Nonlinear Model Predictive Control of Industrial Grinding Circuits using Machine Learning

Inapakurthi, Ravi kiran and Miriyala, Srinivas Soumitri and Kolluri, Suryanarayana and Mitra, Kishalay (2020) Nonlinear Model Predictive Control of Industrial Grinding Circuits using Machine Learning. In: 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 1 December 2020 - 4 December 2020.

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

Abstract

Control problems of engineering interest such as industrial grinding circuit (IGC) are essential for minimizing the energy consumption, maximizing throughput or maintaining product quality to make these processes energy sustainable in future. Detailed physics based models, although provide more insight into the process and yield accurate results, often cannot be used for control purposes owing to the high computational time involved in solving the complicated mass, momentum, energy balance equations used to describe the process. In the current study, the aim is to use optimally designed Recurrent Neural Networks, a type of data-based modeling technique popularly used in the machine learning domain, for modeling transients involved in the IGC and test its effectiveness in set point (SP) tracking under the nonlinear model predictive control (NMPC) framework. SP tracking of throughput (related to the amount of raw materials processed to give products) and recirculation load (related to the energy consumption) is performed. We observe that the developed machine learning based models could effectively perform the SP tracking for nonlinear industrial process like grinding.

[error in script]

IITH Creators:

IITH Creators	ORCiD
Inapakurthi, Ravi Kumar	UNSPECIFIED
Miriyala, Srinivas Soumitri	UNSPECIFIED
Kolluri, Suryanarayana	UNSPECIFIED
Mitra, Kishalay	http://orcid.org/0000-0001-5660-6878

Item Type:

Conference or Workshop Item (Paper)

Uncontrolled Keywords:

Computational time; Control problems; Energy balance equations; Engineering interests; Grinding circuits; Industrial processs; Nonlinear model predictive control; Physics-based models;Energy utilization; Grinding (machining); Intelligent computing; Model predictive control; Nonlinear systems; Predictive analytics; Predictive control systems; Recurrent neural networks; Timing circuits

Subjects:

Chemical Engineering

Divisions:

Department of Chemical Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

28 Jun 2021 10:36

Last Modified:

28 Jun 2021 10:36

URI:

http://raiithold.iith.ac.in/id/eprint/8034