model reference adaptive control scheme for nonlinear plants is presented in this section. 

Hoskins, D.A. Neural Network Based Model-Reference Adaptive Control. Ph. D. Dissertation, University of Washington, UMI Dissertation Services, Ann Arbor, MI (1990)... 

Neural Networks Based Model Predictive Control of the... 

Compared to a mechanistic model, a neural network based model is built more easily and often provides more accurate results. This is due to the empirical character of this type of modeling. The versatility of the neural network approach allows the faithful rendering of experimental data used for training. One still needs to imderstand that the neural network approach does not clarify the reaction mechanism itself and does not answer the great question why So, one should consider that both approaches— mechanistic and neural—are complementary. One side is represented by the mechanistic model which renders chemical and physical laws and can sometimes supply less accurate results the other side is the neural network model with an empirical character and, generally, accurate results [3]. 

ABSTRACT The workload of air traffic controllers plays a crucial role in the safety and efficiency of air traffic as it is the prime factor to determine airspace capacity. Workload is the result of traffic complexity which can be described by different sets of parameters. In order to find a small, yet reliable set of complexity parameters that describe controller workload in the Hungarian Air Traffic Control system, a study with multiple steps was conducted and the results are presented in this paper. Information on the most important complexity factors was aquired from experts through interviews and questionnaires and as a result, different sets of parameters were created. To validate the method for gathering information as well as the applicability of complexity factors for airspace capacity estimation, a neural network based model was used. The results indicate that even smaller sets of parameters can be helpful in estimating workload. 

Gomez-Ruiz, J.A., Karanik, M. Pelaez, J.I. 2010. Estimation of missing judgments in AHP pairwise matrices using a neural network-based model. Applied Mathematics and Computation 216 2959-2975. 

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