煤泥浮选过程模型仿真及控制研究
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摘要
随着原煤入洗量的增大、入洗原煤的煤质变化波动较大以及市场对煤炭产品品质要求日益严格的形势下，作为煤炭洗选过程的主要环节，浮选过程的自动化水平则越来越被人们重视。但是，在煤泥浮选自动化实施的过程中面临着一些难题，阻碍着浮选过程自动化发展的步伐。首先，煤泥浮选过程变量的检测设备和手段严重滞后于控制策略的研究；其次，缺乏有效实用的控制模型来指导浮选过程的自动控制；再次，虽然控制理论的研究已经十分成熟，但是真正应用于浮选过程的控制策略还不完善。因此，论文分别从煤泥浮选过程变量检测、浮选过程建模和浮选过程控制系统构建三个方面进行了研究。
论文是在选煤厂实际生产环境下进行研究的。
论文以薛湖选煤厂浮选设备浮选床及其配套设施为研究对象，针对该厂浮选过程中存在的实际问题进行系统分析，结合煤泥浮选过程控制变量分析，确定了以浮选入料灰分、浮选入料流量和浮选入料浓度为干扰变量；捕收剂和起泡剂的添加量为操作变量；尾矿灰分为被控变量的控制结构。
为了建立煤泥浮选过程的药剂添加量模型，制定了离线试验方案，在现场进行连续20天的采样，采样数据包括原煤灰分、浮选入料灰分、浮选入料流量、浮选入料浓度、浮选精煤和尾矿灰分、捕收剂和起泡剂的加药量。通过对数据的整理分析发现，在采样期间内，原煤灰分和浮选入料灰分波动大，并且保持着较好的相关性；浮选入料流量由于工艺流程的原因，波动范围较大；浮选入料浓度波动范围较小；浮选精煤灰分比要求指标都偏低并且保持在一个较好的水平；尾矿灰分一般偏低并且波动较大。最终确定了以满足尾矿灰分为主要目标。
利用原煤-1.4g/cm~3和+1.8g/cm~3两个密度级的基础灰分样品，配制灰分从6.58%到85%不同灰分级的煤浆样品，分别对两组样品在CIE推荐的（0/45）反射样品测量的标准照明和观察几何条件下进行图像采集。每组样品中不同灰分级的煤浆图像在表观上已经有了良好的区分度，并且随着灰分的增高，图像的灰度也呈现增大的趋势。对其中一组样品进行XRD分析，结果表明，随着煤浆样品灰分的升高，其高岭石的含量也相应增加，煤浆样品图像的灰度也相应增大，因此可以用煤浆样品图像的灰度来预测煤浆的灰分含量。通过原煤灰分和浮选入料灰分采样的数据的分析表明：原煤灰分与浮选入料的灰分保持着相同的变化趋势，因此可以建立原煤灰分和浮选入料灰分的预测模型，通过原煤灰分仪在线预测浮选入料的灰分。
搭建了煤泥浮选控制系统的硬件平台。通过建立的数据采集模块将原煤灰分仪的数据采集到浮选控制系统中，结合浮选入料的软测量模型实现浮选入料灰分的在线监测。改进了煤浆灰分的在线检测传感器，实现了尾矿灰分的在线检测。
应用光照模型，推导了图像灰度值和光强之间的模型，确定了煤浆图像灰度值的大小的主要影响参数是漫反射系数K a和镜面反射系数K S，这两个系数都与被拍摄物质的基本光学属性决定，验证了建立煤浆图像的灰度与煤浆灰分之间的软测量模型来实现煤浆灰分检测的可能性。
选取灰度图像的平均灰度值、方差、平滑度、偏度、能量、熵六个特征值作为煤浆灰度图像特征，对利用基础灰分样品得到的两组煤浆图片的灰度直方图进行分析表明，随着煤浆灰分增大，灰度直方图的平均灰度值、方差、平滑度不断增大，能量则随着灰分的增大不断减小，偏度和熵随着灰分的增大并没有呈现很强的规律性。对煤浆灰度图像的6个特征值进行相关性分析表明：除了灰度图像的偏度与实际灰分值之间的相关性不显著之外，其它5个灰度图像的特征值均与煤浆的实际灰分值之间存在着较强的相关关系。将灰度平均值、方差、平滑度、熵和能量这5个特征向量作为煤浆灰分BP神经网络训练模型的输入建立煤浆灰分的软测量模型。
将用最小二乘法得到的煤浆灰分预测模型和利用BP神经网络建立的模型的MSE和R值对比得到：利用BP神经网络得到的煤浆灰分的软测量模型的准确程度更高。但是对于浮选入料等灰分比较低的煤浆，该方法建立的软测量模型误差较大。建立了原煤灰分与浮选入料之间的关系模型，实现了浮选入料灰分的预测。
将浮选入料流量、浓度灰分和浮选尾矿灰分作为煤泥浮选药剂添加量模型的输入，捕收剂和起泡剂的药剂流量作为该模型的输出。采样得到的浮选入料流量、浓度灰分和浮选尾矿灰分与浮选药剂添加量之间的关系呈离散状态。对浮选药剂添加量模型的输出进行PCA分析表明：输入变量的前三个特征维的贡献率的总和超过了90%，故选取前三个特征维作为煤泥浮选药剂添加量模型的输入。利用采样得到的94组实验数据建立了煤泥浮选过程药剂添加量的GA-SVMR模型，误差分析表明：GA-SVMR的预测能力强于SVMR获得的预测模型。
在建立的GA-SVMR煤浆灰分预测模型的基础上建立的基于模型参考的模糊自适应控制系统，通过仿真表明设计的基于模型参考的模糊自适应控制系统，能够随着对象的变化，自适应地调整自身的结构参数，弥补模型预测中存在的偏差，因此能够有效地和预测模型相结合对系统实现较好的控制性能。
应用串口通讯协议，开发了基于COM口单向数据流的灰分数据采集模块，实现了灰分仪数据与控制系统数据的无缝衔接。构建了基于OPC协议的MATLAB与iFix接口通讯协议，实现了MATLAB与上位机之间的数据共享。
The floatation process is one of the main links in coal preparation process, its automationlevel in floatation process has been paid on more and more attention for the increase of raw coalquantity, fluctuation of raw coal quality, and stricter quality requirement of the market. However,some knotty problems in the automation process of coal slurry flotation are hindering thedevelopment of flotation automation. Firstly, the testing equipment and means for the variable inthe floatation process lag far behind the research on control strategy; secondly, there is noeffective and practical control module guiding the automatic control of flotation process; thirdly,the control strategy in the flotation process is not yet perfect while the research on control theoryis already very mature. Therefore, slurry flotation variable detection, process modeling andprocess control system construction are studied in this thesis.
This thesis is researched in the practical production environment of coal preparation plant.
Flotation bed from Xuehu coal preparation plant is taken as research object in this paper, thecontrol structure is determined where the ash, flow rate and concentration of the flotation feedare taken as disturbance variables, the dosage of collector and frother are taken as operationalvariables, the tailings ash is taken as controlled variable.
Off-line experimental scheme was established to build the reagent dosage model for coalslurry flotation process. A lasting20-day sampling is taken in the preparation plant and duringwhich the ash of raw coal and flotation feed are changeful and there is good correlative them; theash of flotation tailings remains lower and changes a lot. The ash of coal tailings is finally takenas main control object.
Image acquisition under the standard lighting for measurement of reflection samples andgeometric conditions for observation recommended by CIE is carried on the slurry samples withthe ash ranging from6.58%to85%and which are prepared by using basic ash samples of rawcoal whose density fraction is-1.4g/cm~3and+1.8g/cm~3. The image of slurry with differentash shows enough discrimination and the gray scale of the image grow with the ash increase.XRD analysis on one of the samples shows that the content of kaolinite and the gray scale of theimage increase with the ash of the slurry samples grows, thus gray scale of the slurry samplescan be used to predict the slurry ash. The ash of the raw coal and flotation feed remain the samechanging trend which is obtained from data analysis of samples. Therefore the prediction modelcan be established to predict the flotation feed ash online through that of raw coal.
The hardware platform for coal slime flotation control system is built. The data from theraw coal ash apparatus is collected and transported to the flotation control system through thedata acquisition module and applying these data to the soft sensor model, the online monitoring for flotation feed ash can be realized. The online detection of tailings ash is realized through theimproved online monitoring sensor for slurry ash.
The relation model of the image grey level and intensity of illumination is derived using theillumination model thus expressing the diffuse feflectivityK aand specularity factorKSwhich are all decided by the basic optical attributes of the shoot matter mainly influence the greylevel of the image, therefor the soft-sensing model for the slurry image grey level and ash isestablished for slurry ash monitoring.
The average gray level, variance, evenness, skewness, energy, entropy of the gray levelimage were taken as image gray features for slurry, the correlation analysis on the6featuresshows that: all the eigenvalue of the slurry gray level image except the skewness show obviouscorrelativity. Soft-sensing model for slurry ash is established taking average grey level, variance,evenness, energy, entropy as the input eigenvector for BP neural net training model of slurryash.The comparison between the MSE and R value of the slurry ash prediction model got byleast square method and the model established using BP neural net indicate that: the soft-sensingmodel for slurry ash using BP neural net owing higher accuracy. However the error of thissoft-sensing model is bigger for slurry with lower ash like flotation feed. The relation modelbetween the ash of raw coal and floatation feed is established for the predication on of flotationfeed ash.
The PCA analysis on the output of the flotation reagent dosage model shows that the firstthree feature components of the input variable contribute more than90%. The GA-SVMRreagent dosage model is established using the94set of sampled test data which shows that theprediction ability of GA-SVMR model is better than that of SVMR model.
The model-reference based adaptive fuzzy control system is established relying on theGA-SVMR predication model, the simulation shows that this control system can adjust itsstructure parameters adaptively. Thus better control performance can be realized by combiningthis control system with the prediction model.
The data acquisition module for ash relying on COM unidirectional data flow basing isdeveloped using serial communication protocol thus leading to the seamless connection of datafrom ash monitor and control system. The communication protocol of MATLAB based on OPCprotocol and ifix interface realizes the data sharing between MATLAB and upper monitor.
论文是在选煤厂实际生产环境下进行研究的。
论文以薛湖选煤厂浮选设备浮选床及其配套设施为研究对象，针对该厂浮选过程中存在的实际问题进行系统分析，结合煤泥浮选过程控制变量分析，确定了以浮选入料灰分、浮选入料流量和浮选入料浓度为干扰变量；捕收剂和起泡剂的添加量为操作变量；尾矿灰分为被控变量的控制结构。
为了建立煤泥浮选过程的药剂添加量模型，制定了离线试验方案，在现场进行连续20天的采样，采样数据包括原煤灰分、浮选入料灰分、浮选入料流量、浮选入料浓度、浮选精煤和尾矿灰分、捕收剂和起泡剂的加药量。通过对数据的整理分析发现，在采样期间内，原煤灰分和浮选入料灰分波动大，并且保持着较好的相关性；浮选入料流量由于工艺流程的原因，波动范围较大；浮选入料浓度波动范围较小；浮选精煤灰分比要求指标都偏低并且保持在一个较好的水平；尾矿灰分一般偏低并且波动较大。最终确定了以满足尾矿灰分为主要目标。
利用原煤-1.4g/cm~3和+1.8g/cm~3两个密度级的基础灰分样品，配制灰分从6.58%到85%不同灰分级的煤浆样品，分别对两组样品在CIE推荐的（0/45）反射样品测量的标准照明和观察几何条件下进行图像采集。每组样品中不同灰分级的煤浆图像在表观上已经有了良好的区分度，并且随着灰分的增高，图像的灰度也呈现增大的趋势。对其中一组样品进行XRD分析，结果表明，随着煤浆样品灰分的升高，其高岭石的含量也相应增加，煤浆样品图像的灰度也相应增大，因此可以用煤浆样品图像的灰度来预测煤浆的灰分含量。通过原煤灰分和浮选入料灰分采样的数据的分析表明：原煤灰分与浮选入料的灰分保持着相同的变化趋势，因此可以建立原煤灰分和浮选入料灰分的预测模型，通过原煤灰分仪在线预测浮选入料的灰分。
搭建了煤泥浮选控制系统的硬件平台。通过建立的数据采集模块将原煤灰分仪的数据采集到浮选控制系统中，结合浮选入料的软测量模型实现浮选入料灰分的在线监测。改进了煤浆灰分的在线检测传感器，实现了尾矿灰分的在线检测。
应用光照模型，推导了图像灰度值和光强之间的模型，确定了煤浆图像灰度值的大小的主要影响参数是漫反射系数K a和镜面反射系数K S，这两个系数都与被拍摄物质的基本光学属性决定，验证了建立煤浆图像的灰度与煤浆灰分之间的软测量模型来实现煤浆灰分检测的可能性。
选取灰度图像的平均灰度值、方差、平滑度、偏度、能量、熵六个特征值作为煤浆灰度图像特征，对利用基础灰分样品得到的两组煤浆图片的灰度直方图进行分析表明，随着煤浆灰分增大，灰度直方图的平均灰度值、方差、平滑度不断增大，能量则随着灰分的增大不断减小，偏度和熵随着灰分的增大并没有呈现很强的规律性。对煤浆灰度图像的6个特征值进行相关性分析表明：除了灰度图像的偏度与实际灰分值之间的相关性不显著之外，其它5个灰度图像的特征值均与煤浆的实际灰分值之间存在着较强的相关关系。将灰度平均值、方差、平滑度、熵和能量这5个特征向量作为煤浆灰分BP神经网络训练模型的输入建立煤浆灰分的软测量模型。
将用最小二乘法得到的煤浆灰分预测模型和利用BP神经网络建立的模型的MSE和R值对比得到：利用BP神经网络得到的煤浆灰分的软测量模型的准确程度更高。但是对于浮选入料等灰分比较低的煤浆，该方法建立的软测量模型误差较大。建立了原煤灰分与浮选入料之间的关系模型，实现了浮选入料灰分的预测。
将浮选入料流量、浓度灰分和浮选尾矿灰分作为煤泥浮选药剂添加量模型的输入，捕收剂和起泡剂的药剂流量作为该模型的输出。采样得到的浮选入料流量、浓度灰分和浮选尾矿灰分与浮选药剂添加量之间的关系呈离散状态。对浮选药剂添加量模型的输出进行PCA分析表明：输入变量的前三个特征维的贡献率的总和超过了90%，故选取前三个特征维作为煤泥浮选药剂添加量模型的输入。利用采样得到的94组实验数据建立了煤泥浮选过程药剂添加量的GA-SVMR模型，误差分析表明：GA-SVMR的预测能力强于SVMR获得的预测模型。
在建立的GA-SVMR煤浆灰分预测模型的基础上建立的基于模型参考的模糊自适应控制系统，通过仿真表明设计的基于模型参考的模糊自适应控制系统，能够随着对象的变化，自适应地调整自身的结构参数，弥补模型预测中存在的偏差，因此能够有效地和预测模型相结合对系统实现较好的控制性能。
应用串口通讯协议，开发了基于COM口单向数据流的灰分数据采集模块，实现了灰分仪数据与控制系统数据的无缝衔接。构建了基于OPC协议的MATLAB与iFix接口通讯协议，实现了MATLAB与上位机之间的数据共享。
The floatation process is one of the main links in coal preparation process, its automationlevel in floatation process has been paid on more and more attention for the increase of raw coalquantity, fluctuation of raw coal quality, and stricter quality requirement of the market. However,some knotty problems in the automation process of coal slurry flotation are hindering thedevelopment of flotation automation. Firstly, the testing equipment and means for the variable inthe floatation process lag far behind the research on control strategy; secondly, there is noeffective and practical control module guiding the automatic control of flotation process; thirdly,the control strategy in the flotation process is not yet perfect while the research on control theoryis already very mature. Therefore, slurry flotation variable detection, process modeling andprocess control system construction are studied in this thesis.
This thesis is researched in the practical production environment of coal preparation plant.
Flotation bed from Xuehu coal preparation plant is taken as research object in this paper, thecontrol structure is determined where the ash, flow rate and concentration of the flotation feedare taken as disturbance variables, the dosage of collector and frother are taken as operationalvariables, the tailings ash is taken as controlled variable.
Off-line experimental scheme was established to build the reagent dosage model for coalslurry flotation process. A lasting20-day sampling is taken in the preparation plant and duringwhich the ash of raw coal and flotation feed are changeful and there is good correlative them; theash of flotation tailings remains lower and changes a lot. The ash of coal tailings is finally takenas main control object.
Image acquisition under the standard lighting for measurement of reflection samples andgeometric conditions for observation recommended by CIE is carried on the slurry samples withthe ash ranging from6.58%to85%and which are prepared by using basic ash samples of rawcoal whose density fraction is-1.4g/cm~3and+1.8g/cm~3. The image of slurry with differentash shows enough discrimination and the gray scale of the image grow with the ash increase.XRD analysis on one of the samples shows that the content of kaolinite and the gray scale of theimage increase with the ash of the slurry samples grows, thus gray scale of the slurry samplescan be used to predict the slurry ash. The ash of the raw coal and flotation feed remain the samechanging trend which is obtained from data analysis of samples. Therefore the prediction modelcan be established to predict the flotation feed ash online through that of raw coal.
The hardware platform for coal slime flotation control system is built. The data from theraw coal ash apparatus is collected and transported to the flotation control system through thedata acquisition module and applying these data to the soft sensor model, the online monitoring for flotation feed ash can be realized. The online detection of tailings ash is realized through theimproved online monitoring sensor for slurry ash.
The relation model of the image grey level and intensity of illumination is derived using theillumination model thus expressing the diffuse feflectivityK aand specularity factorKSwhich are all decided by the basic optical attributes of the shoot matter mainly influence the greylevel of the image, therefor the soft-sensing model for the slurry image grey level and ash isestablished for slurry ash monitoring.
The average gray level, variance, evenness, skewness, energy, entropy of the gray levelimage were taken as image gray features for slurry, the correlation analysis on the6featuresshows that: all the eigenvalue of the slurry gray level image except the skewness show obviouscorrelativity. Soft-sensing model for slurry ash is established taking average grey level, variance,evenness, energy, entropy as the input eigenvector for BP neural net training model of slurryash.The comparison between the MSE and R value of the slurry ash prediction model got byleast square method and the model established using BP neural net indicate that: the soft-sensingmodel for slurry ash using BP neural net owing higher accuracy. However the error of thissoft-sensing model is bigger for slurry with lower ash like flotation feed. The relation modelbetween the ash of raw coal and floatation feed is established for the predication on of flotationfeed ash.
The PCA analysis on the output of the flotation reagent dosage model shows that the firstthree feature components of the input variable contribute more than90%. The GA-SVMRreagent dosage model is established using the94set of sampled test data which shows that theprediction ability of GA-SVMR model is better than that of SVMR model.
The model-reference based adaptive fuzzy control system is established relying on theGA-SVMR predication model, the simulation shows that this control system can adjust itsstructure parameters adaptively. Thus better control performance can be realized by combiningthis control system with the prediction model.
The data acquisition module for ash relying on COM unidirectional data flow basing isdeveloped using serial communication protocol thus leading to the seamless connection of datafrom ash monitor and control system. The communication protocol of MATLAB based on OPCprotocol and ifix interface realizes the data sharing between MATLAB and upper monitor.
引文
[1]黄晶,阙沛文.煤泥浮选生产过程智能控制系统[J].工业仪表与自动化装置,2003,6:38-40.
[2]谢广元.选矿学[M].江苏，徐州:中国矿业大学出版社,2005:427.
[3] Bouchard J, Desbiens A, del Villar R, et al. Column Flotation Simulation and Control: An Overview [J].Minerals Engineering,2009,22(6):519-529.
[4] O'Connor C T, Mills PJ T, Cilliers J J. Prediction of Large-Scale Column Flotation Cell Performance UsingPilot Plant Data [J]. The Chemical Engineering Journal and the Biochemical Engineering Journal,1995,59(1):1-6.
[5]樊民强.选煤数学模型与数据处理[M].北京:煤炭工业出版社,2004:2-3.
[6]庄劲武,张晓锋,张超.阶跃响应法在浮地交流系统绝缘故障定位中的应用初探[J].继电器,2004,(3):21-25.
[7]肖锋,李欣然,石吉银.阶跃响应法在小电流接地系统故障选线与测距中的应用[J].电力科学与技术学报,2008,(1):83-88.
[8]万春红,柬洪春,张东宁,等.水泥分解炉温度控制过程的阶跃响应建模与仿真[J].化工自动化及仪表,2011,(12):1420-1424.
[9]王可,李国平,李治,等.基于协整和脉冲响应的中国煤炭贸易量变化对其价格影响研究[J].当代经济科学,2011,(3):81-86.
[10]赵家峤,王道明.频率响应法对变压器绕组变形的测试与分析[J].科技信息,2011,(27):322-323.
[11]余英,葛洲,汪伟,等.基于频率响应法的变压器绕组故障分析[J].水电能源科学,2011,(9):175-177.
[12]刘君,韦国,周利军,等.基于频率响应法评估油纸绝缘微水含量[J].天津大学学报,2012,(5):454-460.
[13]吴丽英.相关分析法在地下水位预测中的应用[J].地下水,2011,(6):47-50.
[14]孙跃.相关分析法的实例计算[J].大科技：科技天地,2011,(18):24-25.
[15]孙颖.基于典型相关分析法的顾客生命周期影响因素的实证研究[J].安徽工程大学学报,2011,(3):80-83.
[16]杜志强,杨志远,吴艳,等.煤层含气量评价中灰色关联分析与相关分析法对比[J].煤田地质与勘探,2012,(1):20-23.
[17]张小艳,郭翠.基于matlab绘制原煤可选性曲线方法的研究[J].工矿自动化,2010,(3):122-124.
[18]许燕,林建.煤灰分模型参数最小残差绝对值和准则下的搜索解法[J].黑龙江大学自然科学学报,1998,(2):85-87.
[19]乐建兵,周怀春.综合辨识算法研究及其实际应用分析[J].科技与企业,2012,(6):268-269.
[20]王萍,郭巧,赵静.极大似然法在间接测热系统参数估计中的应用[J].系统工程与电子技术,2003,(11):1404-1406.
[21] Panda R, Chatterji B N. Least Squares Generalized B-Spline Signal and Image Processing [J]. SignalProcessing,2001,81(10):2005-2017.
[22] Markus M, Vernon Knapp H, Tasker G D. Entropy and Generalized Least Square Methods in Assessmentof the Regional Value of Streamgages [J]. Journal of Hydrology,2003,283(1–4):107-121.
[23] Kenward M G. The Distribution of a Generalized Least Squares Estimator with Covariance Adjustment [J].Journal of MultivariateAnalysis,1986,20(2):244-250.
[24]韩学山,柳焯.动态经济调度的新算法—辅助变量法[J].电力系统自动化,1993,(9):35-39.
[25]严晓久,周爱国,林建平, et al.基于辅助变量法的系统参数辨识[J].机床与液压,2006,(12):180-181.
[26] Hong M, S derstr m T. Relations between Bias-Eliminating Least Squares, the Frisch Scheme andExtended Compensated Least Squares Methods for Identifying Errors-in-Variables Systems [J].Automatica,2009,45(1):277-282.
[27] Hung H-M. ANote on Extended Least Squares Methods for Cluster Sampling [J]. Statistics&ProbabilityLetters,1987,5(5):343-346.
[28] Haaland D M, Melgaard D K. New Augmented Classical Least Squares Methods for ImprovedQuantitative Spectral Analyses [J]. Vibrational Spectroscopy,2002,29(1-2):171-175.
[29] Yu S, Zhu K, Diao F. ADynamic All Parameters Adaptive Bp Neural Networks Model and Its Applicationon Oil Reservoir Prediction [J]. Applied Mathematics and Computation,2008,195(1):66-75.
[30] Zhang J-h, Xie A-g, Shen F-m. Multi-Objective Optimization and Analysis Model of Sintering ProcessBased on Bp Neural Network [J]. Journal of Iron and Steel Research, International,2007,14(2):1-5.
[31] Zhang L, Luo J, Yang S. Forecasting Box Office Revenue of Movies with Bp Neural Network [J]. ExpertSystems withApplications,2009,36(3, Part2):6580-6587.
[32]陈爱军.最小二乘支持向量机及其在工业过程建模中的应用[D].2006.
[33]李丽娟.最小二乘支持向量机建模及预测控制算法研究[D].2008.
[34] Schuhmann. Methods for Steady State Study of Flotation Problems [J]. physics and Chemistry,1942,46:891-902.
[35] Yoon R H. The Role of Hydrodynamic and Surface Forces in Bubble-Particle Interaction [J]. InternationalJournal of Mineral Processing,2000,58(1-4):129-143.
[36] Nguyen A V, Ralston J, Schulze H J. On Modelling of Bubble-Particle Attachment Probability in Flotation[J]. International Journal of Mineral Processing,1998,53(4):225-249.
[37] Nguyen-Van A, Kmet S. Probability of Collision between Particles and Bubbles in Flotation: TheTheoretical Inertialess Model Involving a Swarm of Bubbles in Pulp Phase [J]. International Journal ofMineral Processing,1994,40(3-4):155-169.
[38] LYNCHA.J.浮选回路的模拟和控制[M].北京:中国建筑工业出版社,1986:34-38.
[39] Yalcin T. Determination of Flotation Rate Constants from Semi-Batch Flotation Data [J]. MineralsEngineering,1992,5(6):695-706.
[40] Duan J, Fornasiero D, Ralston J. Calculation of the Flotation Rate Constant of Chalcopyrite Particles in anOre [J]. International Journal of Mineral Processing,2003,72(1-4):227-237.
[41] Kracht W, Vallebuona G, Casali A. Rate Constant Modelling for Batch Flotation, as a Function of GasDispersion Properties [J]. Minerals Engineering,2005,18(11):1067-1076.
[42] Polat M, Chander S. First-Order Flotation Kinetics Models and Methods for Estimation of the TrueDistribution of Flotation Rate Constants [J]. International Journal of Mineral Processing,2000,58(1-4):145-166.
[43] Ralston J, Fornasiero D, Grano S, et al. Reducing Uncertainty in Mineral Flotation--Flotation RateConstant Prediction for Particles in an Operating Plant Ore [J]. International Journal of MineralProcessing,2007,84(1-4):89-98.
[44]尹蒂,李松仁,胡为柏.浮选回路动态模型研究(Ⅱ)——连续浮选模型的建立[J].有色金属(选矿部分),1992,(06):32-36.
[45]尹蒂,李松仁,胡为柏.浮选回路动态模型研究(Ⅰ)——分批浮选模型的建立(续)[J].有色金属(选矿部分),1992,(04).
[46]尹蒂,李松仁,胡为柏.浮选回路动态模型研究(Ⅰ)——分批浮选模型的建立[J].有色金属(选矿部分),1992,(03):35-39.
[47] Koh P T L, Schwarz M P. Cfd Modelling of Bubble–Particle Collision Rates and Efficiencies in aFlotation Cell [J]. Minerals Engineering,2003,16(11):1055-1059.
[48]刘文礼,路迈西,任守政,等.各品级煤泥浮选规律的研究[J].中国矿业大学学报（自然科学版）,2000,29(4):358-362.
[49]刘文礼,路迈西,任守政,等.煤泥浮选数学模型及其仿真器的研究[J].中国矿业大学学报,2000,29(06):592-595.
[50]陶有俊刘路.细粒煤浮选数学模型的研究[J].中国矿业大学学报,1999,28(05):425-428.
[51] Abkhoshk E, Kor M, Rezai B. A Study on the Effect of Particle Size on Coal Flotation Kinetics UsingFuzzy Logic [J]. Expert Systems withApplications,2010,37(7):5201-5207.
[52] Jorjani E, Asadollahi Poorali H, Sam A, et al. Prediction of Coal Response to Froth Flotation Based onCoal Analysis Using Regression and Artificial Neural Network [J]. Minerals Engineering,2009,22(11):970-976.
[53]李海清,黄志尧.软测量技术原理及应用[M].背景:化学工业出版社,2000:
[54] Moolman D. W. A C, Van Deventer J.S.J. The Interpretation of Flotation Froth Surfaces by Using DigitalImageAnalysis and Neural Networks [J]. Chemical Engineering Science,1995,50(22):3501-3513.
[55] Holtham P N, Nguyen K K. On-Line Analysis of Froth Surface in Coal and Mineral Flotation UsingJkfrothcam [J]. International Journal of Mineral Processing,2002,64(2–3):163-180.
[56]耿增显,柴天佑.基于ls-Svm的浮选过程工艺技术指标软测量[J].系统仿真学报,2008,(23):6321-6324.
[57]曾荣,沃国经.图像处理技术在镍选矿厂中的应用[J].矿冶,2002,1:37-41.
[58]王介生,张勇.基于anfis的软测量模型在浮选中的应用[J].合肥工业大学学报：自然科学版,2006,(11):1365-1369.
[59]张勇,王介生.基于pca—Rbf神经网络的浮选过程软测量建模[J].南京航空航天大学学报,2006,(B07):116-119.
[60]牟学民,刘金平,桂卫华,等.基于sift特征配准的浮选泡沫移动速度提取与分析[J].信息与控制,2011,(4):525-531.
[61]任会峰,阳春华,周璇,等.基于泡沫图像特征加权svm的浮选工况识别[J].浙江大学学报：工学版,2011,(12):2115-2119.
[62]唐朝晖,孙园园,桂卫华,等.基于小波变换的浮选泡沫图像纹理特征提取[J].计算机工程,2011,(18):206-208.
[63]唐朝晖,朱楚梅,刘金平.基于lbpv的浮选泡沫图像纹理特征提取[J].计算机应用研究,2011,(10):3934-3936.
[64]汪中伟,梁栋华.基于浮选泡沫图像特征参数的应用研究[J].矿冶,2011,(2):82-84.
[65]肖金林,桂卫华.浮选泡沫图像监控系统在冬瓜山铜矿的应用[J].科协论坛：下半月,2012,(6):70-71.
[66]周开军,阳春华,牟学民,等.基于图像特征提取的浮选关键参数智能预测算法[J].控制与决策,2009,(9):1300-1305.
[67]邹莹,苏婷婷,黄易.基于支持向量机的浮选泡沫图像识别[J].科技创新与应用,2012,(16):35-35.
[68] Citir C, Aktas Z, Berber R. Off-Line Image Analysis for Froth Flotation of Coal [J]. Computers&Chemical Engineering,2004,28(5):625-632.
[69] Hargrave J M, Miles N J, Hall S T. The Use of Grey Level Measurement in Predicting Coal FlotationPerformance [J]. Minerals Engineering,1996,9(6):667-674.
[70] Jeanmeure L F C, Zimmerman W B J. ACnn Video Based Control System for a Coal Froth Flotation [A].Cellular Neural Networks and Their Applications Proceedings,1998Fifth IEEE International Workshopon [C].1998.192-197.
[71] Zimmermann W B J. Simple Model of Equilibrium Froth Height for Foams: An Application for CnnImage Analysis [A]. Cellular Neural Networks and their Applications,1996. CNNA-96. Proceedings.,1996Fourth IEEE International Workshop on [C].1996.237-241.
[72] Adel G T, Luttrell G H. Development of a Video-Based Slurry Sensor for on-Line Ash Analysis. SecondQuarterly Technical Progress Report, January1,1995--March31,1995, DOE/PC/94226--2[R].Pennsylvania: Center U S D o E PE T.1996.
[73]陈子鸣,茹青.使用多媒体技术研究浮选泡沫现象的初步探索[J].矿冶工程,1997,17(3):247-248.
[74]刘文礼,路迈西,王凡,等.煤泥浮选泡沫图像纹理特征的提取及泡沫状态的识别[J].化工学报,2003,54(6):830-835.
[75]李珍香,罗宏宇.一种基于PC机的煤泥浮选自动识别系统[J].电脑开发与应用,2000,13(2):29-30.
[76]刘富强,钱建生.基于图像处理与识别技术的煤矿矸石自动分选[J].煤炭学报,2000,25(5):534-537.
[77]李庆利,杨国权,韩忠义,等.煤泥浮选泡沫数字图像处理中的边缘检测算法研究[J].选煤技术,2005,2:7-9.
[78]路迈西,王凡,等Analysis of Texture of Froth Image in Coal Flotation [J].中国矿业大学学报：英文版,2001,11(2):100-103.
[79]王晓晨.泡沫图像特征与浮选精煤指标关系的研究[J].淮南工业学院学报,2001,21(01):41-43.
[80]杨小平,冯绍灌,许德平,等.选煤厂浮选过程检测与控制的研究[J].煤炭科学技术,2001,29(08):1-4.
[81]中国煤炭加工利用协会.选煤实用技术手册（上）[M].徐州:中国矿业大学,2008:432-433.
[82]谢广元,吴玲,欧泽深,等.煤泥分级浮选工艺的研究[J].中国矿业大学学报,2005,34(6):756-760.
[83]张秀捧.煤泥分级浮选工艺的研究与实践[D].徐州:2003.
[84] Xie G Y. The Study Andpractice of Cyclonic Microbabble Flotation Columninash Andpyritic SulfurRejection Fromcoals [A]. Xie H, Golosinski T S. Mining Science and Technology'99[C]. A.A.Balkema,1999.511-514.
[85]张波.煤泥浮选的影响因素与生产操作要领[J].选煤技术,2011,4:39-40.
[86]王泽南,谢广元. Fcmc型浮选柱处理难浮煤的探讨[J].煤炭工程,2006,(5):86-88.
[87]徐初阳,郭立颖,聂容春,等.百善煤的结构特征及可浮选性研究[J].煤炭工程,2004,(5):11-14.
[88]周桂英,张强,曲景奎.煤炭微生物预处理浮选脱硫降灰的试验研究[J].矿产综合利用,2004,(5):11-14.
[89]沈正义,卓金武,匡亚莉,等.基于多目标正交试验的浮选药剂制度优化研究[J].选煤技术,2007,3:1-3.
[90]张勇.粗糙集-神经网络智能系统在浮选过程中的应用研究[D].大连:大连理工大学,2002.5.5-8.
[91] Mauro F, Grundy, M.,. The Application of fotation Columns at Lornex Miningcorporation Ltd.[A].9thDistrict Sixth Meeting. CIM, B.C.,[C].1984.23.
[92] Nicol S, Roberts, T., Bensley, C., Kidd, G., Lamb, R.,. Column fotation of Ultrafne Coal: Experience atBhp-Utah Coal Limited's Riverside Mine.[A]. Sastry,K.(Ed.), SME Annual Meeting, ColumnFlotation’88.[C]. Society of Mining, Metallurgy and Exploration,1988.7-11.
[93] Moys M H, Finch, J.A.,. The Measurement and Control of Level in fotation Column.[A]. Sastry, K.(Ed.),SME Annual Meeting–Column Flotation’88.[C]. Society of Mining, Metallurgy and Exploration,1988.103-112.
[94] Barrière P-A, Dumont, F., Desbiens, A.,. Using Semi-Physical Models for Better Control. Part2:Nonlinear Control of a Pilot fotation Column.[A]. International Federation of Automatic Control (IFAC)Symposium onAutomation in Mining [C]. Mineral and Metal Processing,2001.131-136.
[95] Alexander D, Bilney, T., Schwartz, S.,. Flotation Performance Improvement at Placer Dome KanownaBelle Gold Mine.[A]. Proceedings2005–37th Annual Meeting of the Canadian Mineral Processors.CIM [C].2005.171-201.
[96] Milot M, Desbiens, A., del Villar, R., Hodouin, D.,. Identifcation and Multivariable Nonlinear PredictiveControl of a Pilot fotation Column.[A]. XXI International Mineral Processing Congress.[C]. IFAC,2000.
[97] Pu M, Gupta, Y., Taweel, A.A.,. Model Predictive Control of fotation Columns.[A]. Column’91–Proceedings of an International Conference on Column Flotation,[C]. Canadian Institute of Mining,Metallurgy and Petroleum,,1991.467-478.
[98] Persechini M A M, Peres A E C, Jota F G F G. Control Strategy for a Column Flotation Process [J].Control Engineering Practice,2004,12(8):963-976.
[99] Maldonado M, Desbiens, A., del Villar, R., Girgin, H., Gomez, C.,2008b. Decentralized Control of aPilotfotation Column: A3*3System.[A].6th International Copper Conference [C].2007.241-254.
[100] Carvalho T, Dur o F, Fernandes C. Dynamic Characterization of Column Flotation Process LaboratoryCase Study [J]. Minerals Engineering,1999,12(11):1339-1346.
[101] Hirajima T, Takamori, T., Tsunekawa, T., Matsubara, T., Oshima, K., Imai, T., Sawaki,, K. K, S. TheApplication of Fuzzy Logic to Control Concentrate Grade in Column fotation at Toyoha Mines.[A].Agar, G., Huls, B., Hyma, D.(Eds.), Column’91–Proceedings of an International Conference onColumn Flotation,[C]. Canadian Institute of Mining, Metallurgy and Petroleum,,1991.375-407.
[102] McKay J, Ynchausti, R.,. Expert Supervisory Control of fotation Columns.[A]. Gomez, C.O., Finch,J.A.(Eds.), Column’96–Proceedings of the International Symposium on Column Flotation. MineralSciences and Engineering Section of the Metallurgical Society of CIM and Canadian Mineral ProcessorsDivision of CIM [C].1996.353-367.
[103] Karr C L. Adaptive Process Control Using Biological Paradigms [J]. Journal of Network and ComputerApplications,1996,19(1):21-44.
[104]樊华.大型浮选机自动控制系统的探讨[J].赤子,2012,(12):177-177.
[105]邵之苗,高扬.铜选矿厂浮选过程控制系统的研究与应用[J].矿冶,2012,(1):73-76.
[106]沈孝忠,刘强,赵霞.基于s7—300Plc的浮选自动加药控制系统[J].自动化应用,2012,(5):45-46.
[107]孙振海,罗成名,宋风华.浮选自动跟踪加药控制系统[J].煤炭技术,2011,(4):119-120.
[108]翟延忠,周娟华.基于精密计量泵的浮选加药控制平台[J].选煤技术,2011,(2):53-56.
[109]张敏,张建强,刘炯天,等.浮选柱泡沫层检测控制系统的研究[J].矿山机械,2010,(19):107-110.
[110]钟刚,王东,修德江.浮选加药自动控制系统[J].矿业工程,2012,(2):36-38.
[111]尚海洋.磨矿浮选控制系统开发与应用[J].矿冶,2009,18(2):89-92.
[112]陈子鸣,茹青.用于浮选控制的模糊自适应专家系统结构设计原理[J].有色金属：选矿部分,1993,(5):36-38.
[113]李海波,郑秀萍,柴天佑.浮选过程混合智能优化设定控制方法[J].东北大学学报：自然科学版,2012,(1):1-5.
[114]张勇,丛峰武,王伟.粗集神经网络在阳离子反浮选控制中的应用[J].控制工程,2004,(z1):85-88.
[115]张勇,王峰,潘学军,等.粗糙集理论在阳离子反浮选控制中的应用[J].中南工业大学学报,2003,(4):368-372.
[116]杨小平,田敬秋.煤泥浮选过程控制技术进展[J].煤炭科学技术,1999,(5):38-40.
[117]何胜春,丰日兵.邢台选煤厂煤泥浮选控制系统的研究[J].选煤技术,2000,5:16-17.
[118]杨小平,王汝赡,等.选煤厂浮选过程检测与控制的研究[J].煤炭科学技术,2001,(8):1-4.
[119]杨小平,许德平.煤泥浮选优化控制系统的设计[J].煤炭学报,2000,(2):200-202.
[120]杨小平,吴翠平.煤泥浮选计算机控制的研究[J].煤矿自动化,2000,(4):1-3.
[121]杨小平,王汝赡,等.煤泥浮选测控系统的研究[J].中国矿业大学学报,2001,(1):39-42.
[122]张举丘,杨小平.淮北选煤厂浮选控制系统研究[J].中国煤炭,2011,(3):82-84.[123]陈玉强肖,李桂权.神经网络及模糊控制在振动主动控制中的应用[M].哈尔滨:黑龙江教育出版社,2007:15-19.
[124] Vapnik V. The nature of satistical learing theory [M]. NewYork: Springer-verlag,2000.
[125] Kuwabara S, Tamura N, Yamanaka Y, et al. Sympathetic Sweat Responses and Skin Vasomotor Reflexesin Carpal Tunnel Syndrome [J]. Clinical Neurology and Neurosurgery,2008,110(7):691-695.
[126] Gao L, Ren S. Prediction of Nitrophenol-Type Compounds Using Chemometrics and Spectrophotometry[J]. Analytical Biochemistry,2010,405(2):184-191.
[127] Huang C-J, Lai W K, Luo R-L, et al. Application of Support Vector Machines to Bandwidth Reservationin Sectored Cellular Communications [J]. Engineering Applications of Artificial Intelligence,2005,18(5):585-594.
[128] Thissen U, van Brakel R, de Weijer A P, et al. Using Support Vector Machines for Time Series Prediction[J]. Chemometrics and Intelligent Laboratory Systems,2003,69(1–2):35-49.
[129] Shin H, Cho S. Response Modeling with Support Vector Machines [J]. Expert Systems with Applications,2006,30(4):746-760.
[130] Li Y, Su L, Zhang X, et al. Prediction of Association Constants of Cesium Chelates Based on UniformDesign Optimized Support Vector Machine [J]. Chemometrics and Intelligent Laboratory Systems,2011,105(1):106-113.
[131] Huang Q, Zhuang Y, Qiao X, et al. Predicted Model of Aggregation of Molecules in Chinese HerbalDrugs by Support Vector Machines [J]. Acta Physico-Chimica Sinica,2007,23(8):1141-1144.
[132] Huo H-B, Zhu X-J, Cao G-Y. Nonlinear Modeling of a Sofc Stack Based on a Least Squares SupportVector Machine [J]. Journal of Power Sources,2006,162(2):1220-1225.
[133] Zhao B. Modeling Pressure Drop Coefficient for Cyclone Separators: A Support Vector MachineApproach [J]. Chemical Engineering Science,2009,64(19):4131-4136.
[134] Zhong Z-D, Zhu X-J, Cao G-Y. Modeling a Pemfc by a Support Vector Machine [J]. Journal of PowerSources,2006,160(1):293-298.
[135] Iplikci S. A Support Vector Machine Based Control Application to the Experimental Three-Tank System[J]. ISATransactions,2010,49(3):376-386.
[136] Chen S-W, Li Z-R, Li X-Y. Prediction of Antifungal Activity by Support Vector Machine Approach [J].Journal of Molecular Structure: THEOCHEM,2005,731(1–3):73-81.
[137] Li H, Sun J. Predicting Business Failure Using Support Vector Machines with Straightforward Wrapper:ARe-Sampling Study [J]. Expert Systems withApplications,2011,38(10):12747-12756.
[138] Liu Q, Yang M, Lei J, et al. Modeling and Optimizing Parabolic Trough Solar Collector Systems Usingthe Least Squares Support Vector Machine Method [J]. Solar Energy,2012,86(7):1973-1980.
[139] Wu Q. Hybrid Model Based on Wavelet Support Vector Machine and Modified Genetic AlgorithmPenalizing Gaussian Noises for Power Load Forecasts [J]. Expert Systems with Applications,2011,38(1):379-385.
[140]王志新.智能模糊控制器的若干问题研究[M].北京:知识产权出版社,2009:
[141]曹立学.基于模糊自适应pid的温度控制系统的设计[J].计算机与数字工程,2012,(6):139-141.
[142]张丽萍,马立新,金珍珍.模糊自适应pid炉温控制系统的设计[J].热加工工艺,2012,(14):234-236.
[143]皮柳杨,陈希有,李红.基于模糊自适应pid控制器的智能供水系统[J].电气自动化,2012,(3):79-81.
[144]汤秀芬.模糊自适应pid控制器参数整定的研究[J].工业控制计算机,2012,(2):55-56.
[145]李硬,薛智.多普勒超声波流量计测量中异常现象分析[J].中国仪器仪表,2004,(9):41-42.
[146]黄玉明,杨士强.彩色ccd摄象机的光强—灰度响应线性化及其在颜色定标中的应用[J].机器人,1992,(1):38-44.
[147]周世生.印刷色彩学[M].北京:北京工业出版社,2008:169-170.
[2]谢广元.选矿学[M].江苏，徐州:中国矿业大学出版社,2005:427.
[3] Bouchard J, Desbiens A, del Villar R, et al. Column Flotation Simulation and Control: An Overview [J].Minerals Engineering,2009,22(6):519-529.
[4] O'Connor C T, Mills PJ T, Cilliers J J. Prediction of Large-Scale Column Flotation Cell Performance UsingPilot Plant Data [J]. The Chemical Engineering Journal and the Biochemical Engineering Journal,1995,59(1):1-6.
[5]樊民强.选煤数学模型与数据处理[M].北京:煤炭工业出版社,2004:2-3.
[6]庄劲武,张晓锋,张超.阶跃响应法在浮地交流系统绝缘故障定位中的应用初探[J].继电器,2004,(3):21-25.
[7]肖锋,李欣然,石吉银.阶跃响应法在小电流接地系统故障选线与测距中的应用[J].电力科学与技术学报,2008,(1):83-88.
[8]万春红,柬洪春,张东宁,等.水泥分解炉温度控制过程的阶跃响应建模与仿真[J].化工自动化及仪表,2011,(12):1420-1424.
[9]王可,李国平,李治,等.基于协整和脉冲响应的中国煤炭贸易量变化对其价格影响研究[J].当代经济科学,2011,(3):81-86.
[10]赵家峤,王道明.频率响应法对变压器绕组变形的测试与分析[J].科技信息,2011,(27):322-323.
[11]余英,葛洲,汪伟,等.基于频率响应法的变压器绕组故障分析[J].水电能源科学,2011,(9):175-177.
[12]刘君,韦国,周利军,等.基于频率响应法评估油纸绝缘微水含量[J].天津大学学报,2012,(5):454-460.
[13]吴丽英.相关分析法在地下水位预测中的应用[J].地下水,2011,(6):47-50.
[14]孙跃.相关分析法的实例计算[J].大科技：科技天地,2011,(18):24-25.
[15]孙颖.基于典型相关分析法的顾客生命周期影响因素的实证研究[J].安徽工程大学学报,2011,(3):80-83.
[16]杜志强,杨志远,吴艳,等.煤层含气量评价中灰色关联分析与相关分析法对比[J].煤田地质与勘探,2012,(1):20-23.
[17]张小艳,郭翠.基于matlab绘制原煤可选性曲线方法的研究[J].工矿自动化,2010,(3):122-124.
[18]许燕,林建.煤灰分模型参数最小残差绝对值和准则下的搜索解法[J].黑龙江大学自然科学学报,1998,(2):85-87.
[19]乐建兵,周怀春.综合辨识算法研究及其实际应用分析[J].科技与企业,2012,(6):268-269.
[20]王萍,郭巧,赵静.极大似然法在间接测热系统参数估计中的应用[J].系统工程与电子技术,2003,(11):1404-1406.
[21] Panda R, Chatterji B N. Least Squares Generalized B-Spline Signal and Image Processing [J]. SignalProcessing,2001,81(10):2005-2017.
[22] Markus M, Vernon Knapp H, Tasker G D. Entropy and Generalized Least Square Methods in Assessmentof the Regional Value of Streamgages [J]. Journal of Hydrology,2003,283(1–4):107-121.
[23] Kenward M G. The Distribution of a Generalized Least Squares Estimator with Covariance Adjustment [J].Journal of MultivariateAnalysis,1986,20(2):244-250.
[24]韩学山,柳焯.动态经济调度的新算法—辅助变量法[J].电力系统自动化,1993,(9):35-39.
[25]严晓久,周爱国,林建平, et al.基于辅助变量法的系统参数辨识[J].机床与液压,2006,(12):180-181.
[26] Hong M, S derstr m T. Relations between Bias-Eliminating Least Squares, the Frisch Scheme andExtended Compensated Least Squares Methods for Identifying Errors-in-Variables Systems [J].Automatica,2009,45(1):277-282.
[27] Hung H-M. ANote on Extended Least Squares Methods for Cluster Sampling [J]. Statistics&ProbabilityLetters,1987,5(5):343-346.
[28] Haaland D M, Melgaard D K. New Augmented Classical Least Squares Methods for ImprovedQuantitative Spectral Analyses [J]. Vibrational Spectroscopy,2002,29(1-2):171-175.
[29] Yu S, Zhu K, Diao F. ADynamic All Parameters Adaptive Bp Neural Networks Model and Its Applicationon Oil Reservoir Prediction [J]. Applied Mathematics and Computation,2008,195(1):66-75.
[30] Zhang J-h, Xie A-g, Shen F-m. Multi-Objective Optimization and Analysis Model of Sintering ProcessBased on Bp Neural Network [J]. Journal of Iron and Steel Research, International,2007,14(2):1-5.
[31] Zhang L, Luo J, Yang S. Forecasting Box Office Revenue of Movies with Bp Neural Network [J]. ExpertSystems withApplications,2009,36(3, Part2):6580-6587.
[32]陈爱军.最小二乘支持向量机及其在工业过程建模中的应用[D].2006.
[33]李丽娟.最小二乘支持向量机建模及预测控制算法研究[D].2008.
[34] Schuhmann. Methods for Steady State Study of Flotation Problems [J]. physics and Chemistry,1942,46:891-902.
[35] Yoon R H. The Role of Hydrodynamic and Surface Forces in Bubble-Particle Interaction [J]. InternationalJournal of Mineral Processing,2000,58(1-4):129-143.
[36] Nguyen A V, Ralston J, Schulze H J. On Modelling of Bubble-Particle Attachment Probability in Flotation[J]. International Journal of Mineral Processing,1998,53(4):225-249.
[37] Nguyen-Van A, Kmet S. Probability of Collision between Particles and Bubbles in Flotation: TheTheoretical Inertialess Model Involving a Swarm of Bubbles in Pulp Phase [J]. International Journal ofMineral Processing,1994,40(3-4):155-169.
[38] LYNCHA.J.浮选回路的模拟和控制[M].北京:中国建筑工业出版社,1986:34-38.
[39] Yalcin T. Determination of Flotation Rate Constants from Semi-Batch Flotation Data [J]. MineralsEngineering,1992,5(6):695-706.
[40] Duan J, Fornasiero D, Ralston J. Calculation of the Flotation Rate Constant of Chalcopyrite Particles in anOre [J]. International Journal of Mineral Processing,2003,72(1-4):227-237.
[41] Kracht W, Vallebuona G, Casali A. Rate Constant Modelling for Batch Flotation, as a Function of GasDispersion Properties [J]. Minerals Engineering,2005,18(11):1067-1076.
[42] Polat M, Chander S. First-Order Flotation Kinetics Models and Methods for Estimation of the TrueDistribution of Flotation Rate Constants [J]. International Journal of Mineral Processing,2000,58(1-4):145-166.
[43] Ralston J, Fornasiero D, Grano S, et al. Reducing Uncertainty in Mineral Flotation--Flotation RateConstant Prediction for Particles in an Operating Plant Ore [J]. International Journal of MineralProcessing,2007,84(1-4):89-98.
[44]尹蒂,李松仁,胡为柏.浮选回路动态模型研究(Ⅱ)——连续浮选模型的建立[J].有色金属(选矿部分),1992,(06):32-36.
[45]尹蒂,李松仁,胡为柏.浮选回路动态模型研究(Ⅰ)——分批浮选模型的建立(续)[J].有色金属(选矿部分),1992,(04).
[46]尹蒂,李松仁,胡为柏.浮选回路动态模型研究(Ⅰ)——分批浮选模型的建立[J].有色金属(选矿部分),1992,(03):35-39.
[47] Koh P T L, Schwarz M P. Cfd Modelling of Bubble–Particle Collision Rates and Efficiencies in aFlotation Cell [J]. Minerals Engineering,2003,16(11):1055-1059.
[48]刘文礼,路迈西,任守政,等.各品级煤泥浮选规律的研究[J].中国矿业大学学报（自然科学版）,2000,29(4):358-362.
[49]刘文礼,路迈西,任守政,等.煤泥浮选数学模型及其仿真器的研究[J].中国矿业大学学报,2000,29(06):592-595.
[50]陶有俊刘路.细粒煤浮选数学模型的研究[J].中国矿业大学学报,1999,28(05):425-428.
[51] Abkhoshk E, Kor M, Rezai B. A Study on the Effect of Particle Size on Coal Flotation Kinetics UsingFuzzy Logic [J]. Expert Systems withApplications,2010,37(7):5201-5207.
[52] Jorjani E, Asadollahi Poorali H, Sam A, et al. Prediction of Coal Response to Froth Flotation Based onCoal Analysis Using Regression and Artificial Neural Network [J]. Minerals Engineering,2009,22(11):970-976.
[53]李海清,黄志尧.软测量技术原理及应用[M].背景:化学工业出版社,2000:
[54] Moolman D. W. A C, Van Deventer J.S.J. The Interpretation of Flotation Froth Surfaces by Using DigitalImageAnalysis and Neural Networks [J]. Chemical Engineering Science,1995,50(22):3501-3513.
[55] Holtham P N, Nguyen K K. On-Line Analysis of Froth Surface in Coal and Mineral Flotation UsingJkfrothcam [J]. International Journal of Mineral Processing,2002,64(2–3):163-180.
[56]耿增显,柴天佑.基于ls-Svm的浮选过程工艺技术指标软测量[J].系统仿真学报,2008,(23):6321-6324.
[57]曾荣,沃国经.图像处理技术在镍选矿厂中的应用[J].矿冶,2002,1:37-41.
[58]王介生,张勇.基于anfis的软测量模型在浮选中的应用[J].合肥工业大学学报：自然科学版,2006,(11):1365-1369.
[59]张勇,王介生.基于pca—Rbf神经网络的浮选过程软测量建模[J].南京航空航天大学学报,2006,(B07):116-119.
[60]牟学民,刘金平,桂卫华,等.基于sift特征配准的浮选泡沫移动速度提取与分析[J].信息与控制,2011,(4):525-531.
[61]任会峰,阳春华,周璇,等.基于泡沫图像特征加权svm的浮选工况识别[J].浙江大学学报：工学版,2011,(12):2115-2119.
[62]唐朝晖,孙园园,桂卫华,等.基于小波变换的浮选泡沫图像纹理特征提取[J].计算机工程,2011,(18):206-208.
[63]唐朝晖,朱楚梅,刘金平.基于lbpv的浮选泡沫图像纹理特征提取[J].计算机应用研究,2011,(10):3934-3936.
[64]汪中伟,梁栋华.基于浮选泡沫图像特征参数的应用研究[J].矿冶,2011,(2):82-84.
[65]肖金林,桂卫华.浮选泡沫图像监控系统在冬瓜山铜矿的应用[J].科协论坛：下半月,2012,(6):70-71.
[66]周开军,阳春华,牟学民,等.基于图像特征提取的浮选关键参数智能预测算法[J].控制与决策,2009,(9):1300-1305.
[67]邹莹,苏婷婷,黄易.基于支持向量机的浮选泡沫图像识别[J].科技创新与应用,2012,(16):35-35.
[68] Citir C, Aktas Z, Berber R. Off-Line Image Analysis for Froth Flotation of Coal [J]. Computers&Chemical Engineering,2004,28(5):625-632.
[69] Hargrave J M, Miles N J, Hall S T. The Use of Grey Level Measurement in Predicting Coal FlotationPerformance [J]. Minerals Engineering,1996,9(6):667-674.
[70] Jeanmeure L F C, Zimmerman W B J. ACnn Video Based Control System for a Coal Froth Flotation [A].Cellular Neural Networks and Their Applications Proceedings,1998Fifth IEEE International Workshopon [C].1998.192-197.
[71] Zimmermann W B J. Simple Model of Equilibrium Froth Height for Foams: An Application for CnnImage Analysis [A]. Cellular Neural Networks and their Applications,1996. CNNA-96. Proceedings.,1996Fourth IEEE International Workshop on [C].1996.237-241.
[72] Adel G T, Luttrell G H. Development of a Video-Based Slurry Sensor for on-Line Ash Analysis. SecondQuarterly Technical Progress Report, January1,1995--March31,1995, DOE/PC/94226--2[R].Pennsylvania: Center U S D o E PE T.1996.
[73]陈子鸣,茹青.使用多媒体技术研究浮选泡沫现象的初步探索[J].矿冶工程,1997,17(3):247-248.
[74]刘文礼,路迈西,王凡,等.煤泥浮选泡沫图像纹理特征的提取及泡沫状态的识别[J].化工学报,2003,54(6):830-835.
[75]李珍香,罗宏宇.一种基于PC机的煤泥浮选自动识别系统[J].电脑开发与应用,2000,13(2):29-30.
[76]刘富强,钱建生.基于图像处理与识别技术的煤矿矸石自动分选[J].煤炭学报,2000,25(5):534-537.
[77]李庆利,杨国权,韩忠义,等.煤泥浮选泡沫数字图像处理中的边缘检测算法研究[J].选煤技术,2005,2:7-9.
[78]路迈西,王凡,等Analysis of Texture of Froth Image in Coal Flotation [J].中国矿业大学学报：英文版,2001,11(2):100-103.
[79]王晓晨.泡沫图像特征与浮选精煤指标关系的研究[J].淮南工业学院学报,2001,21(01):41-43.
[80]杨小平,冯绍灌,许德平,等.选煤厂浮选过程检测与控制的研究[J].煤炭科学技术,2001,29(08):1-4.
[81]中国煤炭加工利用协会.选煤实用技术手册（上）[M].徐州:中国矿业大学,2008:432-433.
[82]谢广元,吴玲,欧泽深,等.煤泥分级浮选工艺的研究[J].中国矿业大学学报,2005,34(6):756-760.
[83]张秀捧.煤泥分级浮选工艺的研究与实践[D].徐州:2003.
[84] Xie G Y. The Study Andpractice of Cyclonic Microbabble Flotation Columninash Andpyritic SulfurRejection Fromcoals [A]. Xie H, Golosinski T S. Mining Science and Technology'99[C]. A.A.Balkema,1999.511-514.
[85]张波.煤泥浮选的影响因素与生产操作要领[J].选煤技术,2011,4:39-40.
[86]王泽南,谢广元. Fcmc型浮选柱处理难浮煤的探讨[J].煤炭工程,2006,(5):86-88.
[87]徐初阳,郭立颖,聂容春,等.百善煤的结构特征及可浮选性研究[J].煤炭工程,2004,(5):11-14.
[88]周桂英,张强,曲景奎.煤炭微生物预处理浮选脱硫降灰的试验研究[J].矿产综合利用,2004,(5):11-14.
[89]沈正义,卓金武,匡亚莉,等.基于多目标正交试验的浮选药剂制度优化研究[J].选煤技术,2007,3:1-3.
[90]张勇.粗糙集-神经网络智能系统在浮选过程中的应用研究[D].大连:大连理工大学,2002.5.5-8.
[91] Mauro F, Grundy, M.,. The Application of fotation Columns at Lornex Miningcorporation Ltd.[A].9thDistrict Sixth Meeting. CIM, B.C.,[C].1984.23.
[92] Nicol S, Roberts, T., Bensley, C., Kidd, G., Lamb, R.,. Column fotation of Ultrafne Coal: Experience atBhp-Utah Coal Limited's Riverside Mine.[A]. Sastry,K.(Ed.), SME Annual Meeting, ColumnFlotation’88.[C]. Society of Mining, Metallurgy and Exploration,1988.7-11.
[93] Moys M H, Finch, J.A.,. The Measurement and Control of Level in fotation Column.[A]. Sastry, K.(Ed.),SME Annual Meeting–Column Flotation’88.[C]. Society of Mining, Metallurgy and Exploration,1988.103-112.
[94] Barrière P-A, Dumont, F., Desbiens, A.,. Using Semi-Physical Models for Better Control. Part2:Nonlinear Control of a Pilot fotation Column.[A]. International Federation of Automatic Control (IFAC)Symposium onAutomation in Mining [C]. Mineral and Metal Processing,2001.131-136.
[95] Alexander D, Bilney, T., Schwartz, S.,. Flotation Performance Improvement at Placer Dome KanownaBelle Gold Mine.[A]. Proceedings2005–37th Annual Meeting of the Canadian Mineral Processors.CIM [C].2005.171-201.
[96] Milot M, Desbiens, A., del Villar, R., Hodouin, D.,. Identifcation and Multivariable Nonlinear PredictiveControl of a Pilot fotation Column.[A]. XXI International Mineral Processing Congress.[C]. IFAC,2000.
[97] Pu M, Gupta, Y., Taweel, A.A.,. Model Predictive Control of fotation Columns.[A]. Column’91–Proceedings of an International Conference on Column Flotation,[C]. Canadian Institute of Mining,Metallurgy and Petroleum,,1991.467-478.
[98] Persechini M A M, Peres A E C, Jota F G F G. Control Strategy for a Column Flotation Process [J].Control Engineering Practice,2004,12(8):963-976.
[99] Maldonado M, Desbiens, A., del Villar, R., Girgin, H., Gomez, C.,2008b. Decentralized Control of aPilotfotation Column: A3*3System.[A].6th International Copper Conference [C].2007.241-254.
[100] Carvalho T, Dur o F, Fernandes C. Dynamic Characterization of Column Flotation Process LaboratoryCase Study [J]. Minerals Engineering,1999,12(11):1339-1346.
[101] Hirajima T, Takamori, T., Tsunekawa, T., Matsubara, T., Oshima, K., Imai, T., Sawaki,, K. K, S. TheApplication of Fuzzy Logic to Control Concentrate Grade in Column fotation at Toyoha Mines.[A].Agar, G., Huls, B., Hyma, D.(Eds.), Column’91–Proceedings of an International Conference onColumn Flotation,[C]. Canadian Institute of Mining, Metallurgy and Petroleum,,1991.375-407.
[102] McKay J, Ynchausti, R.,. Expert Supervisory Control of fotation Columns.[A]. Gomez, C.O., Finch,J.A.(Eds.), Column’96–Proceedings of the International Symposium on Column Flotation. MineralSciences and Engineering Section of the Metallurgical Society of CIM and Canadian Mineral ProcessorsDivision of CIM [C].1996.353-367.
[103] Karr C L. Adaptive Process Control Using Biological Paradigms [J]. Journal of Network and ComputerApplications,1996,19(1):21-44.
[104]樊华.大型浮选机自动控制系统的探讨[J].赤子,2012,(12):177-177.
[105]邵之苗,高扬.铜选矿厂浮选过程控制系统的研究与应用[J].矿冶,2012,(1):73-76.
[106]沈孝忠,刘强,赵霞.基于s7—300Plc的浮选自动加药控制系统[J].自动化应用,2012,(5):45-46.
[107]孙振海,罗成名,宋风华.浮选自动跟踪加药控制系统[J].煤炭技术,2011,(4):119-120.
[108]翟延忠,周娟华.基于精密计量泵的浮选加药控制平台[J].选煤技术,2011,(2):53-56.
[109]张敏,张建强,刘炯天,等.浮选柱泡沫层检测控制系统的研究[J].矿山机械,2010,(19):107-110.
[110]钟刚,王东,修德江.浮选加药自动控制系统[J].矿业工程,2012,(2):36-38.
[111]尚海洋.磨矿浮选控制系统开发与应用[J].矿冶,2009,18(2):89-92.
[112]陈子鸣,茹青.用于浮选控制的模糊自适应专家系统结构设计原理[J].有色金属：选矿部分,1993,(5):36-38.
[113]李海波,郑秀萍,柴天佑.浮选过程混合智能优化设定控制方法[J].东北大学学报：自然科学版,2012,(1):1-5.
[114]张勇,丛峰武,王伟.粗集神经网络在阳离子反浮选控制中的应用[J].控制工程,2004,(z1):85-88.
[115]张勇,王峰,潘学军,等.粗糙集理论在阳离子反浮选控制中的应用[J].中南工业大学学报,2003,(4):368-372.
[116]杨小平,田敬秋.煤泥浮选过程控制技术进展[J].煤炭科学技术,1999,(5):38-40.
[117]何胜春,丰日兵.邢台选煤厂煤泥浮选控制系统的研究[J].选煤技术,2000,5:16-17.
[118]杨小平,王汝赡,等.选煤厂浮选过程检测与控制的研究[J].煤炭科学技术,2001,(8):1-4.
[119]杨小平,许德平.煤泥浮选优化控制系统的设计[J].煤炭学报,2000,(2):200-202.
[120]杨小平,吴翠平.煤泥浮选计算机控制的研究[J].煤矿自动化,2000,(4):1-3.
[121]杨小平,王汝赡,等.煤泥浮选测控系统的研究[J].中国矿业大学学报,2001,(1):39-42.
[122]张举丘,杨小平.淮北选煤厂浮选控制系统研究[J].中国煤炭,2011,(3):82-84.[123]陈玉强肖,李桂权.神经网络及模糊控制在振动主动控制中的应用[M].哈尔滨:黑龙江教育出版社,2007:15-19.
[124] Vapnik V. The nature of satistical learing theory [M]. NewYork: Springer-verlag,2000.
[125] Kuwabara S, Tamura N, Yamanaka Y, et al. Sympathetic Sweat Responses and Skin Vasomotor Reflexesin Carpal Tunnel Syndrome [J]. Clinical Neurology and Neurosurgery,2008,110(7):691-695.
[126] Gao L, Ren S. Prediction of Nitrophenol-Type Compounds Using Chemometrics and Spectrophotometry[J]. Analytical Biochemistry,2010,405(2):184-191.
[127] Huang C-J, Lai W K, Luo R-L, et al. Application of Support Vector Machines to Bandwidth Reservationin Sectored Cellular Communications [J]. Engineering Applications of Artificial Intelligence,2005,18(5):585-594.
[128] Thissen U, van Brakel R, de Weijer A P, et al. Using Support Vector Machines for Time Series Prediction[J]. Chemometrics and Intelligent Laboratory Systems,2003,69(1–2):35-49.
[129] Shin H, Cho S. Response Modeling with Support Vector Machines [J]. Expert Systems with Applications,2006,30(4):746-760.
[130] Li Y, Su L, Zhang X, et al. Prediction of Association Constants of Cesium Chelates Based on UniformDesign Optimized Support Vector Machine [J]. Chemometrics and Intelligent Laboratory Systems,2011,105(1):106-113.
[131] Huang Q, Zhuang Y, Qiao X, et al. Predicted Model of Aggregation of Molecules in Chinese HerbalDrugs by Support Vector Machines [J]. Acta Physico-Chimica Sinica,2007,23(8):1141-1144.
[132] Huo H-B, Zhu X-J, Cao G-Y. Nonlinear Modeling of a Sofc Stack Based on a Least Squares SupportVector Machine [J]. Journal of Power Sources,2006,162(2):1220-1225.
[133] Zhao B. Modeling Pressure Drop Coefficient for Cyclone Separators: A Support Vector MachineApproach [J]. Chemical Engineering Science,2009,64(19):4131-4136.
[134] Zhong Z-D, Zhu X-J, Cao G-Y. Modeling a Pemfc by a Support Vector Machine [J]. Journal of PowerSources,2006,160(1):293-298.
[135] Iplikci S. A Support Vector Machine Based Control Application to the Experimental Three-Tank System[J]. ISATransactions,2010,49(3):376-386.
[136] Chen S-W, Li Z-R, Li X-Y. Prediction of Antifungal Activity by Support Vector Machine Approach [J].Journal of Molecular Structure: THEOCHEM,2005,731(1–3):73-81.
[137] Li H, Sun J. Predicting Business Failure Using Support Vector Machines with Straightforward Wrapper:ARe-Sampling Study [J]. Expert Systems withApplications,2011,38(10):12747-12756.
[138] Liu Q, Yang M, Lei J, et al. Modeling and Optimizing Parabolic Trough Solar Collector Systems Usingthe Least Squares Support Vector Machine Method [J]. Solar Energy,2012,86(7):1973-1980.
[139] Wu Q. Hybrid Model Based on Wavelet Support Vector Machine and Modified Genetic AlgorithmPenalizing Gaussian Noises for Power Load Forecasts [J]. Expert Systems with Applications,2011,38(1):379-385.
[140]王志新.智能模糊控制器的若干问题研究[M].北京:知识产权出版社,2009:
[141]曹立学.基于模糊自适应pid的温度控制系统的设计[J].计算机与数字工程,2012,(6):139-141.
[142]张丽萍,马立新,金珍珍.模糊自适应pid炉温控制系统的设计[J].热加工工艺,2012,(14):234-236.
[143]皮柳杨,陈希有,李红.基于模糊自适应pid控制器的智能供水系统[J].电气自动化,2012,(3):79-81.
[144]汤秀芬.模糊自适应pid控制器参数整定的研究[J].工业控制计算机,2012,(2):55-56.
[145]李硬,薛智.多普勒超声波流量计测量中异常现象分析[J].中国仪器仪表,2004,(9):41-42.
[146]黄玉明,杨士强.彩色ccd摄象机的光强—灰度响应线性化及其在颜色定标中的应用[J].机器人,1992,(1):38-44.
[147]周世生.印刷色彩学[M].北京:北京工业出版社,2008:169-170.
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