## 5 Prediction Modeling and System Optimization
### The Implementation Effect of the Optimization Method  
In order to verify the proposed system parameter optimization method, two experimental tests were carried out on a two-stage reciprocating compressor test bench equipped with SCRS as shown in Figure 3.  
In order to accurately know the delayed closing phase of the valve plate, a displacement sensor was installed inside the actuator to test the real-time displacement of the valve plate which is extremely dangerous in practical applications and is not allowed. Figure 22 shows the simulation of valve leakage by making leak holes into the suction valve plate. The comparison of the actuator response tested by the displacement sensor at different hydraulic oil temperatures is shown in Figure 23. It was found that under the same control signal, the actuator's response became faster as the temperature of the hydraulic oil increased. Therefore, it is possible to change the temperature of the hydraulic oil to replace the change in solenoid valve performance and spring stiffness to affect the system regulation performance. Under the normal condition of the SCRS, the given load of the first and the second stage are both 80%. After the capacity regulation was stable, the operating condition of the SCRS was changed. The normal operation condition of the SCRS is that all valves have no leakage and the temperature of the hydraulic oil is 35 \({}^{\circ}\)C. The effectiveness of the optimization method was proved by the following two test experiments. **Experiment 1. **_Valve leaks in the first stage caused the hydraulic temperature driving the high-pressure stage actuator to rapidly drop from 35 to 25 \({}^{\circ}\)C. The optimization result was adjusted by adjusting the adaptive optimization parameter. In this study, the parameter was set as \(\alpha\) = [0.6, 0.6]._  
Figure 24 shows that the system was in normal operation until point A, and the pressure of the first and the second exhaust buffer tank were stable at 251 kPa and 541 kPa, respectively. Leakage holes were made in the first-stage suction valve between point A and point B, and at the same time the second-stage hydraulic oil was rapidly cooled from 35 to 25 \({}^{\circ}\)C. When the steady-state point B was reached, the system optimization function started to work, and the adaptive control parameter compensations were generated. Similarly, the optimized compensations were generated at the stable point C and point D, respectively. The compensation amount and predicted load at different points are shown in Table 6. Since the compensation amount was generated adaptively according to the degradation rate of the system, the compensation amount decreased with the decrease of the degradation rate. At point B, the system degradation rate was 12.28. With the help of optimization, the system degradation rate eventually decreased to 1.41, which is very close to 2.03 under normal conditions. The deviation between the final pressure at the optimized completion point E and the pressure under normal working conditions was 1 kPa. The adaptive optimization strategy ensured that there was no over-optimization in the case of errors in the load prediction, and the duration of the whole optimization was 50 s.  
Figure 23: Solenoid valve response curve at different temperatures. Figure 22: Suction valve plate leakage hole. **Experiment 2**. : _The temperature of the hydraulic oil driving the low-pressure stage actuator increased rapidly from 35 to 45 \({}^{\circ}\)C, while the temperature of the hydraulic oil driving the high-pressure stage actuator decreased rapidly from 35 to 25 \({}^{\circ}\)C._  
The temperature of the hydraulic oil was 35 \({}^{\circ}\)C under normal working conditions. The given load of the first and the second stage were both 80%, and the pressure in the steady state was 253 kPa and 543 kPa, respectively.