Exploiting connectivity structures for decomposing process plants

Bankole, Temitayo; Bhattacharyya, Debangsu

doi:10.1016/j.jprocont.2018.09.002

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…The specific GT-SOFC system that is investigated here is the HyPer (hybrid performance) facility at the U.S. Department of Energy’s National Energy Technology Laboratory (NETL) in Morgantown, West Virginia, USA . Despite several studies on the dynamics of the HyPer facility and its interaction with the hardware, ,,− the CVs for this process are currently selected heuristically. The HyPer facility is a cyber-physical system , where a software model is used for representing the SOFC.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform

Bankole

Bhattacharyya

Pezzini

et al. 2020

Ind. Eng. Chem. Res.

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Hybrid gas turbine−fuel cell systems have immense potential for high efficiency in electrical power generation with cleaner emissions compared with fossilfueled power generation. A systematic controlled variable (CV) selection method is deployed for a hybrid gas turbine−fuel cell system in the HyPer (hybrid performance) facility at the U.S. Department of Energy's National Energy Technology Laboratory (NETL) for maximizing its economic and control performance. A three-stage approach is used for the CV selection comprising a priori analysis, multiobjective optimization, and a posteriori analysis. The a priori analysis helps to screen off several candidate CVs, thus reducing the size of the combinatorial optimization problem for multiobjective CV selection. For optimal CV selection, a transfer function model of the HyPer facility is identified. By considering several candidate models, the final transfer function model is selected using Akaike's Final Prediction Error criterion. Experimental data from the HyPer facility are used to estimate the noise in the measurement data. For solving the combinatorial multiobjective optimization problem for CV selection, a multiagent optimization platform comprising simulated annealing, genetic algorithm, and efficient ant colony optimization algorithms is used. Pareto-optimal CV sets exhibit a high trade-off between the economic and control objective. The a posteriori analysis is undertaken for several top Pareto-optimal CV sets. An optimal CV set is selected that shows the best compromise between process economics and controllability under both nominal and off-design conditions.

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Section: Introductionmentioning

confidence: 99%

Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform

Bankole

Bhattacharyya

Pezzini

et al. 2020

Ind. Eng. Chem. Res.

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“…Computers & Chemical Engineering,109,. Bankole, T. and Bhattacharyya, D., 2018 (Jones et al, 2014). It should be noted that while CV selection is done heuristically or by off-line evaluations that are rarely revisited, the CV sets can be sub-optimal if the control objective of the process changes or the process model or operating constraints change considerably.…”

Section: Journal Publicationsmentioning

confidence: 99%