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Date Author Baughman, D. Metadata Show full item record. Abstract This dissertation introduces the fundamental principles and practical aspects of neural networks, focusing on their applications in bioprocessing and chemical engineering. Figure 5 represents the schematics of propagates input signals through the network to classic three-layer feedforward neural network provide output at the output layer.
And secondly, the architecture. Input Output layer During the backward sweep, values pass along the layer z1 weighted connections in the reverse direction to that f z x f which was taken during the forward sweep; for y instance, a node in the hidden layer will send activation to every unit in the output layer during the xi forward sweep, and so during the backward sweep a yj node in the hidden layer will receive error signals from every unit in the output layer.
This dual sweep is z illustrated in figure 4. A three layered-feedforward network. Propagation of activation and error signals in feedforward neural network 24 Paper T4. The process involves a catalytic dehydrogenation To reduce the likelihood of the weights changes conversion of heptanes to toluene. Steam supplied to the Fault 2:Fouling of the heat exchanger surface in the heat exchanger in the reactor is recycled to the inlet reactor, leading to a decrease of overall heat of the heater via recycle pump 2.
The outlet valve in transfer coefficient, h in the model. The heater is controlled via a Fault 4:Partial plugging of the pipeline connected to process computer that is always in a non-faulty pump 2, leading to a decrease in the volumetric flow operating mode. Josiah, C. H; and David, M. H , using process Fault 5: Partial plugging of the pipeline connected model simulated this system and identified five to pump 2, leading to a decrease in the volumetric possible causes of faults in the process.
A fault occurs when a certain level of deterioration takes place in one or more of these state variables. Qualitative knowledge that relates the causes of recognition or pattern matching.
Several binary No. Cause sh Th C7H8 Decrease combinations were reported in the literature, 1 Decreased Decreased Decreased depending on the application envisaged. Any deviation in the values of the variables following designations would be utilized. Slightly increased I 2. Moderately increased II 3. Slightly decreased D he fault diagnosis problem is a pattern classification 5.
Moderately decreased DD problem that maps all fault scenarios associated 6. Extremely decreased DDD with positive deviations of the given process 7. Unchanged N parameters value to the vector II [] and all negative deviation to the vector DD [], The first three classes; I, II and III constitute the whereas for a parameter at its normal operating positive deviations whereas the next three: D, DD value, to the vector N [].
These set of because; once a variable deviates from the specified value it is expected to upset the process 27 Paper T4. The patterns of The trends of the pattern errors six patterns, the input vector to the network for the five possible representing the five fault scenarios and the normal faults and the normal condition are represented in operating condition for all training runs, for 8, Table 4. These trends showed a sharp decline in pattern Table 2.
Patterns of the input vector to the network error after few presentations of the training Process Process Variables Status exemplars to the network. It was noticed that the state sh Th C7H8 error trends remained relatively constant afterwards, F1 indicating the stability of the knowledge acquired by F2 F3 the network in classifying the patterns presented.
F4 Few instances of misclassification were observed in F5 the course of training as evident from the error N trends when pattern errors oscillate somewhat randomly , however, the network was able to For each pattern, there are 6x3 bits, thus the input readjust itself subsequently after adequate layer of the network would compose of 18 input presentations of training exemplars, enough to bring units. Nonetheless all training patterns would consist of six dimensional vectors. Figure 5.
Patterns of the input vector to the network function of the number of units in the second hidden Process state Expected network output pattern layer. The number of hidden layer nodes must be F1 large enough to form a decision region that is F2 sufficiently complex for the given problem, an F3 arrangement not possible for just one or two hidden F4 nodes.
Increasing the number of units in the second F5 hidden layer results in the decrease in the total N network error, until a minimum total network error was detected. Electronic copy of the software could be obtained Considering figure 5. The number of nodes in the first hidden was chosen to validate the network performance. This implies the ability of the forty The network is The dependence of network performance on the expected to output 0.
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