WO2020134801A1 - Energy-saving optimization method of cement raw material vertical mill system - Google Patents
Energy-saving optimization method of cement raw material vertical mill system Download PDFInfo
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- WO2020134801A1 WO2020134801A1 PCT/CN2019/120980 CN2019120980W WO2020134801A1 WO 2020134801 A1 WO2020134801 A1 WO 2020134801A1 CN 2019120980 W CN2019120980 W CN 2019120980W WO 2020134801 A1 WO2020134801 A1 WO 2020134801A1
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- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000005457 optimization Methods 0.000 title claims abstract description 26
- 239000004568 cement Substances 0.000 title claims abstract description 18
- 239000002994 raw material Substances 0.000 title claims abstract description 16
- 238000005192 partition Methods 0.000 claims abstract description 44
- 238000010801 machine learning Methods 0.000 claims abstract description 6
- 238000009826 distribution Methods 0.000 claims description 25
- 238000012216 screening Methods 0.000 claims description 15
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000006870 function Effects 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 abstract description 10
- 238000003801 milling Methods 0.000 abstract 1
- 230000003044 adaptive effect Effects 0.000 description 9
- 238000001914 filtration Methods 0.000 description 7
- 230000009467 reduction Effects 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 238000012937 correction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000000843 powder Substances 0.000 description 1
Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
- B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
- B02C23/00—Auxiliary methods or auxiliary devices or accessories specially adapted for crushing or disintegrating not provided for in preceding groups or not specially adapted to apparatus covered by a single preceding group
- B02C23/02—Feeding devices
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
- B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
- B02C25/00—Control arrangements specially adapted for crushing or disintegrating
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
Definitions
- the invention relates to the technical field of energy saving and consumption reduction, in particular to an energy saving optimization method of a cement raw material vertical mill system.
- the second difficulty is that it requires workers to operate according to the recommendations of experts, that is, according to the approximate operation setting values given by man. Different experts have different operation setting values, and they cannot achieve energy saving and consumption reduction.
- the present invention provides an energy-saving optimization method for a cement raw material vertical mill system. Without changing any structure and principle of the production equipment, without adding additional measuring points, or without affecting normal production, the machine learning Method, intelligently provide safe, convenient and reasonable auxiliary decision-making.
- the method includes: collecting historical operation data to form multiple historical operation models, the historical operation data including controllable variables, the controllable variables including mill grinding pressure, mill inlet negative pressure, mill outlet negative pressure, Hot air valve opening, cold air valve opening, mill inlet temperature, mill outlet temperature, fan valve opening;
- the feeding amount of the mill is divided into zones;
- the historical operations of the same feed volume partition are sorted to obtain the optimal historical operation records of a feed volume partition, and the optimal historical operation records of all feed volume partitions are merged to form the most Excellent recommendation form;
- the optimal operation recommendation of the device is obtained according to the real-time operating conditions and the optimal recommendation table.
- the historical operation data is subjected to box-line graph distribution statistics through machine learning to obtain an upper limit and a lower limit, and excluding abnormal data other than the upper limit and the lower limit.
- the specific logic is as follows:
- VMIN> ⁇ -3 ⁇ and VMAX> ⁇ +3 ⁇ the range of (VMIN, ⁇ +3 ⁇ ) is taken as the data screening rule
- VMIN ⁇ -3 ⁇ and VMAX ⁇ +3 ⁇ the range of ( ⁇ -3 ⁇ , VMAX) is taken as the data screening rule
- VMIN> ⁇ -3 ⁇ and VMAX ⁇ +3 ⁇ the range of (VMIN, VMAX) is taken as the data screening rule
- VMIN is the minimum value of the variable, the maximum value of the VMAX variable, the mean of the ⁇ variable, and the standard deviation of the ⁇ variable.
- the historical operation data is discretized, and then the historical operation model is adaptively encoded with high quality.
- the frequency of each code in the same feed volume partition is counted and sorted in descending order according to the frequency; the single-electromechanical consumption corresponding to the same code is counted, the same code is integrated and deduplicated to form an unique code, and the corresponding code
- the single electromechanical consumption is equal to the average of all the single electromechanical consumptions with the same encoding. Obtain the single electromechanical consumption corresponding to the top ten unique codes in descending order, and select the lowest single electromechanical consumption as the best historical operation record of the feeding volume partition where it is obtained. The corresponding optimal recommendation table.
- the coding corresponds to the historical operation model one by one, and the coding is a mapping of each variable to the model, which can locate the model, that is, the corresponding model can be quickly found according to the variable, and the coding method can not only greatly reduce the memory of the sample Space, improve model training speed, and can greatly improve learning accuracy.
- the code represents the grinding pressure of the mill, the negative pressure of the mill inlet, the negative pressure of the mill outlet, the opening of the hot air valve, the opening of the cold air valve, the inlet temperature of the mill, the outlet temperature of the mill, the opening of the fan valve and other variables. data range.
- coding rounding function ((variable-minimum value of variable) / variable step size),
- This application uses adaptive high-quality coding, and its minimum value and variable step size will not incorporate any subjective experience, which depends on the data itself and deviates from artificial fixed subjective experience.
- variable step size depends on the amount of change in data accuracy:
- the recommended optimal historical operation record is decoded, and the decoding calculation formula is as follows:
- Variable variable code * variable step + minimum value of variable.
- the operation model generated by the operation forms a real-time encoding, and matches the real-time encoding with the unique encoding .
- the matching distance is zero and the single-electromechanical consumption of the real-time encoding is lower than that of the single-coded single-electromechanical consumption, it is judged as a better operation record, the operation record is stored, and the average value of the single-electromechanical consumption is recalculated to obtain the unique code
- the new single electromechanical consumption and then compare the frequency and single electromechanical consumption with other unique codes of the feed volume partition, so as to update the optimal historical operation record of the feed volume partition; when the match does not match the distance of zero alone
- the real-time code is listed as a new unique code, and then compared with the frequency of the other unique codes of the feed volume partition and the single electro
- the present invention has the following advantages:
- Adopting adaptive filtering rules and adaptive high-quality coding which deviates from the artificial fixed subjective experience, does not incorporate any subjective viewing methods or experience after observing data statistics, first adaptively adjust the filtering rules to extract safe and normal Value data, and then adjust the coding formula parameters through adaptive learning, so as to achieve the potential value of historical data, and the recommended optimization scheme is objective, reasonable, reliable and safe;
- the online push speed of the present invention is fast, and it is only necessary to determine which feed volume partition it belongs to.
- the present invention has fast learning efficiency and includes an online update function.
- iterating the optimal recommendation table there is no need to learn for all the feed volume partition data, to obtain the optimal operation record of each feed volume partition, only to determine the corresponding feed volume partition according to the feed volume, and then to the corresponding Re-learn the partition data, and then just update the optimal historical operation record of the partition in the optimal recommendation table.
- Embodiment 1 is a schematic flowchart of Embodiment 1 of an energy-saving optimization method of a cement raw material vertical mill system of the present invention
- Embodiment 1 An energy-saving optimization method of a cement raw material vertical mill system, which does not change any structure and principle of production equipment, does not add additional measurement points, and does not affect normal production. It provides intelligently through machine learning methods. Safe, convenient and reasonable auxiliary decision-making.
- the method includes: collecting historical operation data to form a plurality of historical operation models, the historical operation data including controllable variables, regular filtering variables and variables with less correlation, the controllable variables including mill grinding pressure, grinding Machine inlet negative pressure, mill outlet negative pressure, hot air valve opening, cold air valve opening, mill inlet temperature, mill outlet temperature, fan valve opening, the rule filtering variables include mill vibration value and powder separator
- this embodiment only considers how to optimize related and controllable variables as optimization suggestions for energy saving and consumption reduction, so as to reduce energy consumption of the mill and circulating fan without affecting normal production.
- the feeding amount of the mill is divided into zones.
- the historical operations of the same feed volume partition are sorted to obtain the optimal historical operation records of a feed volume partition, and the optimal historical operation records of all feed volume partitions are merged to form the most Excellent recommendation form.
- Embodiment 2 Based on the first embodiment, the historical operation data is analyzed and processed, specifically: before obtaining the optimal historical operation record, the box operation graph distribution statistics are performed on the historical operation data by machine learning, Obtain the upper and lower limits, remove the abnormal data except the upper and lower limits, and then, through the normal distribution of self-learning data, adaptively determine different data screening rules, the specific logic is as follows:
- VMIN> ⁇ -3 ⁇ and VMAX> ⁇ +3 ⁇ the range of (VMIN, ⁇ +3 ⁇ ) is taken as the data screening rule
- VMIN ⁇ -3 ⁇ and VMAX ⁇ +3 ⁇ the range of ( ⁇ -3 ⁇ , VMAX) is taken as the data screening rule
- VMIN> ⁇ -3 ⁇ and VMAX ⁇ +3 ⁇ the range of (VMIN, VMAX) is taken as the data screening rule
- VMIN is the minimum value of the variable, the maximum value of the VMAX variable, the mean of the ⁇ variable, the standard deviation of the ⁇ variable, and the rigidity of the initial target mill current and the high-voltage current of the circulating fan meet the following conditions:
- the historical operation data is discretized, and then the historical operation model is adaptively encoded with high quality, such as: X11, X12, X13,..., X1N, see Figure 1.
- the frequency of each code in the same feed volume partition is counted and sorted in descending order according to the frequency; the single electromechanical consumption Y1 corresponding to the same code is counted, and the same code is integrated and deduplicated to form an unique code, and the unique code corresponds to
- the single electromechanical consumption Y2 is equal to the average of all the single electromechanical consumption Y1 of the same code, and the single electromechanical consumption Y12 corresponding to the top ten unique codes in descending order is obtained.
- the lowest single electromechanical consumption is selected as the optimal historical operation of the feed volume partition Record Ybest (Y optimal) and obtain the corresponding optimal recommendation table.
- the coding corresponds to the historical operation model one by one, and the coding is a mapping of each variable to the model, which can locate the model, that is, the corresponding model can be quickly found according to the variable, and the coding method can not only greatly reduce the memory of the sample Space, improve model training speed, and can greatly improve learning accuracy.
- the code represents the grinding pressure of the mill, the negative pressure of the mill inlet, the negative pressure of the mill outlet, the opening of the hot air valve, the opening of the cold air valve, the inlet temperature of the mill, the outlet temperature of the mill, the opening of the fan valve and other variables. data range.
- coding rounding function ((variable-minimum value of variable) / variable step size),
- This application uses adaptive high-quality coding, and its minimum value and variable step size will not incorporate any subjective experience, which depends on the data itself and deviates from artificial fixed subjective experience.
- variable step size depends on the amount of change in data accuracy:
- the recommended optimal historical operation record is decoded, and the decoding calculation formula is as follows:
- Variable variable code * variable step + minimum value of variable.
- the operation model generated by the operation forms a real-time code, and the real-time code is matched with the unique code.
- the matching distance is zero and the single-electromechanical consumption of the real-time code is lower than that of the single-coded single electromechanical, it is judged as a better operation record.
- the operation record is stored, and the average value of the single electromechanical consumption is recalculated, that is, the new single electromechanical consumption of the unique code is obtained, and then the frequency and the single electromechanical consumption are compared with the other unique codes of the feed volume partition to update the feed.
- the best historical operation record of the volume partition
- the real-time code is listed as a new unique code, and then compared with the other unique codes in the feed volume partition where the frequency and single electrical consumption are compared to update the feed volume.
- the best historical operation record of the partition is listed as a new unique code, and then compared with the other unique codes in the feed volume partition where the frequency and single electrical consumption are compared to update the feed volume. The best historical operation record of the partition.
- Adopting adaptive filtering rules and adaptive high-quality coding which deviates from the artificial fixed subjective experience, does not incorporate any subjective viewing methods or experience after observing data statistics, first adaptively adjust the filtering rules to extract safe and normal Value data, and then adjust the coding formula parameters through adaptive learning, so as to achieve the potential value of historical data, and the recommended optimization scheme is objective, reasonable, reliable and safe;
- the online push speed of the present invention is fast. Only by judging which feed volume partition it belongs to, the corresponding recommendation recommendations can be quickly and efficiently extracted from the stored optimal recommendation table. It has fast efficiency and recommendation speed, which is far from satisfactory The need for a recommendation every 3s;
- the present invention has fast learning efficiency and includes an online update function.
- iterating the optimal recommendation table there is no need to learn for all the feed volume partition data, to obtain the optimal operation record of each feed volume partition, only to determine the corresponding feed volume partition according to the feed volume, and then to the corresponding Re-learn the partition data, and then just update the optimal historical operation record of the partition in the optimal recommendation table.
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Abstract
Description
编码后,统计同一个喂料量分区中各个编码出现的频率,并根据频率进行降序排序;统计相同编码对应的单机电耗Y1,将相同编码进行整合去重,形成独自编码,且独自编码对应的单机电耗Y2等于所有相同编码的单机电耗Y1的均值,获得降序排序中前十位独自编码对应的单机电耗Y12,选择最低的单机电耗作为所在喂料量分区的最优历史操作记录Ybest(Y最优),同时获得相应的最优推荐表。[Correction 13.03.2020 in accordance with Rule 91]
After coding, the frequency of each code in the same feed volume partition is counted and sorted in descending order according to the frequency; the single electromechanical consumption Y1 corresponding to the same code is counted, and the same code is integrated and deduplicated to form an unique code, and the unique code corresponds to The single electromechanical consumption Y2 is equal to the average of all the single electromechanical consumption Y1 of the same code, and the single electromechanical consumption Y12 corresponding to the top ten unique codes in descending order is obtained. The lowest single electromechanical consumption is selected as the optimal historical operation of the feed volume partition Record Ybest (Y optimal) and obtain the corresponding optimal recommendation table.
Claims (8)
- 一种水泥原料立磨***的节能优化方法,其特征在于,An energy-saving optimization method for a cement raw material vertical mill system is characterized by:采集历史操作数据,形成多个历史操作模型,所述历史操作数据包括可控变量,所述可控变量包括磨机研磨压力、磨机进口负压、磨机出口负压、热风阀门开度、冷风阀门开度、磨机入口温度、磨机出口温度、风机阀门开度;Collect historical operation data to form multiple historical operation models. The historical operation data includes controllable variables including mill grinding pressure, mill inlet negative pressure, mill outlet negative pressure, hot air valve opening, Cold air valve opening, mill inlet temperature, mill outlet temperature, fan valve opening;根据喂料量获得喂料量分区;According to the feed volume to obtain the feed volume partition;根据单机电耗量的高低,对同一个喂料量分区的历史操作进行排序,获得一个喂料量分区的最优历史操作记录,将所有喂料量分区的最优历史操作记录合并,形成最优推荐表。According to the level of single electromechanical consumption, the historical operations of the same feed volume partition are sorted to obtain the optimal historical operation records of a feed volume partition, and the optimal historical operation records of all feed volume partitions are merged to form the most Excellent recommendation form.
- 根据权利要求1所述的一种水泥原料立磨***的节能优化方法,其特征在于,根据实时工况和所述最优推荐表获得设备的最优操作建议。The energy-saving optimization method of a cement raw material vertical mill system according to claim 1, wherein the optimal operation recommendation of the equipment is obtained according to the real-time working conditions and the optimal recommendation table.A
- 根据权利要求1所述的一种水泥原料立磨***的节能优化方法,其特征在于,在获得最优历史操作记录前,通过机器学习的方式对所述历史操作数据进行箱线图分布统计,得到上限和下限,剔除上限和下限之外的异常数据。An energy-saving optimization method for a cement raw material vertical mill system according to claim 1, characterized in that, before obtaining the optimal historical operation record, the box-line graph distribution statistics are performed on the historical operation data by machine learning, Obtain upper and lower limits, and remove abnormal data except the upper and lower limits.
- 根据权利要求3所述的一种水泥原料立磨***的节能优化方法,其特征在于,剔除历史操作数据中的异常数据后,自适应确定不同数据筛选规则,通过自主学习数据的正态分布情况,具体逻辑如下:An energy-saving optimization method for a cement raw material vertical grinding system according to claim 3, characterized in that after removing abnormal data in the historical operation data, adaptively determine different data screening rules and learn the normal distribution of the data by autonomous learning The specific logic is as follows:若VMIN<μ-3σ且VMAX>μ+3σ,则取(μ-3σ,μ+3σ)范围作为数据筛选规则;If VMIN<μ-3σ and VMAX>μ+3σ, the range of (μ-3σ, μ+3σ) is taken as the data screening rule;若VMIN>μ-3σ且VMAX>μ+3σ,则取(VMIN ,μ+3σ)范围作为数据筛选规则;If VMIN>μ-3σ and VMAX>μ+3σ, the range of (VMIN, μ+3σ) is taken as the data screening rule;若VMIN<μ-3σ且VMAX<μ+3σ,则取(μ-3σ,VMAX)范围作为数据筛选规则;If VMIN<μ-3σ and VMAX<μ+3σ, the range of (μ-3σ, VMAX) is taken as the data screening rule;若VMIN>μ-3σ且VMAX<μ+3σ,则取(VMIN ,VMAX)范围作为数据筛选规则;If VMIN>μ-3σ and VMAX<μ+3σ, the range of (VMIN, VMAX) is taken as the data screening rule;其中,VMIN为变量最小值,VMAX变量最大值,μ变量均值,σ变量标准差。Among them, VMIN is the minimum value of the variable, the maximum value of the VMAX variable, the mean of the μ variable, and the standard deviation of the σ variable.A
- 根据权利要求4所述的一种水泥原料立磨***的节能优化方法,其特征在于,对历史操作数据进行离散化处理且对历史操作模型进行自适应优质编码;统计同一个喂料量分区中各个编码出现的频率,并根据频率进行降序排序;统计相同编码对应的单机电耗,将相同编码进行整合去重,形成独自编码,且独自编码对应的单机电耗等于所有相同编码的单机电耗的均值,获得降序排序中前十位独自编码对应的单机电耗,选择单机电耗最低的作为所在喂料量分区的最优历史操作记录,同时获得相应的最优推荐表。An energy-saving optimization method for a cement raw material vertical mill system according to claim 4, characterized in that the historical operation data is discretized and the historical operation model is adaptively coded with high quality; statistics are in the same feed volume partition The frequency of occurrence of each code, and sort them in descending order according to the frequency; count the single electromechanical consumption corresponding to the same code, integrate and deduplicate the same code to form an unique code, and the single electromechanical consumption corresponding to the unique code is equal to all the single electromechanical consumption of the same code The average value of the first ten digits in the descending order is coded corresponding to the single electromechanical consumption, and the lowest single electromechanical consumption is selected as the optimal historical operation record of the feed volume partition, and the corresponding optimal recommendation table is obtained.A
- 根据权利要求5所述的一种水泥原料立磨***的节能优化方法,其特征在于,所述编码与所述历史操作模型一一对应,具体编码计算公式为:编码=取整函数((变量–变量最低值)/变量步长),An energy-saving optimization method for a cement raw material vertical mill system according to claim 5, characterized in that the codes correspond one-to-one with the historical operation model, and the specific code calculation formula is: code = rounding function ((variable – Variable minimum value)/variable step size),所述变量最低值取决于正太分布情况:The lowest value of the variable depends on the distribution of the positive and negative:若是标准正态分布数据,则变量最低值=均值-3*标准差;In the case of standard normal distribution data, the lowest value of the variable = mean-3 * standard deviation;若是左偏态(偏态系数SK<-0 .1)分布数据(分布的左侧有长尾),当数据集中(峰态系数KT>0),则变量最低值=均值-3*标准差,当数据分散(峰态系数KT<0),则变量最低值=均值-标准差;If it is left skewed (skew coefficient SK<-0.1) distribution data (the left side of the distribution has a long tail), when the data set (kurtosis coefficient KT>0), the lowest value of the variable = mean-3 * standard deviation , When the data is dispersed (kurtosis coefficient KT<0), the lowest value of the variable = mean-standard deviation;若是右偏态(偏态系数SK>0 .1)分布数据(分布的右侧有长尾),当数据集中(峰态系数KT>0),则变量最低值=均值-3*标准差,当数据分散(峰态系数KT<0),则变量最低值=均值-标准差;If it is right-skewed (skewness coefficient SK>0.1) distribution data (the distribution has a long tail on the right side), when the data set (kurtosis coefficient KT>0), then the lowest value of the variable = mean-3 * standard deviation, When the data are scattered (kurtosis coefficient KT<0), the lowest value of the variable=mean-standard deviation;所述变量步长取决于数据精度变化量:The variable step size depends on the amount of change in data accuracy:DIFF=(均值+3*标准差)-(均值-3*标准差)DIFF = (mean + 3 * standard deviation)-(mean-3 * standard deviation)IF DIFF<=5AND DIFF>=0 .5:IF DIFF<=5AND DIFF>=0.5:变量步长=0 .1;Variable step size = 0.1;ELIF DIFF>5AND DIFF<=50:ELIF DIFF>5AND DIFF<=50:变量步长=1;Variable step size = 1;ELIF DIFF<0 .5:ELIF DIFF<0.5:变量步长=0 .01;Variable step size = 0.01;ELIF DIFF>50:ELIF DIFF>50:变量步长=10。Variable step size=10.A
- 根据权利要求5所述的一种水泥原料立磨***的节能优化方法,其特征在于,根据最优推荐表进行最优操作建议时,对推荐的最优历史操作记录的编码进行解码,解码计算公式如下:变量=变量编码*变量步长+变量最低值。An energy-saving optimization method for a cement raw material vertical mill system according to claim 5, characterized in that, when performing the optimal operation recommendation according to the optimal recommendation table, the recommended optimal historical operation record is decoded and decoded and calculated The formula is as follows: variable=variable code*variable step size+variable minimum value.
- 根据权利要求5所述的一种水泥原料立磨***的节能优化方法,其特征在于,根据实时工况,对最优推荐表对应的工况的最优操作建议进行迭代优化,具体迭代优化过程为,操作产生的操作模型形成实时编码,将实时编码与所述独自编码匹配,当匹配距离为零且实时编码的单机电耗低于独自编码的单机电耗时,判断为较优的操作记录,将该操作记录存储,且重新计算单机电耗均值,即获得该独自编码的新的单机电耗,再与所在喂料量分区的其它独自编码比较出现的频率和单机电耗,从而更新所在喂料量分区的最优历史操作记录;当匹配不到距离为零的独自编码时,将所述实时编码列为新的独自编码,再与所在喂料量分区的其它独自编码比较出现的频率和单机电耗,从而更新所在喂料量分区的最优历史操作记录。An energy-saving optimization method for a cement raw material vertical mill system according to claim 5, characterized in that iterative optimization is performed on the optimal operation recommendations for the operating conditions corresponding to the optimal recommendation table according to the real-time operating conditions, and the specific iterative optimization process For the operation model generated by the operation to form a real-time code, the real-time code is matched with the unique code. When the matching distance is zero and the single-electromechanical consumption of the real-time code is lower than the single-coded single electromechanical consumption, it is judged as a better operation record , Store the operation record, and recalculate the average value of the single electromechanical consumption, that is, obtain the new single electromechanical consumption of the unique code, and then compare the frequency and single electromechanical consumption with the other unique codes of the feed volume partition to update the location The optimal historical operation record of the feed volume partition; when no unique code with a distance of zero is matched, the real-time code is listed as a new unique code, and then compared with the frequency of other unique codes in the feed volume partition And single machine consumption, so as to update the optimal historical operation record of the feed volume partition.A
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CN112949183A (en) * | 2021-03-04 | 2021-06-11 | 上海交通大学宁波人工智能研究院 | System and method for detecting abnormal working conditions of cement raw material vertical mill system |
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CN110471375B (en) * | 2019-07-08 | 2020-07-28 | 杭州电子科技大学 | Anti-interference optimization tracking method for cement denitration process |
CN112506134A (en) * | 2019-09-16 | 2021-03-16 | 阿里巴巴集团控股有限公司 | Method, device and equipment for determining control variable value |
TR202006947A2 (en) * | 2020-05-04 | 2021-11-22 | Sabanci Dijital Teknoloji Hizmetleri Anonim Sirketi | System and method for ai controlling mill operations to lower electricity consumption |
CN116474928B (en) * | 2023-06-25 | 2023-09-26 | 中才邦业(杭州)智能技术有限公司 | Cement mill energy consumption optimization method and system |
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