Intelligent decision-making method, system and device for pneumatic conveying system

Abstract

The invention provides an intelligent decision-making method, system and device for a pneumatic conveying system. The intelligent decision-making method comprises the following steps: acquiring equipment parameter information, running state information and related equipment parameter information; determining the energy supply rate and the maximum energy consumption sustainable time in the safety range based on the average air consumption, the number of air compressors, the air compressor displacement, the volume of air storage tanks and the number of the air storage tanks; based on the current boiler total ash amount, the ash content ratio, the current material production amount and the current material sending amount, the collection bin change rate is determined; based on the current sending bin blanking amount, the sending bin volume, the sending bin number and the current cycle period time, the unit time average handling capacity and the matching output value of the current production output are determined; and based on the maximum energy consumption sustainable time in the safety range, the collection bin change rate, the current blanking time and the matched output value of the current production output, the production output, the cycle period time and the system blanking time in unit time are determined. According to the invention, system faults and resource waste are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of pneumatic conveying systems, and particularly to an intelligent decision-making method, system and device for a pneumatic conveying system. Background Art

[0002] Under the background of the continuous and rapid economic development, the environmental pollution generated during the production and transportation processes in some industries has been increasingly emphasized, such as the particulate matter, powder materials, etc. transported by cement plants, pharmaceutical factories, food processing factories, etc. In this context, pneumatic conveying technology has been gradually popularized; pneumatic conveying replaces the traditional mechanical conveying of materials, and it is a process of conveying materials by means of a sealed conveying pipeline. Pneumatic conveying is a material handling technology that uses compressed air flow to convey bulk particles in a pipeline, and it is an effective measure to achieve green, safe and convenient transportation of bulk materials.

[0003] At present, all pneumatic conveying systems operate in a step-sequence control mode, and the control mode has been standardized. The current program control means of the pneumatic conveying system is single, and the operation steps are as follows: STEP1: The collection bin stores the collected materials; STEP2: Open the inlet valve of the sending bin, connect the ash discharge pipe of the collection bin and the sending bin, and start loading (while starting the cycle time counting at the same time); STEP3: After the inlet valve of the sending bin is opened for loading, start the blanking timer (time control) at the same time, and detect the signal of the level switch installed on the sending bin (level control) at the same time. When the level switch is triggered by the falling materials or the blanking timer is triggered, and any one of the conditions is met, the inlet valve of the sending bin is closed, the connecting pipeline between the collection bin and the sending bin is isolated, and the blanking is stopped; STEP4: When the inlet valve of the sending bin is closed, open the conveying air valve of the system; STEP5: According to the conveying pressure transmitter installed at the inlet of the conveying pipeline, detect the real-time conveying pressure. When the conveying pipeline pressure exceeds a high set value, the conveying air valve is temporarily closed. When the conveying pipeline pressure returns to the normal value, it is reopened. When the conveying pipeline pressure drops to a low set value and lasts for a period of time, the system judges that the conveying has been completed, and the system ends the operation; STEP6: When the system ends the operation, the system detects the cycle time started in STEP2. If the cycle time has not arrived, the system will wait. When the cycle time is triggered, the system will trigger a cycle and trigger the cyclic conveying again.

[0004] At present, large domestic power plants and steel mills generally adopt a cyclic conveying mode for pneumatic conveying systems. Under this mode, the cyclic frequency is controlled by setting the "cycle time"; that is, generally according to the high and low unit loads and the amount of materials to be conveyed per unit time, the cycle time is set to control the ash storage amount in the collection bin. And during the conveying process, the blanking time is adjusted according to experience or the conveying state to prevent the single loading amount of the system from being too large, resulting in unsmooth conveying and blockage.

[0005] While current pneumatic conveying systems can achieve cyclical conveying, key conveying parameters such as cycle time and material drop time require staff to determine them based on their sense of responsibility and work experience. This is simple and practical for power plants with stable coal types and stable unit loads, but it places higher demands on staff expertise when burning complex coal types or when unit loads fluctuate significantly. Not only must the cycle time be adjusted based on the unit load, but the material drop time and material drop volume must also be adjusted based on the system's conveying status to avoid excessively frequent (or low) cycles due to low (or high) unit loads, resulting in resource waste (or insufficient output), or excessive single-cycle conveying, resulting in system conveying problems (or resource waste). Typically, staff members perform multiple duties, often operating not only the pneumatic conveying system but also systems within the same production process, such as desulfurization, denitrification, dust collection, and compressed air systems. Frequent operations consume significant staff energy, and untimely system parameter modifications can lead to system failures or resource waste. Furthermore, system failures require manual identification and resolution by experienced staff.

[0006] In summary, the current operation of pneumatic conveying systems relies entirely on human operators, who monitor the operating status of the units and the system in real time. This approach not only requires high professional qualifications from the staff but also requires a great deal of effort. Untimely or improper operation can easily lead to system failures and waste of resources. Therefore, how to achieve intelligent control of system operating parameters to reduce system failures and resource waste is a technical problem that needs to be solved urgently. Summary of the Invention

[0007] In view of this, embodiments of the present invention provide an intelligent decision-making method, system, and device for a pneumatic conveying system to eliminate or improve one or more defects in the prior art.

[0008] One aspect of the present invention provides an intelligent decision-making method for a pneumatic conveying system, the intelligent decision-making method comprising:

[0009] Obtaining equipment parameter information, operating status information, and related equipment parameter information of the pneumatic conveying system. The operating status information includes average gas consumption, current delivery bin discharge volume, current cycle time, current discharge time, and current material delivery volume. The equipment parameter information includes the number of air compressors, air compressor exhaust volume, gas storage tank volume, number of gas storage tanks, delivery bin volume, and number of delivery bins. The related equipment parameter information includes the current total boiler ash content, ash ratio, and current material production volume.

[0010] Determine the energy supply rate and the sustainable time of the maximum energy consumption within the safe range based on the average gas consumption, the number of air compressors, the displacement of the air compressors, the volume of the gas storage tank, and the number of gas storage tanks;

[0011] Determine the change rate of the collection bin based on the current total ash amount of the boiler, the ash ratio, the current material production amount, and the current material sending amount;

[0012] Determine the matching output value of the average processing amount per unit time and the current production output based on the current material dropping amount in the sending bin, the volume of the sending bin, the number of sending bins, and the current cycle time;

[0013] Determine the production output per unit time, the cycle time, and the system material dropping time of the pneumatic conveying system based on the sustainable time of the maximum energy consumption within the safe range, the change rate of the collection bin, the current material dropping time, and the matching output value of the current production output;

[0014] In some embodiments of the present invention, determining the energy supply rate and the sustainable time of the maximum energy consumption within the safe range based on the average gas consumption, the number of air compressors, the displacement of the air compressors, the volume of the gas storage tank, and the number of gas storage tanks includes:

[0015] Calculate the total gas volume based on the number of air compressors and the displacement of the air compressors, and calculate the total gas storage volume based on the volume of the gas storage tank and the number of gas storage tanks;

[0016] Determine the energy supply rate based on the difference between the total gas volume and the average gas consumption; based on the formula Determine the sustainable time of the maximum energy consumption within the safe range; where s n represents the sustainable time of the maximum energy consumption within the safe range, C 总 represents the total gas storage volume, and v n represents the energy supply rate.

[0017] In some embodiments of the present invention, determining the change rate of the collection bin based on the current total ash amount of the boiler, the ash ratio, the current material production amount, and the current material sending amount includes:

[0018] Determine the production change rate based on the product of the current total ash amount of the boiler and the ash ratio;

[0019] Determine the material production amount at the next moment based on the production change rate and the current material production amount;

[0020] Take the difference between the material production amount at the next moment and the current material sending amount as the change rate of the collection bin.

[0021] In some embodiments of the present invention, determining a matching output value of the average processing amount per unit time and the current production output based on the current blanking amount of the sending bin, the volume of the sending bin, the number of sending bins, and the current cycle time includes:

[0022] Calculating the current single - time sending amount based on the product of the current blanking amount of the sending bin, the volume of the sending bin, and the number of sending bins;

[0023] Based on the formula Calculating the current number of cycles of the sending bin; where n is the current number of cycles of the sending bin and t is the current cycle time;

[0024] Determining the dust collector - collected ash amount based on the current total boiler ash amount and the ash ratio;

[0025] Determining the average processing amount per unit time based on the product of the current number of cycles of the sending bin and the current single - time sending amount;

[0026] Based on the formula Determining the matching output value of the current production output, where d p is the matching output value of the current production output, c h is the dust collector - collected ash amount, d s is the average processing amount per unit time, n is the current number of cycles of the sending bin, and d d is the current single - time sending amount.

[0027] In some embodiments of the present invention, determining the production output per unit time, the cycle time, and the system blanking time of the pneumatic conveying system based on the maximum sustainable time of energy consumption within a safe range, the change rate of the collection bin, the current blanking time, and the matching output value of the current production output includes:

[0028] Determining the production output per unit time based on the sum of the matching output value of the current production output and the change rate of the collection bin;

[0029] Determining the system blanking time based on a preset time change value and the current blanking time;

[0030] Based on the formula Calculating the cycle time, where t s represents the cycle time, d c represents the production output per unit time, and d f represents the current material sending amount.

[0031] In some embodiments of the present invention, determining the system blanking time based on a preset time change rate and the current blanking time includes:

[0032] Determine whether the current running time of the system is greater than the maximum sustainable time of energy consumption within the safe range. When it is not greater, determine the system blanking time based on the sum of the preset time change value and the current blanking time.

[0033] In some embodiments of the present invention, save the equipment parameter information, operating status information, related equipment parameter information of the pneumatic conveying system, and the production output per unit time, cycle time, and system blanking time of the corresponding pneumatic conveying system to the database.

[0034] According to another aspect of the present invention, an intelligent decision-making system for a pneumatic conveying system is also disclosed. The system includes:

[0035] An embedded neural network integrated monitoring module for obtaining the equipment parameter information, operating status information, and related equipment parameter information of the pneumatic conveying system. The operating status information includes the average gas consumption, current blanking amount of the sending bin, current cycle time, current blanking time, and current material sending amount. The equipment parameter information includes the number of air compressors, air compressor exhaust volume, storage tank volume, number of storage tanks, sending bin volume, and number of sending bins. The related equipment parameter information includes the current total ash amount of the boiler, ash ratio, and current material production amount;

[0036] An energy consumption algorithm sub-module for determining the energy supply rate and the maximum sustainable time of energy consumption within the safe range based on the average gas consumption, number of air compressors, air compressor exhaust volume, storage tank volume, and number of storage tanks;

[0037] A raw material input algorithm sub-module for determining the collection bin change rate based on the current total ash amount of the boiler, ash ratio, current material production amount, and current material sending amount;

[0038] A collector stacking algorithm sub-module for determining the matching output value of the average processing amount per unit time and the current production output based on the current blanking amount of the sending bin, sending bin volume, number of sending bins, and current cycle time;

[0039] A production operation algorithm sub-module for determining the production output per unit time, cycle time, and system blanking time of the pneumatic conveying system based on the maximum sustainable time of energy consumption within the safe range, collection bin change rate, current blanking time, and matching output value of the current production output.

[0040] According to still another aspect of the present invention, an intelligent decision-making device for a pneumatic conveying system is also disclosed. The device includes a processor, a memory, and a computer program stored in the memory. The processor is used to execute the computer program, and when the computer program is executed, the device implements the steps of the method described in any of the above embodiments.

[0041] According to another aspect of the present invention, a computer-readable storage medium is also disclosed, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in any of the above embodiments are implemented.

[0042] For the intelligent decision-making method of the pneumatic conveying system disclosed in the above embodiments of the present invention, first, the energy supply rate, the maximum sustainable time of energy consumption within the safety range, the change rate of the collection bin, the average processing capacity per unit time, and the matching output value of the current production output are determined. Then, based on the maximum sustainable time of energy consumption within the safety range, the change rate of the collection bin, and the matching output value of the current production output, the production output per unit time, the cycle time, and the system blanking time of the pneumatic conveying system are determined. This method determines the operating parameters of the system at the next moment based on the real-time operating state and equipment parameters of the system, thereby realizing the intelligent adjustment of the operating parameters of the system, enabling the system to always work with the optimal operating parameters, which not only saves manpower, increases the timeliness of system output changes, but also reduces system failures and resource waste.

[0043] The additional advantages, objects, and features of the present invention will be partially described below, and will become partially apparent to those of ordinary skill in the art after studying the following text, or can be learned from the practice of the present invention. The objects and other advantages of the present invention can be achieved and obtained by the structures specifically pointed out in the specification and the drawings.

[0044] Those skilled in the art will understand that the objects and advantages that can be achieved by the present invention are not limited to the above specifically described, and the above and other objects that the present invention can achieve will be more clearly understood according to the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings described herein are used to provide a further understanding of the present invention, form a part of this application, and do not limit the present invention. The components in the drawings are not drawn to scale, but are only for showing the principles of the present invention. In order to facilitate showing and describing some parts of the present invention, the corresponding parts in the drawings may be enlarged, that is, may become larger relative to other components in the exemplary device actually manufactured according to the present invention. In the drawings:

[0046] Figure 1 It is a schematic flowchart of the intelligent decision-making method of the pneumatic conveying system according to an embodiment of the present application.

[0047] Figure 2 It is a schematic architecture diagram of the intelligent decision-making system of the pneumatic conveying system according to an embodiment of the present application.

[0048] Figure 3 It is a schematic calculation flowchart of the intelligent decision-making method of the pneumatic conveying system according to an embodiment of the present invention. Detailed implementation manners

[0049] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the implementation manners and the drawings. Herein, the illustrative implementation manners of the present invention and the descriptions thereof are used to explain the present invention, but do not limit the present invention.

[0050] Herein, it also needs to be noted that in order to avoid obscuring the present invention due to unnecessary details, only the structures and / or processing steps closely related to the solution of the present invention are shown in the drawings, and other details less related to the present invention are omitted.

[0051] It should be emphasized that the term "comprising / including" when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0052] Herein, it also needs to be noted that if not otherwise specified, the term "connection" herein can not only refer to direct connection, but also represent indirect connection with an intermediate, and can not only represent wired connection, but also wireless connection, and can be specifically changed based on the actual application scenario.

[0053] Currently, intelligent chemical plants are being gradually promoted in China, and unattended operation is being gradually realized. The existing pneumatic conveying system relies on the regulation of staff, and its control method cannot achieve unattended operation. Moreover, the operation parameter control method of the existing pneumatic conveying system also has the disadvantages of high system failure rate and resource waste. To solve the above problems, the present invention provides an intelligent decision-making method, system and device for a pneumatic conveying system, which realizes real-time operation state detection of the system through the combination of system software / hardware, and intelligent adjustment of operation parameters according to the detected data, and has the capabilities of intelligent adjustment, error correction and fault handling, reduces the relevant work of the staff, enables the system to operate under the optimal design conditions and reduces energy consumption, and realizes the unattended function.

[0054] To better understand the present invention, the following are the explanations of the terms related to the technical solutions of this application:

[0055] Positive pressure pneumatic conveying system: A fully enclosed pipeline conveying system, where the conveying medium is mostly powdery or granular materials. The positive pressure pneumatic conveying system includes: a collection bin, a sending bin, an energy system, a dense-phase booster, control instruments, a conveying pipeline system, a switching valve system, a control system, a receiving terminal bin (ash silo / bin), etc. Collection bin: The dust-containing gas passes through the bag filter or electrostatic precipitator at the upper part of the collection bin. The dust collector can separate the dust in the dust-containing gas from the gas, and the separated dust will fall into the collection bin below the dust collector for temporary storage. Sending bin: The sending bin is a device that uses the principle of pneumatic conveying to send materials. The sending bin is the core component of the pneumatic sending bin and is used to store the materials to be sent. In the pneumatic conveying system, compressed air or high-pressure gas is introduced into the sending bin to blow the materials out of the sending bin and transport them to the destination through the pipeline. Energy system: The air source of the conveying and instrument compressed air system is provided by an air compressor, and an air storage tank is used as a temporary storage container. When the pneumatic conveying system needs to operate, it is sent to the conveying air valve of the pneumatic conveying system through the compressed air pipeline. Dense-phase booster: It is the core hardware of the intelligent conveying system. The dense-phase booster can automatically sense the pressure in the pipeline. After determining the material flow pressure before the system runs, it adjusts the opening pressure matching value of the dense-phase booster. If the pressure in the pipeline exceeds the set pressure, the dense-phase booster will automatically open to supplement the conveying air to ensure that the ash-gas ratio, operating pressure, and operating flow rate at the supplement point are maintained within a certain range. The dense-phase booster is a pure mechanical structure, and its opening and closing do not require external electrical signal control. When the intelligent conveying system is operating, relying on the physical characteristics of the dense-phase booster, it can ensure that parameters such as the system conveying output value, gas consumption, and conveying time per unit time (or single cycle period) are relatively stable.

[0056] Control instruments: Measuring instruments related to determining the operating parameters of the pneumatic conveying system by neural network intelligent algorithms, such as intelligent algorithm control terminals, neural network integrated monitoring systems, level signal acquisition devices, compressed air system control devices, material loading control devices, pipeline system switching control devices, dense-phase booster systems, etc. Conveying pipeline system: The sending bin is connected to the receiving terminal bin through the conveying pipeline, which includes multiple branch pipelines or terminal bins. All the routes connected by the pipelines are collectively referred to as the conveying pipeline system. The length of the conveying pipeline system between the two bins is generally about dozens of meters to thousands of meters. Switching valve system: A group of sending bins may include multiple branch pipelines or terminal bins. During the operation and conveying of the system, the materials sent from the sending bin need to set switching valves at the branch points to ensure the routing direction and path when transporting to the designated target terminal bin. The system composed of these switching valves is the pipeline switching system. Control system: The core of the system control, which includes actuators, sensors, intelligent information processors, communication interfaces, human-machine interfaces, etc.

[0057] Blanking time: An isolation valve is set between the collection bin and the sending bin. The materials in the collection bin are connected to the sending bin through this isolation valve. When the system needs to transport, this isolation valve opens, and the materials in the collection bin will fall into the lower sending bin through this isolation valve. The amount of loading in the lower sending bin is controlled by the opening time of this isolation valve, and this time is the blanking time. Cycle period time (operation interval time): The volume of the system sending bin is limited, and the maximum single delivery volume is basically the volume of the sending bin. Therefore, after the collection bin continuously receives materials, in order to meet the material transfer and transportation of the system, the sending bin adopts a cyclic transportation mode, that is, the sending bin is loaded, and after the loading is completed, it is transported through the transportation pipeline with the conveying air as the power. After the transportation is completed, it continues to be loaded and transported, and operates in this way cyclically. Cycle period time: The overall process time from the start of loading in the sending bin until the start of the next loading. Cycle failure time: Since the system operates cyclically, according to the volume of the sending bin and the transportation distance, there should be a theoretically maximum transportation time for each transportation cycle. If the system transportation has not ended within this time, the system considers that the transportation process in the system is too long and there is a failure. When the system operation has not ended after this time, an alarm will be triggered.

[0058] In the following, embodiments of the present invention will be described with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0059] Figure 1 It is a schematic flow chart of an intelligent decision-making method for a pneumatic conveying system according to an embodiment of the present application, as Figure 1 shown, the intelligent decision-making method at least includes steps S10 to S50.

[0060] Step S10: Obtain the equipment parameter information, operation status information, and related equipment parameter information of the pneumatic conveying system. The operation status information includes the average gas consumption, the current blanking amount in the sending bin, the current cycle period time, the current blanking time, and the current material sending amount. The equipment parameter information includes the number of air compressors, the exhaust volume of the air compressors, the volume of the gas storage tank, the number of gas storage tanks, the volume of the sending bin, and the number of sending bins. The related equipment parameter information includes the current total ash amount of the boiler, the ash ratio, and the current material production amount.

[0061] In this step, the device parameter information and operating status information can be obtained from the host server of the pneumatic conveying system, or can be input customarily. The relevant device parameter information refers to the device parameters or status parameters of other related devices related to the pneumatic conveying system, such as boilers and other related devices. Specifically, the average air consumption of the pneumatic conveying system refers to the actual amount of compressed air consumed by the system during production operation per unit time under normal operating conditions. The change rate of the collection bin refers to the degree and trend of the change in the material height in the material collection bin per unit time. The current material production volume refers to the total amount of materials produced by the boiler under the current coal feeding amount and ash ratio, and the amount of materials collected in the collection bin according to a fixed ratio of the dust collector output. The current material falling amount in the sending bin refers to the amount of materials falling from the sending bin (or called the feeding bin, dosing bin) during the pneumatic conveying process. The current cycle time refers to the time required for the pneumatic conveying system to complete a complete conveying cycle. The current material sending volume refers to the amount of materials conveyed per unit time by the pneumatic conveying system according to the number of cycles and the single sending volume under the current operating state.

[0062] As Figure 2 shown, the acquisition of the data of the pneumatic conveying system can be realized based on the embedded neural network integrated monitoring module 10. This module and the host of the pneumatic conveying system adopt a C / S system architecture. The embedded neural network integrated monitoring module 10 collects the associated system production data in the host server and the real-time feedback data of the detection instruments, and further analyzes the collected associated system production data and the real-time feedback data of the detection instruments to determine the device parameter information and operating status information of the pneumatic conveying system, so that the valid data such as the device parameter information, operating status information and related device parameter information of the pneumatic conveying system are sent to the intelligent algorithm control module.

[0063] Specifically, the embedded neural network integrated monitoring module 10 can collect the real-time feedback data of the detection instruments on each branch of the pneumatic conveying system, such as pressure detection instruments, flow detection instruments, etc. In addition, the associated system production data collected by the embedded neural network integrated monitoring module 10 includes the number and exhaust volume of air compressors, flow rate, loading and unloading time, pressure of the air storage tank, volume of the air storage tank, etc. In addition, the embedded neural network integrated monitoring module 10 can also determine the current material production volume based on the raw material input algorithm module according to the unit load, the coal feeding amount change trend model, the ash ratio after combustion, the material specific gravity (bulk density), etc. In addition to the above, the embedded neural network integrated monitoring module 10 can also record the production process data such as the production process running time, the production and conveying volume per unit time, and the pressure change trend.

[0064] Step S20: Determine the energy supply rate and the maximum sustainable time of energy consumption within the safe range based on the average air consumption, the number of air compressors, the exhaust volume of air compressors, the volume of the air storage tank, and the number of air storage tanks.

[0065] In this step, the energy replenishment rate and the maximum sustainable time of energy consumption within the safe range of the pneumatic conveying system are further calculated based on the average gas consumption, the number of air compressors, the exhaust volume of the air compressors, the volume of the gas storage tank, and the number of gas storage tanks obtained in step S10. Among them, the energy replenishment rate refers to the speed of energy replenishment required for the system to maintain its operation; the maximum sustainable time of energy consumption within the safe range refers to the longest time period during which the system can continue to operate after the energy consumption reaches the maximum value during the operation of the pneumatic conveying system; the maximum sustainable time of energy consumption within the safe range is used to determine whether the current energy reserve can meet the energy consumption of the later-stage system production. That is, in the operation decision control module, the rate of change of the later-stage materials is used to match the current production operation cycle to ensure the average distribution and use of the energy system, and to avoid excessive instantaneous consumption of the energy system and too many simultaneously started air compressors, resulting in excessive electricity costs and mechanical losses.

[0066] Exemplarily, this step can be implemented based on the intelligent algorithm control module. The intelligent algorithm control module includes multiple sub-modules, that is, all the data obtained in step S10 are assigned to the corresponding sub-modules in the intelligent algorithm control module to calculate the reasonable parameters required for production under the current conditions of the pneumatic conveying system. Exemplarily, the intelligent algorithm control module can at least include an energy consumption algorithm sub-module 21, a raw material input algorithm sub-module 22, a collector stacking algorithm sub-module 23, and a production operation algorithm sub-module 30.

[0067] Specifically, step S20 can be completed based on the energy consumption algorithm sub-module 21 of the intelligent algorithm control module, that is, the energy consumption algorithm sub-module 21 determines the energy replenishment rate and the maximum sustainable time of energy consumption within the safe range based on the average gas consumption, the number of air compressors, the exhaust volume of the air compressors, the volume of the gas storage tank, and the number of gas storage tanks.

[0068] Exemplarily, determining the energy replenishment rate and the maximum sustainable time of energy consumption within the safe range based on the average gas consumption, the number of air compressors, the exhaust volume of the air compressors, the volume of the gas storage tank, and the number of gas storage tanks may specifically include the following steps: calculating the total gas volume based on the number of air compressors and the exhaust volume of the air compressors, calculating the total gas storage based on the volume of the gas storage tank and the number of gas storage tanks; determining the energy replenishment rate based on the difference between the total gas volume and the average gas consumption; based on the formula determine the maximum sustainable time of energy consumption within the safe range; where s n represents the maximum sustainable time of energy consumption within the safe range, C 总 represents the total gas storage, and v n represents the energy replenishment rate. For example Figure 3As shown, when calculating the maximum sustainable time of energy consumption within the specific calculation safety range, the energy replenishment rate can be first calculated based on the average gas consumption, the number of air compressors, and the air displacement of the air compressors. Further, the maximum sustainable time of energy consumption within the safety range can be calculated based on the volume of the gas storage tank, the number of gas storage tanks, and the energy replenishment rate.

[0069] Specifically, the energy consumption algorithm sub-module 21 calculates the energy output that the energy system can provide through the air displacement and the number of air compressors (the total energy output is the total gas volume, where the number of air compressors is the actual number of online units, and those in cold standby or under maintenance are automatically filtered out). At the same time, it calculates the total gas storage through the volume of the gas storage tank and the number of gas storage tanks, calculates the energy replenishment rate based on the obtained average gas consumption, and further determines the maximum sustainable time of energy consumption within the safety range based on the energy replenishment rate. The energy consumption algorithm sub-module 21 further assigns the calculation results to the subsequent intelligent decision-making terminal to determine the operating parameters of the pneumatic conveying system at the next moment.

[0070] In a specific embodiment, the pneumatic conveying system is used for pneumatic conveying of the first electric field and the second electric field. At this time, the total gas volume Nm 3 / min = the air displacement of a single air compressor Nm 3 / min * the number of air compressors, the total gas storage = the volume of a single gas storage tank * the number of gas storage tanks, the energy replenishment rate Nm 3 / min = the total gas volume Nm 3 / min - the average gas consumption Nm 3 / min.

[0071]

[0072] Step S30: Determine the change rate of the collection bin based on the current total ash amount of the boiler, the ash ratio, the current material production amount, and the current material sending amount.

[0073] In this step, the change rate of the collection bin is further determined based on the current total ash amount of the boiler, the ash ratio, the current material production amount, and the current material sending amount obtained in step S10. Among them, the change rate of the collection bin refers to the change speed of the material level height in the collection bin per unit time.

[0074] Exemplarily, this step can be completed based on the raw material input algorithm sub-module 22 of the intelligent algorithm control module, that is, the raw material input algorithm sub-module 22 determines the change rate of the collection bin based on the current total ash amount of the boiler, the ash ratio, the current material production amount, and the current material sending amount.

[0075] Further, determine the change rate of the collection bin based on the current total ash amount of the boiler, the ash ratio, the current material production amount, and the current material sending amount. Specifically, the following steps may be included: determine the production amount change rate based on the product of the current total ash amount of the boiler and the ash ratio; determine the material production amount at the next moment based on the production amount change rate and the current material production amount; use the difference between the material production amount at the next moment and the current material sending amount as the change rate of the collection bin.

[0076] Specifically, the raw material input algorithm sub-module 22 calculates the change rate of the collection bin according to the current total ash amount of the boiler, the ash ratio, the current material production amount, and the current material sending amount. To ensure that the material level in the collection bin always remains at a safe height, the change rate of the collection bin can be further assigned to the subsequent intelligent decision-making terminal to determine the operating parameters of the pneumatic conveying system at the next moment.

[0077] In a specific embodiment, the total ash amount of the boiler ash can be calculated based on the coal feeding amount of the boiler and the ash ratio generated after coal combustion. Further, as Figure 3 shown, the material amount in the collection bin (the amount collected by the dust collector) can be determined based on the total ash amount of the boiler ash and the treatment ratio of the electrostatic precipitator. Calculate the difference in the material sending amount through the material sending amount at the next moment of the electric field and the material amount in the collection bin, and use this difference as the change rate of the collection bin. Exemplarily, when performing pneumatic conveying on multiple electric fields, determine the treatment amount of each electric field based on the characteristics of the dust removal system. For example, when the first electric field dust collector is at full load, the treatment amount is about 80% of the overall ash amount, and the treatment amount of the second electric field is about 10% of the overall ash amount; taking the first electric field as an example, the ash amount collected by the first electric field dust collector per hour = the total ash amount of the boiler ash * the treatment ratio of the first electric field dust collector, and the change rate of the collection bin m 3 / h = the ash amount collected by the first electric field dust collector m 3 / h - the current sending amount of the first electric field m 3 / h. Additionally, it can be understood that when the pneumatic conveying system performs pneumatic conveying on only one electric field, then at this time, the ash amount collected by the dust collector of this electric field is equal to the total ash amount of the boiler ash, that is, the treatment ratio of the dust collector of this electric field is 100%.

[0078] Step S40: Determine the matching output value of the average processing amount per unit time and the current production output based on the current material dropping amount in the sending bin, the volume of the sending bin, the number of sending bins, and the current cycle time.

[0079] In this step, further calculate the matching output value of the average processing amount per unit time and the current production output based on the current blanking amount of the current sending bin, the volume of the sending bin, the number of sending bins, and the current cycle time. Among them, the average processing amount per unit time can be understood as the amount of material processed by the pneumatic conveying system per unit time, and the matching output value of the current production output can be understood as the difference between the total amount of material that needs to be processed per unit time by the pneumatic conveying system at the next moment and the current amount of processed material. It is the conveying capacity that can operate stably and efficiently determined according to the conveying requirements of the total amount of material that needs to be output, the design parameters of the system, and the current working conditions.

[0080] Exemplarily, this step can be completed based on the collector stacking algorithm sub-module 23 of the intelligent algorithm control module, that is, the collector stacking algorithm sub-module 23 determines the matching output value of the average processing amount per unit time and the current production output based on the current blanking amount of the current sending bin, the volume of the sending bin, the number of sending bins, and the current cycle time.

[0081] Exemplarily, determining the matching output value of the average processing amount per unit time and the current production output based on the current blanking amount of the current sending bin, the volume of the sending bin, the number of sending bins, and the current cycle time may specifically include the following steps:

[0082] Calculate the current single sending amount based on the product of the current blanking amount of the current sending bin, the volume of the sending bin, and the number of sending bins;

[0083] Based on the formula Calculate the current cycle times of the sending bin; where n is the current cycle times of the sending bin and t is the current cycle time;

[0084] Determine the dust collector ash collection amount based on the current total boiler ash amount and ash ratio;

[0085] Determine the average processing amount per unit time based on the product of the current cycle times of the sending bin and the current single sending amount;

[0086] Based on the formula Determine the matching output value of the current production output, where d p is the matching output value of the current production output, c h is the dust collector ash collection amount, d s is the average processing amount per unit time, n is the current cycle times of the sending bin, d d is the current single sending amount.

[0087] Specifically, the collector stacking algorithm sub-module 23 first calculates the average processing volume per unit time. To ensure the timely processing of the materials sent by the upstream system equipment, it also calculates the matching output value of the current production output, and assigns the determined calculation result to the subsequent intelligent decision terminal to determine the operating parameters of the pneumatic conveying system at the next moment.

[0088] In a specific embodiment, the current single sending volume of the power plant = (the current falling material volume of the sending bin * the volume of a single sending bin m 3 ) * the number of sending bins, and the average processing volume per unit time m 3 / h = the current cycle times of the sending bin * the current single sending volume of a power plant,

[0089] Among them,

[0090] Step S50: Determine the production output per unit time, the cycle period time, and the system falling material time of the pneumatic conveying system based on the sustainable time of the maximum energy consumption within the safe range, the change rate of the collection bin, the current falling material time, and the matching output value of the current production output.

[0091] In this step, further determine the production output per unit time, the cycle period time, and the system falling material time of the pneumatic conveying system based on the sustainable time of the maximum energy consumption within the safe range, the change rate of the collection bin, and the matching output value of the current production output determined in the above steps S20 to S40. The production output per unit time, the cycle period time, and the system falling material time of the pneumatic conveying system obtained in this step are the system operating parameters corresponding to the next moment.

[0092] Exemplarily, this step can be implemented based on the production operation algorithm sub-module 30, that is, the production operation algorithm sub-module 30 determines the production output per unit time, the cycle period time, and the system falling material time of the pneumatic conveying system based on the sustainable time of the maximum energy consumption within the safe range, the change rate of the collection bin, the current falling material time, and the matching output value of the current production output.

[0093] Exemplarily, determining the production output per unit time, the cycle period time, and the system falling material time of the pneumatic conveying system based on the sustainable time of the maximum energy consumption within the safe range, the change rate of the collection bin, the current falling material time, and the matching output value of the current production output may specifically include the following steps: Determine the production output per unit time based on the sum of the matching output value of the current production output and the change rate of the collection bin; Determine the system falling material time based on the preset time change value and the current falling material time; Calculate the cycle period time based on the formula where t s represents the cycle period time, and d cRepresents the production output per unit time, d f Represents the current material sending amount.

[0094] Specifically, the production output m per unit time 3 / h = the matching output value m of the current production output 3 / h + the change rate of the collection bin m 3 / h. The preset time change value can be exemplarily set to 5s. At this time, the system blanking time = the current blanking time + 5s; in addition, It can be understood that the preset time change values listed in the above embodiments are only examples. In some other embodiments, the preset time change value can also be set to other times other than 5s, such as 6s, 4s, etc.

[0095] Furthermore, determining the system blanking time based on the preset time change rate and the current blanking time includes: judging whether the current running time of the system is greater than the maximum sustainable time of energy consumption within the safe range. When it is not greater, determining the system blanking time based on the sum of the preset time change value and the current blanking time. In this embodiment, further judge whether the current running time of the system is greater than the maximum sustainable time of energy consumption within the safe range; if it is not greater, then use the sum of the preset time change value and the current blanking time as the system blanking time for the next moment, and if it is greater than or equal to, then send in the way of small amounts and multiple times. At this time, exemplarily, the difference between the current blanking time and the preset time change value can be used as the system blanking time for the next moment.

[0096] Through the above embodiments, it can be found that the intelligent decision-making method of the pneumatic conveying system of the present application can intelligently analyze and obtain the system operation parameters for the next moment based on the equipment parameter information and operation status information of the pneumatic conveying system. This method realizes the intelligent decision-making of the operation parameters of the pneumatic conveying system, thus eliminating the need to rely on the experience of the staff, not only saving manpower, but also improving the operation stability of the pneumatic conveying system.

[0097] In addition, the equipment parameter information, operation status information, relevant equipment parameter information of the pneumatic conveying system, as well as the production output per unit time, cycle time, and system blanking time of the corresponding pneumatic conveying system can be further saved to the database; so that in the subsequent cycle process, based on the obtained equipment parameter information and operation status information of the pneumatic conveying system, the system operation parameters of the pneumatic conveying system can be conveniently obtained through cross-comparison from the database.

[0098] Exemplarily, the storage of data can be implemented based on a data screening and classification and fuzzy rule matching algorithm module, which is used to record all valid data, classify and store them in a database to establish a fuzzy rule base, and provide parameter correction and comparison. Specifically, the data screening and classification and fuzzy rule matching algorithm module collects relevant system data through the embedded neural network comprehensive monitoring module 10, and includes the relevant actual work data of each sub-module or cooperating module during the operation and transportation of the system, such as: the total exhaust volume of the air compressor, the pressure drop rate of the air storage tank, the number of opening of the sending bins in the transportation system, the coal feeding amount of the upstream unit, the ash content, the material level height of the collecting bin, the production operation cycle and the single sending amount, etc.; in addition, the operation states of each system corresponding to each different state period or parameter are classified and sorted for storage, providing an infrastructure for fuzzy rule control of the subsequent operation parameters.

[0099] Correspondingly, the present invention also provides an intelligent decision-making system for a pneumatic conveying system, which includes:

[0100] An embedded neural network comprehensive monitoring module 10, which is used to obtain the equipment parameter information, operation state information and relevant equipment parameter information of the pneumatic conveying system. The operation state information includes the average gas consumption, the current material falling amount of the sending bin, the current cycle time, the current material falling time and the current material sending amount. The equipment parameter information includes the number of air compressors, the exhaust volume of the air compressors, the volume of the air storage tank, the number of air storage tanks, the volume of the sending bin and the number of sending bins. The relevant equipment parameter information includes the current total ash amount of the boiler, the ash content ratio, and the current material production amount;

[0101] An energy consumption algorithm sub-module 21, which is used to determine the energy replenishment rate and the maximum sustainable time of energy consumption within a safe range based on the average gas consumption, the number of air compressors, the exhaust volume of the air compressors, the volume of the air storage tank and the number of air storage tanks;

[0102] A raw material input algorithm sub-module 22, which is used to determine the change rate of the collecting bin based on the current total ash amount of the boiler, the ash content ratio, the current material production amount and the current material sending amount;

[0103] A collector stacking algorithm sub-module 23, which is used to determine the matching output value of the average processing amount per unit time and the current production output based on the current material falling amount of the sending bin, the volume of the sending bin, the number of sending bins and the current cycle time;

[0104] A production operation algorithm sub-module 30, which is used to determine the production output per unit time, the cycle time and the system material falling time of the pneumatic conveying system based on the maximum sustainable time of energy consumption within a safe range, the change rate of the collecting bin, the current material falling time and the matching output value of the current production output.

[0105] In one embodiment, the intelligent decision-making system of the pneumatic conveying system is embedded with a system including an intelligent algorithm control terminal, an embedded intelligent control decision terminal, and an optimization database under the control of the control system of the original host (DCS / PLC) of the pneumatic conveying control system. By communicating with the original host of the conveying system and the neural network integrated monitoring module, it reads the real-time operation data of the system equipment status, the networks of each relevant equipment, or the indirectly relevant system equipment. After the core calculations of each module of the intelligent algorithm control terminal, all relevant calculation result values are assigned to the intelligent control decision terminal. The intelligent control decision terminal gives the optimal operation mode and operation parameters and feeds them back to the host in real time through communication. Based on the parameter output results of the intelligent control decision terminal, the host operates the pneumatic conveying system. The neural network integrated monitoring system diagnoses and integrates the execution decision and the actual operation status by monitoring the above operation process and feedback closed-loop, and finally realizes the optimal operation mode of energy-saving operation and unattended operation of the intelligent system. The neural network intelligent algorithm determines and decides the energy-saving pneumatic conveying system, which adopts a modular embedded design and is composed of four parts as a whole: the neural network integrated monitoring system and the optimization database module, the intelligent algorithm control terminal module, the embedded intelligent control decision module, and the embedded maintenance-free inspection module (intelligent boost system).

[0106] In addition, the original host program control logic terminal of the pneumatic conveying system is generally composed of a DCS / PLC system. The DCS / PLC is responsible for the execution action control of the main equipment of the pneumatic conveying system and collects the operation status of adjacent systems, such as: unit load, coal feeding amount, ash ratio, compressed air system (energy system), collection bin level, destination collection bin level, etc. In one embodiment, the pneumatic conveying system can be controlled based on the following process: (1) The operator first selects the destination collection bin required for system conveying according to the production needs in the factory; (2) System self-check, equipment status display (whether the operating conditions are met and alarm information); (3) Operation mode selection (select manual or intelligent unattended operation); (4) Operating parameter setting (if manual operation is selected, operating parameters need to be set); (5) Start the execution of the program control logic (start the system after the parameter setting is completed); (6) System operation (the system operation logic completes the conveying process according to the selected mode).

[0107] According to another aspect of the present invention, there is also provided an intelligent decision-making device for a pneumatic conveying system, the device includes a processor, a memory, and a computer program stored on the memory. The processor is used to execute the computer program, and when the computer program is executed, the device realizes the steps of the method described in any of the above embodiments.

[0108] An embodiment of the present invention also provides a computer-readable storage medium and a computer program product, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in any of the above embodiments are implemented. The computer-readable storage medium may be a tangible storage medium, such as a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, floppy disks, hard disks, removable storage disks, CD-ROMs, or any other form of storage medium known in the art.

[0109] Those of ordinary skill in the art should understand that the various exemplary components, systems, and methods described in connection with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Specifically, whether to implement in hardware or software depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a functional card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The programs or code segments can be stored in a machine-readable medium or transmitted through a data signal carried in a carrier wave on a transmission medium or a communication link.

[0110] It should be clear that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order between steps after understanding the spirit of the present invention.

[0111] In the present invention, the features described and / or illustrated for one embodiment can be used in the same or a similar manner in one or more other embodiments, and / or combined with the features of other embodiments or replace the features of other embodiments.

[0112] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, various changes and modifications can be made to the embodiments of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent decision-making method for a pneumatic conveying system, characterized in that The described intelligent decision-making method includes: Obtain the equipment parameter information, operating status information, and related equipment parameter information of the pneumatic conveying system. The operating status information includes the average gas consumption, current discharging amount of the sending bin, current cycle time, current discharging time, and current material sending amount. The equipment parameter information includes the number of air compressors, the exhaust volume of the air compressors, the volume of the air storage tank, the number of air storage tanks, the volume of the sending bin, and the number of sending bins. The related equipment parameter information includes the current total ash amount of the boiler, the ash ratio, and the current material production amount; Determine the energy replenishment rate and the maximum sustainable time of energy consumption within the safe range based on the average gas consumption, the number of air compressors, the exhaust volume of the air compressors, the volume of the air storage tank, and the number of air storage tanks; Determine the change rate of the collection bin based on the current total ash amount of the boiler, the ash ratio, the current material production amount, and the current material sending amount; Determine the average processing amount per unit time and the matching output value of the current production output based on the current discharging amount of the sending bin, the volume of the sending bin, the number of sending bins, and the current cycle time; Determine the production output per unit time, the cycle time, and the system discharging time of the pneumatic conveying system based on the maximum sustainable time of energy consumption within the safe range, the change rate of the collection bin, the current discharging time, and the matching output value of the current production output; 2. The intelligent decision-making method for the pneumatic conveying system according to claim 1, wherein Determine the energy replenishment rate and the maximum sustainable time of energy consumption within the safe range based on the average gas consumption, the number of air compressors, the exhaust volume of the air compressors, the volume of the air storage tank, and the number of air storage tanks, including: Calculate the total gas volume based on the number of air compressors and the exhaust volume of the air compressors, and calculate the total gas storage volume based on the volume of the air storage tank and the number of air storage tanks; Determine the energy replenishment rate based on the difference between the total gas volume and the average gas consumption rate; based on the formula Determine the maximum sustainable time of energy consumption within the safe range; where, s n represents the maximum sustainable time of energy consumption within the safe range, C 总 represents the total gas storage volume, v n represents the energy replenishment rate.

3. The intelligent decision-making method for the pneumatic conveying system according to claim 1, wherein Determine the change rate of the collection bin based on the current total ash amount of the boiler, the ash ratio, the current material production amount, and the current material sending amount, including: Determine the change rate of the production amount based on the product of the current total ash amount of the boiler and the ash ratio; Determine the material production amount at the next moment based on the change rate of the production amount and the current material production amount; Use the difference between the material production amount at the next moment and the current material sending amount as the change rate of the collection bin.

4. The intelligent decision-making method for the pneumatic conveying system according to claim 3, characterized in that, Determine the average processing amount per unit time and the matching output value of the current production output based on the current discharging amount of the sending bin, the volume of the sending bin, the number of sending bins, and the current cycle time, including: Calculate the current single sending amount based on the product of the current discharging amount of the sending bin, the volume of the sending bin, and the number of sending bins; Based on the formula Calculate the current cycle count of the sending bin; where n is the current cycle count of the sending bin and t is the current cycle period time; Determine the ash collection amount of the dust collector based on the current total ash amount of the boiler and the ash ratio; Determine the average processing amount per unit time based on the product of the current cycle times of the sending bin and the current single sending amount; Based on the formula Determine the matching output value of the current production output, where d p is the matching output value of the current production output, c h is the amount of ash collected by the dust collector, d s is the average processing amount per unit time, n is the number of cycles of the current sending bin, d d is the current single sending amount.

5. The intelligent decision-making method for the pneumatic conveying system according to claim 4, wherein Determine the production output per unit time, the cycle time, and the system discharging time of the pneumatic conveying system based on the maximum sustainable time of energy consumption within the safe range, the change rate of the collection bin, the current discharging time, and the matching output value of the current production output, including: Determine the production output per unit time based on the sum of the matching output value of the current production output and the change rate of the collection bin; Determine the system discharging time based on the preset time change value and the current discharging time; Based on the formula Calculate the cycle time, where t s represents the cycle time, d c represents the production output per unit time, d f represents the current material sending quantity.

6. The intelligent decision-making method for the pneumatic conveying system according to claim 5, characterized in that Determine the system discharging time based on the preset time change rate and the current discharging time, including: Determine whether the current running time of the system is greater than the maximum sustainable time of energy consumption within the safe range. When it is not greater, determine the system blanking time based on the sum of the preset time change value and the current blanking time.

7. The intelligent decision-making method for the pneumatic conveying system according to claim 1, characterized in that Save the equipment parameter information, operating status information, relevant equipment parameter information of the pneumatic conveying system, and the production output per unit time, cycle time, and system blanking time of the corresponding pneumatic conveying system to the database.

8. An intelligent decision-making system for a pneumatic conveying system, characterized in that, The system includes: An embedded neural network comprehensive monitoring module, which is used to obtain the equipment parameter information, operating status information, and relevant equipment parameter information of the pneumatic conveying system. The operating status information includes the average gas consumption, the current blanking volume of the sending bin, the current cycle time, the current blanking time, and the current material sending volume. The equipment parameter information includes the number of air compressors, the exhaust volume of the air compressors, the volume of the air storage tank, the number of air storage tanks, the volume of the sending bin, and the number of sending bins. The relevant equipment parameter information includes the current total ash amount of the boiler, the ash ratio, and the current material production volume; An energy consumption algorithm sub-module, which is used to determine the energy supply rate and the maximum sustainable time of energy consumption within the safe range based on the average gas consumption, the number of air compressors, the exhaust volume of the air compressors, the volume of the air storage tank, and the number of air storage tanks; A raw material input algorithm sub-module, which is used to determine the change rate of the collection bin based on the current total ash amount of the boiler, the ash ratio, the current material production volume, and the current material sending volume; A collector stacking algorithm sub-module, which is used to determine the matching output value of the average processing volume per unit time and the current production output based on the current blanking volume of the sending bin, the volume of the sending bin, the number of sending bins, and the current cycle time; A production operation algorithm sub-module, which is used to determine the production output per unit time, the cycle time, and the system blanking time of the pneumatic conveying system based on the maximum sustainable time of energy consumption within the safe range, the change rate of the collection bin, the current blanking time, and the matching output value of the current production output; 9. An intelligent decision-making device for a pneumatic conveying system, the device comprising a processor, a memory, and a computer program stored on the memory, characterized in that, The processor is used to execute the computer program. When the computer program is executed, the device implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 7.

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