Impact of Compressor Protection System on Rundown Characteristics

Tveit, Gisle B.; Bjørge, Tor; Bakken, Lars E.

doi:10.1115/gt2005-68436

Cited by 6 publications

(3 citation statements)

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“…A parametric study on a compressor station composed of five multistage centrifugal compressors in parallel was carried out in Refs. [42,43].…”

Section: Introductionmentioning

confidence: 98%

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Analysis of biogas compression system dynamics

Morini¹,

Pinelli²,

Venturini³

2009

Applied Energy

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“…A parametric study on a compressor station composed of five multistage centrifugal compressors in parallel was carried out in Refs. [42,43].…”

Section: Introductionmentioning

confidence: 98%

“…Different models [10,[39][40][41][42][43][44] have been presented in literature dealing with modeling compression systems and set up of surge avoidance strategies. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Analysis of biogas compression system dynamics

Morini¹,

Pinelli²,

Venturini³

2009

Applied Energy

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“…A study of transients during emergency shutdown (ESD) showed the importance of the placement of the recycle valve and signal delay time to avoid surge during compressor run-down [5]. Other studies [6] and [7] have reported the experience from the analysis of compressors and driver behaviour during rundown for Troll Kollsnes gas treatment plant compressors. Morini [2] developed a one-dimensional dynamic compressor model based on first principles for simulation of transient behaviour.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Analysis of Compressor Trips in the Sno̸hvit LNG Refrigerant Circuits

Schjølberg

Hyllseth

Skofteland

et al. 2008

Volume 7: Education; Industrial and Cogeneration; Marine; Oil and Gas Applications

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Compressors are key components in the refrigerant circuits of the Snøhvit LNG plant and contain large amounts of mechanical energy. Thus it is imperative that the control system is able to keep the compressor out of surge in case of driver trip. A dynamic process simulator describing the total LNG plant has been developed by Kongsberg Process Simulation and the simulator has been applied in the engineering phase for the design and process verification. The simulator has also been used to verify the robustness of the closed loop refrigerant circuits and to verify that the refrigerant compressors are sufficiently protected after a driver trip. The presented work demonstrates the value of dynamic simulations for verification of compressor protection systems. In addition it shows the importance of using correct data for polar inertia of the rotating equipment, as well as the opening and dead times of the anti-surge valves. It is recommended to include a sensitivity analysis of these parameters as part of plant verification studies. INTRODUCTIONCompressors are key components in the refrigerant circuits of LNG plants. It is imperative that these compressors are properly protected against surge,, Compressors in surge can result in a severe damage to machinery and disturb plant regularity.One of the most serious incidents that can happen to a compressor system is a driver trip. Gas turbines and electrical motors are the most common types of compressor driver and a trip of the driver here is more serious than a process trip. This is because in a process trip the shutdown of the driver itself can be delayed until the surge protection valves have been opened. Important factors controlling the run down of the compressor after a trip are opening times of the anti-surge valves, piping layout, inlet and discharge volumes as well as the polar inertia of the rotating equipment including the compressor, gear and driver. It is essential to be able to validate that the compressor is properly protected during process upsets including the case of a driver trip.The use of dynamic models for analysing compressor transient behaviour has been addressed by several authors in various scientific journals and conference papers and has also been demonstrated by the supplier industry. Patel [1] discusses dynamic simulators as an important tool in the design and operation of compressor systems. A good overview of the scientific research within this area is given in [2].

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Optimization of a Real-Time Simulator Based on Recurrent Neural Networks for Compressor Transient Behavior Prediction

Venturini

2006

Journal of Turbomachinery

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In this paper, feed-forward recurrent neural networks (RNNs) with a single hidden layer and trained by using a back-propagation learning algorithm are studied and developed for the simulation of compressor behavior under unsteady conditions. The data used for training and testing the RNNs are both obtained by means of a nonlinear physics-based model for compressor dynamic simulation (simulated data) and measured on a multistage axial-centrifugal small-size compressor (field data). The analysis on simulated data deals with the evaluation of the influence of the number of training patterns and of each RNN input on model response, both for data not corrupted and corrupted with measurement errors, for different RNN configurations, and different values of the total delay time. For RNN models trained directly on experimental data, the analysis of the influence of RNN input combination on model response is repeated, as carried out for models trained on simulated data, in order to evaluate real system dynamic behavior. Then, predictor RNNs (i.e., those that do not include among the inputs the exogenous inputs evaluated at the same time step as the output vector) are developed and a discussion about their capabilities is carried out. The analysis on simulated data led to the conclusion that, to improve RNN performance, the adoption of a one-time delayed RNN is beneficial, with an as-low-as-possible total delay time (in this paper, 0.1 s) and trained with an as-high-as possible number of training patterns (at least 500). The analysis of the influence of each input on RNN response, conducted for RNN models trained on field data, showed that the single-step-ahead predictor RNN allowed very good performance, comparable to that of RNN models with all inputs (overall error for each single calculation equal to 1.3% and 0.9% for the two test cases considered). Moreover, the analysis of multi-step-ahead predictor capabilities showed that the reduction of the number of RNN calculations is the key factor for improving its performance over a significant time horizon. In fact, when a high test data sampling time is chosen (in this paper, 0.24 s), prediction errors were acceptable (lower than 1.9%)

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Impact of Compressor Protection System on Rundown Characteristics

Cited by 6 publications

References 0 publications

Analysis of biogas compression system dynamics

Analysis of biogas compression system dynamics

Dynamic Analysis of Compressor Trips in the Sno̸hvit LNG Refrigerant Circuits

Optimization of a Real-Time Simulator Based on Recurrent Neural Networks for Compressor Transient Behavior Prediction

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