Valve Dynamic and Thermal Cycle Model in Stepless Capacity Regulation for Reciprocating Compressor

The existing researches of stepless capacity regulation system by depressing the suction valve for reciprocation compressor always adopt hypothesis that the compressor valves are open or close instantaneously, the valve dynamic has not been taken account into thermal cycle computation, the influence of capacity regulation system on suction valves dynamic performance and cylinder thermal cycle operation has not been considered. This paper focuses on theoretical and experimental analysis of the valve dynamic and thermal cycle for reciprocating compressor in the situation of stepless capacity regulation. The valve dynamics equation, gas forces for normal and back flow, and the cylinder pressure varying with suction valve unloader moment during compression thermal cycle are discussed. A new valve dynamic model based on L-K real gas state equation for reciprocating compressor is first deduced to reduce the calculation errors induced by the ideal gas state equation. The variations of valve dynamic and cylinder pressure during part of compression stroke are calculated numerically. The calculation results reveal the non-normal thermal cycle and operation condition of compressor in stepless capacity regulation situation. The numerical simulation results of the valve dynamic and thermal cycle parameters are also verified by the stepless capacity regulation experiments in the type of 3L-10/8 reciprocating compressor. The experimental results agree with the numerical simulation results, which show that the theoretical models proposed are effective and high-precision. The proposed theoretical models build the theoretical foundation to design the real stepless capacity regulation system.

Study on effect analysis and parameter optimizing of stepless capacity control system on reciprocating compressors

An improved model of reciprocating compressor operation cycle with a stepless capacity control system is presented and influence of the key parameters of the system is evaluated. In the stepless capacity control system of a reciprocating compressor,mechanical unloaders are used to partially hold suction valves open for a certain time during the compression stroke. The typical working process of the reciprocating compressor is changed by capacity regulation apparatus. However,some critical parameters like the hydraulic force acting at the unloader have not been rigorously studied in previous researches. Here an improved numerical model of a double acting reciprocating compressor under the control stepless capacity is proposed and verified by experimental trials. Numerical simulations are carried out to select and evaluate the acting force which definitely has an influence on indicator diagrams of compressors. It is observed that the optimized range of 350 N to 380 N is preferable for the unloader force such that the intensity of opening and closing impacts are minimized.

Research on gas leakage through suction valves of reciprocating compressor under varying load conditions

A research concerning the coupling conditions of gas leakage through suction valves and capacity regulation is performed in an industrial reciprocating compressor.Both internal flow and thermodynamic characteristic are discussed in detail.The results show that the capacity of compressor can be regulated steplessly by controlling suction valve closure moment.And then the quantitative relationship between the capacity load and the closing angle of suction valve is revealed.The capacity load and valve leakage rate show obvious different features in P-V diagrams,which makes it easier to define appropriate features for detecting cracked or broken reciprocating compressor valves under varying load conditions.A set of curves of compression work and discharge gas mass are obtained and a method for rating thermal performance of a compressor is presented using these curves.

Fault Diagnosis of Reciprocating Compressors Valve Based on Cyclostationary Method

The relationship between second-order cyclostationary method and time-frequency distribution is studied, and cyclic autocorrelation（CA） function is indicated to be one sort of special time-frequency distribution method. Furthermore, a fault diagnosis method for reciprocating compressors based on empirical mode decomposition （EMD） and CA function is proposed, and then it is applied to the fault diagnosis of reciprocating compressor valve. Firstly, the vibration signal of reciprocating compressor valve is decomposed by using ENID method, and several intrinsic mode functions （IMFs） are obtained. Secondly, the IMFs are evaluated by some denoising criterions to remove the noise and interfering ones. Finally, the CA functions of the remained IMFs are calculated, which will be used to reconstruct the CA function of the original vibration signal. Engineering application indicates that this method can sufficiently inhibit the cross-interference items of CA function. Therefore, more explicit working conditions of reciprocating compressor components can be achieved.

Study of Failure Diagnostic Methods and Intelligent Diagnostic System for Reciprocating Compressors

Three categories of failure diagnostic methods for reciprocating compressors are classified according to the signals adopted by the diagnosis. They are parameter method, vibration method, and oil analysis method. In this paper, the applicable range and operational difficulties of these methods are discussed on the basis of analysis and induction upon normal failure. It is proposed that a compressor's normal failure can be divided into thermodynamical property failure and mechanical function failure. As to the former, the parameter method that takes a cylinder pressure signal as the main diagnostic signal may be applied; and as to the latter, the vibration signal frequency spectrum can be used to diagnose. At the same time, the structure of an intelligent diagnostic system based on neural networks is introduced, and its schematic is given.

Fault diagnosis of reciprocating compressor cylinder based on EMD coherence method

Since there always exist interference components which caused by valve vibration,it is hard to directly use cylinder signal for fault diagnosis of reciprocating compressor. Based on Empirical Mode Decomposition (EMD) coherence,a new method for the fault diagnosis of reciprocating compressor cylinder is presented. Firstly,double channel signals of cylinder and valve are synchronously sampled. Secondly,cyclic cross-correlation function is used to eliminate the time delay between these signals. Finally,EMD coherence method is utilized to remove the interference components from cylinder signal. Thus,more concise and precise working condition information for cylinder and inner components can be obtained. So it is an effective method for fault diagnosis of reciprocating compressor cylinder.

Research on optimization design method of actuator parameters with stepless capacity control system for reciprocating compressor

Actuator and hydraulic system parameters have great influence on the performance,safety and reliability of the stepless capacity control system of reciprocating compressor.Due to the diversity and complex relationship of parameters,traditional parameters selected and calculated based on feasibility can’t make the system run efficiently,have limitations,and may have adverse effects on the system or compressor.Therefore,taking the spring stiffness of the actuator and the impact velocity of ejection,the inlet oil pressure of the hydraulic system,and the indicated power deviation of the compressor as objective functions,the multi-parameter and multi-objective optimization research of the actuator and hydraulic system with the stepless capacity control system based on non-dominated sorting genetic algorithm II(NSGA-II)is carried out.Based on fuzzy analytic hierarchy process(FAHP),the optimal solution is selected from the Pareto front,and compared with the traditional design value,the result is better than that obtained by the traditional design method.

A novel flow control scheme for reciprocating compressor based on adaptive predictive PID control

In the process of capacity regulation of reciprocating compressor, the frequent change of inlet temperature and pressure makes the control of exhaust flow unstable, resulting in the high pressure ratio of the intermediate stage. At last the compressor cannot operate safely. To solve the problem, a novel flow control scheme based on inlet temperature and pressure ratio is proposed. In this scheme, the intake model of the cylinder under the capacity regulation condition is established to calculate the load of the first cylinder. Then, the adaptive predictive PID(APPID) controller is designed to control the pressure ratio of other stages, and the grey prediction model is used to predict the pressure output to overcome the system delay. To solve the problem of control parameters tuning, an improved particle swarm optimization(PSO) algorithm is adopted to obtain the optimal control parameters.The effectiveness of the adaptive predictive PID control method is verified by a two-stage compressor model simulation.Finally, the flow control scheme is applied to the actual four-stage air reciprocating compressor flow control system. Although the temperature difference is greater than 15 ℃, the compressor exhaust flow is maintained at the set value and the pressure ratio is also maintained stable. At the same time, the compressor pressure ratio can be quickly adjusted without overshoot. The application result further verifies the feasibility and effectiveness of the scheme.

Flywheel and Induction Motor Sizing for Torque Fluctuation Optimization in Reciprocating Compressor

Reciprocating compressors are prone to high cyclic loads. The high required performance relies on a good design focusing a torque oscillation applied by the driver. In the first part of this job, dynamic model of reciprocating is presented. Newton-Euler method is used to get motion equations. In the second part, numerical results are presented. Simulations are used for calculating the driving moment as function of crankshaft motion. These results illustrate the effect of the flywheel and motor on its dynamics and are used for induction motor selection and flywheel sizing for optimizing crankshaft torque fluctuation and power consumption reduction.

Reducing the Gas Pressure Drop in Suction Mufflers of Hermetic Reciprocating Compressors

The suction muffler of hermetic reciprocating compressors is installed in order to attenuate the noise generated by the gas pulsation of the flow through the suction valve. However, the installation of the suction muffler affects the operation of the compressor owing to gas pressure drop, which causes volumetric and energetic efficiency loss due to the gas specific volume augmentation. Therefore, there is a compromise between sound attenuation and pressure drop increase, which has to be taken into account by compressor designers. In this work, it presents a numerical solution to the flow through a suction muffler in order to analyze the pressure field and point out the main contributions to the overall pressure drop of the flow. A commercial CFD （computational fluid dynamics） code was used to perform the numerical simulations and the results were validated by using experimental data. After analyzing the pressure field, the geometry of the muffler was modified intending to decrease the flow pressure drop. The geometric modification produced a 28% reduction on the overall pressure drop, without influencing the sound attenuation.

多级压缩机气阀寿命评估与极限含水量预测

针对目前油田天然气回注具有介质含水多、压力高且变化范围宽等特点,多级往复式压缩机容易产生水击破坏,如何预测极限含水量以有效防止水击是亟需解决的关键问题。采用计算流体力学方法,数值模拟并分析了多级压缩机注入湿气条件下各级气阀阀片的动力学特性,得到了不同天然气含水量的气阀阀片载荷分布。进一步结合nCode计算疲劳,预测了多级压缩机气阀服役寿命与极限含水量。JGC-4型压缩机模拟计算结果表明:随着进气含水量增大,压缩机排气压力提高,气缸内不断析出水滴,致使水击产生,且对高压级影响更为显著,压缩机整机进气含水体积分数上限为0.43%。研究结果可为多级往复式压缩机的应用提供技术支撑。

基于数字孪生技术的往复式空气压缩机效率预测方法研究

通过建立往复式空气压缩机数字孪生体模型,实现压缩机效率预测和参数寻优的方法,具有灵活、成本低、通用性好的优势.但是在多变量条件下,传统的基于BP神经网络孪生模型训练时间长、工作量大,寻优过程易陷入局部最优解,不易实现全局最优.针对传统孪生体模型存在的问题,提出了基于CIWOA-BPNN算法的孪生体模型构建方法,通过主成分分析法确定孪生体模型关键指标,在BPNN模型基础之上引入改进的鲸鱼优化算法.研究表明,基于CIWOA-BPNN算法的孪生体模型有效避免了BPNN模型陷入局部最优解.用CIWOA-BPNN算法预测压缩机效率相对误差小于0.6%,决定系数为0.99775,与传统模型相比大幅提升了预测精度.

储气库注采工艺优化研究与应用

储气库建设对供气管网安全、平衡供气具有重要战略意义,在天然气调峰中起到举足轻重的作用。目前,储气库工艺存在压缩机组中体放空对空排放安全隐患和增压气润滑油超标、采出气轻烃超标、甘醇再生系统尾气对空排放、集输管线积盐腐蚀等问题。针对中体放空,设置放空引射装置,将排放物引至火炬集中排放,避免可燃气体站内聚集的隐患;针对润滑油含量超标(体积比为16×10^(−6)),设置高压除油器,降低增压气油含量,消除二次污染;通过对脱水装置吸收塔和再生装置优化,避免甘醇起泡失活;通过对甘醇再生气系统改造,实现再生气回收利用,降低环境污染,节约能耗。

基于GMPE和GWO-MKELM算法的往复压缩机轴承故障诊断

针对往复压缩机内部结构复杂,轴承间隙故障特征提取困难和识别准确率不高等问题,提出了多尺度排列熵和多核极限学习机混合算法的智能诊断新方法。首先,针对多尺度排列熵在多尺度过程中,利用均值粗粒化的方式在一定程度上“中和”了原始信号的动力学突变行为,降低了熵值分析的准确性,提出了一种广义多尺度排列熵算法;然后,为解决核极限学习机处理复杂数据样本分类存在的局限性,将高斯核函数、多项式核函数和感知器核函数进行线性叠加,构建混合核函数,提出了多核极限学习机模型。仿真实验结果表明,该故障诊断方法识别准确率高达98%,高效地实现了轴承不同种类故障的智能诊断。

基于修正共形接触的往复压缩机轴承碰磨研究

传统Hertz接触模型在分析往复压缩机轴承碰磨故障时,存在计算误差大的问题。为此,针对滑动轴承碰磨故障的共形接触特点,提出一种适用于轴承碰磨故障的修正共形接触力模型。该模型在L-N接触力模型的基础上引入修正刚度系数Kc和修正刚度指数nc,得到修正共形接触力模型。基于拉格朗日第二类动力学方程,建立含有往复压缩机连杆大头处轴承间隙的动力学方程;利用RecurDyn软件仿真验证修正共形接触力模型的正确性;分析不同轴承间隙对十字头加速度和轴承接触力的影响,并研究了轴承最大磨损间隙下的轴心轨迹。研究结果表明:修正共形接触力模型相比于L-N接触力模型,更适用于小间隙的载荷接触,且修正共形接触力模型相比L-N接触力模型误差最大可减少约82%。所提出的修正共形接触力模型更适应于往复压缩机轴承间隙碰磨故障的描述。

基于VMD-多尺度排列熵和SVM的船用空压机故障诊断方法

船用机械振动信号存在非线性、非平稳性问题,故障特征难提取,通过变分模态分解(variational mode decomposition,VMD)多尺度排列熵(multiscale permutation entropy,MPE)与支持向量机(support vector machine,SVM)融合的故障诊断方法,对振动信号进行研究。以空压机为例,首先,模拟6种空压机工况,对各工况的热工参数进行测试,分析各工况热工参数的变化程度,并对采集的振动信号进行频域分析。然后通过VMD对振动信号进行分解,得到一系列固有模态分量,计算与原始信号的互相关系数筛选敏感固有模态分量。最后计算出敏感固有模态分量的多尺度排列熵,将其作为特征向量,输入到SVM中,进行故障辨识。实验结果表明:VMD多尺度排列熵与SVM融合的空压机故障辨识方法,能有效地识别故障类型,整体准确率可保持在98.6667%,将此方法与其他方法进行对比,证明此方法有效。

往复式压缩机中间接筒与曲轴箱的连接螺栓断裂失效分析

针对某石化公司重整单元新氢往复压缩机2023年所发生的中间接筒与曲轴箱连接螺栓断裂问题,根据其外观形貌、螺栓材质、螺栓受力情况分析其可能的断裂原因,并从工艺操作、设备安装要求、螺栓紧固方式方面,提出解决方案,保障装置的安稳长满优运行。

往复压缩机润滑油在线监测技术及应用确保设备高效运行

随着往复压缩机在油气田的广泛应用,其安全和可靠的运行成为了现场设备管理的一项重要挑战。报告对往复压缩机的润滑油在线监测技术进行了深入研究,旨在实现压缩机润滑油及设备运行状态的实时监控。研究的成果对推动往复压缩机的管理工作向自动化、网络化和智能化转型具有重要意义。

缓冲罐对往复压缩机橇脉动抑制作用的影响研究

针对往复压缩机在运行过程中所产生的气流脉动问题,详细地阐述了脉动分析技术的基本理论,并对管路系统中起脉动抑制起关键作用的缓冲罐进行了重点研究,从合理确定缓冲罐尺寸与内部结构型式两个方面进行了深入分析,进而提出了缓冲罐的设计建议。同时,结合某实际工程项目对研究成果进行了有效验证,并给出了一般的缓冲罐内部结构型式。

基于GA-BP的往复式压缩机状态监测与预测性维护

针对某增压站现有压缩机状态监控系统识别故障信号困难,故障发生前未能提前预警的问题,开发了面向预知维修的往复式压缩机状态监测与预测系统,建立一套完整的软件及硬件体系。气阀作为往复式压缩机的重要运动部件,其状态监测的测点位于阀盖外表面中心处,以气阀状态监测为例,建立了气阀盖振动力学模型。综合运用时域、频域和小波特征提取方法进行振动信号分析,得到正常状态和故障状态的信号特征,提取状态指标。运用遗传算法优化神经网络模型,建立基于GA-BP的往复式压缩机振动趋势预测模型,试验测得模型的预测误差小于0.04。现场应用结果表明,该系统可实现全天候实时在线监测与预测,实现预知维修,减少工作人员的维保压力,提高机组运行安全性和效率,节省运维成本。