摘要
随着中国经济的高速发展,国家大力发展城镇化建设,以及人们对生活水平的要求不断提高,如何让城镇居民的供暖质量提高越来越受到人们的关注。目前,我国北方主要城市的供暖仍然以煤为主要燃料,不但每年要消耗大量的煤炭资源,同时也造成严重的环境污染。在世界能源日益紧张、现有常规能源面临枯竭、煤炭价格逐年上涨的背景下,采用成本低、环保性好、具有“零排放”特点的生物质燃料作为供暖锅炉的燃料已成为必然的趋势。因此,研究和开发以生物质为燃料、具有优良控制水平的集中供暖锅炉,对节省宝贵的煤炭资源、有效的减少温室气体排放、保护人类的生存环境,具有重要的经济意义和环保意义。
本论文是以国家星火计划项目(2007EA105008)为依托,对现有生物质供暖锅炉结构进行改造设计,并对其喂料及其配风进行自动控制研究和设计,以期达到既节省燃料,又提高供热效能的目的。本文在对生物质成型燃料燃烧特性分析的基础上,对燃料燃烧的发热量进行了分析和计算,着重研究了配风对燃烧速度的影响,并对送料机进行了选型计算；给出了生物质集中供暖锅炉喂料与配风控制系统的总体设计方案；对控制系统下位机PLC的硬件与软件进行了设计；根据锅炉燃烧系统数学建模困难、热传递延时大且不稳定等特点,首次采用RBF(Radial Basis Function)神经网络作为控制器的算法,将一种融合了聚类算法、正交最小二乘学习算法及梯度下降法的递增式RBF神经网络算法应用于该控制系统的建模,并运用MATLAB进行了仿真,结果表明,所选用的方法及所建立的模型适用于生物质集中供暖锅炉喂料及配风控制要求。本论文研究结果对今后生物质供暖锅炉的自动化技术研究与开发具有一定的参考价值。
With China's rapid economic development, urbanization countries develop and people's living standard continues to improve, and how to improve the heating quality of urban residents will be attract more and more people's attention.Currently, the main cities in North China are mainly using coal-burning heating still, and this will consume a large amount of coal resources. Under the background of the increasing tension in the world's energy, facing with the existing conventional energy depletion and the rising coal prices each year, using biomass briquettes of this low-cost, efficient fuel for heating has become the inevitable trend; what's more,conventional energy use produces a lot of"CO2", a huge threat to the global environment, and therefore, biomass briquettes "zero discharge" of advantages will help alleviate the damage to the environment of human.
This article is based on "National Spark Program (2007EA105008)" scientific research projects to study the basis of existing transformation of biomass heating boilers, which is able to feed materials with air volume control, to not only save fuel, but also improve the heating efficiency. Through the analysis of the distribution on the combustion velocity of the wind in the biomass briquette combustion, the heat of fuel combustion are calculated, and the selection of feeding were calculated; the central heating boiler biomassfeed and air distribution control system design program is provided; design the hardware and software of the lower computer PLC control system. According to the difficulties in the mathematical model of the boiler combustion system and the large and unsteady heat transfer delay characteristics, this paper uses the RBF(Radial Basis Function) neural network as a controller algorithm; based on the learning algorithm in the study, the Fusion of the clustering algorithm, orthogonal least squares learning algorithm and the incremental gradient descent method are applied to the RBF neural networkcontrol system. Verify the feasibility of this incremental algorithm through the simulation by using MATLAB.
There has a lot of research focusing on biomass heating boilers in the country.This is the first time to utilize RBF neural network to feed the biomass boiler heating system with air control, and to train the neural network. The simulation results have achieved the expected goal, which provide automated operationgood guidance and reference in the future of biomass heating boilers.
引文
[1]马孝琴.生物质(秸秆)成型燃料燃烧动力学特性及液压秸秆成型机改进设计研究.河南农业大学博士学位论文,2002
[2]王补宣,工程传热传质学[M].科学出版社,1998(5)
[3]马孝琴.秸秆着火及燃烧、燃烬特性的试验研究[J].河南形技术师范学院学报,2002,16(2)：69-73
[4]邵国军,吴强.循环流化床锅炉对燃料的适应性[M].东北电力技术,2002(10)：69-71
[5]朱永吉,变频器在锅炉风机中的节能应用[J].节能技术,2003(14)：38-40
[6]Powell M J D, Radial basis functions for multivariable interpolations:a review[J].In IMA Conference on Algrorithms for the Approximation of Functions and Data. RMCS, Shrivenham UK,1985:141-170
[7]Dong-Ho Shin and Youdan Kim,Reconfigurable Flight Control System Design using adaptive neural networks,IEEE Transactions on control systems technology,2004,12(1):87-102
[8]B.S.Kim and A.1.Calise, Nonlinear adaptive flight using neural networke, J.Guidance,Control,Dynamitic,1997,20(1):26-33
[9]Moody J, Darken C,Learning with localized receptive fields, In Sejnowski T Touretzky D, Hinton G, editors, Connectionist Models Summer School Carnegie Mellon University, 1988
[10]工学雷,邵惠鹤,李亚芬.一种径向基函数神经网络在线训练算法及其在非线性控制中的应用[J].信息与控制,2001(3)：249-253
[11]周开利,邓春晖,MATLAB基础及其应用教程[M].北京大学出版社,2007(1)：1-10
[12]程备久,卢向阳,蒋立科.生物质能学[M].化学工业出版社,2007
[13]刘圣勇,赵迎芳,张百良.生物质成型燃料燃烧理论分析.能源研究与利用[M],2002(6)：26-28
[14]李岱森.简明供暖设计手册.中国建筑工业出版社[M]：588-592
[15]林鹏.秸秆类生物质层燃特性的试验研究.上海：上海交通大学硕士学位论文.2008
[16]姚向君,田宜水.生物质能资源清洁转化利用技术.化学工业出版社：2004
[17]张百良,李保谦.HPB-Ⅱ型生物质成型机的应用研究.太阳能学报,1999,20(3)：234-237
[18]Bernberger I.Decentralized Biomass Combustion State of the Art and Future Development.Biomass and Bioenergy.1998,14(1):91-94
[19]Alferov S.A. Relationships in the Compression of Straw. Selkhozmashina 1957,(3):8-15.
[20]NIEM NEN M, VEDONEN K. Bioenergy T echnologies A pplied In EU Countries in VTT Project Report 2005(43)
[21]BEND IXEN K. USC Technology In CCT Boilers Applying Biomass Cofiring in Powergen Europe 2005,Milano
[22]SATHISH THIRUVENGADACHARI, B.E., Mechanical Engineering Anna University, Chennai, India,2001, Experimental and neural network-based model for human-machine systems reliability
[23]姜玉春,吴红燕.PID控制器参数的整定[J].莱钢科技,2006(2)
[24]吴志敏.基于新型PID神经网络的自适应控制系统研究[J].科学技术与工程,2008(1).
[25]杨亚辉,苏玉鑫,张立科,陈孝栋.机器人系统同步协调自适应控制[J].系统仿真学报,2008,(01)
[26]TILLMAND A. Biomass cofiring:The technology, the experience, the combustion consequences.Biomass and Bioenergy,2000,19(6):365-384
[27]ASADULLAH M,MIYAZAWA T,ITO S,KUNIMORI K,TOMISHIGE K. Demonstration of real biomass gasification drastically promotedby effective catalyst.Appl Catal A,2003, 246(1):103-116
[28]谢军,吴创之,阴秀丽,陈平.流化床中二次风对生物质气化的影响[A]2005年中国生物质能技术与可持续发展研讨会论文集[C],2005
[29]邓立新.生物质的洁净转化和综合利用[J]化学教育,2004,(2)
[30]BAXTER L. Biomass-coal co-combustion:Opportunity for affordable renewable energy.Fuel,2005,84(10):1295-1302
[31]王晶,李玉兰,蔡自兴,沈理.基于遗传算法的模糊系统优化设计方法,控制理论与应用.1999
[32]张明,袁益超,刘聿拯.生物质直接燃烧技术的发展研究能.源研究与信息,2005,21(1)：15-20
[33]J. Monaco, D. Ward, R. Baryon and R. Bird.Implementation and flight test assessment of an adaptive, reconfigurable fight control system. AIAA97-3738,1997
[34]Grover P.D,Mishra S.K.Proceedings of the International Workshop on Biomass Briqueting.New Delhi.India,1996
[35]徐丽娜.神经网络控制[M].电子工业出版社,2003：21-28
[36]J. Monaco, D. Ward, R. Barron and R. Bird.Implementation and flight testassessment of an adaptive, reconfigurable fight control system. AIAA97-3738,1997
[37]张百良,李保谦.HPB-I型生物质成型机的试验研究.农业工程学报,1999,15(3)：133-136
[38]张锡虎,住宅供暖方式的发展动态.北京市建筑设计标准办公室,2003(30)
[39]周藤.现行住宅采暖技术综述.科技,2004,(10)：124-128
[40]R. Rotenbach and T. Linn, Nanofiltration bioreactor and nanofiltration/crystallization, two examples forthe potential of nanofiltration in wastewater treat-ment. Third Gulf Water Conference, Muscat, Oman,1997
[41]Abulbasher M. Shahalam, Ali Al-Harthy, Alaa Al-Zawhry,Feed water pretreatment in RO systems:unit processes in the Middle East,Department of Civil Engineering, Sultan Qaboos University, PO Box 33,June 2002
[42]马孝琴.秸秆着火及燃烧燃烬特性的试验研究.河南职业技术师范学院学报,2002,16(2)：69-73
[43]吴创之.欧洲生物质能利用的研究现状及特点(J).新能源,1999(3)：30-35
[44]张广全,董建勋,肖艳.秸杆直燃发电技术的特点和工艺探索.东北电力技术,2007(8)：40
[45]NIEM NEN M, VEDONEN K. Bioenergy T echnologies A pplied In EU Countriesin VTT Project Report 2005(43)
[46]BEND IXEN K. USC Technology In CCT Boilers Applying Biomass Cofiringin Powergen Europe 2005,Milano
[47]邵国军,吴强.循环流化床锅炉对燃料的适应性[M].东北电力技术,2002(10)：39-41
[48]祝贺,魏毅立,田海等.热水锅炉供暖的动态自动调节[J].暖通空调,2003,(3)：127-129
[49]俞金寿.工业过程先进控制[M].北京：中国石化出版社,2002
[50]魏海坤.神经网络结构设计的理论与方法[M].北京：国防工业出版社,2005
[51]王旭东,邵惠鹤.RBF神经网络理论及其在控制中的应用[J].信息与控,1997：272-284
[52]白兆兴.生物质锅炉技术现状与存在问题[M].工业锅炉,2008,(2)：29-32
[53]工业锅炉基础知识及理论[M].北京：化学工业出版社,2008
[54]王杰,吴强.锅炉烟气含氧量的混合Fuzzy-PI+PD控制.锅炉技术[J],2009,(11)
[55]李燕东.生物质成型颗粒燃料气化锅炉的设计研究.机电产品开发与创新,2009,(4)
[56]济南引进丹麦BWE公司的130T生物质锅炉设计说明书和使用说明书.中国电力联盟,2009,(6)
[57]陆耀庆.供暖通风设计手册.中国建筑工业出版社,1987：628-636