摘要
燃料电池是一种新兴的、高效率的且清洁的发电装置。从能量转换角度考虑,燃料电池与普通的一次电源和二次电源相同,都是直接将化学能转变为电能的装置。从实际应用来考虑,燃料电池有其独特的性质,其运行所需燃料和氧化剂并非存储在其内部,而是源源不断地由电池外部设备供给。直接内部重整固体氧化物燃料电池(DIR-SOFC)除了具有燃料电池共有的优点(高效率、低污染、低噪声、高能量密度和高可靠性)外,其凭借自身运行温度高、电极催化剂特殊的特点,可以直接利用天然气、煤气或者其它碳氢化合物作为燃料,因此DIR-SOFC无需外部燃料重整器,从而简化了电池系统结构。另外,DIR-SOFC可以与燃气轮机或蒸汽轮机等构成联合发电系统,对电池排出的含有大量未使用燃料和热量的尾气加以再利用,以提高系统总的发电效率(其系统总体效率可以超过80%)和进一步降低对环境的污染。而且,SOFC系统发电容量大,用途更为广泛。
本课题是国家高技术研究发展计划资助项目(863计划)“中温平板型SOFC模块发电系统的研制”和国家自然科学基金项目“燃料电池-燃气轮机混合动力系统非线性对象的协调控制”课题的一部分。本文以DIR-SOFC发电系统为研究对象,以甲烷蒸汽混合气体作为其燃料,对DIR-SOFC系统的建模、数值模拟、控制策略等问题进行了研究。以甲烷蒸汽重整反应动力学、DIR-SOFC系统机理模型为研究基础,详细地分析讨论了DIR-SOFC系统在不同运行条件下的静态和动态特性。在此基础上,采用基于数据的小波网络(WN)方法(免疫优化WN(IOWN)和自递归WN(SRWN))对DIR-SOFC系统建立了输入输出黑盒模型。根据对DIR-SOFC系统动态特性的分析讨论,设计了适合其的控制方案(包括模糊控制和预测控制),并对不同控制方法的结果进行了比较分析。本文的主要工作包括:
1.在甲烷蒸汽重整动力学理论的基础上,建立了甲烷蒸汽重整过程中各个反应的动力学模型。依据模型,在MATLAB/SIMULINK仿真环境里,建立了重整反应平衡组成计算模块和反应速度计算模块。基于建立的计算模块,对甲烷蒸汽重整过程进行了热力学计算分析,并对其稳定状态和动态行为进行了仿真,考察了运行温度、压力、S/C等重要参数对重整反应的影响,给出了不同运行条件下,甲烷转化率、反应产物平衡组成(甲烷、氢气、一氧化碳、二氧化碳)、以及重整过程中不同反应进行速度的计算结果。同时,分别对不同条件下的结果进行了对比,并对反应中出现的各种状态进行了分析讨论。
2.基于甲烷蒸汽重整反应动力学、化学能电能转换原理、质量和能量传递原理建立了DIR-SOFC的机理数学模型。模型中采用了微分方程组来描述DIR-SOFC内部的各种物理和化学过程,为了求得模型方程的解,结合本研究的需要并采用计算流体动力学方法推导了DIR-SOFC模型的二维矩阵形式进而对其进行了数值计算。在MATLAB环境中编写仿真程序(m文件),将其封装成SIMULINK中可调用的子模块,并整合为便于操作和调用的DIR-SOFC模块。针对电池运行中的重要变量和参数(SOLID厚度、燃料气流、空气气流、压力、S/C和电压),考察了它们对交叉流场电池运行状态及性能的影响,给出了由各种输入和参数的阶跃变化引起的电池二维参数分布动态变化过程,并对结果进行了分析和讨论。DIR-SOFC性能研究表明:1)在燃料入口处,DIR-SOFC温度有明显的下降,在达到最低值之后,会沿着阳极流道逐渐上升,在阳极流道中部阴极流道出口附近达到温度的最大值,接着沿流道缓慢下降;2)沿着阴极流道,电池温度逐渐上升,在阴极流道出口附近达到最大值;3)阳极和阴极入口处的电流密度较小,沿着阳极流道电流密度逐渐升高,在温度最高点附近达到电流密度的最大值,接着逐渐下降,而沿着阴极流道,电流密度总的趋势是逐渐增大的,在阳极出口附近,沿着阴极流道方向,电流密度是逐渐减小的,这与阳极出口处燃料消耗殆尽有关;4) DIR-SOFC中甲烷气体浓度沿着阳极流道逐渐减小,氢气的浓度沿着阳极流道先增大然后逐渐减小,水蒸汽的浓度则是先减小而后逐渐增大,氧气的浓度沿着阴极流道逐渐减小,并在电流密度最大处达到其最小值;5)增加SOLID厚度,会降低电池的平均温度、平均电流密度、燃料和氧气的利用率;6)燃料气流量减少20%,会减低电池的平均温度、平均电流密度,增加燃料利用率,减小氧气利用率;7)燃料气流量增加20%,使得平均电流密度增加,但是平均温度下降了,燃料利用率相应减小,氧气利用率会突然增加而后回落;8)空气流量减少20%,会增大电池的平均温度、平均电流密度、燃料利用率和氧气利用率;9)空气流量增加10%,降低了电池的平均温度、平均电流密度、燃料利用率和氧气利用率;10)压力从3bar变为2bar,降低了电池的平均温度、平均电流密度、燃料利用率和氧气利用率;11)压力从3bar变为5bar,增加了电池的平均温度、平均电流密度、燃料利用率和氧气利用率;12) S/C 2.14→1.8,增加了电池的平均温度、平均电流密度、燃料利用率和氧气利用率;13) S/C 2.14→3.0,降低了电池的平均温度、平均电流密度、燃料利用率和氧气利用率;14)单电池电压从0.7V升至0.75V,降低了电池的平均温度、平均电流密度、燃料利用率和氧气利用率。
3.在WN建模方法基础上采用多目标免疫优化算法(MOIA)对原始WN模型的网络结构和初始状态进行优化,用以达到提高WN模型精度的目的。为了提高模型对DIR-SOFC及其系统的动态行为的学习能力,本文采用改进了的SRWN。为了进一步实现在线模型辨识,采用了带遗忘因子的二次函数作为目标函数,并推导了SRWN中参数的更新公式(包括小波尺度和平移参数的梯度更新公式,以及权重参数的迭代更新公式)。接着,对其参数更新算法的收敛性进行了证明,提出了参数学习速度自适应调整律。仿真结果表明IOWN和SRWN(采用带遗忘因子的目标函数)均能够成功地对DIR-SOFC建模,并能获得较高的精度,其中SRWN具有更好的在线学习能力。
4.根据质量、能量守恒定律以及经验公式建立了DIR-SOFC发电系统中其它各部件(燃烧室、压气机、透平、热交换器)的模型,并创建了SIMULINK中可调用的子模块。以这些部件模块为基础,构建了190kW级DIR-SOFC发电系统的SIMULINK模型,通过该模型,设置了多种运行条件,进行了仿真实验,获得了DIR-SOFC系统在不同工况下的动态运行特性,分析了各变量对系统性能的影响。基于对DIR-SOFC系统的动态仿真实验,确定了各操纵量(入口燃料和空气流量以及燃烧室添加燃料流量)来控制发电系统的运行状态和输出性能(电堆温度、燃料利用率和系统输出功率)。应用智能控制中具有代表性的模糊控制和预测控制方法设计了针对该DIR-SOFC发电系统的控制策略。在模糊控制算法中采用了改进的非均匀分布模糊变量隶属度函数、T-S模糊规则以及加权平均反模糊方法。在预测控制算法中,采用SRWN模型作为DIR-SOFC系统的预测模型,并对DIR-SOFC系统进行了在线建模和预测仿真实验,结果表明DIR-SOFC系统的SRWN模型具有较好的在线辨识能力和适应性,而且具有较高的预测精度。在控制实验中,为了展示基于SRWN的预测控制方法的优越性,还采用了IOWN模型替换预测控制中的SRWN模型。控制仿真实验结果表明,与模糊控制和基于IOWN的预测控制相比,基于在线更新SRWN模型的预测控制器具有最短的调节时间和平稳的控制过程,能够快速稳定地跟随控制变量设定值,获得了良好的控制精度。
The fuel cell is a kind of new and developing clean power generation equipment with high efficiency. From the viewpoint of the energy conversion, the fuel cell, as well as the primary and the secondary power sources, directly converts the chemical energy into electrical energy. From the viewpoint of the practical application, the fuel cell has unique properties. The fuel and oxidant needed in fuel cell operation are continuously supplied by external devices instead of being stored within fuel cell itself. The direct internal reforming solid oxide fuel cell (DIR-SOFC) not only has the general advantages of fuel cells (high efficiency, low pollution, low noise, high energy density and high reliability), but also has high operating temperature and special electrode catalyst. The DIR-SOFC can thus be directly fueled with the natural gas, coal gas and other hydrocarbons, and does not need any external fuel reformer. Therefore, the SOFC configuration can be simplified. Further, to increase the total efficiency of the power system and reduce pollution to the environment, the DIR-SOFC can be combined with gas and steam turbines to recycle the fuel cell exhaust gas which contains a lot of unused fuel and heat (the overall efficiency can thus be more than 80%). Additionally, the SOFC system has a large power generation capacity and covers a wide range of applications.
The work is part of the project“Intermediate temperature planar SOFC modularized generation system”supported by the National Hi-Tech Research and Development Program (863) of China and the project“Harmonious nonlinear control of fuel cell-gas turbine hybrid generation system”supported by the National Natural Science Foundation of China. The dissertation takes the DIR-SOFC system fueled with methane-steam gas mixture as the study object to develop the model, numerical simulation technique and control strategy of the power plant. Based on the kinetics of the methane steam-reforming and the mechanism model of the DIR-SOFC system, the steady and dynamic performances of the DIR-SOFC system under different operating conditions are detailedly analyzed and discussed. Based on the above work, the data-driven wavelet network (WN) methods (immune optimized WN (IOWN) and self recurrent WN (SRWN)) are applied to establish the input-output black-box model of the DIR-SOFC system. According to the analysis and discussion of the dynamic performance, the control schemes (fuzzy control and predictive control) suitable for the DIR-SOFC system are designed. The results obtained by different control methods are compared and analyzed. The main contributions and achievements of this dissertation are given below:
1. The kinetic models of reactions in the methane steam-reforming process are established based on the kinetic theory of the methane steam-reforming. According to the models, the calculation modules of the equilibrium composition and reaction rates for the reforming process are built in MATLAB/SIMULINK simulation environment. The thermodynamic analysis of the reforming reactions and the simulation for the steady and dynamic behavior are performed based on the built calculation modules. The influences of important parameters (operating temperature, pressure, S/C, etc.) on the reforming process are investigated. The methane conversion, product equilibrium composition (methane, hydrogen, carbon monoxide, carbon dioxide) and reaction rates in the reforming process under different conditions are calculated. The simulation results are compared, and the states in reactions are analyzed and discussed.
2. The mechanism model of the DIR-SOFC is established based on the kinetics of methane steam-reforming, principles of chemical energy-electrical energy conversion, and fundamentals of mass and energy transfer. The simultaneous differential equations are employed to describe the physical and chemical behaviors occurring in the DIR-SOFC. To obtain the solutions of the model equations, the computational fluid dynamics (CFD) method, combined with the need for research, is applied to deduce the two-dimensional matrix form of the DIR-SOFC model equations to perform the numerical calculation. The simulation codes (m files) are programmed in MATLAB, and masked blocks are generated so that they can be invoked in SIMULINK. Then the blocks are integrated together to build DIR-SOFC module which can facilitate manipulating and invoking. The influences of important variables and parameters (SOLID thickness, fuel flow, air flow, pressure, S/C and voltage) on operating states and performances of the cross-flow fuel cell are investigated. Dynamic processes of two-dimensional parameter distributions of fuel cell caused by step changes of inputs and parameters are presented, analyzed and discussed. The research results of DIR-SOFC performances show that: 1) At the fuel inlet, the temperature of DIR-SOFC reduces significantly. After the temperature reaches its minimum, it gradually rises along the anode flow channel and reaches its maximum in the middle of the anode channel near the cathode channel outlet. Then, the temperature gradually decreases along the channel. 2) Along the cathode channel, the cell temperature gradually rises and reaches its maximum near the cathode channel outlet. 3) At the anode and cathode inlet, the current density is relatively low. Along the anode channel, the current density gradually increases and reaches its maximum near the maximum-temperature point. Then, it gradually decreases. Along the cathode channel, the general trend of the current density is gradually increasing. Near the anode outlet, along the cathode channel, the current density gradually reduces because the fuel is almost consumed near this position. 4) Along the anode channel in the DIR-SOFC, the methane concentration gradually decreases, the hydrogen concentration first increases and then decreases, while the steam concentration first decreases and then increases. Along the cathode channel, the oxygen concentration gradually reduces and reaches its minimum near the maximum-current density point. 5) The increased SOLID thickness decreases the cell mean temperature, mean current density, fuel and oxygen utilizations. 6) The fuel flow rate decreased by 20% results in the decrease in cell mean temperature and mean current density. The fuel utilization accordingly rises and the oxygen utilization reduces. 7) The fuel flow rate increased by 20% results in the increase in mean current density, while the mean temperature reduces. The fuel utilization accordingly reduces, while the oxygen utilization gradually decreases following the sudden increase. 8) The air flow rate decreased by 20% results in the increase in cell mean temperature, mean current density, fuel and oxygen utilizations. 9) The air flow rate increased by 10% results in the decrease in cell mean temperature, mean current density, fuel and oxygen utilizations. 10) The pressure changed from 3bar to 2bar results in the decrease in cell mean temperature, mean current density, fuel and oxygen utilizations. 11) The pressure changed from 3bar to 5bar results in the increase in cell mean temperature, mean current density, fuel and oxygen utilizations. 12) S/C 2.14→1.8 results in the increase in cell mean temperature, mean current density, fuel and oxygen utilizations. 13) S/C 2.14→3.0 results in the decrease in cell mean temperature, mean current density, fuel and oxygen utilizations. 14) The cell voltage changed from 0.7V to 0.75V results in the decrease in cell mean temperature, mean current density, fuel and oxygen utilizations.
3. Based on the WN modeling method, the multiobjective optimization immune algorithm (MOIA) is employed to optimize the network structure and initial state of the original WN to improve the accuracy of the WN model. In order to enhance the capability of model in learning from dynamic behavior of the DIR-SOFC and system, the improved SRWN is used. To implement the on-line model identification, the quadratic function with forgetting factor is taken as the objective function, and parameter updating formulas for the SRWN are deduced (including the updating formulas for dilation and translation parameters and iterative updating formulas for weights). Then, the convergence of the parameter updating algorithm is proved, and the adaptive law for the learning rates of parameters is proposed. Simulation results show that the IOWN and the SRWN (using objective function with forgetting factor) both succeed in modeling DIR-SOFC with relatively high accuracy. Further, the SRWN has better on-line learning ability.
4. According to the conservation law of mass and energy and empirical formulas, models of components (combustor, compressor, turbine, heat exchanger) of DIR-SOFC system are established. The sub-modules for these component models are constructed in SIMULINK. Based on component modules, a 190kW DIR-SOFC generation system model is also constructed in SIMULINK. Several operating cases are set up to perform simulation experiments via the system model. Dynamic performances of the DIR-SOFC system under different conditions are obtained. Influences of different variables on system performances are analyzed. The manipulated variables (inlet fuel and air flow rates and added fuel flow rate for the combustor) are determined to control operating states and output performance of the generation system (stack temperature, fuel utilization and system output power). The typical intelligent control methods, fuzzy control and predictive control, are employed to design the control strategy for the DIR-SOFC generation system. Improved non-uniform membership functions for fuzzy variables, T-S fuzzy rules and weighted average defuzzification are applied in the fuzzy control algorithm. In the predictive control, the SRWN model is taken as the predictive model for the DIR-SOFC system. The simulation experiments on the DIR-SOFC system for on-line modeling and prediction are performed. The results show better on-line identification ability, adaptability and relatively high predictive accuracy of the SRWN model for the DIR-SOFC system. In order to show the advantage of the SRWN based predictive control method, the IOWN model is used as a substitute for the SRWN model in the predictive control. The simulation experiment results for control show that the SRWN based predictive controller generates smaller overshoot and shorter setting time compared to the fuzzy controller and IOWN based predictive controller. Further, the SRWN based predictive controller can also track the set points of control variables with high precision in a speedy and stable manner.
引文
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