WO2022233101A1 - Intelligent optimization control device for low-temperature thermal system - Google Patents
Intelligent optimization control device for low-temperature thermal system Download PDFInfo
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- 238000005457 optimization Methods 0.000 title claims abstract description 66
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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]
- G05B19/4185—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 the network communication
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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
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/31—From computer integrated manufacturing till monitoring
- G05B2219/31088—Network communication between supervisor and cell, machine group
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- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Definitions
- the invention relates to the fields of oil refining and chemical industry, in particular to an intelligent optimization control device of a low temperature thermal system.
- the purpose of the present invention is to provide a kind of intelligent optimization control equipment of low temperature thermal system, so as to overcome the above-mentioned technical problems caused by the limitations and defects of the related art at least to a certain extent.
- an intelligent optimization control device for a low-temperature thermal system includes: a heat source unit, a heat sink unit, and a heat intelligent control center, and the heat intelligent control center is used for the heat transfer and heat balance between the heat source unit and the heat sink unit;
- the intelligent optimization control equipment includes:
- a plurality of sensor units each of which is used to collect the sensing parameters of the heat source unit, the heat sink unit and the heat intelligent control center;
- An edge computing unit that performs the following steps:
- a central optimization unit for performing the following steps: inputting the acquired sensor parameters and training a prediction model;
- the low temperature thermal system performs heat transfer through a water cycle
- the sensing parameters include one or more of the following sensor parameters: heating medium water flow rate, heating medium water inlet and outlet temperature, heating medium water inlet and outlet pressure, process flow, process inlet and outlet temperature, conductivity of heating medium water, Dissolved oxygen, PH value, oil content, turbidity;
- the heat exchange parameters include one or more of the following parameters: the temperature of the heat source unit and the heat sink unit after process heat exchange, and the temperature of the heat medium water after heat exchange;
- the control parameters of the heat intelligent control center include one or more of the following control parameters: the flow rate of heat medium water and the flow rate of heat medium water pumps entering each heat source unit and heat sink unit.
- the confirming whether to adjust the control parameters of the heat intelligent control center according to the predicted heat exchange parameters includes:
- control parameters of the heat intelligent control center are adjusted according to the difference between the existing heat exchange parameters and the target heat exchange parameters.
- the prediction model is obtained by training based on historical sensing parameters and heat exchange parameters, and iterative optimization training is performed based on real-time collected sensing parameters and real-time heat exchange parameters.
- the prediction model is a feedforward neural network model
- the hidden layer node transfer function of the feedforward neural network model includes a hyperbolic tangent sigmoid function
- the output layer node transfer function includes a linear function
- the central optimization unit in communication with the edge computing unit,
- the central optimization unit is used to perform:
- the central optimization unit is further configured to perform the following steps:
- the visually processed sensing parameters collected by the plurality of sensor units and the control parameters of the heat intelligent control center are displayed on the intelligent optimization control equipment client or the control device page in the browser.
- the intelligent heat regulation center includes:
- the heat source water end and the heat source water discharge end are connected to the heat source unit;
- the water inlet end of the heat trap and the water outlet end of the heat trap are connected to the heat trap unit;
- the upper water collecting unit is connected between the water outlet end of the heat source and the water inlet end of the heat trap;
- a return water collecting unit connected between the water outlet end of the heat trap and the water inlet end of the heat source
- the water replenishing unit is connected between the return water collecting unit and the water outlet end of the heat source.
- the edge computing unit also inputs environmental parameters into the prediction model.
- the sensor unit includes one or more wireless sensors.
- FIG. 1 is a schematic diagram of a low temperature thermal system according to an embodiment of the present invention.
- FIG. 2 is a schematic diagram of an intelligent optimization control device according to an embodiment of the present invention.
- FIG 3 is a schematic diagram of a low-temperature thermal system in which the intelligent thermal control center includes intelligent optimization control equipment according to an embodiment of the present invention.
- Example embodiments will now be described more fully with reference to the accompanying drawings.
- Example embodiments can be embodied in various forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
- the same reference numerals in the drawings denote the same or similar structures, and thus their detailed descriptions will be omitted.
- FIG. 1 is a schematic diagram of a low temperature thermal system according to an embodiment of the present invention.
- FIG. 2 is a schematic diagram of an intelligent optimization control device according to an embodiment of the present invention.
- 3 is a schematic diagram of a low temperature thermal system heat intelligent control center including intelligent optimization control equipment according to an embodiment of the present invention.
- the low temperature thermal system 10 includes a heat source unit 11 , a heat sink unit 12 and an intelligent heat regulation center 13 .
- the heat intelligent regulation center 13 is used for heat transfer and heat balance between the heat source unit 11 and the heat sink unit 12.
- the low temperature heat system 10 recovers the heat of the materials 1 , 2 , and 3 and sends them to various places to provide the materials 4 , 5 , and 6 with low temperature heat.
- the original temperatures of materials 1, 2, and 3 are different from 100 to 135 °C, but they are all cooled to a lower temperature (generally below 50 °C) by an air cooler or water cooler and then enter the downstream.
- the heat of 1, 2, and 3 is lost to the atmosphere through air cooling and water cooling (consumption of electrical energy).
- the original temperature of materials 4, 5, and 6 varies from 70 to 110 °C. They are all heated to the required temperature by steam.
- the steam can be purchased externally or produced by burning coal, natural gas, etc. Therefore, in the existing implementation, the heat lost by the materials 1, 2, and 3 and the heat required by the materials 4, 5, and 6 are not related to each other, resulting in waste of heat.
- the low temperature thermal system 10 in FIG. 1 may establish a hot water cycle.
- the heat medium water is pressurized by the pump in the heat intelligent control center 13 and sent to the materials 1, 2 and 3 of the heat source unit 11 for heat exchange and temperature rise, and then returned to the heat intelligent control center 13 after heating, and sent to the heat sink unit 12 after the water volume control.
- the raw materials 4, 5, and 6 replace the steam at the original 4, 5, and 6 for heating.
- the water temperature is adjusted by the air cooler or water cooler of the heat intelligent control center 13, and then pressurized by the pump, and sent to the materials 1, 2, and 3 to obtain heat through the water volume control. cycle.
- the materials 1, 2, and 3 reduce the cooling consumption of the air cooler or the water cooler, and the materials 4, 5, and 6 reduce the consumption of heating steam and achieve the utilization of heat.
- the heat intelligent control center includes the heat source water end and the heat source water end, the heat trap water end and the heat trap water end, the feed water collector, the return water collector, the replenishment end, and the sewage end. And intelligent optimization control equipment.
- the heat source water end is connected to the heat source unit 11 , so that the heat medium water flows from the heat source unit 11 into the heat intelligent regulation center 13 .
- the water-removing end of the heat source is connected to the heat source unit 11 , so that the heat medium water flows into the heat source unit 11 from the heat intelligent regulation center 13 .
- the water inlet end of the heat sink is connected to the heat sink unit 12 , so that the heat medium water flows from the heat sink unit 12 into the heat intelligent regulation center 13 .
- the water-removing end of the heat sink is connected to the heat sink unit 12 , so that the heat medium water flows into the heat sink unit 12 from the heat intelligent regulation center 13 .
- the upper water collector is connected between the water outlet end of the heat source and the water inlet end of the heat sink, so as to adjust the flow rate of the heat medium water to be removed from the heat source.
- the return water collector is connected between the water outlet end of the heat trap and the water inlet end of the heat source to adjust the flow rate of the heat medium water to the heat trap.
- the water supply end is arranged in the heat intelligent control center, so as to supply water to the hot water circulation of the low temperature heating system 10 .
- the sewage end is set in the heat intelligent control center, and the sewage is discharged according to the online analysis results of the heat medium water quality.
- Description of the symbols in Fig. 3 hot water circulation pump 1; hot water air cooler 2; regulating valve 3-(1-6); flow sensor 4; pressure sensor 5; temperature sensor 6; conductivity detection 7; oil content detection in water 8; PH detection 9; dissolved oxygen detection 10; upper water collector 11A; return water collector 12A.
- all the sensors, detection instruments, control valve signals, pump air cooler operation signals and other data of the heat intelligent allocation center enter the intelligent optimization control equipment.
- the intelligent optimization control equipment 20 provided by the present invention will be described below with reference to FIG. 2 .
- the intelligent optimization control equipment 20 includes a plurality of sensor units 21 (for the sake of clarity, only one sensor unit is shown in the figure, and the present invention does not limit the number of sensor units), an edge computing unit 22 and a central optimization unit 24 .
- the plurality of sensor units 21 are used to collect sensing parameters of the low temperature thermal system of the thermal intelligent control center.
- the sensing parameters of the low-temperature thermal system of the intelligent heat control center may include, but are not limited to, the flow rate of the heat medium water, the inlet and outlet temperatures of the heat medium water, and the temperature of the heat medium water.
- the inlet and outlet pressures are collected from multiple temperature sensors, pressure sensors, and flow sensors arranged at different positions).
- the sensor unit 21 can also be used to collect the process flow rate and process inlet and outlet temperature of the heat source unit and the heat sink unit (that is, the temperature and flow rate of the materials 1-6 before and after heat treatment).
- the sensor unit 21 includes one or more wireless sensors, so that the communication with the edge computing unit 22 can be realized through the wireless gateway 22 .
- the edge computing unit 22 may be configured to perform the following steps: acquiring the sensing parameters collected by the plurality of sensor units; and calculating the heat transfer data of each heat exchanger.
- the central optimization unit 24 can be used to perform the following steps: inputting the acquired sensor parameters and training a prediction model; predicting heat exchange parameters based on the prediction model; Whether to adjust the control parameters of the thermal intelligent control center. Specifically, the following steps can be used to confirm whether to adjust the control parameters of the heat intelligent control center according to the predicted heat exchange parameters: judging whether the predicted heat exchange parameters meet the target heat exchange parameters; if so, do not adjust the The control parameters of the heat intelligent control center; if not, adjust the control parameters of the heat intelligent control center according to the difference between the existing heat exchange parameters and the target heat exchange parameters.
- the heat exchange parameters include but are not limited to: the temperature of the heat source unit and the heat sink unit after process heat exchange, and the temperature of the heat medium water after heat exchange.
- the control parameters of the heat intelligent control center include but are not limited to: the flow rate of the heat medium water and the flow rate of the heat medium water pump entering each heat source unit and the heat sink unit.
- the prediction model can be obtained by training based on historical sensing parameters and heat exchange parameters, and iterative optimization training can be performed based on real-time collected sensing parameters and real-time heat exchange parameters.
- the prediction model can be modeled by using an open source program (eg, matlab), and established by using a feedforward neural network algorithm with strong nonlinear mapping capability.
- the hidden layer node transfer function of the neural network model includes but is not limited to a hyperbolic tangent sigmoid function, and the output layer node transfer function includes but is not limited to a linear function.
- real-time data can be collected every 10 minutes to 30 minutes, and the data of the system running for 15 days to 1 month is used as input and output parameters to train the model, and the output under different control parameters is performed based on the training model. predict.
- an open source program (such as: matlab) can be used to perform optimization calculation through a heuristic algorithm to obtain target control parameters under the condition of benefit optimization.
- the input parameters of the prediction model include but are not limited to: the temperature and flow of the process stream of the heat source and the heat sink before heat exchange, the temperature and flow of the heat medium water before heat exchange, and environmental parameters such as actual air temperature data.
- the output parameters of the prediction model include but are not limited to: the post-heat temperature of the process stream of the heat source and the heat sink, and the post-heat temperature of the heat medium water.
- the central optimization unit 24 may also be configured to perform the following steps: visualizing the sensing parameters collected by the plurality of sensor units and the control parameters of the heat intelligent control center; The collected sensing parameters and the control parameters of the heat intelligent control center are displayed on the control device page 25 in the intelligent optimization control equipment client or the browser. Further, the central optimization unit 24 can also communicate with the automatic control unit 26, so as to send the optimization calculation result to the automatic control control unit 26 for execution, so as to realize automatic adjustment of the heat medium water flow and temperature in the low-temperature heating system to ensure that The low-temperature thermal system operates stably and optimally under changing production conditions.
- the model input parameters include the flow rates of 4 process streams before heat exchange (instantaneous values are 116t/h, 165t/h, 82t/h, 60t/h respectively), the temperature of 4 process streams before heat exchange (instantaneous values The values are 104°C, 158°C, 152°C, and 179°C respectively), the flow rate and temperature of the heat medium water before heat exchange (the instantaneous values are 1210t/h and 78°C, respectively).
- the output parameters of the model include the temperature of the heat medium water after heat exchange ( The instantaneous value is 90°C).
- the model training and optimization calculation are carried out through the algorithm preset by the central optimization unit, and the target heat transfer temperature after heat transfer is 94°C, and the control value of the heat transfer water flow is 820t/h.
- the central optimization unit sends the control value to the automatic control unit, and adjusts the flow rate of the circulating water pump through the actuator.
- the intelligent optimization control equipment of the low-temperature thermal system provided by the present invention has the following advantages:
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Abstract
The present invention provides an intelligent optimization control device for a low-temperature thermal system. The low-temperature thermal system comprises: a heat source unit, a heat sink unit, and an intelligent heat regulation center, the intelligent heat regulation center being used for heat transfer and heat balance between the heat source unit and the heat sink unit. The intelligent optimization control device comprises: a plurality of sensor units, each respectively used for collecting sensing parameters of the heat source unit, the heat sink unit, and the intelligent heat regulation center; an edge computing unit, used for executing the following steps: acquiring the sensing parameters collected by the plurality of sensor units, and performing quantitative computation on heat balance and heat transfer parameters; and a central optimization unit, used for inputting acquired sensor parameters into a prediction model for training, predicting a heat exchange parameter on the basis of the prediction model, and determining, on the basis of the predicted heat exchange parameter, whether to adjust control parameters of the intelligent heat regulation center with an optimization goal of benefit maximization.
Description
本发明涉及炼油、化工领域，特别涉及一种低温热***的智能优化控制装备。The invention relates to the fields of oil refining and chemical industry, in particular to an intelligent optimization control device of a low temperature thermal system.
近年来，石化企业在“大***能量综合利用”的框架下对基于全厂的低温热***进行了卓有成效的探索，设计建设了涵盖多套生产装置的新型低温热综合利用***，有效降低了企业能耗，获得了显著的经济效益，但运行中普遍存在以下几个问题：In recent years, petrochemical enterprises have carried out fruitful explorations on the low-temperature thermal system based on the whole plant under the framework of "comprehensive utilization of large-scale system energy", and designed and constructed a new low-temperature thermal comprehensive utilization system covering multiple sets of production units, effectively reducing the cost of enterprises. Energy consumption has achieved significant economic benefits, but the following problems generally exist in operation:
1)***庞大复杂，涉及装置多，热源和热阱分散，部分关键节点缺乏测量仪表，关键操作参数缺乏自动控制手段，难以统一管理和及时控制；1) The system is huge and complex, involving many devices, scattered heat sources and heat sinks, lack of measuring instruments at some key nodes, and lack of automatic control means for key operating parameters, making it difficult to manage and control them in a timely manner;
2)***运行后，不能适应极端工况变化、季节变化和部分装置开停工的变化，无法根据工况、天气等变化提前进行调度调整；2) After the system is in operation, it cannot adapt to changes in extreme working conditions, seasonal changes, and changes in the start-up and shutdown of some devices, and cannot make scheduling adjustments in advance according to changes in working conditions, weather, etc.;
3)***运行管理停留在经验阶段，缺乏合理的在线数学模型为现场技术人员提供决策支持和优化指导，受加工方案、加工负荷、气候环境，以及管理人员业务水平限制，***运行常偏离最优点，节能效果达不到设计值；3) The system operation and management remain in the experience stage, lacking a reasonable online mathematical model to provide decision support and optimization guidance for field technicians. Limited by the processing plan, processing load, climate environment, and the professional level of managers, the system operation often deviates from the optimal point , the energy saving effect cannot reach the design value;
4)***运行过程中缺少优化计算结果与控制装置的闭环回路，难以保证数学模型提供的决策支持和优化结果获得实施，手动调节不仅增加现场人员工作量，也常常导致运行效果达不到预期。4) In the process of system operation, there is a lack of optimized calculation results and a closed-loop loop of the control device, and it is difficult to ensure that the decision support provided by the mathematical model and the implementation of the optimization results are implemented.
由此可见，如何对低温热***进行优化控制，是本领域亟待解决的技术问题。It can be seen that how to optimize the control of the low-temperature thermal system is a technical problem to be solved urgently in the art.
发明内容SUMMARY OF THE INVENTION
本发明的目的在于提供一种低温热***的智能优化控制装备，从而至少在一定 程度上克服由于相关技术的限制和缺陷而导致的上述技术问题。The purpose of the present invention is to provide a kind of intelligent optimization control equipment of low temperature thermal system, so as to overcome the above-mentioned technical problems caused by the limitations and defects of the related art at least to a certain extent.
根据本发明的第一个方面，提供一种低温热***的智能优化控制装备，所述低温热***包括：热源单元、热阱单元以及热量智能调控中心，所述热量智能调控中心用于所述热源单元与所述热阱单元之间的热量传递和热量平衡；According to a first aspect of the present invention, an intelligent optimization control device for a low-temperature thermal system is provided. The low-temperature thermal system includes: a heat source unit, a heat sink unit, and a heat intelligent control center, and the heat intelligent control center is used for the heat transfer and heat balance between the heat source unit and the heat sink unit;
所述智能优化控制装备包括：The intelligent optimization control equipment includes:
多个传感器单元，各所述传感器单元分别用于采集所述热源单元、所述热阱单元以及所述热量智能调控中心的传感参数；a plurality of sensor units, each of which is used to collect the sensing parameters of the heat source unit, the heat sink unit and the heat intelligent control center;
边缘计算单元，用于执行如下步骤：An edge computing unit that performs the following steps:
获取所述多个传感器单元采集的传感参数；acquiring sensing parameters collected by the plurality of sensor units;
对热量平衡和热量传递参数进行量化计算；Quantitative calculation of heat balance and heat transfer parameters;
中心优化单元，用于执行如下步骤：将所获取的传感器参数输入和训练预测模型；a central optimization unit for performing the following steps: inputting the acquired sensor parameters and training a prediction model;
基于所述预测模型预测换热参数；predicting heat transfer parameters based on the prediction model;
基于所预测的换热参数、以效益最大化为优化目标，确认是否调整所述热量智能调控中心的控制参数。Based on the predicted heat exchange parameters and with the optimization goal of maximizing benefits, it is confirmed whether to adjust the control parameters of the heat intelligent control center.
在本申请的一些实施例中，所述低温热***通过水循环进行热量传递，In some embodiments of the present application, the low temperature thermal system performs heat transfer through a water cycle,
所述传感参数包括如下传感器参数中的一项或多项：热媒水流量、热媒水的进出温度、热媒水的进出压力、工艺流量、工艺进出温度、热媒水的电导率、溶解氧、PH值、油含量、浊度；The sensing parameters include one or more of the following sensor parameters: heating medium water flow rate, heating medium water inlet and outlet temperature, heating medium water inlet and outlet pressure, process flow, process inlet and outlet temperature, conductivity of heating medium water, Dissolved oxygen, PH value, oil content, turbidity;
所述换热参数包括如下参数中的一项或多项：所述热源单元以及所述热阱单元的工艺换热后的温度，热媒水换热后的温度；The heat exchange parameters include one or more of the following parameters: the temperature of the heat source unit and the heat sink unit after process heat exchange, and the temperature of the heat medium water after heat exchange;
所述热量智能调控中心的控制参数包括如下控制参数中的一项或多项：进入各 热源单元以及热阱单元的热媒水流量、热媒水泵流量。The control parameters of the heat intelligent control center include one or more of the following control parameters: the flow rate of heat medium water and the flow rate of heat medium water pumps entering each heat source unit and heat sink unit.
在本申请的一些实施例中，所述根据所预测的换热参数确认是否调整所述热量智能调控中心的控制参数包括：In some embodiments of the present application, the confirming whether to adjust the control parameters of the heat intelligent control center according to the predicted heat exchange parameters includes:
判断所预测的换热参数是否符合目标换热参数；Determine whether the predicted heat transfer parameters conform to the target heat transfer parameters;
若是，则不调整所述热量智能调控中心的控制参数；If so, do not adjust the control parameters of the heat intelligent control center;
若否，则根据现有的换热参数以及所述目标换热参数的差异，调整所述热量智能调控中心的控制参数。If not, the control parameters of the heat intelligent control center are adjusted according to the difference between the existing heat exchange parameters and the target heat exchange parameters.
在本申请的一些实施例中，所述预测模型基于历史传感参数以及换热参数训练获得，并基于实时采集的传感参数以及实时的换热参数进行迭代优化训练。In some embodiments of the present application, the prediction model is obtained by training based on historical sensing parameters and heat exchange parameters, and iterative optimization training is performed based on real-time collected sensing parameters and real-time heat exchange parameters.
在本申请的一些实施例中，所述预测模型为前馈神经网络模型，所述前馈神经网络模型的隐含层节点传递函数包括双曲正切S形函数，输出层节点传递函数包括线性函数。In some embodiments of the present application, the prediction model is a feedforward neural network model, the hidden layer node transfer function of the feedforward neural network model includes a hyperbolic tangent sigmoid function, and the output layer node transfer function includes a linear function .
在本申请的一些实施例中，所述中心优化单元，与所述边缘计算单元相通信，In some embodiments of the present application, the central optimization unit, in communication with the edge computing unit,
所述中心优化单元用于执行：The central optimization unit is used to perform:
所述预测模型的训练；the training of the predictive model;
所述热量智能调控中心的控制参数的优化；Optimization of control parameters of the heat intelligent control center;
所述传感参数、换热参数以及所述控制参数的储存。Storage of the sensing parameters, heat transfer parameters and the control parameters.
在本申请的一些实施例中，所述中心优化单元还用于执行如下步骤：In some embodiments of the present application, the central optimization unit is further configured to perform the following steps:
对所述多个传感器单元采集的传感参数以及所述热量智能调控中心的控制参数进行可视化处理；Visually process the sensing parameters collected by the plurality of sensor units and the control parameters of the heat intelligent control center;
将可视化处理的所述多个传感器单元采集的传感参数以及所述热量智能调控中心的控制参数显示于智能优化控制装备客户端或者浏览器中的控制装置页面中。The visually processed sensing parameters collected by the plurality of sensor units and the control parameters of the heat intelligent control center are displayed on the intelligent optimization control equipment client or the control device page in the browser.
在本申请的一些实施例中，所述热量智能调控中心包括：In some embodiments of the present application, the intelligent heat regulation center includes:
热源来水端以及热源去水端，连接至所述热源单元；The heat source water end and the heat source water discharge end are connected to the heat source unit;
热阱来水端以及热阱去水端，连接至所述热阱单元；The water inlet end of the heat trap and the water outlet end of the heat trap are connected to the heat trap unit;
上水集水单元，连接于所述热源去水端以及所述热阱来水端之间；The upper water collecting unit is connected between the water outlet end of the heat source and the water inlet end of the heat trap;
回水集水单元，连接于所述热阱去水端以及所述热源来水端之间；a return water collecting unit, connected between the water outlet end of the heat trap and the water inlet end of the heat source;
补水单元，连接于所述回水集水单元和所述热源去水端之间。The water replenishing unit is connected between the return water collecting unit and the water outlet end of the heat source.
在本申请的一些实施例中，所述边缘计算单元还将环境参数输入所述预测模型。In some embodiments of the present application, the edge computing unit also inputs environmental parameters into the prediction model.
在本申请的一些实施例中，所述传感器单元包括一个或多个无线传感器。In some embodiments of the present application, the sensor unit includes one or more wireless sensors.
本发明提供的低温热***的智能控制装置具有如下优势：The intelligent control device of the low-temperature thermal system provided by the present invention has the following advantages:
通过低温热***智能化优化及控制装备以无线数据采集***、通信传输为基础，以大数据建模算法和启发式优化算法为核心，将边缘计算设备与大数据深度学习算法和自动控制装置集成为一体，从而实现实时数据采集监控、低温热***在线模拟、关键操作参数优化计算、操作参数自动控制等功能，确保低温热***的闭环实时优化控制。Through intelligent optimization and control equipment of low-temperature thermal system, based on wireless data acquisition system and communication transmission, with big data modeling algorithm and heuristic optimization algorithm as the core, edge computing equipment, big data deep learning algorithm and automatic control device are integrated. Become one, so as to realize the functions of real-time data acquisition and monitoring, online simulation of low-temperature thermal system, optimization calculation of key operating parameters, automatic control of operating parameters, etc., to ensure closed-loop real-time optimal control of low-temperature thermal system.
为使能更进一步了解本申请的特征及技术内容，请参阅以下有关本申请的详细说明与附图，但是这里的详细说明以及附图仅是用来说明本申请，而非对本申请的权利要求范围作任何的限制。In order to further understand the features and technical content of the present application, please refer to the following detailed description and drawings related to the present application, but the detailed description and drawings here are only used to describe the present application, rather than the claims of the present application any limitation on the scope.
通过参照附图详细描述其示例实施方式，本申请的上述和其它特征及优点将变得更加明显。The above and other features and advantages of the present application will become more apparent from the detailed description of example embodiments thereof with reference to the accompanying drawings.
图1为本发明的一个实施例的低温热***的示意图。FIG. 1 is a schematic diagram of a low temperature thermal system according to an embodiment of the present invention.
图2为本发明的一个实施例的智能优化控制装备的示意图。FIG. 2 is a schematic diagram of an intelligent optimization control device according to an embodiment of the present invention.
图3为本发明的一个实施例的热量智能调控中心包含智能优化控制装备的低温热***的示意图。3 is a schematic diagram of a low-temperature thermal system in which the intelligent thermal control center includes intelligent optimization control equipment according to an embodiment of the present invention.
现在将参考附图更全面地描述示例实施方式。然而，示例实施方式能够以多种形式实施，且不应被理解为限于在此阐述的实施方式；相反，提供这些实施方式使得本申请将全面和完整，并将示例实施方式的构思全面地传达给本领域的技术人员。在图中相同的附图标记表示相同或类似的结构，因而将省略它们的详细描述。Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments, however, can be embodied in various forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and thus their detailed descriptions will be omitted.
此外，所描述的特征、结构或特性可以以任何合适的方式结合在一个或更多实施例中。在下面的描述中，提供许多具体细节从而给出对本发明的实施例的充分理解。然而，本领域技术人员将意识到，可以实践本发明的技术方案而没有所述特定细节中的一个或更多，或者可以采用其它的结构、部件、步骤、方法等。在其它情况下，不详细示出或描述公知结构、部件或者操作以避免模糊本发明的各方面。Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided in order to give a thorough understanding of embodiments of the present invention. However, one skilled in the art will appreciate that the technical solutions of the present invention may be practiced without one or more of the specific details, or with other structures, components, steps, methods, etc., may be employed. In other instances, well-known structures, components, or operations are not shown or described in detail to avoid obscuring aspects of the present invention.
本发明的其它特性和优点将通过下面的详细描述变得显然，或部分地通过本发明的实践而习得。Other features and advantages of the present invention will become apparent from the following detailed description, or may be learned in part by practice of the present invention.
结合图1至图3对本发明提供的低温热***的智能优化控制装备进行说明。图1为本发明的一个实施例的低温热***的示意图。图2为本发明的一个实施例的智能优化控制装备的示意图。图3为本发明的一个实施例的包含智能优化控制装备的低温热***热量智能调控中心的示意图。The intelligent optimization control equipment for the low temperature thermal system provided by the present invention will be described with reference to FIGS. 1 to 3 . FIG. 1 is a schematic diagram of a low temperature thermal system according to an embodiment of the present invention. FIG. 2 is a schematic diagram of an intelligent optimization control device according to an embodiment of the present invention. 3 is a schematic diagram of a low temperature thermal system heat intelligent control center including intelligent optimization control equipment according to an embodiment of the present invention.
所述低温热***10包括热源单元11、热阱单元12以及热量智能调控中心13。所述热量智能调控中心13用于所述热源单元11与所述热阱单元12之间的热量传 递和热量平衡。The low temperature thermal system 10 includes a heat source unit 11 , a heat sink unit 12 and an intelligent heat regulation center 13 . The heat intelligent regulation center 13 is used for heat transfer and heat balance between the heat source unit 11 and the heat sink unit 12.
如图1示出的低温热***10的一个具体实施例中，低温热***10回收物料1、2、3的热量，分别送至各处，为物料4、5、6提供低温热量。在现有的实现中，物料1、2、3原来的温度100-135℃各不一样，但都是通过空冷器或者水冷器冷却到较低温度(一般为50℃以下)后进入下游，物料1、2、3的热量通过空冷和水冷(消耗电能)损失到大气中。物料4、5、6原来的温度70-110℃各不一样，都是通过蒸汽加热到需要的温度，蒸汽可以是外部购买或者通过燃烧煤、天然气等自行生产。由此，现有的实现中，物料1、2、3损失的热量，以及物料4、5、6所需的热量互不关联，从而导致热量的浪费。In a specific embodiment of the low temperature heat system 10 shown in FIG. 1 , the low temperature heat system 10 recovers the heat of the materials 1 , 2 , and 3 and sends them to various places to provide the materials 4 , 5 , and 6 with low temperature heat. In the existing implementation, the original temperatures of materials 1, 2, and 3 are different from 100 to 135 °C, but they are all cooled to a lower temperature (generally below 50 °C) by an air cooler or water cooler and then enter the downstream. The heat of 1, 2, and 3 is lost to the atmosphere through air cooling and water cooling (consumption of electrical energy). The original temperature of materials 4, 5, and 6 varies from 70 to 110 °C. They are all heated to the required temperature by steam. The steam can be purchased externally or produced by burning coal, natural gas, etc. Therefore, in the existing implementation, the heat lost by the materials 1, 2, and 3 and the heat required by the materials 4, 5, and 6 are not related to each other, resulting in waste of heat.
为此，图1中的低温热***10可以建立热水循环。热媒水在热量智能调控中心13通过泵加压后送到热源单元11的物料1、2、3处进行换热升温，升温后返回热量智能调控中心13，经过水量调控送到热阱单元12的物料4、5、6处替代原4、5、6处的蒸汽进行加热。热媒水温度降低后再返回热量智能调控中心13，由热量智能调控中心13的空冷器或者水冷器调节水温后再通过泵加压，通过水量调控送去物料1、2、3处取热完成循环。通过低温热***10的运用，物料1、2、3减少了空冷器或者水冷器的冷却消耗，物料4、5、6减少了加热蒸汽的消耗，达到了热量的利用。To this end, the low temperature thermal system 10 in FIG. 1 may establish a hot water cycle. The heat medium water is pressurized by the pump in the heat intelligent control center 13 and sent to the materials 1, 2 and 3 of the heat source unit 11 for heat exchange and temperature rise, and then returned to the heat intelligent control center 13 after heating, and sent to the heat sink unit 12 after the water volume control. The raw materials 4, 5, and 6 replace the steam at the original 4, 5, and 6 for heating. After the temperature of the heat medium water is lowered, it returns to the heat intelligent control center 13. The water temperature is adjusted by the air cooler or water cooler of the heat intelligent control center 13, and then pressurized by the pump, and sent to the materials 1, 2, and 3 to obtain heat through the water volume control. cycle. Through the use of the low temperature heating system 10, the materials 1, 2, and 3 reduce the cooling consumption of the air cooler or the water cooler, and the materials 4, 5, and 6 reduce the consumption of heating steam and achieve the utilization of heat.
具体而言，继续参考图3，图3示出本发明的包含智能优化控制装备的低温热***热量智能调控中心的具体结构。如图3所示，热量智能调控中心包括热源来水端以及热源去水端、热阱来水端以及热阱去水端、上水集水器、回水集水器以及补水端、排污端和智能优化控制装备。热源来水端连接至所述热源单元11，以供热媒水自热源单元11流入热量智能调控中心13。热源去水端连接至所述热源单元11， 以供热媒水自热量智能调控中心13流入热源单元11。热阱来水端连接至所述热阱单元12，以供热媒水自热阱单元12流入热量智能调控中心13。热阱去水端连接至所述热阱单元12，以供热媒水自热量智能调控中心13流入热阱单元12。上水集水器连接于所述热源去水端以及热阱来水端之间，以调整去热源的热媒水的流量。回水集水器连接于所述热阱去水端以及热源来水端之间，以调整去热阱的热媒水的流量。补水端设置在热量智能调控中心，以便于向低温热***10的热水循环进行供水。排污端设置在热量智能调控中心，根据热媒水水质在线分析结果进行排污。图3中各标号说明：热水循环泵1；热水空冷器2；调节阀3-(1～6)；流量传感器4；压力传感器5；温度传感器6；电导率检测7；水中油含量检测8；PH检测9；溶解氧检测10；上水集水器11A；回水集水器12A。其中，热量智能调配中心所有传感器、检测仪表、调节阀信号、泵空冷器运行信号等数据均进入智能优化控制装备。Specifically, continue to refer to FIG. 3 , which shows the specific structure of the intelligent control center for low temperature thermal system heat including intelligent optimization control equipment of the present invention. As shown in Figure 3, the heat intelligent control center includes the heat source water end and the heat source water end, the heat trap water end and the heat trap water end, the feed water collector, the return water collector, the replenishment end, and the sewage end. And intelligent optimization control equipment. The heat source water end is connected to the heat source unit 11 , so that the heat medium water flows from the heat source unit 11 into the heat intelligent regulation center 13 . The water-removing end of the heat source is connected to the heat source unit 11 , so that the heat medium water flows into the heat source unit 11 from the heat intelligent regulation center 13 . The water inlet end of the heat sink is connected to the heat sink unit 12 , so that the heat medium water flows from the heat sink unit 12 into the heat intelligent regulation center 13 . The water-removing end of the heat sink is connected to the heat sink unit 12 , so that the heat medium water flows into the heat sink unit 12 from the heat intelligent regulation center 13 . The upper water collector is connected between the water outlet end of the heat source and the water inlet end of the heat sink, so as to adjust the flow rate of the heat medium water to be removed from the heat source. The return water collector is connected between the water outlet end of the heat trap and the water inlet end of the heat source to adjust the flow rate of the heat medium water to the heat trap. The water supply end is arranged in the heat intelligent control center, so as to supply water to the hot water circulation of the low temperature heating system 10 . The sewage end is set in the heat intelligent control center, and the sewage is discharged according to the online analysis results of the heat medium water quality. Description of the symbols in Fig. 3: hot water circulation pump 1; hot water air cooler 2; regulating valve 3-(1-6); flow sensor 4; pressure sensor 5; temperature sensor 6; conductivity detection 7; oil content detection in water 8; PH detection 9; dissolved oxygen detection 10; upper water collector 11A; return water collector 12A. Among them, all the sensors, detection instruments, control valve signals, pump air cooler operation signals and other data of the heat intelligent allocation center enter the intelligent optimization control equipment.
以上仅仅是示意性地描述本发明提供的低温热***10，本发明并非以此为限制。The above is only a schematic description of the low-temperature thermal system 10 provided by the present invention, and the present invention is not limited thereto.
下面结合图2描述本发明提供的智能优化控制装备20。智能优化控制装备20包括多个传感器单元21(为了清楚起见，图中仅示出一个传感器单元，本发明并不限制传感器单元的数量)、边缘计算单元22以及中心优化单元24。The intelligent optimization control equipment 20 provided by the present invention will be described below with reference to FIG. 2 . The intelligent optimization control equipment 20 includes a plurality of sensor units 21 (for the sake of clarity, only one sensor unit is shown in the figure, and the present invention does not limit the number of sensor units), an edge computing unit 22 and a central optimization unit 24 .
多个传感器单元21用于采集所述热量智能调控中心低温热***的传感参数。在前述的低温热***10通过水循环进行热量传递的实施例中，所述热量智能调控中心低温热***的传感参数可以包括但不限于热媒水流量、热媒水的进出温度、热媒水的进出压力(如图3示出的多个设置在不同位置的温度传感器、压力传感器、流量传感器采集获得)。所述传感器单元21还可以用于采集所述热源单元以及所述热阱单元的工艺流量、工艺进出温度(也就是物料1-6经热处理前后的温度以及 流量)。在一些优选的实施例中，所述传感器单元21包括一个或多个无线传感器，从而可以通过无线网关22实现与边缘计算单元22之间的通信。The plurality of sensor units 21 are used to collect sensing parameters of the low temperature thermal system of the thermal intelligent control center. In the aforementioned embodiment in which the low-temperature thermal system 10 transfers heat through water circulation, the sensing parameters of the low-temperature thermal system of the intelligent heat control center may include, but are not limited to, the flow rate of the heat medium water, the inlet and outlet temperatures of the heat medium water, and the temperature of the heat medium water. The inlet and outlet pressures (as shown in FIG. 3 are collected from multiple temperature sensors, pressure sensors, and flow sensors arranged at different positions). The sensor unit 21 can also be used to collect the process flow rate and process inlet and outlet temperature of the heat source unit and the heat sink unit (that is, the temperature and flow rate of the materials 1-6 before and after heat treatment). In some preferred embodiments, the sensor unit 21 includes one or more wireless sensors, so that the communication with the edge computing unit 22 can be realized through the wireless gateway 22 .
边缘计算单元22可以用于执行如下步骤：获取所述多个传感器单元采集的传感参数；对各换热器的热量传递数据进行计算。中心优化单元24可以用于执行如下步骤：将所获取的传感器参数输入和训练预测模型；基于所述预测模型预测换热参数；根据所预测的换热参数、以效益最大化为优化目标，确认是否调整所述热量智能调控中心的控制参数。具体而言，可以通过如下步骤实现根据所预测的换热参数确认是否调整所述热量智能调控中心的控制参数：判断所预测的换热参数是否符合目标换热参数；若是，则不调整所述热量智能调控中心的控制参数；若否，则根据现有的换热参数以及所述目标换热参数的差异，调整所述热量智能调控中心的控制参数。其中，所述换热参数包括但不限于：所述热源单元以及所述热阱单元的工艺换热后的温度，热媒水换热后的温度。所述热量智能调控中心控制参数包括但不限于：进入各热源单元以及热阱单元的热媒水流量、热媒水泵流量。The edge computing unit 22 may be configured to perform the following steps: acquiring the sensing parameters collected by the plurality of sensor units; and calculating the heat transfer data of each heat exchanger. The central optimization unit 24 can be used to perform the following steps: inputting the acquired sensor parameters and training a prediction model; predicting heat exchange parameters based on the prediction model; Whether to adjust the control parameters of the thermal intelligent control center. Specifically, the following steps can be used to confirm whether to adjust the control parameters of the heat intelligent control center according to the predicted heat exchange parameters: judging whether the predicted heat exchange parameters meet the target heat exchange parameters; if so, do not adjust the The control parameters of the heat intelligent control center; if not, adjust the control parameters of the heat intelligent control center according to the difference between the existing heat exchange parameters and the target heat exchange parameters. Wherein, the heat exchange parameters include but are not limited to: the temperature of the heat source unit and the heat sink unit after process heat exchange, and the temperature of the heat medium water after heat exchange. The control parameters of the heat intelligent control center include but are not limited to: the flow rate of the heat medium water and the flow rate of the heat medium water pump entering each heat source unit and the heat sink unit.
具体而言，所述预测模型可以基于历史传感参数以及换热参数训练获得，并可以基于实时采集的传感参数以及实时的换热参数进行迭代优化训练。在上述实施例的一个具体实现中，预测模型可以利用开源程序(如：matlab)实现建模，采用具有很强非线性映射能力的前馈神经网络算法建立。神经网络模型的隐含层节点传递函数包括但不限于双曲正切S形函数，输出层节点传递函数包括但不限于线性函数。在进行建模时，可以每10分钟～30分钟采集一次实时数据，以***运行15天～1个月的数据作为输入和输出参数对模型进行训练，基于训练模型对不同控制参数下的输出进行预测。进一步地，可以利用开源程序(如：matlab)通过启发式算法进行优化计算，获得效益优化条件下的目标控制参数。Specifically, the prediction model can be obtained by training based on historical sensing parameters and heat exchange parameters, and iterative optimization training can be performed based on real-time collected sensing parameters and real-time heat exchange parameters. In a specific implementation of the above embodiment, the prediction model can be modeled by using an open source program (eg, matlab), and established by using a feedforward neural network algorithm with strong nonlinear mapping capability. The hidden layer node transfer function of the neural network model includes but is not limited to a hyperbolic tangent sigmoid function, and the output layer node transfer function includes but is not limited to a linear function. When modeling, real-time data can be collected every 10 minutes to 30 minutes, and the data of the system running for 15 days to 1 month is used as input and output parameters to train the model, and the output under different control parameters is performed based on the training model. predict. Further, an open source program (such as: matlab) can be used to perform optimization calculation through a heuristic algorithm to obtain target control parameters under the condition of benefit optimization.
其中，预测模型的输入参数包括但不限于：热源、热阱的工艺物流换热前温度和流量，热媒水换热前温度和流量以及诸如实际气温数据的环境参数。预测模型的输出参数包括但不限于：热源、热阱的工艺物流换热后温度，热媒水换热后温度。Wherein, the input parameters of the prediction model include but are not limited to: the temperature and flow of the process stream of the heat source and the heat sink before heat exchange, the temperature and flow of the heat medium water before heat exchange, and environmental parameters such as actual air temperature data. The output parameters of the prediction model include but are not limited to: the post-heat temperature of the process stream of the heat source and the heat sink, and the post-heat temperature of the heat medium water.
所述中心优化单元24还可以用于执行如下步骤：对所述多个传感器单元采集的传感参数以及所述热量智能调控中心的控制参数进行可视化处理；将可视化处理的所述多个传感器单元采集的传感参数以及所述热量智能调控中心的控制参数显示于智能优化控制装备客户端或者浏览器中的控制装置页面25中。进一步地，所述中心优化单元24还可以与自动控制单元26之间进行通信，以将优化计算结果发送给自控控制单元26执行，实现低温热***中热媒水流量、温度等自动调整，确保生产工况变化下的低温热***稳定、优化运行。The central optimization unit 24 may also be configured to perform the following steps: visualizing the sensing parameters collected by the plurality of sensor units and the control parameters of the heat intelligent control center; The collected sensing parameters and the control parameters of the heat intelligent control center are displayed on the control device page 25 in the intelligent optimization control equipment client or the browser. Further, the central optimization unit 24 can also communicate with the automatic control unit 26, so as to send the optimization calculation result to the automatic control control unit 26 for execution, so as to realize automatic adjustment of the heat medium water flow and temperature in the low-temperature heating system to ensure that The low-temperature thermal system operates stably and optimally under changing production conditions.
在一个具体实现中，模型输入参数包括4股工艺物流换热前流量(瞬时值分别为116t/h、165t/h、82t/h、60t/h)、4股工艺物流换热前温度(瞬时值分别为104℃、158℃、152℃、179℃)、热媒水换热前流量和温度(瞬时值分别为1210t/h、78℃)，模型输出参数包括热媒水换热后温度(瞬时值为90℃)。通过中心优化单元预设的算法进行模型训练和优化计算，得到目标热媒水换热后温度为94℃，热媒水流量控制值为820t/h。之后，中心优化单元将该控制值发送给自动控制单元，通过执行机构调整循环水泵流量。本发明提供的低温热***的智能优化控制装备具有如下优势：In a specific implementation, the model input parameters include the flow rates of 4 process streams before heat exchange (instantaneous values are 116t/h, 165t/h, 82t/h, 60t/h respectively), the temperature of 4 process streams before heat exchange (instantaneous values The values are 104°C, 158°C, 152°C, and 179°C respectively), the flow rate and temperature of the heat medium water before heat exchange (the instantaneous values are 1210t/h and 78°C, respectively). The output parameters of the model include the temperature of the heat medium water after heat exchange ( The instantaneous value is 90°C). The model training and optimization calculation are carried out through the algorithm preset by the central optimization unit, and the target heat transfer temperature after heat transfer is 94℃, and the control value of the heat transfer water flow is 820t/h. After that, the central optimization unit sends the control value to the automatic control unit, and adjusts the flow rate of the circulating water pump through the actuator. The intelligent optimization control equipment of the low-temperature thermal system provided by the present invention has the following advantages:
通过低温热***智能化优化及控制装备以无线数据采集***、通信传输为基础，以大数据建模算法和启发式优化算法为核心，将边缘计算设备与大数据深度学习算法和自动控制装置集成为一体，从而实现实时数据采集监控、低温热***在线模拟、关键操作参数优化计算、操作参数自动控制等功能，确保低温热***的闭环 实时优化控制。Through intelligent optimization and control equipment of low-temperature thermal system, based on wireless data acquisition system and communication transmission, with big data modeling algorithm and heuristic optimization algorithm as the core, edge computing equipment, big data deep learning algorithm and automatic control device are integrated. Become one, so as to realize the functions of real-time data acquisition and monitoring, online simulation of low-temperature thermal system, optimization calculation of key operating parameters, automatic control of operating parameters, etc., to ensure closed-loop real-time optimal control of low-temperature thermal system.
本发明已由上述相关实施例加以描述，然而上述实施例仅为实施本发明的范例。必需指出的是，已揭露的实施例并未限制本发明的范围。相反地，在不脱离本发明的精神和范围内所作的更动与润饰，均属本发明的专利保护范围。The present invention has been described by the above-mentioned related embodiments, however, the above-mentioned embodiments are only examples for implementing the present invention. It must be pointed out that the disclosed embodiments do not limit the scope of the present invention. On the contrary, changes and modifications made without departing from the spirit and scope of the present invention belong to the scope of patent protection of the present invention.
Claims (10)
- 一种低温热***的智能优化控制装备，其特征在于，所述低温热***包括：热源单元、热阱单元以及热量智能调控中心，所述热量智能调控中心用于所述热源单元与所述热阱单元之间的热量传递和热量平衡；An intelligent optimization control device for a low-temperature thermal system, characterized in that the low-temperature thermal system includes: a heat source unit, a heat sink unit, and a heat intelligent control center, and the heat intelligent control center is used for the heat source unit and the heat Heat transfer and heat balance between trap cells;所述智能优化控制装备包括：The intelligent optimization control equipment includes:多个传感器单元，各所述传感器单元分别用于采集所述热源单元、所述热阱单元以及所述热量智能调控中心的传感参数；a plurality of sensor units, each of which is used to collect the sensing parameters of the heat source unit, the heat sink unit and the heat intelligent control center;边缘计算单元，用于执行如下步骤：An edge computing unit that performs the following steps:获取所述多个传感器单元采集的传感参数；acquiring sensing parameters collected by the plurality of sensor units;对热量平衡和热量传递参数进行量化计算；Quantitative calculation of heat balance and heat transfer parameters;中心优化单元，用于执行如下步骤：A central optimization unit that performs the following steps:将所获取的传感器参数输入和训练预测模型；Input the acquired sensor parameters and train the prediction model;基于所述预测模型预测换热参数；predicting heat transfer parameters based on the prediction model;基于所预测的换热参数，确认是否调整所述热量智能调控中心的控制参数。Based on the predicted heat exchange parameters, it is confirmed whether to adjust the control parameters of the heat intelligent control center.
- 如权利要求1所述的低温热***的智能优化控制装备，其特征在于，所述低温热***通过水循环进行热量传递，The intelligent optimization control equipment for a low-temperature thermal system according to claim 1, wherein the low-temperature thermal system transfers heat through a water cycle,所述传感参数包括如下传感器参数中的一项或多项：热媒水流量、热媒水的进出温度、热媒水的进出压力、工艺流量、工艺进出温度、热媒水的电导率、溶解氧、PH值、油含量、浊度；The sensing parameters include one or more of the following sensor parameters: heating medium water flow rate, heating medium water inlet and outlet temperature, heating medium water inlet and outlet pressure, process flow, process inlet and outlet temperature, conductivity of heating medium water, Dissolved oxygen, PH value, oil content, turbidity;所述换热参数包括如下参数中的一项或多项：所述热源单元以及所述热阱单元的工艺换热后的温度，热媒水换热后的温度；The heat exchange parameters include one or more of the following parameters: the temperature of the heat source unit and the heat sink unit after process heat exchange, and the temperature of the heat medium water after heat exchange;所述热量智能调控中心的控制参数包括如下控制参数中的一项或多项：进入各 热源单元以及热阱单元的热媒水流量、热媒水泵流量。The control parameters of the heat intelligent control center include one or more of the following control parameters: the flow rate of heat medium water and the flow rate of heat medium water pumps entering each heat source unit and heat sink unit.
- 如权利要求2所述的低温热***的智能优化控制装备，其特征在于，所述根据所预测的换热参数确认是否调整所述热量智能调控中心的控制参数包括：The intelligent optimization control equipment for a low-temperature thermal system according to claim 2, wherein the confirming whether to adjust the control parameters of the heat intelligent control center according to the predicted heat exchange parameters comprises:判断所预测的换热参数是否符合目标换热参数；Determine whether the predicted heat transfer parameters conform to the target heat transfer parameters;若是，则不调整所述热量智能调控中心的控制参数；If so, do not adjust the control parameters of the heat intelligent control center;若否，则根据现有的换热参数以及所述目标换热参数的差异，调整所述热量智能调控中心的控制参数。If not, the control parameters of the heat intelligent control center are adjusted according to the difference between the existing heat exchange parameters and the target heat exchange parameters.
- 如权利要求1所述的低温热***的智能优化控制装备，其特征在于，所述预测模型基于历史传感参数以及换热参数训练获得，并基于实时采集的传感参数以及实时的换热参数进行迭代优化训练。The intelligent optimization control equipment for a low-temperature thermal system according to claim 1, wherein the prediction model is obtained by training based on historical sensing parameters and heat exchange parameters, and based on real-time collected sensing parameters and real-time heat exchange parameters Perform iterative optimization training.
- 如权利要求4所述的低温热***的智能优化控制装备，其特征在于，所述预测模型为前馈神经网络模型，所述前馈神经网络模型的隐含层节点传递函数包括双曲正切S形函数，输出层节点传递函数包括线性函数。The intelligent optimization control equipment for a low-temperature thermal system according to claim 4, wherein the prediction model is a feedforward neural network model, and the hidden layer node transfer function of the feedforward neural network model includes the hyperbolic tangent S Shape function, output layer node transfer function includes linear function.
- 如权利要求1所述的低温热***的智能优化控制装备，其特征在于，所述中心优化单元，与所述边缘计算单元相通信，The intelligent optimization control equipment for a low temperature thermal system according to claim 1, wherein the central optimization unit communicates with the edge computing unit,所述中心优化单元还用于执行：The central optimization unit is also used to perform:所述预测模型的训练；the training of the predictive model;所述热量智能调控中心的控制参数的优化；Optimization of control parameters of the heat intelligent control center;所述传感参数、换热参数以及所述控制参数的储存。Storage of the sensing parameters, heat transfer parameters and the control parameters.
- 如权利要求6所述的低温热***的智能优化控制装备，其特征在于，所述中心优化单元还用于执行如下步骤：The intelligent optimization control equipment for a low temperature thermal system according to claim 6, wherein the central optimization unit is further configured to perform the following steps:对所述多个传感器单元采集的传感参数以及所述热量智能调控中心的控制参 数进行可视化处理；Visually process the sensing parameters collected by the plurality of sensor units and the control parameters of the heat intelligent control center;将可视化处理的所述多个传感器单元采集的传感参数以及所述热量智能调控中心的控制参数显示于智能优化控制装备客户端或者浏览器中的控制装置页面中。The visually processed sensing parameters collected by the plurality of sensor units and the control parameters of the heat intelligent control center are displayed on the intelligent optimization control equipment client or the control device page in the browser.
- 如权利要求2所述的低温热***的智能优化控制装备，其特征在于，所述热量智能调控中心包括：The intelligent optimization control equipment for a low-temperature thermal system according to claim 2, wherein the thermal intelligent control center comprises:热源来水端以及热源去水端，连接至所述热源单元；The heat source water end and the heat source water discharge end are connected to the heat source unit;热阱来水端以及热阱去水端，连接至所述热阱单元；The water inlet end of the heat trap and the water outlet end of the heat trap are connected to the heat trap unit;上水集水单元，连接于所述热源去水端以及所述热阱来水端之间；The upper water collecting unit is connected between the water outlet end of the heat source and the water inlet end of the heat trap;回水集水单元，连接于所述热阱去水端以及所述热源来水端之间；a return water collecting unit, connected between the water outlet end of the heat trap and the water inlet end of the heat source;补水单元，连接于所述回水集水单元和所述热源去水端之间。The water replenishing unit is connected between the return water collecting unit and the water outlet end of the heat source.
- 如权利要求1所述的低温热***的智能优化控制装备，其特征在于，所述边缘计算单元还将环境参数输入所述预测模型。The intelligent optimization control equipment for a low temperature thermal system according to claim 1, wherein the edge computing unit further inputs environmental parameters into the prediction model.
- 如权利要求1至9任一项所述的低温热***的智能优化控制装备，其特征在于，所述传感器单元包括一个或多个无线传感器。The intelligent optimization control equipment for a low temperature thermal system according to any one of claims 1 to 9, wherein the sensor unit comprises one or more wireless sensors.
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