原稳加热炉建模及参数优化算法的研究与实现

发布时间：2018-03-04 16:02

本文选题：BP神经网络　切入点：粒子群算法　出处：《东北石油大学》2015年硕士论文　论文类型：学位论文

【摘要】：在原油稳定工艺中原油加热的温度直接关系到不凝气、轻烃的产量,原稳加热炉在原油稳定过程中起着至关重要的作用,原稳加热炉出口温度的控制直接影响着产品的产量。因此,通过提高原油稳定工艺中原油温度控制的技术水平,从而提高原油稳定装置的产品率具有重要的意义。本文采用多学科相融合的方法,对神经网络与粒子群优化算法在原稳加热炉参数优化方面进行了深入研究。通过理论分析和实际研究,分析某原稳装置加热炉运行的现状和存在的问题,得出影响原稳加热炉出口温度的因素主要有以下几个方面:原油加热炉入口温度、原油进加热炉流量、原油进加热炉入口压力、原油加热炉燃料气流量、原油出加热炉压力、加热炉入口调节阀开度。通过分析和研究现有的神经网络算法的特点、优势以及适用范围,结合加热炉的工艺参数与研究目标之间的关系特点,选用BP神经网络算法建立加热炉的数学模型。然后,分析对比各种优化算法,最终确定采用粒子群优化算法对参数进行优化,采用求取目标函数最小值的办法寻找原稳加热炉工艺参数的最优参数组合。通过本文的研究,得到了原稳加热炉出口温度控制的最优参数组合,并进行现场应用,对比优化前与优化后变化,达到了良好的温度控制结果。该方法有效的提高了原油稳定加热炉的性能,解决了优化前原稳加热炉四条支管温差大、偏流结焦、温度波动大,调整频繁等问题。有利于操作人员有针对性的对参数进行调整,较大的提高了参数控制水平,为原油稳定加热炉工艺参数的调整提供重要的理论依据和实践指导。
[Abstract]:In crude oil stabilization process, the heating temperature of crude oil is directly related to the output of uncondensed gas and light hydrocarbon, and the original stable heating furnace plays an important role in the process of crude oil stabilization. The control of the outlet temperature of the original stable heating furnace directly affects the output of the product. Therefore, by improving the technical level of the crude oil temperature control in the crude oil stabilization process, Therefore, it is of great significance to improve the product rate of crude oil stabilizer. The neural network and particle swarm optimization (PSO) algorithm are studied in this paper. Through theoretical analysis and practical research, the present situation and existing problems of a primary stabilizer furnace are analyzed. The main factors affecting the outlet temperature of the original stable heating furnace are as follows: the inlet temperature of crude oil furnace, the flow rate of crude oil furnace, the inlet pressure of crude oil furnace, the fuel gas flow rate of crude oil heating furnace, the pressure of crude oil reheating furnace. Through the analysis and research on the characteristics, advantages and applicable range of the existing neural network algorithms, the characteristics of the relationship between the process parameters of the furnace and the research objectives are analyzed and studied. BP neural network algorithm is used to establish the mathematical model of reheating furnace. Then, after analyzing and comparing various optimization algorithms, the particle swarm optimization algorithm is used to optimize the parameters. The optimal parameter combination of the process parameters of the original stable reheating furnace is found by using the method of finding the minimum value of the objective function. Through the research in this paper, the optimal parameter combination of the outlet temperature control of the original stable heating furnace is obtained, and the field application is carried out. Compared with the changes before and after optimization, the results of temperature control are good. This method can effectively improve the performance of crude oil stable heating furnace, and solve the problem that the temperature difference of the four branches of the original stable heating furnace before optimization is large, the slanting coking, and the temperature fluctuating greatly. It is helpful for the operators to adjust the parameters, improve the control level of the parameters, and provide important theoretical basis and practical guidance for the adjustment of the process parameters of crude oil stable heating furnace.
【学位授予单位】：东北石油大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TE963

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