Enabling online optimization and control of complex models through smart surrogates based on ANNs

Miriyala, Srinivas Soumitri and Devi Pantula, Priyanka and Majumdar, Saptarshi and Mitra, Kishalay (2016) Enabling online optimization and control of complex models through smart surrogates based on ANNs. In: Indian Control Conference (ICC), 4-6 Jan. 2017, Hyderabad, India.

Full text not available from this repository. (Request a copy)


Implementation of online optimization and control of complex processes near impossible in given time frame owing to large computational time of the models. Fast and efficient surrogate models such as ANN offer a credible solution to this problem. Optimization of the complex model can proceed with a data based surrogate model, thereby making it much faster than the conventional run. However, the process of surrogate model construction involves certain number of parameters that are usually fixed based on some heuristic, thereby constructing an inefficient model. This paper aims at presenting a novel parameter free surrogate building algorithm specifically focusing on artificial neural networks (ANN) for emulating the physics based models. The proposed algorithm intelligently designs the configuration of the network along with simultaneous determination of sample size required for training it to predict results with maximum accuracy. The novel sample size determination algorithm is based on a potential concept of hypercube sampling technique, which assures convergence to results in very less computational time. The multi-objective optimization framework ensures parsimonious behavior of the network. The proposed algorithm is utilized to build optimal ANN models for highly nonlinear polyvinyl acetate polymer reaction network model. Optimization of this complicated reaction network with the ANN surrogate in place is achieved nearly 12 times faster than the first principle based model. All this being possible with a surrogate building algorithm, which just takes the simulation model as input.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Majumdar, SaptarshiUNSPECIFIED
Mitra, Kishalayhttp://orcid.org/0000-0001-5660-6878
Item Type: Conference or Workshop Item (Paper)
Subjects: Chemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: Team Library
Date Deposited: 04 Jun 2019 07:04
Last Modified: 04 Jun 2019 07:04
URI: http://raiith.iith.ac.in/id/eprint/5432
Publisher URL: http://doi.org/10.1109/INDIANCC.2016.7441131
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 5432 Statistics for this ePrint Item