Oct 25, 2021 · The proposed approach, which combines machine learning and classical control of linear processes, consists in the design of a controller for a ...
The proposed approach, which combines machine learning and classical control of linear processes, consists in the design of a controller for a waste heat ...
The proposed approach, which combines machine learning and classical control of linear processes, consists in the design of a controller for a waste heat ...
Because the gains are different, each mode exhibits different properties in terms of speed of convergence and robustness to measurement noise.
Johan Peralez, Francesco Galuppo, Pascal Dufour, Christian Wolf, Madiha Nadri. Data-driven multi-model control for a waste heat recovery system.
... Data-driven multi-model control for a waste heat recovery system. 2020 59th IEEE Conference on Decision and Control (CDC), Dec 2020, Jeju (virtual) ...
Sep 7, 2024 · The data-driven models can better characterize complex systems while reducing the complexity of both modeling and computation [13]. Zhang et al.
May 15, 2023 · Organic Rankine Cycle (ORC) stands out in low-grade waste heat recovery (WHR) technology for its significant performance.
Experimental validation of a multiple model predictive control for waste heat recovery organic Rankine cycle systems. Article. May 2021; APPL THERM ENG.
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Downloadable (with restrictions)! Organic Rankine Cycle (ORC) stands out in low-grade waste heat recovery (WHR) technology for its significant performance.