WO2023221446A1 - 一种scr脱硝效能自动寻优调控方法和系统 - Google Patents

一种scr脱硝效能自动寻优调控方法和系统 Download PDF

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WO2023221446A1
WO2023221446A1 PCT/CN2022/133901 CN2022133901W WO2023221446A1 WO 2023221446 A1 WO2023221446 A1 WO 2023221446A1 CN 2022133901 W CN2022133901 W CN 2022133901W WO 2023221446 A1 WO2023221446 A1 WO 2023221446A1
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working condition
data
value
indicator
automatic optimization
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PCT/CN2022/133901
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English (en)
French (fr)
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冯前伟
张杨
杜振
江建平
裴煜坤
郭栋
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华电电力科学研究院有限公司
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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]
    • G05B19/41865—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] characterised by job scheduling, process planning, material flow
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D53/00—Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases, aerosols
    • B01D53/34—Chemical or biological purification of waste gases
    • B01D53/74—General processes for purification of waste gases; Apparatus or devices specially adapted therefor
    • B01D53/86—Catalytic processes
    • B01D53/8621—Removing nitrogen compounds
    • B01D53/8625—Nitrogen oxides
    • B01D53/8628—Processes characterised by a specific catalyst
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D53/00—Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases, aerosols
    • B01D53/34—Chemical or biological purification of waste gases
    • B01D53/74—General processes for purification of waste gases; Apparatus or devices specially adapted therefor
    • B01D53/86—Catalytic processes
    • B01D53/8621—Removing nitrogen compounds
    • B01D53/8625—Nitrogen oxides
    • B01D53/8631—Processes characterised by a specific device
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32252—Scheduling production, machining, job shop
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Definitions

  • This application relates to the technical field of industrial waste gas purification, and in particular to a method and system for automatic optimization and control of SCR denitration efficiency.
  • SCR (Selective Catalytic Reduction) denitrification also called selective catalytic reduction, is currently the most widely used flue gas denitrification technology in the world.
  • Ammonia (NH3) is usually used as a reducing agent to selectively reduce the generated NOx to N2. It has the advantages of no by-products, no secondary pollution, simple device structure, high extraction efficiency (up to more than 90%), reliable operation, and easy maintenance.
  • Low-nitrogen combustion + SCR flue gas denitrification technology is the mainstream technology for denitrification and ultra-low emissions of coal-fired boilers. Under the situation of deep peak shaving, combustion in the furnace is faced with increased difficulty in controlling NOx and reduced boiler efficiency.
  • the SCR denitrification system is faced with the problem of air pollution. There are a series of problems such as preheater blockage and high operating costs.
  • the embodiments of the present application provide a method and system for automatic optimization and control of SCR denitration efficiency, so as to at least solve the problem in related technologies of how to balance boiler operation efficiency and SCR denitration operation cost.
  • embodiments of the present application provide a method for automatic optimization and control of SCR denitration efficiency.
  • the method includes:
  • the corresponding working condition interval is obtained by matching based on the current values of the working condition indicators in the actual data
  • the data values of each working condition indicator in the working condition interval are sorted in a preset order through the automatic optimization model
  • the optimal value of each working condition index is calculated through the automatic optimization model.
  • historical data is obtained, and the historical data is divided based on the working condition dividing points to obtain data of several working condition intervals including:
  • the above working condition division points include main steam flow and unit load.
  • the corresponding working condition intervals obtained by matching include:
  • sorting the data values of each working condition indicator in the working condition interval in a preset order through the automatic optimization model includes:
  • the data values of each working condition indicator are sorted in order from large to small index value
  • the data values of each working condition indicator are sorted in order from small to large index values.
  • calculating the optimal value of each working condition indicator includes:
  • the data outside the range of ⁇ 3 times the standard deviation of the average value of each working condition indicator is removed, and then the average value of the first 10%-15% of the data is taken as the optimal value of each working condition indicator. value.
  • inventions of the present application provide an automatic optimization and control system for SCR denitration efficiency.
  • the system includes a data processing module, a model building module, a matching module, a sorting module and an optimization module;
  • the data processing module is used to obtain historical data, divide the historical data based on working condition dividing points, and obtain data of several working condition intervals;
  • the model building module is used to construct an automatic optimization model for regulating the NOx generation concentration based on the data in the working condition interval;
  • the matching module is used in actual operations to match and obtain the corresponding working condition interval based on the current value of the working condition indicator in the actual data;
  • the sorting module is used to sort the data values of each working condition indicator in the working condition interval in a preset order through the automatic optimization model;
  • the optimization module is used to calculate the optimal value of each working condition indicator through the automatic optimization model based on the sorted data.
  • the data processing module is also used to obtain historical data; set the number of divisions, according to (the upper limit value of the working condition dividing point - the lower limit value of the working condition dividing point)/the number of divisions,
  • the historical data are divided evenly to obtain data of several working condition intervals, where the working condition dividing points include main steam flow and unit load.
  • the matching module is also used in actual operations to use the formula Calculate the Euclidean distance between the actual data and each working condition interval, and match the working condition interval with the smallest Euclidean distance.
  • x i is the current value of the working condition index i in the actual data
  • y i is the working condition in the working condition interval.
  • the sorting module is also used to configure the index value of the working condition indicator in the working condition interval through the automatic optimization model; if the index value is larger, the data of the corresponding working condition indicator The better the value, the data values of each working condition indicator will be sorted in order from large to small; if the indicator value is smaller, the data value of the corresponding working condition indicator will be better, then the data values of each working condition indicator will be sorted in order from large to small. Sort the data values of each working condition indicator in order of highest order.
  • the optimization module is also used to use the automatic optimization model to remove data outside the range of the average ⁇ 3 times of the standard deviation of each working condition indicator, and then take the top 10% - The average value of 15% of the data is used as the optimal value of each working condition indicator.
  • embodiments of the present application provide a method and system for automatic optimization and control of SCR denitration efficiency.
  • the historical data is divided based on working condition division points to obtain data of several working condition intervals; based on Based on the data of the working condition interval, an automatic optimization model for regulating the concentration of NOx is constructed; in actual operations, the corresponding working condition interval is obtained by matching according to the current value of the working condition indicator in the actual data; the automatic optimization model is used to The data values of each working condition indicator in the working condition interval are sorted according to the preset order; based on the sorting results, the optimal value of each working condition indicator is calculated through the automatic optimization model, which solves how to take into account the boiler operating efficiency and To address the issue of SCR denitration operating costs, the optimization model is used to calculate the optimal values of various operating conditions in the boiler unit, effectively control the concentration of NOx in the furnace, and effectively balance the boiler operating efficiency and SCR denitration operating costs.
  • Figure 1 is a step flow chart of an automatic optimization and control method for SCR denitration efficiency according to an embodiment of the present application
  • Figure 2 is a structural block diagram of an automatic optimization and control system for SCR denitration efficiency according to an embodiment of the present application
  • Figure 3 is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application.
  • an embodiment means that a particular feature, structure or characteristic described in connection with the embodiment may be included in at least one embodiment of the application.
  • the appearances of this phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.
  • Words such as “connected”, “connected”, “coupled” and the like mentioned in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.
  • the "plurality” mentioned in this application refers to two or more than two.
  • “And/or” describes the relationship between related objects, indicating that three relationships can exist. For example, “A and/or B” can mean: A alone exists, A and B exist simultaneously, and B exists alone.
  • the character “/” generally indicates that the related objects are in an “or” relationship.
  • the terms “first”, “second”, “third”, etc. used in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.
  • Figure 1 is a step flow chart of the automatic optimization and control method of SCR denitrification efficiency according to the embodiment of the present application. As shown in Figure 1, the method includes the following steps :
  • Step S102 obtain historical data, divide the historical data based on the working condition dividing points, and obtain data of several working condition intervals;
  • the type of the working condition indicators (such as SCR ammonia supply flow, SCR ammonia supply pressure, SCR inlet flue gas flow, SOF air proportion, etc.) is numerical type, and the number of divisions of the working condition indicators is required, and the minimum Fill in 1, the upper and lower limits can be left empty when divided into one working condition interval, and the upper and lower limits need to be filled in when divided into multiple working conditions; when divided into multiple working conditions, the upper limit of the working condition dividing point - the lower limit Value)/number of divisions, divide the working condition indicators evenly.
  • Step S104 Based on the data in the working condition interval, construct an automatic optimization model for regulating the NOx generation concentration;
  • the indicator for automatic optimization of SCR denitration efficiency is configured as the NOx concentration value at the denitration inlet
  • an automatic optimization model is constructed that associates the working condition indicators in the working condition interval with the NOx concentration value at the denitration inlet.
  • the model is used to calculate the optimal value of the working condition index in the working condition interval, thereby regulating the generation concentration of NOx in the boiler (that is, the NOx concentration value at the denitrification inlet).
  • NOx is the general term for nitrogen oxide compounds, which usually includes NO and NO2. Except for nitrogen dioxide, other nitrogen oxides are extremely unstable and turn into nitrogen dioxide and nitric oxide when exposed to light, moisture, and heat. Nitric oxide turns into nitrogen dioxide, such as nitrous oxide (N2O) , nitric oxide (NO), dioxide (NO2), dinitrogen trioxide (N2O3), dinitrogen tetroxide (N2O4), etc.
  • a boiler unit equipment can correspond to multiple types of automatic optimization models, each The type can create a model in use and a backup model. The model in use cannot be modified, and the backup model can be modified and activated.
  • Step S106 In actual operations, match the current values of the working condition indicators in the actual data to obtain the corresponding working condition interval;
  • Step S108 use the automatic optimization model to sort the data values of each working condition indicator in the working condition interval in a preset order
  • the index value of the working condition indicator in the working condition interval is configured through the automatic optimization model; if the index value is larger, the data value of the corresponding working condition indicator is better (larger), then the index value is ordered from large to small. , sort the data values of each working condition indicator separately; if the smaller the index value is, the better the data value of the corresponding working condition indicator is (smaller), then sort the data of each working condition indicator in the order of index value from small to large. The values are sorted separately.
  • Step S110 According to the sorting results, the optimal value of each working condition index is calculated through the automatic optimization model.
  • the data outside the range of ⁇ 3 times the standard deviation of the average value of each working condition indicator is removed, and then the average value of the first 10%-15% of the data is taken as the optimal value of each working condition indicator.
  • step S106 to step S110 are executed cyclically according to a preset period, and the optimal value of the working condition index obtained in each execution is stored in the database for subsequent big data analysis, wherein the preset period It can be set according to requirements, such as one minute, that is, steps S106 to S110 are executed every minute, and the optimal value of the working condition index is obtained every minute.
  • the problem of how to take into account the boiler operating efficiency and the SCR denitration operating cost is solved, and the optimal values of various working condition indicators in the boiler unit are calculated based on the optimization model, effectively Regulate the generation concentration of NOx in the furnace, effectively taking into account the boiler operating efficiency and SCR denitration operating costs; calculate the optimal points of working condition indicators under different loads based on historical data and the iterative method of large optimization and small optimization, and use it as a real-time adjustment for operation The optimal value, and then perform real-time analysis and optimization control based on the optimal value during operation adjustment. It has the advantages of systematic, accurate, and strong operability. It can ensure the stable, reliable and economic operation of the SCR denitrification system, and has the ability to produce significant results. Environmental protection benefits, safety benefits and economic benefits have broad application prospects.
  • FIG. 2 is a structural block diagram of the automatic optimization and control system for SCR denitrification efficiency according to the embodiment of the present application. As shown in Figure 2, the system includes a data processing module 21. Model building module 22, matching module 23, sorting module 24 and optimization module 25;
  • the data processing module 21 is used to obtain historical data, divide the historical data based on the working condition dividing points, and obtain data of several working condition intervals;
  • the model building module 22 is used to build an automatic optimization model for regulating the NOx generation concentration based on the data of the working condition interval;
  • the matching module 23 is used in actual operations to match and obtain the corresponding working condition interval based on the current value of the working condition indicator in the actual data;
  • the sorting module 24 is used to sort the data values of each working condition indicator in the working condition interval in a preset order through the automatic optimization model;
  • the optimization module 25 is used to calculate the optimal value of each working condition index through the automatic optimization model based on the sorted data.
  • the data processing module 21 is also used to obtain historical data; set the number of divisions, and calculate the The historical data are averagely divided to obtain data of several working condition intervals.
  • the working condition dividing points include main steam flow and unit load.
  • the matching module 23 is also used in actual operations to use the formula Calculate the Euclidean distance between the actual data and each working condition interval, and match the working condition interval with the smallest Euclidean distance.
  • x i is the current value of the working condition index i in the actual data
  • y i is the working condition in the working condition interval.
  • the sorting module 24 is also used to configure the index value of the working condition index in the working condition interval through the automatic optimization model; if the index value is larger, the data value of the corresponding working condition index is better, then according to the index Sort the data values of each working condition indicator in order from large to small; if the smaller the indicator value is, the better the data value of the corresponding working condition indicator is, then sort the data values of each working condition in order from small to large.
  • the data values of the indicator are sorted individually.
  • the optimization module 25 is also used to use the automatic optimization model to remove data outside the range of the average ⁇ 3 times of the standard deviation of each working condition indicator, and then obtain the first 10%-15% of the data before sorting. The average value is taken as the optimal value of each working condition indicator.
  • each of the above modules can be a functional module or a program module, and can be implemented by software or hardware.
  • each of the above-mentioned modules can be located in the same processor; or each of the above-mentioned modules can also be located in different processors in any combination.
  • This embodiment also provides an electronic device, including a memory and a processor.
  • a computer program is stored in the memory, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.
  • the above-mentioned electronic device may further include a transmission device and an input-output device, wherein the transmission device is connected to the above-mentioned processor, and the input-output device is connected to the above-mentioned processor.
  • embodiments of the present application can provide a storage medium to achieve this.
  • the storage medium stores a computer program; when the computer program is executed by the processor, any one of the SCR denitrification efficiency automatic optimization and control methods in the above embodiments is implemented.
  • a computer device which may be a terminal.
  • the computer equipment includes a processor, memory, network interface, display screen and input device connected by a system bus.
  • the processor of the computer device is used to provide computing and control capabilities.
  • the memory of the computer device includes non-volatile storage media and internal memory.
  • the non-volatile storage medium stores operating systems and computer programs. This internal memory provides an environment for the execution of operating systems and computer programs in non-volatile storage media.
  • the network interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements an automatic optimization and control method for SCR denitration efficiency.
  • the display screen of the computer device may be a liquid crystal display or an electronic ink display.
  • the input device of the computer device may be a touch layer covered on the display screen, or may be a button, trackball or touch pad provided on the computer device shell. , it can also be an external keyboard, trackpad or mouse, etc.
  • Figure 3 is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application.
  • an electronic device is provided.
  • the electronic device can be a server, and its internal structure diagram can be as shown in Figure 3 shown.
  • the electronic device includes a processor, a network interface, an internal memory and a non-volatile memory connected through an internal bus, wherein the non-volatile memory stores an operating system, a computer program and a database.
  • the processor is used to provide computing and control capabilities
  • the network interface is used to communicate with external terminals through a network connection
  • the internal memory is used to provide an environment for the operation of the operating system and computer programs.
  • Performance automatic optimization and control method database is used to store data.
  • FIG. 3 is only a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the electronic equipment to which the solution of the present application is applied.
  • Specific electronic devices can May include more or fewer parts than shown, or combine certain parts, or have a different arrangement of parts.
  • Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Synchlink DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
  • SRAM static RAM
  • DRAM dynamic RAM
  • SDRAM synchronous DRAM
  • DDRSDRAM double data rate SDRAM
  • ESDRAM enhanced SDRAM
  • SLDRAM synchronous chain Synchlink DRAM
  • Rambus direct RAM
  • DRAM direct memory bus dynamic RAM
  • RDRAM memory bus dynamic RAM

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Abstract

本申请涉及一种SCR脱硝效能自动寻优调控方法和系统，其中，该方法包括：获取历史数据，基于工况划分点对历史数据进行划分，得到若干工况区间的数据；基于工况区间的数据，构建用于调控NOx生成浓度的自动寻优模型；在实际作业中，根据实际数据中工况指标的当前值，匹配得出对应的工况区间；通过自动寻优模型将工况区间中各个工况指标的数据值，按预设顺序分别进行排序；根据排序的结果，通过自动寻优模型，计算出各个工况指标的最优值。通过本申请，解决了如何兼顾锅炉运行效率和SCR脱硝运行成本的问题，实现了基于寻优模型计算出锅炉机组中各项工况指标的最优值，有效调控炉内NOx的生成浓度进而兼顾效率与成本。

Description

一种SCR脱硝效能自动寻优调控方法和系统 技术领域
本申请涉及工业废气净化技术领域，特别是涉及一种SCR脱硝效能自动寻优调控方法和系统。
背景技术
SCR(Selective Catalytic Reduction)脱硝也叫选择性催化还原法，是目前国际上应用最为广泛的烟气脱硝技术，通常采用氨(NH3)作为还原剂将生成的NOx选择性地还原成N2。其具有无副产物，不产生二次污染，装置结构简单，并且脱出效率高(可达到90％以上)，具有运行可靠、便于维护等优点。低氮燃烧+SCR烟气脱硝技术是燃煤锅炉脱硝超低排放的主流技术，在深度调峰的大形势下炉内燃烧面临着NOx控制难度增大、锅炉效率降低，SCR脱硝系统面临着空预器堵塞、运行成本高等一系列问题。
通过调整低氮运行方式降低NOx浓度生成，有利于降低脱硝系统成本，但相应会牺牲锅炉效率，提高锅炉侧的运行成本；反之，高浓度的炉内NOx生成能避免锅炉效率的牺牲，但会造成脱硝系统成本提升，如何兼顾低氮NOx燃烧生成和SCR脱硝运行成本最优经济运行是当前电力环保领域急需解决的技术难题。随着燃煤电厂趋向于智慧化，现有优化调整方式仍然无法有效解决当前燃煤锅炉面临的高效燃烧、低NOx排放二者之间的突出矛盾。
目前针对相关技术中如何兼顾锅炉运行效率和SCR脱硝运行成本的问题，尚未提出有效的解决方案。
发明内容
本申请实施例提供了一种SCR脱硝效能自动寻优调控方法和系统，以至少解决相关技术中如何兼顾锅炉运行效率和SCR脱硝运行成本的问题。
第一方面，本申请实施例提供了一种SCR脱硝效能自动寻优调控方法，所述方法包括：
获取历史数据，基于工况划分点对所述历史数据进行划分，得到若干工况区间的数据；
基于所述工况区间的数据，构建用于调控NOx生成浓度的自动寻优模型；
在实际作业中，根据实际数据中工况指标的当前值，匹配得出对应的工况 区间；
通过所述自动寻优模型将所述工况区间中各个工况指标的数据值，按预设顺序分别进行排序；
根据所述排序的结果，通过所述自动寻优模型，计算出各个工况指标的最优值。
在其中一些实施例中，获取历史数据，基于工况划分点对所述历史数据进行划分，得到若干工况区间的数据包括：
获取历史数据；
设置划分个数，根据(工况划分点的上限值-工况划分点的下限值)/划分个数，将所述历史数据进行平均划分，得到若干工况区间的数据，其中，所述工况划分点包括主蒸汽流量和机组负荷。
在其中一些实施例中，在实际作业中，根据实际数据中工况指标的当前值，匹配得出对应的工况区间包括：
在实际作业中，通过公式
Figure PCTCN2022133901-appb-000001
计算出实际数据与各个工况区间的欧式距离，匹配得出欧氏距离最小的工况区间，其中，x i为实际数据中工况指标i的当前值，y i为工况区间中工况指标i的中心值。
在其中一些实施例中，通过所述自动寻优模型将所述工况区间中各个工况指标的数据值，按预设顺序分别进行排序包括：
通过所述自动寻优模型配置所述工况区间中工况指标的指标值；
若所述指标值越大，对应工况指标的数据值越优，则按指标值从大到小的顺序，将各个工况指标的数据值分别进行排序；
若所述指标值越小，对应工况指标的数据值越优，则按指标值从小到大的顺序，将各个工况指标的数据值分别进行排序。
在其中一些实施例中，根据所述排序的结果，通过所述自动寻优模型，计算出各个工况指标的最优值包括：
通过所述自动寻优模型，去掉各个工况指标的平均值±3倍的标准差范围外的数据，再取所述排序前10％-15％数据的平均值作为各个工况指标的最优值。
第二方面，本申请实施例提供了一种SCR脱硝效能自动寻优调控系统，所述系统包括数据处理模块、模型构建模块、匹配模块、排序模块和寻优模块；
所述数据处理模块，用于获取历史数据，基于工况划分点对所述历史数据 进行划分，得到若干工况区间的数据；
所述模型构建模块，用于基于所述工况区间的数据，构建用于调控NOx生成浓度的自动寻优模型；
所述匹配模块，用于在实际作业中，根据实际数据中工况指标的当前值，匹配得出对应的工况区间；
所述排序模块，用于通过所述自动寻优模型将所述工况区间中各个工况指标的数据值，按预设顺序分别进行排序；
所述寻优模块，用于根据所述排序后的数据，通过所述自动寻优模型，计算出各个工况指标的最优值。
在其中一些实施例中，所述数据处理模块，还用于获取历史数据；设置划分个数，根据(工况划分点的上限值-工况划分点的下限值)/划分个数，将所述历史数据进行平均划分，得到若干工况区间的数据，其中，所述工况划分点包括主蒸汽流量和机组负荷。
在其中一些实施例中，所述匹配模块，还用于在实际作业中，通过公式
Figure PCTCN2022133901-appb-000002
计算出实际数据与各个工况区间的欧式距离，匹配得出欧氏距离最小的工况区间，其中，x i为实际数据中工况指标i的当前值，y i为工况区间中工况指标i的中心值。
在其中一些实施例中，所述排序模块，还用于通过所述自动寻优模型配置所述工况区间中工况指标的指标值；若所述指标值越大，对应工况指标的数据值越优，则按指标值从大到小的顺序，将各个工况指标的数据值分别进行排序；若所述指标值越小，对应工况指标的数据值越优，则按指标值从小到大的顺序，将各个工况指标的数据值分别进行排序。
在其中一些实施例中，所述寻优模块，还用于通过所述自动寻优模型，去掉各个工况指标的平均值±3倍的标准差范围外的数据，再取排序前10％-15％数据的平均值作为各个工况指标的最优值。
相比于相关技术，本申请实施例提供的一种SCR脱硝效能自动寻优调控方法和系统，通过获取历史数据，基于工况划分点对历史数据进行划分，得到若干工况区间的数据；基于工况区间的数据，构建用于调控NOx生成浓度的自动寻优模型；在实际作业中，根据实际数据中工况指标的当前值，匹配得出对应的工况区间；通过自动寻优模型将工况区间中各个工况指标的数据值，按预设 顺序分别进行排序；根据排序的结果，通过自动寻优模型，计算出各个工况指标的最优值，解决了如何兼顾锅炉运行效率和SCR脱硝运行成本的问题，实现了基于寻优模型计算出锅炉机组中各项工况指标的最优值，有效调控炉内NOx的生成浓度，有效兼顾锅炉运行效率和SCR脱硝运行成本。
附图说明
此处所说明的附图用来提供对本申请的进一步理解，构成本申请的一部分，本申请的示意性实施例及其说明用于解释本申请，并不构成对本申请的不当限定。在附图中：
图1是根据本申请实施例的SCR脱硝效能自动寻优调控方法的步骤流程图；
图2是根据本申请实施例的SCR脱硝效能自动寻优调控系统的结构框图；
图3是根据本申请实施例的电子设备的内部结构示意图。
附图说明：21、数据处理模块；22、模型构建模块；23、匹配模块；24、排序模块；25、寻优模块。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行描述和说明。应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。基于本申请提供的实施例，本领域普通技术人员在没有作出创造性劳动的前提下所获得的所有其他实施例，都属于本申请保护的范围。
显而易见地，下面描述中的附图仅仅是本申请的一些示例或实施例，对于本领域的普通技术人员而言，在不付出创造性劳动的前提下，还可以根据这些附图将本申请应用于其他类似情景。此外，还可以理解的是，虽然这种开发过程中所作出的努力可能是复杂并且冗长的，然而对于与本申请公开的内容相关的本领域的普通技术人员而言，在本申请揭露的技术内容的基础上进行的一些设计，制造或者生产等变更只是常规的技术手段，不应当理解为本申请公开的内容不充分。
在本申请中提及“实施例”意味着，结合实施例描述的特定特征、结构或特性可以包含在本申请的至少一个实施例中。在说明书中的各个位置出现该短语并不一定均是指相同的实施例，也不是与其它实施例互斥的独立的或备选的实施例。本领域普通技术人员显式地和隐式地理解的是，本申请所描述的实施 例在不冲突的情况下，可以与其它实施例相结合。
除非另作定义，本申请所涉及的技术术语或者科学术语应当为本申请所属技术领域内具有一般技能的人士所理解的通常意义。本申请所涉及的“一”、“一个”、“一种”、“该”等类似词语并不表示数量限制，可表示单数或复数。本申请所涉及的术语“包括”、“包含”、“具有”以及它们任何变形，意图在于覆盖不排他的包含；例如包含了一系列步骤或模块(单元)的过程、方法、系统、产品或设备没有限定于已列出的步骤或单元，而是可以还包括没有列出的步骤或单元，或可以还包括对于这些过程、方法、产品或设备固有的其它步骤或单元。本申请所涉及的“连接”、“相连”、“耦接”等类似的词语并非限定于物理的或者机械的连接，而是可以包括电气的连接，不管是直接的还是间接的。本申请所涉及的“多个”是指两个或两个以上。“和/或”描述关联对象的关联关系，表示可以存在三种关系，例如，“A和/或B”可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。字符“/”一般表示前后关联对象是一种“或”的关系。本申请所涉及的术语“第一”、“第二”、“第三”等仅仅是区别类似的对象，不代表针对对象的特定排序。
本申请实施例提供了一种SCR脱硝效能自动寻优调控方法，图1是根据本申请实施例的SCR脱硝效能自动寻优调控方法的步骤流程图，如图1所示，该方法包括以下步骤：
步骤S102，获取历史数据，基于工况划分点对历史数据进行划分，得到若干工况区间的数据；
具体地，获取历史数据，设置划分个数，根据(工况划分点的上限值-工况划分点的下限值)/划分个数，将历史数据进行平均划分，得到若干工况区间的数据，其中，工况划分点包括主蒸汽流量和机组负荷。
优选地，工况指标(如SCR氨气供应流量、SCR供氨压力、SCR入口烟气流量、SOF风占比等)的类型为数值型，工况指标的划分个数为必填项，最小填1，划分成1个工况区间时上下限可为空，划分成多个工况时需填写上下限；划分成多个工况时，根据(工况划分点的上限值-下限值)/划分个数，将工况指标平均划分。例如，若工况划分点为机组负荷，对于300MW机组，划分个数为5，即(300MW-0MW)/5＝60MW，负荷模设置20％(对应60MW)，则0-20％(对应0-60MW)是一个工况区间，20-40％(对应60-120MW)是一个工况区间，40-60(对应120-180MW)是一个工况区间，60-80％(对应180-240MW)是一个工况区间，80-100％(对应240-300MW)是一个工况区间。
步骤S104，基于工况区间的数据，构建用于调控NOx生成浓度的自动寻优模型；
具体地，基于工况区间的数据，配置SCR脱硝效能自动寻优的指标为脱硝入口NOx浓度值，构建将工况区间中工况指标与脱硝入口NOx浓度值关联起来的自动寻优模型，该模型用于计算工况区间中工况指标的最优值，以此调控锅炉中NOx的生成浓度(即脱硝入口NOx浓度值)。
需要说明的是，NOx是氮氧化合物的总称，通常包括NO和NO2等。除二氧化氮之外，别的氮氧化物均极不稳定，遇光、湿、热变为二氧化氮及一氧化氮，一氧化氮又变成二氧化氮，如一氧化二氮(N2O)，一氧化氮(NO)，二氧化(NO2)，三氧化二氮(N2O3)，四氧化二氮(N2O4)等，此外，一个锅炉机组设备可以对应多种类型的自动寻优模型，每个类型可以创建一个使用中模型，及一个备用模型，使用中的模型不可修改，备用模型可以修改和启用。
步骤S106，在实际作业中，根据实际数据中工况指标的当前值，匹配得出对应的工况区间；
具体地，在实际作业中，通过公式
Figure PCTCN2022133901-appb-000003
计算出实际数据与各个工况区间的欧式距离，匹配得出欧氏距离最小的工况区间，其中，x i为实际数据中工况指标i的当前值，y i为工况区间中工况指标i的中心值。
步骤S108，通过自动寻优模型将工况区间中各个工况指标的数据值，按预设顺序分别进行排序；
具体地，通过自动寻优模型配置工况区间中工况指标的指标值；若指标值越大，对应工况指标的数据值越优(大优)，则按指标值从大到小的顺序，将各个工况指标的数据值分别进行排序；若指标值越小，对应工况指标的数据值越优(小优)，则按指标值从小到大的顺序，将各个工况指标的数据值分别进行排序。
步骤S110，根据排序的结果，通过自动寻优模型，计算出各个工况指标的最优值。
具体地，通过自动寻优模型，去掉各个工况指标的平均值±3倍的标准差范围外的数据，再取排序前10％-15％数据的平均值作为各个工况指标的最优值。
进一步地，按预设周期，循环执行步骤S106至步骤S110，并将每次执行得到的工况指标的最优值存储到数据库中，以用于后续的大数据分析，其中，该 预设周期可根据需求设定，如一分钟，即每一分钟执行一次步骤S106至步骤S110，每一分钟得到一次工况指标的最优值。
通过本申请实施例中的步骤S102至步骤S110，解决了如何兼顾锅炉运行效率和SCR脱硝运行成本的问题，实现了基于寻优模型计算出锅炉机组中各项工况指标的最优值，有效调控炉内NOx的生成浓度，有效兼顾锅炉运行效率和SCR脱硝运行成本；基于历史数据及大优、小优迭代的方式来计算不同负荷下工况指标的最优点，并将其作为运行实时调整的最优值，进而在运行调整中根据最优值来进行实时的分析与寻优调控，具有系统、准确、操作性强等优点，能够保障SCR脱硝系统稳定可靠经济运行，具有良好产生显著的环保效益、安全效益以及经济效益，具有广发的应用前景。
需要说明的是，在上述流程中或者附图的流程图中示出的步骤可以在诸如一组计算机可执行指令的计算机系统中执行，并且，虽然在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤。
本申请实施例提供了一种SCR脱硝效能自动寻优调控系统，图2是根据本申请实施例的SCR脱硝效能自动寻优调控系统的结构框图，如图2所示，该系统包括数据处理模块21、模型构建模块22、匹配模块23、排序模块24和寻优模块25；
数据处理模块21，用于获取历史数据，基于工况划分点对历史数据进行划分，得到若干工况区间的数据；
模型构建模块22，用于基于工况区间的数据，构建用于调控NOx生成浓度的自动寻优模型；
匹配模块23，用于在实际作业中，根据实际数据中工况指标的当前值，匹配得出对应的工况区间；
排序模块24，用于通过自动寻优模型将工况区间中各个工况指标的数据值，按预设顺序分别进行排序；
寻优模块25，用于根据排序后的数据，通过自动寻优模型，计算出各个工况指标的最优值。
在其中一些实施例中，数据处理模块21，还用于获取历史数据；设置划分个数，根据(工况划分点的上限值-工况划分点的下限值)/划分个数，将历史数据进行平均划分，得到若干工况区间的数据，其中，工况划分点包括主蒸汽流量和机组负荷。
在其中一些实施例中，匹配模块23，还用于在实际作业中，通过公式
Figure PCTCN2022133901-appb-000004
计算出实际数据与各个工况区间的欧式距离，匹配得出欧氏距离最小的工况区间，其中，x i为实际数据中工况指标i的当前值，y i为工况区间中工况指标i的中心值。
在其中一些实施例中，排序模块24，还用于通过自动寻优模型配置工况区间中工况指标的指标值；若指标值越大，对应工况指标的数据值越优，则按指标值从大到小的顺序，将各个工况指标的数据值分别进行排序；若指标值越小，对应工况指标的数据值越优，则按指标值从小到大的顺序，将各个工况指标的数据值分别进行排序。
在其中一些实施例中，寻优模块25，还用于通过自动寻优模型，去掉各个工况指标的平均值±3倍的标准差范围外的数据，再取排序前10％-15％数据的平均值作为各个工况指标的最优值。
需要说明的是，上述各个模块可以是功能模块也可以是程序模块，既可以通过软件来实现，也可以通过硬件来实现。对于通过硬件来实现的模块而言，上述各个模块可以位于同一处理器中；或者上述各个模块还可以按照任意组合的形式分别位于不同的处理器中。
本实施例还提供了一种电子装置，包括存储器和处理器，该存储器中存储有计算机程序，该处理器被设置为运行计算机程序以执行上述任一项方法实施例中的步骤。
可选地，上述电子装置还可以包括传输设备以及输入输出设备，其中，该传输设备和上述处理器连接，该输入输出设备和上述处理器连接。
需要说明的是，本实施例中的具体示例可以参考上述实施例及可选实施方式中所描述的示例，本实施例在此不再赘述。
另外，结合上述实施例中的SCR脱硝效能自动寻优调控方法，本申请实施例可提供一种存储介质来实现。该存储介质上存储有计算机程序；该计算机程序被处理器执行时实现上述实施例中的任意一种SCR脱硝效能自动寻优调控方法。
在一个实施例中，提供了一种计算机设备，该计算机设备可以是终端。该计算机设备包括通过系统总线连接的处理器、存储器、网络接口、显示屏和输入装置。其中，该计算机设备的处理器用于提供计算和控制能力。该计算机设 备的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有操作系统和计算机程序。该内存储器为非易失性存储介质中的操作系统和计算机程序的运行提供环境。该计算机设备的网络接口用于与外部的终端通过网络连接通信。该计算机程序被处理器执行时以实现一种SCR脱硝效能自动寻优调控方法。该计算机设备的显示屏可以是液晶显示屏或者电子墨水显示屏，该计算机设备的输入装置可以是显示屏上覆盖的触摸层，也可以是计算机设备外壳上设置的按键、轨迹球或触控板，还可以是外接的键盘、触控板或鼠标等。
在一个实施例中，图3是根据本申请实施例的电子设备的内部结构示意图，如图3所示，提供了一种电子设备，该电子设备可以是服务器，其内部结构图可以如图3所示。该电子设备包括通过内部总线连接的处理器、网络接口、内存储器和非易失性存储器，其中，该非易失性存储器存储有操作系统、计算机程序和数据库。处理器用于提供计算和控制能力，网络接口用于与外部的终端通过网络连接通信，内存储器用于为操作系统和计算机程序的运行提供环境，计算机程序被处理器执行时以实现一种SCR脱硝效能自动寻优调控方法，数据库用于存储数据。
本领域技术人员可以理解，图3中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的电子设备的限定，具体的电子设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，该计算机程序可存储于一非易失性计算机可读取存储介质中，该计算机程序在执行时，可包括如上述各方法的实施例的流程。其中，本申请所提供的各实施例中所使用的对存储器、存储、数据库或其它介质的任何引用，均可包括非易失性和/或易失性存储器。非易失性存储器可包括只读存储器(ROM)、可编程ROM(PROM)、电可编程ROM(EPROM)、电可擦除可编程ROM(EEPROM)或闪存。易失性存储器可包括随机存取存储器(RAM)或者外部高速缓冲存储器。作为说明而非局限，RAM以多种形式可得，诸如静态RAM(SRAM)、动态RAM(DRAM)、同步DRAM(SDRAM)、双数据率SDRAM(DDRSDRAM)、增强型SDRAM(ESDRAM)、同步链路(Synchlink)DRAM(SLDRAM)、存储器总线(Rambus)直接RAM(RDRAM)、直接存储器总线动态RAM(DRDRAM)、以及存储器总线动态RAM(RDRAM)等。
本领域的技术人员应该明白，以上所述实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
以上所述实施例仅表达了本申请的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出若干变形和改进，这些都属于本申请的保护范围。因此，本申请专利的保护范围应以所附权利要求为准。

Claims (10)

  1. 一种SCR脱硝效能自动寻优调控方法，其特征在于，所述方法包括：
    获取历史数据，基于工况划分点对所述历史数据进行划分，得到若干工况区间的数据；
    基于所述工况区间的数据，构建用于调控NOx生成浓度的自动寻优模型；
    在实际作业中，根据实际数据中工况指标的当前值，匹配得出对应的工况区间；
    通过所述自动寻优模型将所述工况区间中各个工况指标的数据值，按预设顺序分别进行排序；
    根据所述排序的结果，通过所述自动寻优模型，计算出各个工况指标的最优值。
  2. 根据权利要求1所述的方法，其特征在于，获取历史数据，基于工况划分点对所述历史数据进行划分，得到若干工况区间的数据包括：
    获取历史数据；
    设置划分个数，根据(工况划分点的上限值-工况划分点的下限值)/划分个数，将所述历史数据进行平均划分，得到若干工况区间的数据，其中，所述工况划分点包括主蒸汽流量和机组负荷。
  3. 根据权利要求1所述的方法，其特征在于，在实际作业中，根据实际数据中工况指标的当前值，匹配得出对应的工况区间包括：
    在实际作业中，通过公式
    Figure PCTCN2022133901-appb-100001
    计算出实际数据与各个工况区间的欧式距离，匹配得出欧氏距离最小的工况区间，其中，x i为实际数据中工况指标i的当前值，y i为工况区间中工况指标i的中心值。
  4. 根据权利要求1所述的方法，其特征在于，通过所述自动寻优模型将所述工况区间中各个工况指标的数据值，按预设顺序分别进行排序包括：
    通过所述自动寻优模型配置所述工况区间中工况指标的指标值；
    若所述指标值越大，对应工况指标的数据值越优，则按指标值从大到小的顺序，将各个工况指标的数据值分别进行排序；
    若所述指标值越小，对应工况指标的数据值越优，则按指标值从小到大的顺序，将各个工况指标的数据值分别进行排序。
  5. 根据权利要求1所述的方法，其特征在于，根据所述排序的结果，通过所述自动寻优模型，计算出各个工况指标的最优值包括：
    通过所述自动寻优模型，去掉各个工况指标的平均值±3倍的标准差范围外的数据，再取所述排序前10％-15％数据的平均值作为各个工况指标的最优值。
  6. 一种SCR脱硝效能自动寻优调控系统，其特征在于，所述系统包括数据处理模块、模型构建模块、匹配模块、排序模块和寻优模块；
    所述数据处理模块，用于获取历史数据，基于工况划分点对所述历史数据进行划分，得到若干工况区间的数据；
    所述模型构建模块，用于基于所述工况区间的数据，构建用于调控NOx生成浓度的自动寻优模型；
    所述匹配模块，用于在实际作业中，根据实际数据中工况指标的当前值，匹配得出对应的工况区间；
    所述排序模块，用于通过所述自动寻优模型将所述工况区间中各个工况指标的数据值，按预设顺序分别进行排序；
    所述寻优模块，用于根据所述排序后的数据，通过所述自动寻优模型，计算出各个工况指标的最优值。
  7. 根据权利要求6所述的系统，其特征在于，所述数据处理模块，还用于获取历史数据；设置划分个数，根据(工况划分点的上限值-工况划分点的下限值)/划分个数，将所述历史数据进行平均划分，得到若干工况区间的数据，其中，所述工况划分点包括主蒸汽流量和机组负荷。
  8. 根据权利要求6所述的系统，其特征在于，所述匹配模块，还用于在实际作业中，通过公式
    Figure PCTCN2022133901-appb-100002
    计算出实际数据与各个工况区间的欧式距离，匹配得出欧氏距离最小的工况区间，其中，x i为实际数据中工况指标i的当前值，y i为工况区间中工况指标i的中心值。
  9. 根据权利要求6所述的系统，其特征在于，所述排序模块，还用于通过所述自动寻优模型配置所述工况区间中工况指标的指标值；若所述指标值越大，对应工况指标的数据值越优，则按指标值从大到小的顺序，将各个工况指标的数据值分别进行排序；若所述指标值越小，对应工况指标的数据值越优，则按指标值从小到大的顺序，将各个工况指标的数据值分别进行排序。
  10. 根据权利要求6所述的系统，其特征在于，所述寻优模块，还用于通过所述自动寻优模型，去掉各个工况指标的平均值±3倍的标准差范围外的数据，再取排序前10％-15％数据的平均值作为各个工况指标的最优值。
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