基于Flownex®仿真环境和人工智能的燃煤锅炉控制性能优化

L. V. D. Westhuizen, I. Gorlach
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引用次数: 0

摘要

可再生能源、抽水蓄能电厂和联合循环燃气轮机的内在可变性意味着,南非设计用于连续基本负荷发电的燃煤电厂现在必须用于可变负荷。这对这些工厂的整体效率和预期寿命有负面影响。因此,挑战在于平衡网络需求与电站运行、热效率、可用性和延长的电厂预期寿命之间的关系。当前的研究重点是通过对锅炉子系统的暂态建模来监测和优化锅炉运行和控制的效率。Flownex®仿真环境用于模拟通用锅炉和锅炉控制系统,以模拟热流体过程和关键锅炉控制器。建立的模型基于工厂数据进行评估,然后通过PID控制器和机器学习算法进行优化。对于选定的控制器,如:锅炉负荷控制和蒸汽压力控制,机器学习算法获得的过程参数优于PID控制器。附加关键词:发电,锅炉控制,锅炉建模。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Optimisation of Coal-fired Boiler Control using Flownex® Simulation Environment and AI
ABSTRACT The inherent variability of renewable energy sources, pump storage plants and combined cycle gas turbines implies that coal-fired plants designed for continuous base load generation in South Africa must now be used for variable load. This has a negative effect on the overall efficiency and life expectancy of these plants. The challenge is, therefore, to balance the network demands with the power station operation, its thermal efficiency, availability and extended plant life expectancy. The focus of the current research is to monitor and optimise the efficiency of the boiler operation and control through modelling of the boiler subsystems during transient states. Flownex® Simulation Environment was used to model a generic boiler and a boiler control system in order to simulate thermo-fluid processes and critical boiler controllers. The developed model was evaluated based on plant data and optimised afterwards by means of PID controllers and Machine Learning algorithms. The process parameters obtained from the Machine Learning algorithms outperform that of the PID controllers for the selected controllers, such as: boiler load control and steam pressure control. Additional keywords: Power generation, boiler control, boiler modelling.
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