Modeling the steel case carburizing quenching process using statistical and machine learning techniques

Deshpande, Parijat; Gautham, B. P.; Gupta, Ujjawal; Khan, Danish

doi:10.1109/iciinfs.2014.7036589

Cited by 3 publications

(1 citation statement)

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“…In order to reduce simulation time and approximate complex physical phenomena or optimization problems that are difficult to model, research on data-driven modeling techniques by surrogating numerical simulations has progressed in various fields [26], [28]- [32]. For example, the surrogate models have been utilized for solving wind farm layout optimization problems [28], and used for predicting surface hardness in the carburization quenching processes [29]. The estimation of the output of numerical simulations using surrogate modeling is effective and powerful way for the selection of the appropriate parameters in the complex output surface of the simulation results [30], [31].…”

Section: A Outline Of the Surrogate Modelmentioning

confidence: 99%

Operational Planning of a Residential Fuel Cell System for Minimizing Expected Operational Costs Based on a Surrogate Model

et al. 2020

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This study proposes a novel operational planning method for polymer electrolyte fuel cell cogeneration systems (PEFC-CGSs). PEFC-CGSs provide hot water by utilizing waste heat produced in the electricity generation process, and hot water is stored in an attached tank. Generating and storing hot water based on an optimal operational plan according to household demand leads to further energy saving; therefore, operational planning methods based on household demand prediction have received significant attention. However, the improvement in the demand prediction accuracy does not necessarily lead to efficient PEFC-CGS operation in terms of operational costs; in other words, the accuracy in the demand prediction does not directly indicate the resulting operational efficiency. In this study, the authors propose a novel approach based on a surrogate model for deriving an appropriate plan that minimizes the expected operational costs among the operational plan candidates. In the proposed scheme, the error between expected and actual operational costs explicitly represents the relevance of the operational plan, so that the optimal operational plan can be selected directly from the perspective of the resulting operational efficiency. The practicality of the proposed approach is evaluated with the existing demand prediction-based approach via numerical simulations using real-world measurements of multiple customers in Japan. The proposed method reveals 30% reduction of the excessive operational costs by avoiding the inefficient operation of the auxiliary gas-heater in the experiments and will further enhance the value of introducing highly efficient residential fuel cell system that contributes to a low-carbon society. INDEX TERMS Cost minimization, machine learning, operational planning, polymer electrolyte fuel cell cogeneration systems, surrogate model.

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