By I. M. Mujtaba, Mehboob A. Hussain
This quantity is a follow-up to the IChemE symposium on "Neural Networks and different studying Technologies", held at Imperial university, London, in may well 1999. The curiosity proven by means of the contributors, in particular these from the undefined, has been instrumental in generating the publication. The papers were written through individuals to the symposium and specialists within the box from world wide. They current the entire very important elements of neural community usage in addition to express the flexibility of neural networks in a variety of points of strategy engineering difficulties - modelling, estimation, keep watch over, optimization and business purposes.
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Extra resources for Application of Neural Networks and Other Learning Technologies in Process Engineering
Neural Networks in Process Engineering 50 100 150 200 time (h) 250 Figure 3. Identification data Set 1: RUS inputs 300 350 33 RBFN Identification of an Industrial Polymerization Reactor Model G 150 200 250 time (h) Figure 4. Identification data Set 2: RDS inputs 300 350 Neural Networks in Process Engineering 150 200 time (h) Figure 5. Validation data Set 3: RUS inputs. RBFN Identification of an Industrial Polymerization Reactor Model 35 Table 4. 07 The identification results for data in Sets 1 and 2 using stepwise regression with varying values for the width parameter /3 are shown in Table 4.
N-butanol/water, iso-butanol/water, cumene/water, toluene/water, kerosine/water and Clairsol 350/water. A range of column sizes is involved. This allowed the construction of a training (74 exemplars) and a test data set (15 exemplars) against which the performance of the network could be measured. e. the drop size was modelled in terms of the interfacial tension of the system, as well as the geometry of the column. The differences in densities and viscosities were ignored for simplicity. The network consisted of an input layer with 6 input nodes (for the six input variables), a sigmoidal hidden layer with 4 hidden nodes, and a single-node sigmoidal output layer (for the output variable, d^)- The network converged rapidly (after approximately 10 000 iterations or less than a minute of training on a 486 DX personal computer or better), and could then be used to predict d32-values based on data not used during training of the network.
Although no physical meaning can be attached to the weights in the network, their collective effect enables the network to accurately predict drop sizes and hold-up in an existing column. Trial and error procedure is required to set up the simplest network possible (to minimize the number of weighting parameters), but this is not a long process. An alternative procedure using a mechanistic model such as that discussed by Cauwenberg et al. (1997) requires much more computational effort and has no potential for learning the consequences of inevitable changes with time often experienced in industrial plants.
Application of Neural Networks and Other Learning Technologies in Process Engineering by I. M. Mujtaba, Mehboob A. Hussain