Analysis of Artificial Neural Networks For Building Automated Surrogate Algorithms

Miriyala, S S (2015) Analysis of Artificial Neural Networks For Building Automated Surrogate Algorithms. Masters thesis, Indian Institute of Technology Hyderabad.

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While attaining the objective of online optimization of complex chemical processes, the possibility of using the first principle based models is rarely an option, since such models demand large computational time. Surrogate models, which can emulate first principle based models, offer a credible solution to this problem, by ensuring faster optimization. Thus, the entire challenge of enabling online optimization of complex models depends on construction of efficient surrogate models. Often, the surrogate building algorithms have certain parameters that are usually fixed based on some heuristic, thereby inviting potential errors in building such surrogate models. This work aims at presenting an elaborate study on the effect of various parameters affecting the predictability of artificial neural networks viz.(a) architecture of ANN, (b) sample size required by the ANN, (c) maximum possible accuracy of prediction, (d) a robust sampling plan and (e) transfer function choice for node activation. The ANNs are then utilized as surrogates for a highly nonlinear industrial sintering process, the optimization of which is then realized nearly 7 times faster than the optimization study using the expensive phenomenological model.

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Item Type: Thesis (Masters)
Uncontrolled Keywords: ANNs, nonlinear models, online optimization and control, Parameter in surrogate construction, Surrogate models, sintering process, TD326
Subjects: Chemical Engineering > Biochemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: Library Staff
Date Deposited: 03 Jul 2015 06:08
Last Modified: 13 May 2019 11:52
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