CN115059924A - Intelligent combustion control method and device for garbage incinerator, equipment and storage medium - Google Patents
Intelligent combustion control method and device for garbage incinerator, equipment and storage medium Download PDFInfo
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Abstract
The disclosure provides an intelligent combustion control method and device for a garbage incinerator, electronic equipment and a computer storage medium, and relates to the field of combustion process monitoring. The method comprises the steps that a distributed control system collects input reference quantity required by each control loop in the waste incinerator in real time; transmitting the input reference quantity required by each control loop in the waste incinerator to an intelligent combustion control calculation model corresponding to the control loop for calculation to obtain operation parameters; and returning the operation parameters to the distributed control system and controlling the action of each actuator of the incinerator in the incineration process. The method overcomes the defects that the traditional ACC system excessively depends on the accuracy of instruments and the like, is a control system which is suitable for the waste incineration characteristics of China and has an international advanced level, aims to realize the unmanned operation of waste incineration control, meets the production requirements of environmental protection, standard reaching, safety and stability, reasonably prolongs the service life of each device, and realizes the purposes of energy conservation and efficiency improvement.
Description
Technical Field
The present disclosure relates to the field of garbage disposal, and in particular, to a method and an apparatus for controlling operation of a garbage incineration process, an electronic device, and a computer storage medium.
Background
At present, Automatic Control of a garbage incinerator at home and abroad mainly adopts an Automatic Combustion Control system, namely an Automatic Combustion Control (ACC) system, the ACC system technology is mainly introduced from abroad, the technical scheme is that an operator sets parameters such as garbage heat value, target steam quantity, garbage specific gravity and the like, and Automatic Control is realized through calculation of process formulas of various loops, and the core idea of the technology is process calculation.
The existing ACC system has certain limitations, for example, the ACC system control can only be realized under certain conditions, and is difficult to adapt to the characteristics of high water content and large component fluctuation of domestic garbage, the ACC system is mainly calculated according to a formula, and the experience of operators cannot be set in the control system; the ACC system is too dependent on the precision of instruments and meters, and if some instruments and meters are damaged or the precision is insufficient, the ACC system is difficult to accurately operate; the ACC system needs to rely on a detailed and accurate database of garbage heating values; this results in the ACC system difficult to carry out extensive popularization in domestic waste incineration plant, and domestic waste incineration degree of automation is lower.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
In order to solve the above problems, an object of the present disclosure is to provide an intelligent combustion control method and apparatus for a waste incinerator, which can adapt to the characteristics of large inertia, hysteresis, nonlinearity, time-varying property, working environment and disturbance uncertainty of the waste incineration system, improve the uniform heat release of the waste in the whole incineration system, and reduce the formation of pollutants.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to one aspect of the disclosure, a method for controlling intelligent combustion of a garbage incinerator comprises the following steps:
the distributed control system collects input reference quantity required by each control loop in the waste incinerator in real time;
transmitting the input reference quantity required by each control loop in the waste incinerator to an intelligent combustion control calculation model corresponding to the control loop for calculation to obtain operation parameters;
and returning the operation parameters to the distributed control system and controlling the action of each actuator of the incinerator in the incineration process.
Further, the intelligent combustion control method for the garbage incinerator further comprises the following steps:
determining input reference quantity and output controlled quantity required by each control loop in the waste incinerator;
screening input reference quantity and output controlled quantity required by each control loop in the waste incinerator from historical combustion condition information, and processing the input reference quantity and the output controlled quantity to generate a training set;
training the intelligent combustion control calculation model of each control loop in the waste incinerator by using the training set to obtain the intelligent combustion control calculation model of each control loop in the waste incinerator;
and combining and integrally packaging the calculation models of each control loop in the garbage incinerator to generate the calculation model of the intelligent combustion control method of the garbage incinerator.
Further, the inputting the reference quantity includes: temperature parameters, pressure parameters, load parameters and pollutant parameters in the incinerator; the outputting the controlled quantity includes: the control parameters of the feeding system, the control parameters of the grate movement period, the control parameters of the primary air system of the combustion air system and the control parameters of the secondary air system of the combustion air system.
Further, the inputting the reference amount further includes: oxygen content at the outlet, the thickness of the garbage layer and the liquid level of the steam drum.
Furthermore, the acquisition time span and the acquisition frequency required by acquiring data of the input reference quantity and the output controlled quantity required by each control loop in the waste incinerator are determined.
Furthermore, the processing method of the data of the input reference quantity and the output controlled quantity required by each control loop in the waste incinerator is to analyze the absolute value and the relative trend of each data and eliminate the data of which the absolute value exceeds the limited range.
Further, the training set and the test set are divided in time order.
Furthermore, the intelligent combustion control calculation model of each control loop in the waste incinerator is a machine learning model and a neural network model for processing regression problems.
According to another aspect of the present disclosure, there is provided a garbage incinerator intelligent combustion control apparatus, comprising:
the collection module is used for collecting input reference quantity and output controlled quantity required by each control loop in the waste incinerator;
the data processing module is used for processing the input reference quantity and the output controlled quantity required by each control loop in the waste incinerator to generate a training set;
the calculation model training module is used for training the learning model of each control loop in the waste incinerator to obtain a calculation model of each control loop in the waste incinerator;
the packaging module is used for combining and integrally packaging the calculation models of each control loop in the waste incinerator to generate an intelligent combustion control method calculation model;
the calculation module is used for calculating operation parameters according to the input reference quantity acquired by the decentralized control system in real time;
and the communication module is used for carrying out data transmission with the distributed control system, the distributed control system acquires the input reference quantity in real time and transmits the input reference quantity to the intelligent combustion control method calculation model for calculation, and the calculated operation parameters are transmitted back to the distributed control system and control the action of each actuator of the incinerator in the incineration process.
Further, the device can also comprise a storage module for storing data of the input reference quantity, the output controlled quantity and the operation parameter.
Furthermore, the device can also comprise a control module which is used for controlling the operation of the acquisition module, the data processing module, the calculation model training module, the packaging module, the calculation module, the communication module and the storage module.
According to still another aspect of the present disclosure, there is provided a garbage incinerator intelligent combustion control apparatus, including:
at least one processor and a memory communicatively coupled to the at least one processor; the memory stores commands executable by the at least one processor, the commands being executable by the at least one processor to enable the at least one processor to perform a method of intelligent combustion control for a waste incinerator as described above in the first aspect.
According to yet another aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a garbage incinerator intelligent combustion control method as described above in the first aspect.
According to yet another aspect of the present disclosure, there is provided a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a processor, cause the processor to perform a method of intelligent combustion control for a waste incinerator as described in the first aspect above.
According to the technical scheme, massive working condition data of waste incineration are learned through a big data technology, a deep learning technology and an artificial intelligence technology, an intelligent control algorithm of the incinerator is formed, the combustion state in the incinerator is automatically judged, intelligently analyzed and a control strategy is automatically adjusted, unmanned operation of incineration control is achieved, stable operation of the incinerator is guaranteed, and the purposes of energy conservation and efficiency improvement are achieved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
Fig. 1 is a flowchart of an intelligent combustion control method for a garbage incinerator according to an exemplary embodiment of the present application;
fig. 2 is a flow of generating a calculation model of an intelligent combustion control method for a garbage incinerator according to an exemplary embodiment of the present application;
FIG. 3 is a schematic view of an intelligent combustion control device of a garbage incinerator according to an exemplary embodiment of the present application;
fig. 4 is a schematic diagram of an intelligent combustion control device of a garbage incinerator according to an exemplary embodiment of the present application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
According to the scheme, the regulation and control model is used for deep learning of massive working condition data of waste incineration, the model is high in precision, the calculation processing capacity of real-time data is high, the defects that a traditional system excessively depends on the precision of an instrument and the like are overcome, the purpose that the waste incineration is controlled to be unmanned operation is achieved, and the purposes of energy conservation and efficiency improvement are achieved. For ease of understanding, the following first explains several terms referred to in this application.
Artificial Intelligence (AI) is a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human Intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is the research of the design principle and the realization method of various intelligent machines, so that the machines have the functions of perception, reasoning and decision making.
The artificial intelligence technology is a comprehensive subject and relates to the field of extensive technology, namely the technology of a hardware level and the technology of a software level. The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
Machine Learning (ML) is a multi-domain cross subject, and relates to multi-domain subjects such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and the like. The method specially studies how a computer simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve the performance of the computer. Machine learning is the core of artificial intelligence, is the fundamental approach to make computers have intelligence, and is applied in various fields of artificial intelligence. Machine learning and deep learning generally include techniques such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and formal education learning.
The scheme provided by the embodiment of the application relates to the technologies such as artificial intelligence combustion process monitoring and the like, and is specifically explained by the following embodiments:
the control principle of the incinerator comprises: collecting working condition data of the incinerator, and calculating the working condition data by using a process calculation formula to obtain an air flow control value and a speed value. The operating condition data 902 includes a garbage Heat Value LHV (Low Heat Value), a main steam flow, a main steam pressure, a main steam temperature, a leachate injection flow, and a urea water injection flow. The speed value is adjusted to adjust the movement cycle of the actuator of the incinerator.
The air flow control value comprises drying grate standard air flow, combustion grate standard air flow and burn-out grate standard air flow, and the air flow control value is used for controlling the air flow entering the drying grate, the combustion grate and the burn-out grate. And calculating the working condition data and the excess air parameter according to a process calculation formula to obtain standard air flow, and simultaneously obtaining the standard air flow of the drying grate, the standard air flow of the combustion grate and the standard air flow of the burn-out grate according to the standard air flow. Wherein the standard air flow rate is m 3 N/h。
In addition, the speed values comprise the pusher speed, the drying grate speed, the combustion grate speed and the burning grate speed, the working condition data is calculated according to a process calculation formula to obtain the weight of the garbage, and on one hand, the thickness control value of the garbage layer in the incinerator is determined according to the weight of the garbage. On the other hand, the required garbage volume is calculated according to the garbage weight and the garbage appearance specific gravity, a standard speed value is determined based on the required garbage volume, the pusher speed and the drying grate speed are calculated according to the garbage layer thickness control value and the standard speed value, and the burning grate speed are determined according to the standard speed.
The calculation formula for calculating the volume Vr of the garbage required to be fed into the incinerator according to the weight Wr and the appearance specific gravity Sg of the garbage is as follows:
Vr＝Wr/Sg (1)
wherein the unit of the volume Vr of the required garbage is m 3 The unit of the weight Wr of the garbage is t/h, and the unit of the appearance specific gravity Sg of the garbage is t/m 3 。
The key technologies included in the intelligent combustion control system are as follows:
(1) and the collected temperature field information, combustion state information and combustion condition information are input into the intelligent combustion control system as input variables through the distributed control system.
(2) And processing the field operation data by adopting a big data analysis method.
(3) And performing model training on the processed field operation data by adopting a machine learning method to obtain a high-precision regulation and control model.
(4) The model is trained by changing the combination form among the input variables, so that the model learns the change trend of the input variables of the system along with time, and the dependence on the high precision of a single-point single instrument is eliminated.
(5) The system has high robustness, and if a single instrument or an actuator is damaged, the stable operation of other parts of the system is not influenced.
(6) The control idea completely different from that of the existing automatic garbage incineration control system is adopted, dependence on a garbage heat value database is eliminated, and the automatic garbage incineration control system can adapt to the characteristics of high garbage heat value and high organic matter content.
(7) Has high universality and is suitable for various incinerators, including but not limited to mechanical grate incinerators, fluidized bed incinerators and rotary incinerators.
Fig. 1 is a flowchart of an intelligent combustion control method for a garbage incinerator according to an exemplary embodiment of the present application, including the following steps:
s101, collecting input reference quantity required by each control loop in the waste incinerator in real time by a decentralized control system;
s102, transmitting input reference quantity required by each control loop in the waste incinerator to an intelligent combustion control calculation model corresponding to the control loop for calculation to obtain operation parameters;
and S103, returning the operation parameters to the distributed control system and controlling the action of each actuator of the incinerator in the incineration process.
As shown in fig. 2, the above-mentioned method for controlling the intelligent combustion of the waste incinerator further comprises:
s201, determining input reference quantity and output controlled quantity required by each control loop in the waste incinerator;
s202, screening input reference quantity and output controlled quantity required by each control loop in the waste incinerator from historical combustion condition information, and processing the input reference quantity and the output controlled quantity to generate a training set;
s203, training the intelligent combustion control calculation model of each control loop in the waste incinerator by using the training set to obtain the intelligent combustion control calculation model of each control loop in the waste incinerator;
and S204, combining and integrally packaging the calculation models of each control loop in the garbage incinerator to generate the intelligent combustion control method calculation model of the garbage incinerator.
In an exemplary embodiment of the disclosure, by reading current combustion condition information, a plurality of data measuring points containing the data are transmitted to a database for storage through an OPC (Object Linking and Embedding for process control) communication protocol, variable information of each model is read and model calculation is performed based on a control algorithm, after the model calculation is completed, a calculation result of the model is output as an operation parameter, the operation parameter is transmitted to the database for storage, and the operation parameter is transmitted to a distributed control system control actuator. Based on the learning of historical combustion condition information, a control algorithm library of the incinerator is formed, the judgment speed of the combustion condition information is increased, the information processing efficiency of the incinerator is improved, the automation level of the incinerator is improved, the operation stability and safety of the incinerator are improved, and the energy efficiency utilization rate is improved.
The historical combustion condition information includes, but is not limited to, a main steam flow, an outlet oxygen amount, a garbage material layer thickness, a drying grate temperature, a combustion grate upper portion temperature, a furnace mean temperature, and a high-temperature superheater temperature.
The intelligent combustion control method of the garbage incinerator will be described with reference to the following embodiments.
The method comprises the following steps: and collecting historical combustion condition information.
The collection of the historical combustion condition information requires determining the variables to be collected, i.e., the input and output variables mentioned above, the collection time span, and the sampling frequency. The time span and the sampling frequency of collecting samples can be determined according to the performance requirements of the model, the collecting time span is an important factor influencing the richness of the historical combustion working condition information, and the collected historical combustion working condition information is richer and the generalization of the model is higher.
Step two: and training the neural network model.
(1) Selecting input variables of the model: the variables input into the model comprise temperature parameters, pressure parameters, load parameters, pollutant parameters, working condition state parameters and flame combustion information of the reaction furnace.
Wherein the temperature parameters comprise furnace temperature, grate temperature, furnace average temperature, high-temperature superheater temperature, main steam temperature, ash temperature and the like. The pressure parameters comprise primary air pressure, secondary air pressure, steam drum pressure, main steam pressure, hearth pressure and the like. The load parameters comprise main steam flow, combustion chamber heat load, grate mechanical load and the like. Contaminant parameters include hydrogen chloride, carbon dioxide, nitrogen oxides, particulates, carbon monoxide, and the like. The working condition state parameters comprise the oxygen content of outlet gas, the thickness of a garbage material layer, the liquid level of a steam drum and the like.
The model related in the embodiment of the disclosure is more than one, so that a data set after the same set of data processing method is not adopted for models of different control loops, but models are respectively established for the models of each control loop, and the process of establishing the models in the embodiment of the disclosure is different from the existing method, the existing method comprises the steps of firstly performing sample processing, data analysis and the like, and then establishing the models, and the embodiment of the disclosure performs the variable selection process of each control loop and the model establishment process of each control loop while performing the data processing process.
(2) Processing input data: the parent set of data is processed first and then the subset of data is selected. The data set of the raw data after rough processing is called a mother set, and the data set corresponding to the variable related to each control loop is selected as a subset. When the data of the mother set is processed, the working condition data of an unstable state and an abnormal state are removed by taking the whole working condition as a guide, and all variables at certain time points are deleted due to the extensive processing mode. In processing the subset data, the selection process of the subset variables is performed by interleaving. The subset variables are selected with the aid of the results of the historical data analysis, guided by the correlation in the process logic. The purpose of data processing is to screen out excellent manual experience for model learning, and the quality of data determines the upper and lower limits of model precision, so that data samples should be selected as much as possible from data operated by skilled firemen.
(3) Building a neural network model: each selected subset is divided into a test set and a training set, and a neural network model is trained using the training set, the neural network model including, but not limited to, a neural network model that handles regression problems (i.e., outputs are continuous variables), such as a decision tree regression model, a support vector regression model, a BP (back propagation) neural network model, a long-term and short-term memory network model, and the like.
The neural network model construction of the invention is different from the common machine learning in that: the method for randomly dividing the training set and the test set is adopted before the common machine learning model is built, and random division is feasible because common data samples are unordered and independent and do not influence. However, the data sample involved in the invention is exactly a panel data, and is a time sequence which is continuous from front to back on a time axis, the waste incineration system has large hysteresis, the working condition of the previous period of time has great influence on the working condition of the next period of time, and the working conditions of adjacent time points have high similarity, if the training set and the test set are randomly divided, the intersection of the training set and the test set can be caused essentially, and the purpose of testing the model effect can not be achieved. The invention is an important technical point, and if the test set and the training set are not divided according to the continuous division principle, the neural network model has good performance during training, but has poor effect during actual use.
(4) Simplifying the neural network model: the total output of the complete system of the embodiment of the disclosure is 22, and the simplification processing can be properly carried out, for example, the operation parameters of the grate motion cycles on the left side and the right side are simplified into the operation parameters of the grate motion cycles, and 4 neural network models can be reduced. The embodiment of the disclosure carries out linkage processing on the operation parameters of the left and right side grate motion cycle classes, and only a neural network model of 18 operation parameters needs to be established.
In addition, except for the running parameters of the grate motion cycles on the left side and the right side, other running parameters can be properly linked according to the relation among the running parameters, and the running parameters of a single neural network model are linked with proper running parameters, so that the robustness of the system is guaranteed, the output result of the neural network model is simplified, the complexity of the neural network model is reduced, and the training speed of the neural network model is increased.
(5) Optimizing input variables and output operation parameters of the neural network model, wherein the number of the neural network models is large, the number of the parameters of each neural network model is large, the optimal parameter index of the neural network model is found by training the neural network models, and the historical working condition state and the current working condition state are mapped to the action of each actuator, so that the continuous stability of the working condition of the reaction furnace is achieved.
Indexes expressed by the neural network model are different from indexes of the traditional regression type machine learning model, and loss function values such as MSE (Mean Square Error) and the like are not directly used as standards for evaluating the quality of the model. Because the sample data is generated by manual regulation, the selection of specific numerical values often contains certain subjective factors, and therefore, the control output within a certain range can achieve similar control effects. The invention defines a new model accuracy index, namely after the neural network model is trained, the difference between the original value and the model calculated value on the test set is calculated, the proportion of the difference value within a certain tolerance range is counted, and the higher the proportion within the tolerance is, the higher the control accuracy of the neural network model is.
Step three: and outputting the operating parameters of the intelligent control system.
The operation parameters output by the neural network model comprise operation parameters for controlling a feeding system, operation parameters for controlling a grate movement period, operation parameters for controlling a primary air system of a combustion air system and operation parameters for controlling a secondary air system of the combustion air system.
(1) The operating parameters of the feeding system comprise the left-side pusher proportional valve parameters and the right-side pusher proportional valve parameters.
(2) The running parameters of the grate motion period comprise a left side drying grate motion period parameter, a right side drying grate motion period parameter, a left side combustion grate motion period parameter, a right side combustion grate motion period parameter, a left side burn-out grate motion period parameter and a right side burn-out grate motion period parameter.
(3) The operation parameters of the primary air system of the combustion air system comprise a primary fan frequency parameter, a primary air inlet valve parameter, a primary air pre-heater opening degree parameter, a primary air pre-heater branch parameter, a drying grate primary air valve parameter, a combustion grate first-section primary air valve parameter, a combustion grate second-section primary air valve parameter, a combustion grate third-section primary air valve parameter, a combustion grate first-section primary air valve parameter and a combustion grate second-section primary air valve parameter.
(4) The operation parameters of the secondary air system of the combustion air system comprise secondary fan frequency parameters, secondary air inlet valve parameters, secondary air pre-heater opening degree parameters and secondary air pre-heater branch parameters.
Step four: testing the intelligent combustion control system.
(1) And obtaining a required system output control model through an intelligent combustion control system data acquisition and model training module.
(2) And packaging the neural network model, and transmitting data between the control program and the distributed control system, so that the real-time data of the distributed control system can be transmitted to a core control algorithm, the core control algorithm reads the real-time data into the trained neural network model for calculation, and transmits a calculated control instruction back to the distributed control system to command the action of each actuator in the incineration system.
(3) Before the system is put into use formally, the system needs to be tested off-line and on-line. The off-line test is to continuously input historical combustion condition information into a core control algorithm, compare the difference between a manual operation value and an algorithm calculated value under the previous working condition, test a plurality of groups of values, and if the difference is within an acceptable range, perform on-line test.
(4) After the off-line test is completed, the system also needs to be tested on line. The on-line test needs to make objective evaluation for each function of the combustion control system, preset the function index of the on-line test effect, and evaluate the on-line test of the combustion control system according to the index, wherein the function index comprises load index main steam flow, temperature index furnace average temperature and index outlet oxygen quantity influencing pollutant emission.
(5) And during on-line testing, adding a process constraint condition on process logic to ensure that the working condition can be quickly recovered from abnormal conditions to normal conditions. In order to ensure that the system is tested on line by using the algorithm adjusting capability under the normal working condition, a proper amount of process constraint needs to be added aiming at the abnormal working condition which exceeds the algorithm adjusting capability.
(6) After the online test is completed, the intelligent combustion control system can be kept stable when the waste incineration system is taken over. At the moment, an input interface of an intelligent combustion control system is needed, a required target steam quantity can be set, and the target steam quantity needs to be combined with a model of a feeding system in an algorithm to complete the adjustment of the overall load level of the garbage incinerator.
The intelligent and efficient big data technology is integrated, the control accuracy and the information processing efficiency of the system are improved through online learning of historical data, and the following control effects are achieved:
(1) the stability of the total flow of the steam of the boiler is improved.
(2) The control effect of the thickness of the garbage layer is improved.
(3) The controllability of the garbage burning position is improved.
(4) The loss degree of the hot scorch is reduced.
(5) The stability of the furnace temperature is improved.
(6) The environmental protection standard reaching rate, the safety and the stability of the operation indexes in the production requirement are improved.
(7) The adaptability to high water content and high organic matter content of garbage is improved.
(8) The service life of the equipment is prolonged, and the equipment cost is reduced.
(9) The energy saving and efficiency increasing of the waste incineration power plant are improved.
(10) The automatic intelligent control effect of the waste incineration is improved.
(11) The identification result of the flame temperature field by non-contact temperature measurement is improved.
(12) The judgment speed and the information processing efficiency of the flame combustion state are improved.
In addition, this public incinerator combustion control system combines big data technique and artificial intelligence technique to and combine distributed control system, through discerning and judging the combustion state of furnace, thereby real-time analysis, and adopt artificial intelligence technique to control the combustion state of burning furnace simultaneously.
Furthermore, in the intelligent combustion control system, the control principle, the big data technology and the artificial intelligence technology are used for analyzing historical data, the data of the intelligent combustion control system are analyzed and calculated, the optimal operation parameters of all controller variables in the waste incineration system are obtained, and then the optimal operation parameters are returned to the distributed control system so as to control the intelligent combustion control system. Under the even release thermal prerequisite of assurance rubbish, improved the burnout volume of flue gas and residue, reduced the formation volume of pollutant, improved refuse treatment's innoxious degree, improved refuse treatment's minimizing degree to refuse treatment's efficiency utilization ratio has been improved.
The neural network model is trained based on advanced process control methods such as fuzzy control and the like, statistical analysis, a machine learning technology, a deep learning technology and a machine vision AI technology, abundant operation experience of field operators is integrated into the model, and the model is high in precision and strong in applicability. The above technical solutions are only used for illustrating the present disclosure, and are not idle for the protection scope of the present disclosure, and modifications and variations of the present disclosure by other intelligent control methods, machine learning methods and deep learning methods also fall within the scope of the claims of the present disclosure and their equivalent technologies.
Corresponding to the method embodiment, the present disclosure also provides an operation control device of a reaction furnace, which can be used for executing the method embodiment.
Fig. 3 is a garbage incinerator intelligent combustion control device in an exemplary embodiment of the present disclosure, in the garbage incinerator intelligent combustion control device 300, the following modules are included but not limited to: the system comprises an acquisition module 310, a data processing module 320, a calculation model training module 330, a packaging module 340, a calculation module 350 and a communication module 360.
The collecting module 310 is configured to collect an input reference quantity and an output controlled quantity required by each control loop in the waste incinerator.
And the data processing module 320 is configured to process the input reference quantity and the output controlled quantity required by each control loop in the waste incinerator to generate a training set.
And the calculation model training module 330 is configured to train the learning model of each control loop in the waste incinerator to obtain a calculation model of each control loop in the waste incinerator.
And the packaging module 340 is configured to combine the calculation models of each control loop in the waste incinerator and package the combination to generate an intelligent combustion control method calculation model.
And a calculating module 350, configured to calculate the operating parameter according to the input reference quantity obtained by the distributed control system in real time.
And the communication module 360 is used for carrying out data transmission with the distributed control system, the distributed control system acquires the input reference quantity in real time and transmits the input reference quantity to the intelligent combustion control method calculation model for calculation, and the calculated operation parameters are transmitted back to the distributed control system and control the action of each actuator of the incinerator in the incineration process.
Preferably, the intelligent combustion control device 300 of the garbage incinerator can further include a storage module 370 for storing data of the input reference amount and the output controlled amount.
Optionally, the intelligent combustion control device 300 of the garbage incinerator may further include a control module 380 for controlling operations of the acquisition module 310, the data processing module 320, the calculation model training module 330, the encapsulation module 340, the calculation module 350, the communication module 360 and the storage module 370.
Specifically, the intelligent combustion control device 300 of the garbage incinerator collects historical combustion condition information through the collection module 310, the data processing module 320 refers to a data set obtained by roughly processing the collected historical combustion condition information as a mother set, a data set corresponding to variables related to each control loop is selected as a subset, the calculation model training module 330 divides each selected subset into a test set and a training set, a neural network model is trained by using the training set, the trained neural network model is packaged by the packaging module 340, and data transmission is performed between a control program and a decentralized control system through the communication module 360. The acquisition module 310 acquires a current input reference amount, i.e. a working condition state, in the distributed control system, for example, a key working condition parameter at a certain time is: the main steam flow is 38.07t/h, the outlet oxygen analyzer is 6.95%, the garbage layer thickness is 0.28kpa, the combustion grate temperature (A: 167.92 ℃, B: 175.24 ℃, C: 160.04 ℃ and D: 170.83 ℃), the combustion grate upper temperature is 1069.74 ℃, the burn-out grate upper temperature is 919.39 ℃, the furnace temperature is 1051.20 ℃ and the high temperature superheater temperature is 642.18 ℃, the communication module 360 transmits a plurality of data measuring points containing the data to the database for storage through an OPC communication protocol, the calculating module 350 reads corresponding data for calculation according to variable information required by each model, if the input variables of the secondary fan frequency adjusting model are the outlet oxygen analyzer, the furnace temperature and the high temperature superheater, the current values of the three variables are input into the secondary fan frequency model, and the adjusting value of the secondary fan frequency is obtained. When all the models are calculated, obtaining the output reference quantity under the working condition, namely the regulating quantity is set as: the frequency of a primary air fan is 30.5HZ, the primary air inlet valve is 100 percent, the opening of the primary air preheater inlet valve is 0 percent, the opening of a primary air preheater branch valve is 100 percent, the primary air valve of a drying grate is 40 percent, the primary air valve of a first section of a combustion grate is 50 percent, the primary air valve of a second section of the combustion grate is 50 percent, the primary air valve of a third section of the combustion grate is 80 percent, the primary air valve of a first section of a burn-out grate is 50 percent, the secondary air inlet valve is 20HZ, the secondary air inlet valve is 100 percent, the opening of the secondary air preheater inlet valve is 0 percent, the opening of the secondary air preheater branch valve is 100 percent, the motion period of a left side drying grate and a right side drying grate is 80S, the motion period of a left side burning grate and a right side burning grate is 88S, the motion period of the left side burning grate and the right side burning grate is 150S, and the proportional valve of the left side pusher is 38 percent. The communication module 360 transmits the calculated operation parameters back to the decentralized control system and controls the actions of each actuator of the incinerator in the incineration process.
It should be noted that, since the intelligent combustion control device of the garbage incinerator in the present embodiment is based on the same inventive concept as the intelligent combustion control method of the garbage incinerator in any of the above embodiments, the corresponding contents in the method embodiments are also applicable to the system embodiments, and will not be described in detail herein.
Fig. 4 illustrates a garbage incinerator intelligent combustion control device provided in an exemplary embodiment of the present application, where the garbage incinerator intelligent combustion control device 400 may be any type of terminal, such as a mobile phone, a game console, a tablet PC, an e-book reader, smart glasses, a mobile terminal such as MP4(moving picture Experts Group Audio Layer IV, motion picture Experts compression standard Audio Layer 4) player, a smart home device, an AR (Augmented Reality) device, a VR (Virtual Reality) device, and the like, or the garbage incinerator intelligent combustion control device 300 may also be a Personal Computer (Personal Computer, PC), a mobile phone such as a laptop Computer and a desktop Computer, a tablet PC, a Personal Computer, and the like.
Wherein, the garbage incinerator intelligent combustion control device 400 can be installed with an application program for providing the garbage incinerator intelligent combustion control method.
Preferably, the garbage incinerator intelligent combustion control apparatus 400 comprises: one or more processors 410 and memory 420, with one processor 410 being an example in fig. 4.
The processor 410 and the memory 420 may be connected by a bus or other means, with fig. 4 taking the example of a connection by a bus.
The memory 420 is a non-transitory computer readable storage medium, and can be used to store non-transitory software programs, non-transitory computer executable programs, and units, such as program instructions/units corresponding to the garbage incinerator intelligent combustion control device 400 in the embodiment of the present invention, for example, the acquisition module 310, the data processing module 320, the calculation model training module 330, the encapsulation module 340, the calculation module 350, and the communication module 360 shown in fig. 3. The processor 410 executes various functional applications and data processing of the intelligent combustion control device 300 of the garbage incinerator by running non-transitory software programs, instructions and units stored in the memory 420, namely, the intelligent combustion control method of the garbage incinerator, which implements the above method embodiments.
The memory 420 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of one of the garbage incinerator intelligent combustion control apparatuses 300, and the like. Further, the memory 420 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 420 may optionally include memory located remotely from the processor 410, which may be connected to the one waste incinerator intelligent combustion control apparatus 300 via a network.
Alternatively, the network connection may be a wireless network or a wired network using standard communication techniques and/or protocols. The Network is typically the Internet, but may be any Network including, but not limited to, a Local Area Network (LAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a mobile, wireline or wireless Network, a private Network, or any combination of virtual private networks. In some embodiments, data exchanged over a network is represented using techniques and/or formats including Hypertext Mark-up Language (HTML), Extensible markup Language (XML), and the like. All or some of the links may also be encrypted using conventional encryption techniques such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet protocol Security (IPsec). In other embodiments, custom and/or dedicated data communication techniques may also be used in place of, or in addition to, the data communication techniques described above.
The one or more units are stored in the memory 420, and when executed by the one or more processors 410, perform a method of controlling intelligent combustion of a garbage incinerator in any of the above-described method embodiments. For example, the above-described method steps S101 to S103 in fig. 1 and the method steps S201 to S204 in fig. 2 are executed to implement the functions of the module 310 and 360 in fig. 3.
Embodiments of the present invention further provide a computer-readable storage medium, where the computer-readable storage medium stores computer-executable instructions, which are executed by one or more processors, for example, by one processor 410 in fig. 4, and can cause the one or more processors 410 to execute a garbage incinerator intelligent combustion control method in the foregoing method embodiments, execute the above-described method steps S101 to S103 in fig. 1, and the method steps S201 to S204 in fig. 2, and implement the functions of the module 310 and the module 360 in fig. 3.
Embodiments of the present invention further provide a computer program product, where the computer program product includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions, when the program instructions are executed by a computer, for example, by one of the processors 410 in fig. 4, the one or more processors 410 may be caused to execute a garbage incinerator intelligent combustion control method in the above method embodiments, for example, execute the above-described method steps S101 to S103 in fig. 1, and the method steps S201 to S204 in fig. 2, and implement the functions of the module 310 and 360 in fig. 3.
A program product for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
Optionally, the clients of the applications installed in different devices 400 are the same, or the clients of the applications installed on two devices 400 are clients of the same type of application of different control system platforms. Based on different terminal platforms, the specific form of the client of the application program may also be different, for example, the client of the application program may be a mobile phone client, a PC client, or a World Wide Web (Web) client.
One skilled in the art will appreciate that the number of devices 400 described above may be greater or fewer. For example, the number of the terminals may be only one, or several tens or hundreds of the terminals, or more. The number of terminals and the type of the device are not limited in the embodiments of the present application.
It is to be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the method according to an exemplary embodiment of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Accordingly, various aspects of the present invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (11)
1. An intelligent combustion control method of a garbage incinerator is characterized by comprising the following steps:
the distributed control system collects input reference quantity required by each control loop in the waste incinerator in real time;
transmitting the input reference quantity required by each control loop in the waste incinerator to an intelligent combustion control calculation model corresponding to the control loop for calculation to obtain operation parameters;
and returning the operation parameters to the distributed control system and controlling the action of each actuator of the incinerator in the incineration process.
2. The intelligent combustion control method for the garbage incinerator according to claim 1, characterized by further comprising:
determining an input reference quantity and an output controlled quantity required by each control loop in the waste incinerator;
screening input reference quantity and output controlled quantity required by each control loop in the waste incinerator from historical combustion condition information, and processing the input reference quantity and the output controlled quantity to generate a training set;
training the intelligent combustion control calculation model of each control loop in the waste incinerator by using the training set to obtain the intelligent combustion control calculation model of each control loop in the waste incinerator;
and combining and integrally packaging the calculation models of each control loop in the garbage incinerator to generate the calculation model of the intelligent combustion control method of the garbage incinerator.
3. The intelligent combustion control method of a garbage incinerator according to claim 2, characterized in that said input references include: temperature parameters, pressure parameters, load parameters and pollutant parameters in the incinerator; the outputting the controlled quantity includes: the control parameters of the feeding system, the control parameters of the grate movement period, the control parameters of the primary air system of the combustion air system and the control parameters of the secondary air system of the combustion air system.
4. The intelligent combustion control method of a garbage incinerator according to claim 2, characterized in that said input references further comprise: oxygen quantity at outlet, thickness of garbage layer and liquid level of steam drum.
5. The intelligent combustion control method for a garbage incinerator according to claim 2, characterized in that the collection time span and collection frequency determined by collecting data of input reference quantity and output controlled quantity required by each control loop in the garbage incinerator are collected.
6. The intelligent combustion control method for the garbage incinerator according to claim 2, characterized in that the processing method of the data of the input reference quantity and the output controlled quantity required by each control loop in the garbage incinerator is to analyze the absolute value and the relative trend of each data and eliminate the data of which the absolute value exceeds the limited range.
7. The intelligent combustion control method of a garbage incinerator according to claim 2, characterized in that the training set and the test set are divided in time sequence.
8. The intelligent combustion control method for a garbage incinerator according to claim 2, characterized in that the intelligent combustion control calculation model for each control loop in the garbage incinerator is a machine learning model and a neural network model which deal with regression problem.
9. The utility model provides a waste incinerator intelligence combustion control device which characterized in that includes following module:
the collection module is used for collecting input reference quantity and output controlled quantity required by each control loop in the waste incinerator;
the data processing module is used for processing the input reference quantity and the output controlled quantity required by each control loop in the waste incinerator to generate a training set;
the calculation model training module is used for training the learning model of each control loop in the waste incinerator to obtain a calculation model of each control loop in the waste incinerator;
the packaging module is used for combining and integrally packaging the calculation models of each control loop in the waste incinerator to generate an intelligent combustion control method calculation model;
the calculation module is used for calculating operation parameters according to the input reference quantity acquired by the decentralized control system in real time;
and the communication module is used for carrying out data transmission with the distributed control system, the distributed control system acquires the input reference quantity in real time and transmits the input reference quantity to the intelligent combustion control method calculation model for calculation, and the calculated operation parameters are transmitted back to the distributed control system and control the action of each actuator of the incinerator in the incineration process.
10. The utility model provides a waste incinerator intelligence combustion control electronic equipment which characterized in that includes:
at least one processor and a memory communicatively coupled to the at least one processor; the memory stores commands executable by the at least one processor, and the commands are executed by the at least one processor so as to enable the at least one processor to execute the intelligent combustion control method of the garbage incinerator according to any one of claims 1-8.
11. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for controlling the intelligent combustion of a garbage incinerator according to any one of claims 1 to 8.
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