Performance of Gas-Steam Combined Cycle Cogeneration Units Influenced by Heating Network Terminal Steam Parameters

The determination of source-side extracted heating parameters is of great significance to the economic operation of cogeneration systems.This paper investigated the coupling performance of a cogeneration heating and power system multidimensionally based on the operating characteristics of the cogeneration units,the hydraulic and thermodynamic characteristics of the heating network,and the energy loads.Taking a steam network supported by a gas-steam combined cycle cogeneration system as the research case,the interaction effect among the source-side prime movers,the heating networks,and the terminal demand thermal parameters were investigated based on the designed values,the plant testing data,and the validated simulation.The operating maps of the gas-steam combined cycle cogeneration units were obtained using THERMOFLEX,and the minimum source-side steam parameters of the steam network were solved using an inverse solution procedure based on the hydro-thermodynamic coupling model.The cogeneration operating maps indicate that the available operating domain considerably narrows with the rise of the extraction steam pressure and flow rate.The heating network inverse solution demonstrates that the source-side steam pressure and temperature can be optimized from the originally designed 1.11 MPa and 238.8°C to 1.074 MPa and 191.15°C,respectively.Under the operating strategy with the minimum source-side heating parameters,the power peak regulation depth remarkably increases to 18.30%whereas the comprehensive thermal efficiency decreases.The operation under the minimum source-side heating steam parameters can be superior to the originally designed one in the economy at a higher price of the heating steam.At a fuel price of$0.38/kg and the power to fuel price of 0.18 kg/(kW·h),the critical price ratio of heating steam to fuel is 119.1 kg/t.The influence of the power-fuel price ratio on the economic deviation appears relatively weak.

Modeling and Optimization of the Steam Turbine Network of an Ethylene Plant

In this paper,we developed a hybrid model for the steam turbines of a utility system,which combines an improved neural network model with the thermodynamic model.Then,a nonlinear programming(NLP) model of the steam turbine network is formulated by utilizing the developed steam turbine models to minimize the total steam cost for the whole steam turbine network.Finally,this model is applied to optimize the steam turbine network of an ethylene plant.The obtained results demonstrate that this hybrid model can accurately estimate and evaluate the performance of steam turbines,and the significant cost savings can be made by optimizing the steam turbine network operation at no capital cost.

Fault Detection and Isolation Based on Neural Networks Case Study: Steam Turbine

The real-time fault diagnosis system is very important for steam turbine generator set due serious fault results in a reduced amount of electricity supply in power plant. A novel real-time fault diagnosis system is proposed by using Levenberg-Marquardt algorithm related to tuning parameters of Artificial Neural Network (ANN). The model of novel fault diagnosis system by using ANN are built and analyzed. Cases of the diagnosis are simulated. The results show that the real-time fault diagnosis system is of high accuracy and quick convergence. It is also found that this model is feasible in real-time fault diagnosis. The steam turbine is used as a power generator by SONELGAZ, an Algerian company located at Cap Djinet town in Boumerdes district. We used this turbine as our main target for the purpose of this analysis. After deep investigation, while keeping our focus on the most sensitive parts within the turbine, the weakest and the strongest points of the system were identified. Those are the points mostly adequate for failure simulations and at which the designed system will be better positioned for irregularities detection during the production process.

Fault Diagnosing System of Steam Generator for Nuclear Power Plant Based on Fuzzy Neural Networks

All kinds of reasons are analysed in theory and a fault repository combined with local expert experiences is establishedaccording to the structure and the operation characteristic of steam generator in this paper. At the same time, Kohonen algo-rithm is used for fault diagnoses system based on fuzzy neural networks. Fuzzy arithmetic is inducted into neural networks tosolve uncertain diagnosis induced by uncertain knowledge. According to its self-association in the course of default diagnosis. thesystem is provided with non-supervise, self-organizing, self-learning, and has strong cluster ability and fast cluster velocity.

The Research on the Methods of Diagnosing the Steam Turbine Based on the Elman Neural Network

This paper introduces a kind of diagnosis principle and learning algorithm of steam turbine fault diagnosis which based on Elman neural network. Comparing the results of the Elman neural network and the traditional BP neural network diagnosis, the results shows that Elman neural network is an effective way to improve the learning speed , effectively suppress the minimum defects that the traditional neural network easily trapped in, and shorten the autonomous learning time. All these proves that the Elman neural network is an effective way to diagnose the steam turbine.

Fault Diagnostics on Steam Boilers and Forecasting System Based on Hybrid Fuzzy Clustering and Artificial Neural Networks in Early Detection of Chamber Slagging/Fouling

The slagging/fouling due to the accession of fireside deposits on the steam boilers decreases boiler efficiency and availability which leads to unexpected shut-downs. Since it is inevitably associated with the three major factors namely the fuel characteristics, boiler operating conditions and ash behavior, this serious slagging/fouling may be reduced by varying the above three factors. The research develops a generic slagging/fouling prediction tool based on hybrid fuzzy clustering and Artificial Neural Networks (FCANN). The FCANN model presents a good accuracy of 99.85% which makes this model fast in response and easy to be updated with lesser time when compared to single ANN. The comparison between predictions and observations is found to be satisfactory with less input parameters. This should be capable of giving relatively quick responses while being easily implemented for various furnace types.

基于AHP-模糊综合评价的蒸汽管网风险评估方法及应用

蒸汽管网是石化和电力行业中实现能源利用率最大化的重要途径,风险的准确评估对其安全运行具有重要意义,而当前针对石油化工的风险评估方法对蒸汽管网适用性有限。本文通过考虑蒸汽管网潜在风险类型对失效可能性的影响,提出基于层次分析-模糊综合评价的蒸汽管网风险评估方法,并在某企业蒸汽管网的风险评估中进行应用。结果表明该评估方法更接近蒸汽管网实际情况,从而提高了蒸汽管网风险评估的准确性。

燃煤机组过热汽温宽负荷模型前馈控制

为了对燃煤机组过热汽温宽负荷运行时进行更精确地前馈控制,提出一种基于物理引导神经网络(PGNN)的预测前馈信号模型,并基于间隙度量法确定了多模型的负荷段分配。多模型间隙度量PGNN预测方法采用多模型间隙度量方法对负荷区段进行合理划分,结合过热器机理引导的长短期记忆神经网络,可以对强耦合、大惯性的过热汽温宽负荷前馈信号进行精准预测。结果表明:在机组宽负荷运行时,随着负荷降低控制对象的非线性程度逐渐增强,需要更多的模型数量,采用多模型间隙度量PGNN前馈控制方法可以在不同工况下采用与当前工况相适应的前馈信号,有效提升过热汽温的调节精度和稳定性。

单轴燃气-蒸汽联合循环机组FCB控制策略研究与应用

为强化本地支撑电源建设,提高应对突发因素造成的大面积停电事故,广东电网对联合循环机组孤网运行能力及“黑启动”功能提出了明确要求。依托某燃机电厂二期基建调试项目,开展了两套AE94.3A型燃气-蒸汽联合循环“一拖一”单轴式机组快速甩负荷(fast cut back,简称FCB)试验。试验模拟了电网外送回路故障,机组通过快切线路断路器,最终实现了汽机跳闸,燃机快速切负荷、输出功率降至带厂用电的“孤网运行”目的,试验过程各参数变化平稳,主辅机设备均为安全停运,试验取得了较好的效果。通过对汽机旁路系统、锅炉过热/再热减温水系统、汽包液位系统、凝结水及真空系统、轴封系统等的控制策略进行针对性的优化与完善,使得机组具备了完整可靠的FCB功能,显著增强了电源侧供电可靠性。相关经验可供后续同类机组参考。

蒸汽管网钢桁梁桥结构设计与分析

本文以河北沧州渤海新区蒸汽管网工程中的蒸汽管道钢桁梁桥为研究背景,采用大型有限元软件Midas Civil开展桥梁全结构的受力分析与承载力及强度计算,相关计算结果为同类钢桁梁桥的设计与后续施工提供了理论依据。

某长距离输蒸汽管网EPC项目新型管网优化设计

“源-网-荷-储”协调优化运营模式,为我国能源转型和发展提供一种新的发展思路,长距离输热管网作为渠道和桥梁,为能源高效、节能、安全和持续稳定开发利用保驾护航。以某长输蒸汽管网EPC项目为例,按照新型管网设计理念,从项目优化设计、管材选购和项目运行效果等方面进行分析研究,通过投产和运行测试,管网达到冬季表面积雪不化的超低能耗热网输配效果。

考虑时延因素的神经网络主汽温预测

火电机组在灵活调峰调频时,其负荷的大范围变动导致锅炉的部分运行参数也随之频繁变动,进而影响主汽温的稳定性。为了便于对主汽温进行超前控制,采用LSTM神经网络预测模型,对主汽温的未来变化进行预测,并且针对主汽温的主要影响因子存在的迟延问题,提出了互信息法,解决参数时序对齐问题;采用发电机组实际运行数据进行仿真,结果表明此神经网络模型能够有效对主汽温进行预测。

基于神经网络的汽轮机及其辅助系统控制策略优化研究

文章针对汽轮机及其辅助系统控制策略的优化问题,提出了一种基于神经网络的研究方法。通过建立神经网络模型,实现对汽轮机运行状态的智能监测和优化控制。通过对实际案例的仿真与分析,验证了该方法的有效性和实用性。研究结果表明,基于神经网络的汽轮机控制策略优化能够提高系统的稳定性和效率,为汽轮机运行管理提供了新的技术途径。

基于改进神经网络PID的主蒸汽温度优化控制研究

针对电厂主蒸汽温度PID串级控制系统参数整定繁琐、自适应性较差的问题,提出一种改进神经网络PID串级控制方法。为了降低主蒸汽温度控制系统的不确定性,基于最小误差熵(MEE)准则训练串级控制中的主神经网络PID控制器,并利用滚动时域窗法递归估计跟踪误差的熵,提升算法运行效率。将主蒸汽温度误差序列和部分可测扰动输入神经网络PID控制器输入层,实现反馈控制与前馈控制相融合,提升控制系统抗干扰能力。通过与采用最小误差平方和(MSE)准则的神经网络PID控制器对比,采用MEE的神经PID控制器可以减小过热汽温的波动,减少控制系统的随机性。

基于KPCA-IPOA-BiGRU的联合循环余热锅炉主蒸汽参数预测

余热锅炉主蒸汽参数对于联合循环机组的健康运行判断至关重要。针对余热锅炉运行参数非线性和时延性导致主蒸汽参数预测精度低的问题,提出了一种联合循环余热锅炉主蒸汽参数预测模型。首先,采集燃机电厂的SIS运行数据,通过灰色相关性分析法确定输入变量;其次,通过核主成分分析(kernel principal component analysis,KPCA)提取输入参数的特征信息,并根据主成分贡献率选取输入维度;最后,利用改进的鹈鹕优化算法(improved pelican optimization algorithm,IPOA)优化双向门控循环神经网络(bi-directional gated recirculation neural network,BiGRU),并构建KPCA-IPOA-BiGRU进行三压余热锅炉主蒸汽参数的预测测验。结果表明,现场采集的10000组数据点,其中8000组用作训练集,2000组用作测试集,提出的模型将28维输入参数降低至8维,可准确预测3个压力级的蒸汽参数,且R2均大于98%,为预测时延性的联合循环余热锅炉主蒸汽参数提供了技术支持。

基于深度学习的3D打印混凝土蒸汽养护力学性能研究和抗压强度预测

3D打印混凝土(3DCP)技术近年来获得广泛关注,然而,关于养护条件如何影响3DCP的力学性能的研究仍然较少。本研究主要探讨不同蒸汽养护条件(升温速率、恒温时间和恒温温度)对3D打印混凝土材料的力学性能影响规律。为了获得最佳蒸汽养护条件,通过正交试验研究了不同蒸汽养护条件下打印胶凝材料的力学各向异性。此外,基于室内试验数据,建立了条件表格生成对抗网络(CTGAN)用于扩充数据集,由291条数据扩充为1000条数据,建立了一维残差卷积神经网络(1D-Residual CNN)用于预测3DCP的抗压强度,并建立了5个机器学习(ML)模型用于对比,试验结果表明,CTGAN的数据增强技术可以有效提升1D-Residual CNN模型在3DCP抗压强度上的预测精度,R2最高为0.92。

常山胡柚果皮精油提取工艺模型筛选优化、化学成分及抗氧化活性研究

研究常山胡柚果皮(Citrus×aurantium Changshan-huyou pericarp,CP)精油化学成分,并对提取条件进行优化。采用水蒸气蒸馏法提取CP精油,利用气相色谱-质谱法进行分析,通过单因素试验考察不同提取条件对提取率的影响,并利用响应面、人工神经网络进行工艺优化。结果表明,CP精油中主要成分为D-柠檬烯;最佳工艺条件:粒径24目、蒸馏水浸泡1 h、液固比34.86 mL/g、提取温度300.00℃、提取时间166.21 min,在此条件下,CP精油提取率为0.82%,响应面与人工神经网络相结合,可有效地利用已有数据对目标寻优。同时CP精油对DPPH自由基有明显的清除作用(IC_(50)值为65.01 mg/mL),表明CP精油具有一定的抗氧化活性。

Neuro-fuzzy generalized predictive control of boiler steam temperature

Power plants are nonlinear and uncertain complex systems. Reliable control of superheated steam temperature is necessary to ensure high efficiency and high load-following capability in the operation of modem power plant. A nonlinear generalized predictive controller based on neuro-fuzzy network （NFGPC） is proposed in this paper. The proposed nonlinear controller is applied to control the superheated steam temperature of a 200MW power plant. From the experiments on the plant and the simulation of the plant, much better performance than the traditional controller is obtained,

针对汽轮机转子故障样本不足的典型故障检测方法研究

目前深度学习在机械系统的故障诊断方面得到较大普及和发展,这些智能模型需要大量的训练数据确保其泛化能力。然而实际汽轮机转子故障数据缺乏或者难以获取,这给智能故障诊断带来新的挑战。提出一种基于汽轮机转子数值模拟生成故障数据并进行智能故障诊断的方法,通过建立转子有限元模型,生成能够反映其工作状态的故障信息,从而为智能模型提供数据样本。采用根据实际转子信号建立的高精度转子有限元模型,能够有效解决故障样本不足的问题,从而提高智能诊断准确率。通过将有限元技术与深度卷积神经网络相结合,所提出的方法能够在故障样本不足及部分故障信号难以测量的情况下实现汽轮机转子端到端的智能故障诊断,具有准确率高、鲁棒性强的特点。

基于模糊粗糙集的大型汽轮机组设备故障识别方法

针对大型汽轮机组设备故障种类多,提出基于模糊粗糙集的大型汽轮机组设备故障识别方法。首先,模糊化处理大型汽轮机组设备故障信息,将复杂的故障信息转化为简单的模糊编码后,使用故障类型-征兆特征决策表生成方法构建特征决策表,表中各行代表故障类型,各列代表故障征兆特征;将决策表数据输入基于改进可拓神经网络聚类的故障分类模型中,决策表的历史数据作为训练数据,当下机组设备运行状态数据作为测试数据,通过判断当下设备运行状态是否与某故障类型-征兆特征决策表的数据匹配,完成设备故障识别。实验中,此方法可有效识别16种汽轮机组设备故障。