Abstract
The regulating performance degradation of the stepless capacity regulation system for reciprocating compressors occurs frequently in long-term operations. It affects the safe and stable operation of the compressor seriously. The degradation mechanisms in a stepless capacity regulation system are mainly caused by valve leakage, degeneration of the reset spring of the unloader, and (or) deviation of the solenoid valve’s characteristic parameters. In this study, to research the system performance degradation mechanisms and the influence of control parameters on system behavior, a multi-subsystem mathematics model which integrates compressor, gas pipeline, buffer tank, and actuator was built. In order to calculate the rate of degradation, a load prediction model based on a modified back-propagation neural network was established. The rate of degradation can be calculated using the predicted results. In order to optimize system regulation performance, a degradation-based optimization framework was developed which determines optimum control parameter compensation to achieve a minimum degradation rate. In addition, in order to avoid over-compensation, an adaptive control parameter compensation optimization method was adopted. According to the deviation between the given load and the prediction load, the control parameter compensations are obtained adaptively. Finally, two optimization experiments are carried out to show the effectiveness of the developed framework. The optimization results illustrate the degradation rate of the system gradually returning to normal during 60s without any over-compensation.
Highlights
Reciprocating compressors are key equipment most commonly used in oil extraction, gas production, oil refining, chemical industries, refrigeration, and gas transmission
If the solenoid valve changed in response, the accuracy of the capacity regulation decreased under the original design parameters
The initial thresholds and weights of the back propagation (BP) neural network are obtained through particle swarm optimization (PSO) optimization
Summary
Reciprocating compressors are key equipment most commonly used in oil extraction, gas production, oil refining, chemical industries, refrigeration, and gas transmission. If there is a decrease in system regulation performance, it is extremely challenging, even impossible, to locate the fault through the technical method of single factor analysis To address this issue, it is necessary to study the influence law between the system regulation performance and each coupling component and explore the degradation law of system performance caused by valve leakage, characteristic parameter deviation of the solenoid valve, and reset spring stiffness degradation. Based on the coupling regulation multi-system model established in [12], an improved multi-subsystem integrated system mathematical model,fails including compressor, gas controller failure computer-controlled to regulation pipeline, buffer tank, and actuator, was established, which took the solenoid valve dynamics and valve leakage into consideration.
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