影响下吸式气化炉产气质量的原因及对策

影响下吸式生物质气化炉产气量及燃气成分的因素很多，其中的主要因素包括炉体结构、进风量、物料特性及操作方法。通过对以上几个因素的分析，提出一些解决问题的方法，以促进生物质气化技术在广大农村地区的推广应用。     

生物质气化/燃烧双反应器的冷态试验研究

采用所搭建的生物质气化/燃烧双反应器冷态试验台,研究了生物质气化效果的影响因素.双反应器中气化炉内径为211 mm,高为1.7m,为移动床形式;燃烧炉内径为100mm,高为5m,为循环流化床形式.2个反应器由气动返料装置进行连接,通过炉内的灰循环实现耦合.在此试验台上进行了物料循环量的试验研究,考察了循环量与燃烧炉一次风速、下返料风速的关系,通过理论计算,得到了气化炉循环灰所携带的热量和物料在气化炉内的停留时间,为热态试验台的设计提供理论基础.     

户用型生物质气化炉主要技术性能的试验研究

在实验台上针对改进型户用生物质气化炉进行了主要技术性能的试验研究,其中包括生物质原料、鼓风量、气化强度等参数对气化性能的影响,净化装置的脱除效果研究以及焦油回流装置对气化指标的影响等。实验表明,生物质原料的有关参数对气化性能有明显影响;鼓风量是气化炉生产负荷最简单迅速的调节手段之一,鼓风量存在一个最佳运行的鼓风量范围;户用型气化炉设计的净化装置脱除效果显著;焦油回流装置设计合理,提高了燃气品质和气化效果。     

模糊神经网络控制器在CFBB床层温度控制中的应用

由于循环流化床锅炉(CFBB)的燃烧过程是一个多参数、非线性、时变以及变量强耦合的过程,很难建立被控对象的准确数学模型,根据CFBB床层温度被控对象的动态特性,设计了一个模糊神经网络自适应控制系统,引用模糊高斯基函数神经网络结构,并采用基于变尺度优化学习算法的改进型学习算法(MDFP),其学习信号由神经网络辨识器(NNI)提供.理论分析和仿真结果表明该控制系统具有良好的控制性能,其稳定性、鲁棒性等控制品质明显优于常规模糊控制.     

中型下吸式生物质颗粒正压气化炉的研制和运行优化

采用新设计的中型下吸式生物质颗粒气化炉供能系统，针对用户实际需要进行了试验研究。特剐是对供风量与火力大小的关系、生物质的密度与气化强度间的关系和最快启动方式、最优封火方式等进行了试验，试验结果可为气化炉高效运行及进一步的改进提供依据。     

神经网络模糊多模型软测量在磨煤机存煤量测量方面的应用

基于钢球磨煤机的机理模型,采用神经网络模糊多模型软测量的方法解决球磨机存煤量测量问题,首先建立钢球磨煤机的机理模型,然后将FCM聚类与RBF神经网络多模型理论相结合深入探讨了神经网络模糊多模型软测量方法的实现,最后进行了球磨机存煤量测量的仿真试验,并与RBF神经网络单模型的仿真结果进行了比较.结果表明：神经网络模糊多模型软测量的预测输出的误差较小,训练速度更快,具有更好的泛化能力;将神经网络模糊多模型应用于球磨机存煤量的测量是可行的.     

循环流化床燃烧的补偿式解耦控制系统

循环流化床锅炉是一个分布参数,非线性、时变、多交量紧密耦合的被控制对象,其中主汽压力和料床温度是关键参数,又是具有紧密关系强耦合变量,它们均是通过调节给煤量和一次送风量来实现控制的,这就给燃烧控制系统的设计带来困难,难以实现精确的控制。本文提出了一在于多变量频域解耦控制理论的补偿式解耦控制系统,解决了主汽压力和料床温度的燃烧系统控制难题,经在35t/h循环流化床实用,取得了满意的结果。     

基于自适应神经网络模糊PID的磨煤机控制研究

以衡丰发电厂钢球磨煤机辨识结果为研究对象,基于MATLAB/Simulink平台搭建自适应神经网络模糊PID的磨煤机控制系统仿真模型,通过自适应神经网络对模糊规则进行训练和学习,改进磨煤机出口温度控制、入口负压控制以及负荷控制策略.仿真结果表明:自适应神经网络模糊PID控制优化效果明显,在磨煤机50％工况下,相比传统P...     

ALSTOM气化炉的模糊增益调度预测控制

ALSTOM气化炉具有强的非线性及大惯性,常规的控制方法难以满足全工况运行条件下的各项控制指标.采用模糊推理方法,将气化炉在多个工况条件下的局部线性模型综合成全局模糊模型,并在此基础上应用预测控制技术,提出了一种新型的基于过程全局模糊模型的模糊增益调度预测控制方法,并应用于气化炉多变量非线性优化控制.仿真结果表明:即使各个输入受到运行条件的严格约束,各个输出变量仍能较好地维持在ALSTOM气化炉所要求的范围内,具有较好的控制品质,为气化炉的全局优化控制提供了一个较好的方法.     

生物质电厂锅炉燃烧调整试验研究

分析了生物质发电现状,针对某生物质电厂秸秆锅炉,进行了燃烧调整试验研究,考察了一次风、二次风、燃料性质和锅炉漏风对燃烧的影响.结果表明,加强燃烧中心区通风,协调一、二次风量,可降低锅炉不完全燃烧损失和排烟损失,提高锅炉效率;控制人厂燃料含土量与水分,减少杂物,可提高燃烧安全性;降低锅炉漏风量、调整锅炉配风、可保证锅炉正常运行.为生物质锅炉设计、改造和运行提供了参考.     

基于BP神经网络的温度控制系统

文中介绍了基于BP(Back Pmpagation)的神经网络气化炉温度控制系统。对BP神经网络控制算法作了详细的介绍，运用模糊逻辑控制概念赋予隐层含义，并决定其节点数，同时用高斯核函数作为节点激励函数，并做了仿真研究，叙述了系统的硬件与软件构成，试验表明所设计的系统操作方便、安全可靠，所选择的控制算法适应性强，控制效果良好。     

Hydrogen-rich syngas production via integrated configuration of pyrolysis and air gasification processes of various algal biomass: Process simulation and evaluation using Aspen Plus software

In the present study, hydrogen-rich syngas production via integrated configuration of pyrolysis and air gasification processes of different algal biomass is investigated at relevant industrial condition. A comprehensive steady state equilibrium simulation model is developed using Aspen Plus software, to investigate and evaluate the performance of pyrolysis and air gasification processes of different algal biomass (Algal waste, Chlorella vulgaris, Rhizoclonium sp and Spirogyra). The model can be used as a predictive tool for optimization of the gasifier performance. The developed process consists of three general stages including biomass drying, pyrolysis and gasification. The model validation using reported experimental results for pyrolysis of algal biomass indicated that the predicted results are in good agreement with experimental data. The effect of various operational parameters, such as gasifier temperature, gasifier pressure and air flow rate on the gas product composition and H₂/CO was investigated by sensitivity analysis of parameters. The achieved optimal operating condition to maximize the hydrogen and carbon monoxide production as the desirable products were as follows: gasifier temperature of 600 °C, gasifier pressure of 1 atm and air flow rate of 0.01 m³/h.     

水煤浆热解气与氨复合还原超低NOx排放控制技术

为达到火电厂大气污染物排放标准,应控制燃煤锅炉烟气中污染物排放,研发污染物控制技术和超低排放技术。针对烟气中NOx的治理,提出了水煤浆热解气与氨复合还原超低NOx排放技术,并针对水煤浆气化炉控制,提出一个有效气流量和气化炉温度协同控制策略。以煤浆流量和空气流量为输入量、有效气流量和气化炉温度为输出量,建立水煤浆气化炉双输入、双输出耦合模型。在双回路完全解耦的基础上,设计单回路控制系统并进行适当优化,实现热解气的稳定制备和气化炉的安全运行。     

多功能太阳能空调制热兼制热水模式负荷平衡控制策略的研究

为了解决多功能太阳能空调制热兼制热水工况时空调和热水的能量输入与各自的负荷平衡问题,提出了通过调节热水换热器水体积流量以改变空调传热量和热水传热量的控制策略.以空调负荷作为控制目标,建立了神经网络辨识和模糊控制模型,并进行了仿真研究.结果表明,神经网络预测和模糊控制相结合的方法对热水换热器水体积流量的智能调节能有效地解决负荷平衡问题.     

A numerical model of a fluidized bed biomass gasifier

A one-dimensional, steady state, numerical model was developed for a fluidized bed biomass gasifier. The gasifier model consists of a fuel pyrolysis model, an oxidation model, a gasification model and a freeboard model. Given the bed temperature, ambient air flow rate and humidity ratio, fuel moisture content and reactor parameters, the model predicts the fuel feed rate for steady state operation, composition of the producer gas and fuel energy conversion. The gasifier model was validated with experimental results. The effects of major mechanisms (fuel pyrolysis and the chemical and the physical rate processes) were assessed in a sensitivity study of the gasification model. A parametric study was also conducted for the gasifier model. It is concluded that the model can be used for gasifier performance analysis.     

Investigation of syngas exergy value and hydrogen concentration in syngas from biomass gasification in a bubbling fluidized bed gasifier by using machine learning

In this study, an artificial neural network (ANN) model as a machine learning method has been employed to investigate the exergy value of syngas, where the hydrogen content in syngas reached maximum in bubbling fluidized bed gasifier which is developed in Aspen Plus® and validated from experimental data in literature. Levenberg-Marquardt algorithm has been used to train ANN model, where oxygen, hydrogen and carbon contents of sixteen different biomass, gasification temperature, steam and fuel flow rates were selected as input parameters of the model. Moreover, four different biomass samples, which hadn't been used in training and testing, have been used to create second validation. The hydrogen mole fraction of syngas was also evaluated at the different steam to fuel ratio and gasification temperature and the exergy value of syngas at the point where the hydrogen content in syngas reached maximum were estimated with low relative error value.     

生物质气化过程的混合神经网络模拟

用几种生物质的料进行了水蒸汽流化条件下的常压气化实验。为得到各种生物质的气化特性,用混合神经网络模型于气化过程进行了模拟。模型得到的气化产率与实验数据吻合得较好。神经网络给出的气化特性能正确地反映实际的生物质气化过程。模拟结果还显示,草本生物质和木本生物质在气化过程中,各种煤气成分的释放有不同的规律。     

Sensitivity Analysis on Neural Network Algorithm for Primary Superheater Spray Modeling

ABSTRACT

Nonlinear, large inertia with long dead time is always associated with the main steam temperature parameter in coal fired power plant. Successful control of the main steam temperature within ±2°C of its setpoint is the ultimate target for coal-fired power plant operators. Two of the most common main steam temperature circuit are primary superheater spray and secondary superheater spray. Various methods were used to model the primary superheater spray control valve opening, and the neural network remains one of the most popular choices among researchers. It remains inconclusive which neural network algorithm types, setup, number of layers, and training algorithm will give the best result. As such, the paper shows the best setup for the neural network algorithm based on sensitivity analysis methodology for one hidden layer. The inputs selected for the neural network are generator output, main steam flow, total spray flow, and secondary superheater outlet steam temperature, while the output selected is primary spray flow control valve opening.     

Production of hydrogen energy through biomass (waste wood) gasification

Biomass gasification, conversion of solid carbonaceous fuel into combustible gas by partial combustion, is a prominent technology for the production of hydrogen from biomass. The concentration of hydrogen in the gas generated from gasification depends mainly upon moisture content, type and composition of biomass, operating conditions and configuration of the biomass gasifier. The potential of production of hydrogen from wood waste by applying downdraft gasification technology is investigated. An experimental study is carried out using an Imbert downdraft biomass gasifier covering a wide range of operating parameters. The producer gas generated in the downdraft gasifier is analyzed using a gas chromatograph (NUCON 5765) with thermal conductivity detector (TCD). The effects of air flow rate and moisture content on the quality of producer gas are studied by performing experiments. The performance of the biomass gasifier is evaluated in terms of equivalence ratio, composition of producer gas, and rate of hydrogen production.