WO2020078008A1 - 一种智慧化电除尘节能方法 - Google Patents
一种智慧化电除尘节能方法 Download PDFInfo
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- WO2020078008A1 WO2020078008A1 PCT/CN2019/089475 CN2019089475W WO2020078008A1 WO 2020078008 A1 WO2020078008 A1 WO 2020078008A1 CN 2019089475 W CN2019089475 W CN 2019089475W WO 2020078008 A1 WO2020078008 A1 WO 2020078008A1
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- machine learning
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- saving method
- electric dust
- dust removal
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B03—SEPARATION OF SOLID MATERIALS USING LIQUIDS OR USING PNEUMATIC TABLES OR JIGS; MAGNETIC OR ELECTROSTATIC SEPARATION OF SOLID MATERIALS FROM SOLID MATERIALS OR FLUIDS; SEPARATION BY HIGH-VOLTAGE ELECTRIC FIELDS
- B03C—MAGNETIC OR ELECTROSTATIC SEPARATION OF SOLID MATERIALS FROM SOLID MATERIALS OR FLUIDS; SEPARATION BY HIGH-VOLTAGE ELECTRIC FIELDS
- B03C3/00—Separating dispersed particles from gases or vapour, e.g. air, by electrostatic effect
- B03C3/34—Constructional details or accessories or operation thereof
- B03C3/66—Applications of electricity supply techniques
- B03C3/68—Control systems therefor
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- the invention relates to the field of energy saving and consumption reduction, in particular to a smart energy-saving method of electric dust removal.
- Electric dust collector is a necessary supporting equipment for thermal power plants. Its function is to remove the particulate smoke in the flue gas discharged from coal or oil-fired boilers, thereby greatly reducing the amount of smoke discharged into the atmosphere, which is to improve environmental pollution. , Important environmental protection equipment to improve air quality.
- Thermal power plants are usually installed with dry electric dust collectors and wet electric dust collectors at the same time. Due to the forward-looking performance of environmental protection equipment and the improvement of national requirements for coal quality, the current total power of dust collectors far exceeds the practical needs, but the on-site workers lack effective guidance. It is easy to cause waste of electrical energy and increase operating costs.
- the invention provides a smart energy-saving method for electric dust removal.
- the steps of the method include:
- S10 Collect basic working condition information through the data flow trajectory tracking module in the system
- S40 Collect the changes of the influencing factors of the real-time dust emission through the data flow tracing module in the system, and the influencing factors include the power supply mode, current limit and voltage limit of each electric field of the dust collector;
- the machine learning module in the system is triggered to perform machine learning to obtain a target result, which includes the current dust collector operation scheme and the corresponding current total energy consumption And the corresponding current dust emission concentration; and transmit the target results to the online knowledge network.
- a target result which includes the current dust collector operation scheme and the corresponding current total energy consumption And the corresponding current dust emission concentration; and transmit the target results to the online knowledge network.
- different target results are sorted according to the current total energy consumption level, which can be set by the system. The name is reserved and the best operation plan is obtained, otherwise skip.
- existing algorithms such as quick sorting algorithm and random deep forest algorithm can be used.
- S60 Query the online knowledge network to obtain the historical operation plan with the same model instance and the lowest total energy consumption under the emission standard, recommend the optimization plan to the site staff, and guide the site workers to operate the dust collector reasonably.
- algorithms such as the random deep forest algorithm can be used. .
- step S30 the monitoring of the real-time emission of dust can be carried out at full time or at intervals, and the sequence of steps in S30 can be adjusted arbitrarily.
- the basic working condition information includes boiler load information and chemical sub-information, and the boiler load information and chemical sub-information are divided into several levels, for example, the boiler load is divided into below 40%, 40% -60%, 60 % -80%, 80% and above.
- the chemical information includes received base ash, received base carbon, received base hydrogen, received base moisture, received base nitrogen, received base oxygen, received base low calorific value, received One or more combinations of base sulfur divide the received bases into 16 equal parts, and each equal part corresponds to a model instance.
- the dust collector includes a dry electric dust collector and a wet electric dust collector.
- the learning time is equal to the sum of the current learning time and the delayed learning time, that is, during the learning process, the system learns the current operation and also learns to further adjust the current operation on site.
- the machine learning module when the machine learning module performs machine learning, it detects that an electric spark occurs in the electric field, the voltage is zero, and the dust removal capacity is zero. The voltage needs to be reduced to prevent the generation of electric sparks, and the learning is terminated at this time.
- the target result is processed using a weighted average method, and historical knowledge points of the same working condition are merged. The closer the time is to the current time, the higher the experience weight.
- the S50 includes S51: the dust collector is operated by an experienced technician for a period of time, and the system can only guide the on-site workers to operate after the system performs machine learning.
- the S60 includes S61: a small-step iterative test, that is, the on-site worker operates with a small amplitude, and the system judges whether the operation direction is reasonable.
- the intelligent electric dust removal energy-saving method proposed by the present invention has the following advantages:
- on-site workers can iterate in small steps to fine-tune the best plan to further optimize the operation plan.
- FIG. 1 is a flow chart of a method for intelligent energy saving of electric dust removal according to the present invention.
- Embodiment 1 A smart energy-saving method for electric dust removal, the steps of the method include:
- S10 Collect basic working condition information through the data flow trajectory tracking module in the system
- the basic working condition information is graded, and each basic working condition corresponds to a model instance.
- the basic working condition information includes boiler load information and chemical sub-information, and the boiler load information and The chemical information is divided into several levels. If the boiler load is divided into four grades below 40%, 40% -60%, 60% -80%, and above 80%, each grade corresponds to a model instance.
- the chemical information includes received base ash, received base carbon, received base hydrogen, received base moisture, received base nitrogen, received base oxygen, received base low calorific value, received base sulfur A combination of one or more types divides each received base into 16 equal parts, and each equal part corresponds to a model instance.
- the influencing factors of the real-time dust emission change include the power supply mode, current limit and voltage limit of each electric field of the dry electric dust collector and wet electric dust collector;
- the machine learning module in the system is triggered to perform machine learning, generate a learning plan, execute the learning plan and obtain a target result, the target result includes the current dust collector operation plan, Corresponding current total energy consumption and corresponding current dust emission concentration; upload the target results to the online knowledge network.
- different target results are sorted according to the current total energy consumption, which can be set by the system , If the top n are reserved, and get the best operation plan, otherwise skip.
- experienced technicians operate the dust collector for a period of time. After the system performs machine learning, the system can guide the on-site workers to operate.
- the learning time is equal to the sum of the current learning time and the delayed learning time, that is, during the learning process, the system learns the current operation, and also learns to further adjust the current operation on the spot.
- the system conducts regular inspections such as zero spark, and detects electric sparks in the electric field. At this time, the voltage is zero and the dust removal capacity is zero. The voltage needs to be reduced to prevent the generation of electric sparks. At this time, the learning is terminated.
- S60 Query the online knowledge network to obtain the historical operation plan with the same model instance and the lowest total energy consumption under the emission standard, recommend the optimization plan to the field staff, and guide the field workers to operate the dust collector reasonably.
- On-site workers can perform small-step iterative testing on the basis of the best operation plan when they are operating, that is, on-site workers pass a small-scale operation, and the system determines whether the operation direction is reasonable.
- Embodiment 2 Considering that the aging of the electric field device and the device update will cause different power consumption and dust emission results under the same working conditions and influencing factors, the machine learning module uses the weighted average method for the target result when performing machine learning Processing, merging historical knowledge points of the same working condition, the closer the time is to the current time, the higher the experience weight. This method makes the knowledge obtained by the online knowledge network have a time series nature.
- the present invention has the following advantages:
- on-site workers can iterate in small steps to fine-tune the best plan to further optimize the operation plan
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Abstract
Description
Claims (9)
- 一种智慧化电除尘节能方法,其特征在于,所述方法的步骤包括:S10:通过系统中的数据流轨迹跟踪模块采集基础工况信息;S20:将所述基础工况信息划分等级,每一种基础工况对应一种模型实例;S30:采集粉尘实时排放量,并对比排放标准;S40:通过系统中的数据流轨迹跟踪模块采集粉尘实时排放量的影响因素变化,所述影响因素包括除尘器的各电场的供电方式、电流极限和电压极限;S50:在同一种模型实例中,当所述影响因素发生变化时,触发系统中的机器学习模块进行机器学习,获取目标结果,所述目标结果包括当前除尘器操作方案、对应的当前总能耗和对应的当前粉尘排放浓度;并将目标结果传送到在线知识网中,同种模型实例中,不同的目标结果根据当前总能耗的高低进行排序,并得到最佳操作方案;S60:查询在线知识网,将获取同种模型实例且达到排放标准条件下的当前总能耗最低的历史操作方案,,指导现场工人合理操作除尘器。
- 根据权利要求1所述的一种智慧化电除尘节能方法,其特征在于,所述基础工况信息包括锅炉负荷信息和化学分信息,并将锅炉负荷信息和化学分信息划分为若干个等级。
- 根据权利要求2所述的一种智慧化电除尘节能方法,其特征在于,所述化学分信息包括收到基灰份、收到基碳、收到基氢、收到基水分、收到基氮、收到基氧、收到基低位发热量、收到基硫中的一种或多种的组合,将各种收到基做16等分,每一个等分对应一种模型实例。
- 根据权利要求1所述的一种智慧化电除尘节能方法,其特征在于,所述除尘器包括干电除尘器和湿电除尘器。
- 根据权利要求1所述的一种智慧化电除尘节能方法,其特征在于,所述机器学习模块进行机器学习时,学习时间等于当前学习时间和延迟学习时间之和。
- 根据权利要求1所述的一种智慧化电除尘节能方法,其特征在于,所述机器学习模块进行机器学习时,检测到电场出现电火花,则终止学习。
- 根据权利要求1所述的一种智慧化电除尘节能方法,其特征在于,所述机器学习模块进行机器学习时,对目标结果使用加权求平均法处理,合并相同工况的历史知识点,时间距离当前时间越近的,经验权重越高。
- 根据权利要求1所述的一种智慧化电除尘节能方法,其特征在于,所述S50包括S51:由有经验的技术人员操作除尘器,系统进行机器学习。
- 根据权利要求1所述的一种智慧化电除尘节能方法,其特征在于,所述S60包括S61:小步迭代测试,即现场工人通过小幅度操作,由系统判断操作方向是否合理。
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CN109225640A (zh) * | 2018-10-15 | 2019-01-18 | 厦门邑通软件科技有限公司 | 一种智慧化电除尘节能方法 |
CN113426264A (zh) * | 2021-07-15 | 2021-09-24 | 国电环境保护研究院有限公司 | 一种烟气净化岛智慧运行管控方法及管控平台 |
CN114114921A (zh) * | 2021-11-26 | 2022-03-01 | 华能平凉发电有限责任公司 | 一种除尘电源的控制方法及装置 |
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