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[原创]大型电站燃煤锅炉运行优化技术研究及应用

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电力baby 发表于 2008-11-27 08:40:45 | 只看该作者 回帖奖励 |倒序浏览 |阅读模式
<p class="1" style="MARGIN: 5pt 0cm;"><font face="黑体" size="6"><strong>大型电站燃煤锅炉运行优化技术研究及应用</strong></font></p><p class="2" style="MARGIN: 0cm 0cm 5pt;"><span lang="EN-US" style="COLOR: black;"><a href="http://film.eptchina.cn/"><font size="5"><font face="仿宋_GB2312"><strong><span lang="EN-US"><span lang="EN-US">司风琪<span style="mso-spacerun: yes;">&nbsp; </span></span></span><span lang="EN-US"><span lang="EN-US">周建新<span style="mso-spacerun: yes;">&nbsp; </span></span></span><span lang="EN-US"><span lang="EN-US">徐治皋</span></span></strong></font></font></a><p></p></span></p><p class="3" style="MARGIN: 0cm 0cm 7pt;"><font face="楷体_GB2312"><strong>(东南大学能源与环境学院,江苏南京,<span lang="EN-US">210096</span>)</strong></font></p><p class="4" style="MARGIN: 0cm 0cm 0pt;"><strong><span style="FONT-FAMILY: 黑体;">摘要:</span><font face="宋体">提出了一种锅炉运行优化的系统框架,并着重对建模方法和优化算法进行了讨论。对常规在线支持向量机方法进行了改进,提出了新的样本剔除与学习规则,并与常规支持向量机模型相结合,形成了一种自适应建模方法,以适应煤质与锅炉实际运行工况的变化。对多目标优化算法进行讨论,并介绍了<span lang="EN-US">NSGA II</span>遗传算法在锅炉优化中的应用。以某大型电站锅炉为对象,对本文方法进行了应用研究,所建模型的趋势分析和测试结果均表明了本文算法的正确性,所提出的优化模型以<span lang="EN-US">NO<sub>x</sub></span>为目标函数,综合考虑了锅炉运行经济性和安全性等约束条件,优化结果表明通过运行参数的优化调整可有效降低锅炉污染物的排放。</font></strong></p><p class="5" style="MARGIN: 6pt 0cm 10pt;"><font size="2"><strong><span style="FONT-FAMILY: 黑体; mso-ascii-font-family: 宋体;">关键词:</span><span lang="EN-US"><a href="http://bbs.eptchina.cn/index.asp"><span lang="EN-US"><span lang="EN-US"><font face="宋体">燃煤锅炉</font></span></span></a></span><font face="宋体">;优化;模型;算法</font></strong></font></p><p class="6" style="MARGIN: 0cm 0cm 0pt;"><span lang="EN-US" style="COLOR: black;"><font size="5"><font face="Times New Roman"><strong>Study on Boiler Operation Optimization with Applications in Coal-fired Power Plant<p></p></strong></font></font></span></p><p class="7" style="MARGIN: auto 0cm 0pt;"><strong><span lang="EN-GB" style="COLOR: black; mso-ansi-language: EN-GB;"><font face="Times New Roman">Si Feng-qi</font></span><span style="COLOR: black; FONT-FAMILY: 宋体; mso-hansi-font-family: 'Times New Roman'; mso-ansi-language: EN-GB; mso-ascii-font-family: 'Times New Roman';">,</span><span lang="EN-GB" style="COLOR: black; mso-ansi-language: EN-GB;"><font face="Times New Roman">Zhou Jian-xin</font></span><span style="COLOR: black; FONT-FAMILY: 宋体; mso-hansi-font-family: 'Times New Roman'; mso-ansi-language: EN-GB; mso-ascii-font-family: 'Times New Roman';">,</span><span lang="EN-GB" style="COLOR: black; mso-ansi-language: EN-GB;"><font face="Times New Roman">Xu Zhi-gao<p></p></font></span></strong></p><p class="MsoNormalIndent" align="center" style="MARGIN: 0cm 0cm 0pt; TEXT-ALIGN: center; mso-char-indent-count: 0;"><span><strong><font face="Times New Roman">(School of Energy and Environment</font>,<font face="Times New Roman">Southeast University</font>,<font face="Times New Roman">Nanjing 210096, China)<p></p></font></strong></span></p><p class="MsoNormal" style="MARGIN: 0cm 0cm 0pt; mso-layout-grid-align: none;"><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="COLOR: black; mso-bidi-font-size: 10.5pt;"><p><font face="Times New Roman">&nbsp;</font></p></span></b></p><p class="MsoNormal" style="MARGIN: 0cm 0cm 0pt; mso-layout-grid-align: none;"><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="COLOR: black; mso-bidi-font-size: 10.5pt;"><font face="Times New Roman">ABSTRACT</font></span></b><b style="mso-bidi-font-weight: normal;"><span style="COLOR: black; FONT-FAMILY: 宋体; mso-bidi-font-size: 10.5pt; mso-hansi-font-family: 'Times New Roman'; mso-ascii-font-family: 'Times New Roman';">:</span></b><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="COLOR: black; mso-bidi-font-size: 10.5pt;"><font face="Times New Roman">A new boiler operation optimization system, based on a new adaptive modeling method and genetic algorithm, was proposed in this paper. An improved support vector regression method was given with a modified criterion for selection of the unwanted trained sample while continuously training of prediction model. The improved method together with a conventional SVR model was used for adaptively prediction, such that the deviation caused from the variation of coal quality and different operating points can be effectively compensated. The multi-objective optimization algorithm, NAGA II, is included in the proposed optimization scheme. The scheme is further illustrated in a real process of a coal-fired boiler. The analysis and test results of the parametric models were presented. An optimization model, including objective function and constraints, was also given in this paper, and the optimization results revealed the validity of the proposed method.<p></p></font></span></b></p><p class="8" style="MARGIN: 6pt 0cm 10pt;"><span lang="EN-US" style="COLOR: black;"><strong><font face="Times New Roman">KEY WORDS:coal-fired boiler; optimization; model; algorithm<p></p></font></strong></span></p>
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