燃煤锅炉脱硝系统大范围变工况运行的负荷适应性研究

In 2015, the state put forward the ultra-low emission requirements for NOx in coal-fired power plants. At the same time, in the process of energy structure transformation, new energy power need access to power grid and this put forward operational flexibility requirements for coal-fired units. When the unit load is changing fast and widely, how can it meet the ultra-low emission requirements at the same time? This is a serious problem facing coal-fired power plants. This project is aimed at the load adaptability of the denitrification system of coal-fired units under wide range of variable operating conditions. Firstly, the mechanism model of SCR denitrification system is simplified, the complexity is reduced while the dynamic characteristics are retained. The data driven model is used to correct the deviation between the mechanism model and the actual system, and the whole operating condition dynamic hybrid model is constructed to describe the characteristics of SCR denitrification system accurately. Aiming at the strong coupling problem between the denitrification system and the boiler combustion condition, the data driven modelling method is used to construct the intelligent feedforward controller, so that the denitrification system can follow the change of the operating condition in time. Considering the NOx emission, ammonia escape and economic cost, multi-objective optimization index is constructed. Combined with the nonlinear model predictive control idea, the multi-objective optimization control strategy of denitrification system is constructed to realize the load adaptability and accurate control of denitrification system. The research results of this project are in line with the national energy-saving emission reduction and ultra-low emission policy requirements, which have important theoretical significance and application value.

2015年国家对燃煤电站的NOx提出了超低排放要求，同时，在能源结构转型过程中，新能源电力规模化接入电网对燃煤机组提出了运行灵活性要求。机组如何能在快速深度变负荷的同时达到超低排放要求，是燃煤电站面临的严峻问题。本项目针对燃煤机组脱硝系统大范围变工况运行的负荷适应性问题开展研究。首先，对SCR脱硝系统机理模型进行简化，在保留动态特性的同时降低复杂度，采用数据模型修正机理模型与实际系统间的偏差，并构建全工况混合动态模型来准确描述SCR脱硝系统特性；针对脱硝系统与锅炉燃烧工况的强耦合问题，采用数据建模方法构建智能前馈控制器，使脱硝系统及时跟随机组的工况变化；兼顾出口NOx、氨逃逸和经济成本建立多目标优化指标，结合非线性模型预测控制思想构建脱硝系统全工况多目标优化控制策略，实现脱硝系统的负荷适应性和准确控制。此项目的研究成果符合国家节能减排和超低排放的政策要求，具有重要的理论意义和应用价值。

新能源电力规模化接入电网对燃煤电站提出了灵活运行的要求，燃烧产生的NOx浓度会随着机组工况变化而变化，如何在机组快速深度变负荷的同时满足超低排放要求，是燃煤电站面临的严峻问题。本项目考虑脱硝系统与锅炉燃烧的耦合关系，研究SCR脱硝系统的全工况建模和优化控制，实现脱硝系统大范围变工况运行时的超低排放。首先，对SCR脱硝系统的机理模型进行了简化，模型具备动态性能，但存在因简化带来的准确性损失；研究偏最小二乘支持向量机（LSSVM）算法来建立动态数据模型，为了提高模型精度，采用数据模型来修正机理模型与实际系统之间的偏差，提出了一种机理+数据的混合建模方法，准确地进行了SCR特性的全工况动态建模。针对脱硝系统与锅炉燃烧工况的强耦合问题，对炉内NOx生成过程进行数据建模，利用锅炉侧输入参数实时预测SCR入口NOx参数，从而构建智能前馈信号，在变工况时及时对脱硝系统喷氨量进行调节；在控制策略研究中采用非线性模型预测控制，兼顾出口NOx、氨逃逸和经济成本建立多目标优化指标，解决机组大范围变工况时的脱硝系统多目标优化控制问题。项目研究过程中，共发表学术论文5篇，其中SCI收录的学术论文3篇，申请发明专利1项，培养硕士生2人，完成了项目的预期目标与研究内容。项目所提出的混合建模和多目标优化控制策略除了在燃煤机组脱硝系统中可以应用，也可以推广到其他复杂工业过程，具有重要的理论意义和应用价值。