变负荷工况下NOx排放量预测控制

doi:10.16085/j.issn.1000-6613.2017-0716

化工进展 ›› 2018, Vol. 37 ›› Issue (01): 343-349.DOI: 10.16085/j.issn.1000-6613.2017-0716

变负荷工况下NO_x排放量预测控制

唐振浩, 张海洋, 曹生现

东北电力大学自动化工程学院, 吉林吉林 132012

收稿日期:2017-04-20 修回日期:2017-07-11 出版日期:2018-01-05 发布日期:2018-01-05
通讯作者: 唐振浩(1985-),男,博士,副教授,主要研究方向为发电设备智能建模、优化与控制。
作者简介:唐振浩(1985-),男,博士,副教授,主要研究方向为发电设备智能建模、优化与控制。E-mail:tangzhenhao@neepu.edu.cn。
基金资助:
国家自然科学基金（61503072、71402021）及吉林省科学技术基金（20166009）项目。

Model predictive control of NO_x emission under variable load condition

TANG Zhenhao, ZHANG Haiyang, CAO Shengxian

School of Automation Engineering, Northeast Electric Power University, Jilin 132012, Jilin, China

Received:2017-04-20 Revised:2017-07-11 Online:2018-01-05 Published:2018-01-05

摘要/Abstract

摘要： NO_x是火电厂排放的主要污染物之一，降低NO_x的排放是火电厂面临的主要问题。针对火电厂变负荷工况下的NO_x排放量最小化问题，本文提出了一种基于最小二乘支持向量机（LSSVM）的非线性模型预测控制算法。根据电站锅炉实际历史数据建立锅炉负荷预测模型和NO_x排放预测模型，并以交叉验证的方法优化模型参数，从而获得高精度模型。在此基础上以NO_x的排放量最小为优化目标，考虑锅炉负荷约束，构建锅炉燃烧优化模型。采用差分进化算法求解优化模型得到控制参数的最优设定值。为了验证本文提出算法的有效性，采用实际生产数据进行实验。实验结果表明本方法能够在变负荷工况下有效降低NO_x排放量，在不增加电厂改造成本上，为电厂提供了有效的控制手段，具有一定应用前景。

关键词: 煤燃烧, 优化, 氮氧化物, 差分算法, 最小二乘支持向量机, 模型预测控制

Abstract: NO_x is one of the main pollutants for coal-fired power plant emissions. The main problem for the plants today is reducing NO_x emission. A nonlinear model predictive control method based on least square support vector machine (LSSVM)is proposed in this paper to solve the boiler NO_x emission minimization problem considering varying load in coal-fired power plants. The boiler load model and NO_x emissions model are constructed based on practical data. And then, the model parameters can be optimized by cross validation to obtain accuracy models. Based on these models, the boiler combustion optimization model is constructed. The optimization model aiming at minimizing the NO_x emission considers the boiler load as a constraint. This optimization model is solved to obtain the optimal control variable settings by different evolution (DE)algorithm. To testify the effectiveness of the proposed approach, the experiments based on real operational data are designed. The experiments results illustrate that the proposed method could reduce NO_x emissions effectively under varying load. It provides an effective means at no additional cost and has a certain application prospect.

Key words: coal combustion, optimization, nitrogen oxide, differential evolution algorithm, least squares support vector, model-predictive control

中图分类号:

TK224

唐振浩, 张海洋, 曹生现. 变负荷工况下NO_x排放量预测控制[J]. 化工进展, 2018, 37(01): 343-349.

TANG Zhenhao, ZHANG Haiyang, CAO Shengxian. Model predictive control of NO_x emission under variable load condition[J]. Chemical Industry and Engineering Progress, 2018, 37(01): 343-349.

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变负荷工况下NO_x排放量预测控制

Model predictive control of NO_x emission under variable load condition

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变负荷工况下NOx排放量预测控制

Model predictive control of NOx emission under variable load condition

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变负荷工况下NO_x排放量预测控制

Model predictive control of NO_x emission under variable load condition