Retrofit of 350-MW Supercritical Boiler Based on the Analysis on Exhaust Gas Temperature

Since the first batch of 350-MW supercritical utility boilers was put into operation, the exhaust flue gas temperature of the boilers has always been higher than the designed value. The main reason is that the heat absorbed by the air heater is not sufficient. In Huaneng Dongfang Power Plant, the exhaust flue gas temperature is lowered through modifications to the economizer and the air heater. The experimental results reveal that every year, each boiler could save 3 850 tons of standard coal and reduce 85 tons of SO2 and 9 000 tons of CO2 respectively after retrofit.

Correlation of Performance, Exhaust Gas Temperature and Speed of a Spark Ignition Engine Using Kiva4

The objective of this study was to investigate performance characteristics of a spark ignition engine, particularly, the correlation between performance, exhaust gas temperature and speed, using Kiva4. Test data to validate kiva4 simulation results were conducted on a 3-cylinder, four-stroke Volkswagen (VW) Polo 6 TSI 1.2 gasoline engine. Three different tests were, therefore, carried out. In one set, variations in exhaust gas temperature were studied by varying the engine load, while keeping the engine speed constant. In another test, exhaust gas temperature variations were studied by keeping the engine at idling whilst varying the speeds. A third test involved studying variations in exhaust gas temperature under a constant load with variable engine speeds. To study variations in exhaust gas temperatures under test conditions, a basic grid/mesh generator, K3PREP, was employed to write an itape17 file comprising of a 45° asymmetrical mesh. This was based on the symmetry of the combustion chamber of the engine used in carrying out experimental tests. Simulations were therefore performed based on the input parameters established in the conducted tests. Simulations with the kiva4 code showed a significant predictability of the performance characteristics of the engine. This was evident in the appreciable agreement obtained in the simulation results when compared with the test data, under the considered test conditions. A percentage error, between experimental results and results from simulations with the kiva4 code of only between 2% to 3% was observed.

A step parameters prediction model based on transfer process neural network for exhaust gas temperature estimation after washing aero-engines

The prediction of Exhaust Gas Temperature Margin(EGTM)after washing aeroengines can provide a theoretical basis for airlines not only to evaluate the energy-saving effect and emission reduction,but also to formulate reasonable maintenance plans.However,the EGTM encounters step changes after washing aeroengines,while,in the traditional models,a persistence tendency exists between the prediction results and the previous data,resulting in low accuracy in prediction.In order to solve the problem,this paper develops a step parameters prediction model based on Transfer Process Neural Networks(TPNN).Especially,“step parameters”represent the parameters that can reflect EGTM step changes.They are analyzed in this study,and thus the model concentrates on the prediction of step changes rather than the extension of data trends.Transfer learning is used to handle the problem that few cleaning records result in few step changes for model learning.In comparison with Long Short-Term Memory(LSTM)and Kernel Extreme Learning Machine(KELM)models,the effectiveness of the proposed method is verified on CFM56-5B engine data.

737NG飞机发动机EGT指示系统超限分析

介绍了发动机EGT指示系统原理,为减少EGT指示系统故障对发动机性能和航班运行的影响,对EGT指示故障原因进行了分析并给出了排故思路。基于预防性维修方案,通过一定维修间隔内对EGT系统定期检查和执行新的排故方案,结果表明EGT超限故障明显降低,有效提高发动机性能和航班效率。

V2500-A5发动机排气温度真实超限分析

以一起V2500-A5发动机排气温度(Exhaust Gas Temperature,EGT)的真实超限故障为切入点,阐述了影响发动机EGT的因素,以及发动机各主要部件对EGT的影响程度。通过发动机监控系统和原理分析锁定故障源,以期为同类故障的快速处置和排除提供参考。

非线性去噪对发动机排气温度预测精度影响

针对传统方法难以进一步提高发动机排气温度(EGT)预测精度的问题,提出一种基于非线性去除噪声的正则化混沌预测算法。为了消除其他因素和离群点对EGT预测精度的影响,研究了实际测量数据的预处理方法。由于观测数据中的噪声和混沌相空间重构参数对EGT预测精度有较大影响,提出利用搜索算法寻找使EGT预测精度较优的非线性去噪参数和重构相空间的延迟时间和嵌入维变量。仿真结果表明,该算法用于某型航空发动机EGT序列的预测,获得了满意的结果。

RVM在航空发动机故障诊断中的应用研究

针对支持向量机算法存在的不足,研究了一种基于稀疏贝叶斯框架的机器学习方法——相关向量机在航空发动机故障检测中的应用。排气温度是进行发动机监控与故障诊断的重要依据,应用相关向量机对其进行预测。通过仿真实验,证明了相关向量机方法在样本数据较少的情况下只产生了很少的相关向量,并且能够及时准确地预测出发动机排气温度;同时可以使用真实值与预测值的相对误差作为系统是否发生故障的判断依据。

Performance Characteristics of n-Butanol-Diesel Fuel Blend Fired in a Turbo-Charged Compression Ignition Engine

In this study, n-butanol-diesel blends were burned in a turbo-charged, direct injection diesel engine where the brake thermal efficiency, (BTE) or brake specific fuel consumption, (BSFC) was compared with that of ethanol-diesel or methanol-diesel blends in another study by other authors. The test blends used were B5, B10 and B20 (where B5 is 5% n-butanol by volume and 95% diesel fuel-DF). In this study, the BTE was higher and the BSFC improved more than in the other study. Because of improved BTE with increasing brake mean effective pressure, BMEP, the BSFC reduced, however the increased shared volume of n-butanol in DF increased BSFC. Adding n-butanol in DF slightly derated the torque, brake power output with increasing speed, and caused a fall in exhaust gas temperatures, (EGT) which improves the volumetric efficiency and reduces compression work. Therefore, a small-shared volume of n-butanol in DF fired in a turbo-charged diesel engine performs better in terms of BTE and BSFC than that of ethanol or methanol blending in DF.

Remaining Useful Life Prediction for Aero-Engines Combining Sate Space Model and KF Algorithm

The key to failure prevention for aero-engine lies in performance prediction and the exhaust gas temperature margin(EGTM)is used as the most important degradation parameter to obtain the operating performance of the aero-engine.Because of the complex environment interference,EGTM always has strong randomness,and the state space based degradation model can identify the noisy observation from the true degradation state,which is more close to the actual situations.Therefore,a state space model based on EGTM is established to describe the degradation path and predict the remaining useful life(RUL).As one of the most effective methods for both linear state estimation and parameter estimation,Kalman filter(KF)is applied.Firstly,with EGTM degradation data,state space model approach is used to set up a state space model for aero-engine.Secondly,RUL of aero-engine is analyzed,and expected RUL and distribution of RUL are determined.Finally,the sate space model and KF algorithm are applied to an example of CFM-56aero-engine.The expected RUL is predicted,and corresponding probability density distribution(PDF)and cumulative distribution function(CDF)are given.The result indicates that the accuracy of RUL prediction reaches 7.76%ahead 580 flight cycles(FC),which is more accurate than linear regression,and therefore shows the validity and rationality of the proposed method.

燃气轮机的控制及其应用

介绍了出口乏气温度、入口空气温度对PGT10燃气透平的影响;MS5002C燃气透平的使用经验;应用MARKⅥ三冗余控制系统的经验。

Performance Optimization of Dual Pressure Heat Recovery Steam Generator (HRSG) in the Tropical Rainforest

This work evaluates the performance optimization of heat recovery steam generator system in Afam VI power plant, Rivers State. Nigeria. Steady state monitoring and direct collection of data from the plant was performed including logged data for a period of 12 months. The data were analysed using various energy equations. Hysys software was used to model the temperature across the heating surfaces, and MATLAB software was used to determine the heat transfer coefficient, heat duties, steam flow, effectiveness of the HRSG. The optimization technique was carried out by varying the exhaust gas flow, exhaust gas temperature, steam pressure and the theoretical introduction of duct burner for supplementary firing. The results show that between 490℃ and 526℃, the percentage increase in the overall heat absorbed in the HRSG is 37.39%. It also show that for an increase in the exhaust gas mass flow by 80 kg/s, the steam generation increase by 19.29% and 18.18% for the low and high pressure levels respectively. The overall result indicates an improvement in the HRSG energy efficiency and steam generation. As the exhaust gas mass flow and temperature increases, the steam generation and system effectiveness greatly improved under the various considerations, which satisfy the research objective.

基于改进PSO-LSTM的APU排气温度预测研究

为了提高辅助动力装置排气温度的预测精度,采用基于粒子群算法优化长短期记忆网络的方法。通过分析不同参数间相关性,选出影响排气温度的主要参数作为预测模型的输入,构造惯性权重和学习因子的动态调节函数来解决粒子群算法存在的早熟收敛问题,得到一种改进粒子群算法。结果表明,改进PSO-LSTM模型比传统的RNN模型、LSTM模型、基本PSO-LSTM模型、标准PSO-LSTM模型具有更高的预测精度。上述研究结果可为短期APU性能变化趋势预测提供一定的参考。

基于BAM和CNN-GRU混合模型的辅助动力装置排气温度预测方法

对飞机辅助动力装置(Auxiliary Power Unit,APU)排气温度(Exhaust Gas Temperature,EGT)的准确预测可为APU健康管理提供重要信息。传统方法在长周期预测中精度较低。提出一种基于特征与时序的双侧注意力机制(Bilateral Attention Mechanism,BAM)和卷积神经网络(Convolutional Neural Network,CNN)-门控循环单元(Gated Recurrent Unit,GRU)的混合模型,选取5个与排气温度关联度较高的特征参数对EGT进行多变量预测。引入BAM可自动量化输入变量与EGT的关联度,并加强历史关键信息对预测输出的表达;引入CNN可提取反映EGT非平稳动态变化的高维特征。实验结果表明:所提出的混合模型在单步与多步的长时间序列和多变量输入EGT预测均取得很好的效果。相比于BAM-GRU模型、CNN-GRU模型、GRU模型、长短期记忆(Long Short-Term Memory,LSTM)模型、支持向量机(Support Vector Machine,SVM)模型和反向传播(Back Propagation,BP)模型,混合模型的预测精度有较大程度提高。

Improving EGT sensing data anomaly detection of aircraft auxiliary power unit

The reliability of the on-wing aircraft Auxiliary Power Unit(APU)decides the cost and the comfort of flight to a large degree.The most important function of APU is to help start main engines by providing compressed air.Especially on the condition of sudden shutdown in the air,APU can offer additional thrust for landing.Therefore,its condition monitoring has drawn much attention from the academic and industrial field.Among the on-wing sensing data which can reflect its condition,Exhaust Gas Temperature(EGT)is one of the most important parameters.To ensure the reliability of EGT,one kind of data-driven anomaly detection framework for EGT sensing data is proposed based on the Gaussian Process Regression and Kernel Principal Component Analysis.The situations of one-dimensional and two-dimensional input data for EGT anomaly detection are considered,respectively.The cross-validation experiments are carried out by utilizing the real condition data of APU,which are provided by China Southern Airlines Company Limited Shenyang Maintenance Base.The anomalous stuck condition of EGT sensing data is also detected.Experimental results show that the proposed EGT sensing data anomaly detection method can achieve better performance of false positive ratio,false negative ratio and accuracy.

V2500发动机EGT电缆磨损的处理

分析了V2500型发动机EGT电缆磨损发生的原因,并给出了电缆磨损后的处理方法。