基于计算流体力学和多目标遗传算法的气液搅拌反应器模拟与优化

发布时间：2018-03-25 22:20

本文选题：多目标优化　切入点：多相流　出处：《浙江大学》2017年硕士论文

【摘要】：气液搅拌反应器因具有操作灵活性强、传质效果好、混合效率高等优点在过程工业中广泛应用。反应器结构是影响内部物料流动、混合、传质及反应的重要因素,对气液搅拌反应器结构进行优化意义重大。早期对气液搅拌釜的优化研究依赖于实验手段,测量方法受限,需反复试验,太过耗时且成本较高。随着计算流体力学(computational fluid dynamics,CFD)技术的发展,能快速而相对准确地获取反应器内部详细的流场信息。然而基于CFD的优化过程通常只考虑单参数变化,涉及多个参数则需要通过大量的模拟才能筛选出较优结果,加上多相体系固有的复杂性,导致计算量巨大且只能得到局部最优解。多目标遗传算法(multi-objective evolutionary algorithm,MOEA)具有全局优化、并行搜索、快速收敛等特点。本文针对上述问题,提出了将CFD技术与优化算法相结合的解决方法。以双层桨气液搅拌反应器为例进行结构优化,验证了该方法的可行性和有效性。主要工作与研究结果如下:(1)基于实验与CFD分析,建立了一种适用于气液搅拌反应器设计的多目标优化方法。借助双电导电极探针和扭矩测量等实验技术验证CFD模型,并在MATLAB平台上整合CFD分析模块和优化算法模块,引入参数化建模和自动网格生成技术,利用CFD模拟获取反应器内部流场信息,以此指导快速非支配排序遗传算法(non-dominated sorting genetic algorithm,NSGA-Ⅱ)在庞大的求解空间中高效并行寻优。通过创建模块接口,实现全自动优化过程,可以显著地减少计算量,获取全局最优解。(2)在转速300 rpm,表观气速0.02 m/s的空气-水体系中,采用均一气泡尺寸假设,将多目标优化方法应用于双层桨气液搅拌釜的优化,从而实现节能和良好的气体分散。首先建立以桨叶结构参数为优化变量,以最大气含率和最小搅拌功率为目标函数的优化命题,利用CFD和NSGA-Ⅱ算法耦合求解,得到了PCBDT-PTD(下层斜凹叶圆盘涡轮桨-上层下压斜叶桨)优化桨组合。随后探讨了桨组合类型和设计变量对目标函数的影响,发现上层桨为上翻斜叶桨(PTU)时气体分布效果较差,为PTD时气体分散性能最好,随着桨叶倾斜角度增大,气体分布更均匀,搅拌功率先增加,待倾角大于90°后逐渐减小;下层桨为凹叶桨时载气性能良好,凹叶片的长径比增大,载气性能提高,而叶片切角越大,搅拌功率越低。最后考察了优化结果的可靠性,测得优化桨组合的气含率较高且沿轴向均匀分布,明显改善了两桨之间的气体分散状况。优化后能耗大幅降低,较标准的RT-RT(双层六直叶圆盘涡轮桨)组合至少降低了 25%。(3)针对反应器内部气泡大小分布不均的问题,引入了关联湍流耗散率与气泡直径的气泡尺寸模型,在模型验证的基础上,对双层桨气液搅拌反应器进行多目标优化。首先以桨叶结构参数为优化变量,以最大气液比相界面积和最小搅拌功率为目标建立优化命题,得到了 PCBDT-PTU(斜凹叶圆盘涡轮桨-上翻斜叶桨)和PCBDT-PTD(斜凹叶圆盘涡轮桨-下压斜叶桨)两种优化桨组合。然后利用不同桨型揭示了反应器内部气泡尺寸分布规律,阐明了桨组合类型对目标函数的影响。研究发现,叶轮区的气泡尺寸沿着排出流方向先变小后逐渐变大,在循环区和液面附近气泡相对较大。此外,PCBDT-PTU优化桨组合的局部相界面积峰值最高,而PCBDT-PTD优化桨组合的相界面积分布最均匀,均能在低功耗下实现高效传质。最后,验证了优化结果的准确性,实验测得PCBDT-PTU优化桨组合的氧传质系数接近RT-RT标准桨组合的两倍,能耗较RT-RT降低了 29%。
[Abstract]:The gas-liquid stirred reactor because of its operating flexibility, good mass transfer effect, higher mixing efficiency has been widely used in process industry. The reactor structure is the internal material flow, mixing, mass transfer and reaction of the important factors on the structure of the reactor, gas-liquid mixing optimize significant. Early studies on Optimization of gas-liquid stirred tank depends on experimental method, measurement method is limited, the repeated test, too time-consuming and high cost. With the computational fluid dynamics (computational fluid, dynamics, CFD) technology development, can quickly and accurately obtain relatively detailed flow information within the reactor. However, the optimization of CFD is usually considered based on single parameter changes, involving many a parameter is required by a lot of simulations to optimal results, the inherent complexity of multiphase system and lead to the great amount of calculation, and can only get the local optimal solution . the multi-objective genetic algorithm (multi-objective evolutionary algorithm, MOEA) with global optimization, parallel search, fast convergence characteristics. Aiming at these problems, put forward the solution method of combining CFD technique and optimization algorithm. With double impeller gas-liquid stirred reactor with structure optimization, proves the method is feasible and effective. The main work and research results are as follows: (1) analysis and experiment based on CFD, established a suitable gas-liquid stirred reactor design of the multi-objective optimization method. By means of double electrode electric conductivity probe and torque measurement technique to validate the CFD model, and integrates the CFD analysis module and optimization module on the MATLAB platform. The introduction of parametric modeling and automatic mesh generation technology, simulation of internal flow field information acquisition reactor using CFD, so as to guide the fast non dominated sorting genetic algorithm (non-dominated sorting Genetic algorithm, NSGA- II) efficient parallel optimization in solving large space. By creating a module interface, realize the automatic optimization process, can significantly reduce the amount of computation to obtain the global optimal solution. (2) at the speed of 300 rpm, the air - water system of superficial gas velocity of 0.02 m/s, using uniform bubble the size optimization hypothesis, the multi-objective optimization method is applied to dual impeller gas-liquid stirred tank, in order to achieve energy saving gas and good dispersion. The first to establish the blade structure parameters as optimization variables, the minimum rate and stirring power containing the atmosphere as the optimization objective function, using CFD and NSGA- II algorithm coupled solution, get the PCBDT-PTD (pressure lower pitched blade paddle helical concave blade disk turbine blade under the upper impeller combination. Then optimization) to investigate the effect of impeller combination type and design variables on the objective function, found the upper impeller to turn on the cable when the gas Ye Jiang (PTU) The cloth effect is poor, PTD gas dispersion performance of the best, with the blade tilt angle increases, the gas distribution is more uniform, the stirring power increased, when the angle is larger than 90 degree decrease; the lower impeller is a concave blade when the carrier gas has good performance, concave blade length diameter ratio increases, improve the carrier gas performance, and leaves cutting angle is larger, the lower stirring power. Finally we investigated the reliability of the optimization results, high rate and uniform distribution along the axial direction with the measured optimization of impeller combination gas, improve the gas between the two pitch dispersion conditions. The optimized energy consumption is greatly reduced, compared with the standard RT-RT (double six straight blade turbine impeller) the combination of reduced at least 25%. (3) to solve the problem of bubble reactor size distribution are introduced into the model, the bubble size relation of turbulent dissipation rate and bubble diameter, on the basis of the model validation, the dual Impeller Stirred gas-liquid reactor for multiple targets The blade structure parameters optimization. Firstly as optimization variables, with the maximum specific gas-liquid interfacial area and the minimum mixing power optimization proposition as the goal, the PCBDT-PTU (oblique concave blade disk turbine blade double impeller) and PCBDT-PTD (oblique concave blade disk turbine blade under pressure impeller) two optimization different types of impeller impeller combination. Then reveals the reactor internal bubble size distribution, illustrates the influence of impeller combination type of objective function. The study found that the bubble size along the impeller discharge flow direction decreases first and then increases gradually, in the vicinity of the circulation zone and the bubble level is relatively large. In addition, local optimization PCBDT-PTU impeller combination of interfacial area and the highest peak, PCBDT-PTD optimization of impeller combination of interfacial area distribution is most uniform, which can achieve high efficiency of mass transfer in low power consumption. Finally, verifying the accuracy of the optimization results, the measured PCBDT-PTU optimization The oxygen mass transfer coefficient of the paddle combination is close to two times that of the RT-RT standard paddle combination, and the energy consumption is lower than that of RT-RT by 29%.

【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TQ052.5

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