A novel data-driven sampling strategy for optimizing industrial grinding operation under uncertainty using chance constrained programming

Sharma, Surbhi and Pantula, Priyanka Devi and Miriyala, Srinivas Soumitri and Mitra, Kishalay (2021) A novel data-driven sampling strategy for optimizing industrial grinding operation under uncertainty using chance constrained programming. Powder Technology, 377. pp. 913-923. ISSN 00325910

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Multi-objective optimization of an integrated grinding circuit considering various sources of uncertainties has been targeted in this work using Chance constrained programming (CCP). Success of CCP depends on accurate transcription of uncertain parameter space for correct estimation of statistical measures, e.g. probability, which is challenging in practical scenarios, where the data available is sparse and difficult to fit using known statistical distributions. To tackle this situation, a novel Data based Intelligent Sampling strategies for CCP (DISC) has been proposed amalgamating the machine learning techniques with novel Fuzzy C-means algorithm. It identifies the data clusters in the sparse uncertain parameter space followed by sampling strictly inside those clusters using the Sobol scheme, which is often not accurately performed by the conventional techniques. Ten parameters depicting uncertainties in the model and feed stream have been considered for optimizing conflicting objectives of productivity, quality and energy savings. A comprehensive comparison displays 42 and 34% improvements over the conventional box and budget sampling techniques, respectively, demonstrating efficacy of the proposed technique.

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IITH Creators:
IITH CreatorsORCiD
Pantula, Priyanka DeviUNSPECIFIED
Miriyala, Srinivas SoumitriUNSPECIFIED
Mitra, Kishalayhttp://orcid.org/0000-0001-5660-6878
Item Type: Article
Uncontrolled Keywords: Chance-constrained programming; Comprehensive comparisons; Conflicting objectives; Conventional techniques; Fuzzy C-means algorithms; Machine learning techniques; Sources of uncertainty; Statistical distribution
Subjects: Chemical Engineering
Chemical Engineering > Explosives, fuels, related products
Divisions: Department of Chemical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 28 Jun 2021 09:13
Last Modified: 28 Jun 2021 09:13
URI: http://raiith.iith.ac.in/id/eprint/8027
Publisher URL: http://doi.org/10.1016/j.powtec.2020.09.024
OA policy: https://iith.irins.org/profile/62206
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