Control of Organic Rankine Cycle, a neuro-fuzzy approach

Undesirable emissions in internal combustion engines are of major concern due to their negative effect on the human health and global warming. Implementing an organic Rankine cycle (ORC) in mobile applications can help to mitigate their negative effect by transferring some of their wasted heat into...

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Bibliographic Details
Published in:	Control engineering practice Vol. 109; p. 104728
Main Authors:	Enayatollahi, H., Fussey, P., Nguyen, B.K.
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01-04-2021
Subjects:	Energy harvesting Heavy-duty diesel engines Inverse neuro-fuzzy control Organic Rankine Cycle Waste heat recovery Inverse neuro-fuzzy control Waste heat recovery Heavy-duty diesel engines Energy harvesting Organic Rankine Cycle
Online Access:	Get full text
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Summary:	Undesirable emissions in internal combustion engines are of major concern due to their negative effect on the human health and global warming. Implementing an organic Rankine cycle (ORC) in mobile applications can help to mitigate their negative effect by transferring some of their wasted heat into useful electrical or mechanical energy. Considering the stringent regulations in automobile industry ensuring the safety of equipment before implementing them on the vehicles is vital. In this study, a novel control approach is proposed to ensure the safe operation of Organic Rankine Cycle (ORC) waste heat recovery (WHR) system and stabilize its work output when subjected to transient heat sources in a range of waste heat from heavy-duty diesel engines. The control strategy comprises a neuro-fuzzy controller based on the inverse dynamics of the ORC system to control the superheating at the evaporator outlet by adjusting the pump speed and a PI controller to maintain the expander work output by regulating the mass flow rate at the expander inlet. The performance of the control strategy is investigated with respect to set-point tracking and its robustness is tested in the presence of noises. The simulation results indicate an enhancement in the controller performance by combination of feedforward and feedback controllers based on neuro-fuzzy technologies. The proposed control scheme not only can obtain satisfactory transient response under various loading conditions, but also can achieve desirable disturbance rejection performance.
ISSN:	0967-0661 1873-6939
DOI:	10.1016/j.conengprac.2021.104728