Real-time modeling, control and optimization of turbo-machinery auxiliary plants. (March 2018)

• Record Type: Journal Article
• Authors: D'Adamio, Pierluca; Galardi, Emanuele; Meli, Enrico; Nocciolini, Daniele; Pugi, Luca; Rindi, Andrea
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 232:Number 3(2018)
• Page Start: 244
• Page End: 257
• Publication Date: 2018-03
• Subjects: Real-time simulation models -- auxiliary lubrication plants -- real-time control -- control optimization -- thermal-hydraulic modeling; Mechanical engineering -- Periodicals; Automatic control -- Periodicals; Systems engineering -- Periodicals; 621.3
• DOI: 10.1177/0959651817743624
• Languages: English
• ISSNs: 0959-6518

Abstract

In the Oil & Gas industry, the testing of auxiliary lubrication plants represents a mandatory preliminary activity before the whole turbo-machinery train (including the auxiliary lubrication plant) can be put in operation. To this end, the employment of both efficient and accurate plant models becomes very important to synthesize satisfactory control strategies for both testing and normal operation purposes. This way, many benefits (e.g. in terms of safety, times and costs of execution) can be achieved. Therefore, this article focuses on the development of innovative and efficient real-time models and control architectures to describe and regulate the auxiliary lubrication systems. All the new strategies have been validated through accurate real experimental campaign and real-time hardware. According to the Bond-Graph modeling strategy, an efficient lumped parameter model of the lube oil console has been built to properly optimize the description of such system and the efficiency of the control strategies. The code has been compiled and uploaded on a commercial real-time platform that has been used to regulate the pressure control valve of the physical plant (to this aim, a new regulator has been developed). In particular, the control was designed according to a novel model-based methodology, including both identification and control optimization techniques (based on flexible simplex techniques). The comparison between the data obtained from the simulated system and the ones acquired from the physical plant shows the good agreement, reliability and efficiency of the proposed model and the control strategy. Both the modeling approach and the control strategy have been developed in collaboration with Baker and Hughes General Electric, while the experimental data were acquired in a plant located in Massa (Italy).