A method, system and device for optimizing compressed air demand and air compressor cluster control in cement production

Abstract

This invention relates to the field of industrial automation control technology, and discloses a method, system, and equipment for optimizing compressed air demand and air compressor cluster control in cement production. The method includes: acquiring the operating status sequence of multiple distributed air-consuming devices within a cement industrial production area, and retrieving the unit air consumption corresponding to each distributed air-consuming device; calculating the predicted air consumption sequence of each distributed air-consuming device within a preset future time period; extracting the corresponding lag time parameters based on the pipeline network topology; obtaining the time-aligned air consumption of each node; summarizing the time-aligned air consumption of each node to generate a total demand load curve, and generating frequency conversion adjustment commands and start / stop scheduling commands for the air compressor cluster based on the total demand load curve. This invention can solve the lag in pressurized air supply and improve the continuous stability of each stage of cement production.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control technology, and in particular to a method, system and equipment for optimizing the compressed air demand and air compressor cluster control in cement production. Background Technology

[0002] The cement industry production process encompasses multiple stages, including raw material grinding, coal powder preparation, clinker calcination, cement grinding, and packaging and shipping. These distributed production scenarios involve numerous pneumatic devices that consume compressed air, such as the cleaning pulse valves of pulse bag filters, pneumatic control valves in pneumatic conveying systems, and pneumatic actuator cylinders. Currently, the control logic of air compressor stations in cement plants generally adopts single-loop feedback control based on the pressure of the main air supply pipeline. Specifically, a pressure sensor is installed at the outlet of the main air supply pipeline. When the pipeline pressure is detected to be lower than the set lower limit, the control system issues a command to increase the load of the air compressor or start the standby air compressor; when the pressure is higher than the upper limit, a command is issued to unload or shut down the compressor.

[0003] However, the aforementioned control method based on main pipe pressure feedback suffers from an insurmountable physical lag defect. On the one hand, compressed air takes time to travel over long distances and through complex pipeline topologies. When distributed gas-consuming devices at the end suddenly consume gas in a concentrated manner, causing a sharp drop in local pressure, it takes time for this pressure fluctuation to be transmitted to the main pipe on the supply side. On the other hand, the air compressor also needs response time from receiving the command to the motor accelerating and the actual increase in air production. This lag in pressurization causes the dynamic response on the supply side to always lag behind the actual consumption on the demand side, which can easily lead to severe pressure fluctuations at the end and equipment failure. To cover up this defect, on-site operators often have to raise the lower limit of the main pipe's set pressure, which causes the air compressor to operate in a high-pressure ineffective state for a long time, resulting in a huge waste of electricity.

[0004] Therefore, how to solve the lag in air inflation has become an urgent problem to be solved. Summary of the Invention

[0005] This invention provides a method, system, and equipment for optimizing the compressed air demand and air compressor cluster control in cement production, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a method for optimizing the compressed air demand and air compressor cluster control in cement production, comprising: S101, obtain the operating status sequence of multiple distributed gas-consuming devices in the cement industry production area, and retrieve the unit gas consumption corresponding to each of the distributed gas-consuming devices; S102, calculate the predicted gas consumption sequence for each of the distributed gas-consuming devices in a future preset time period based on the operating status sequence and the unit gas consumption; S103, obtain the pipeline topology from the gas supply side to the nodes where each of the distributed gas-consuming devices is located, and extract the corresponding lag time parameters based on the pipeline topology; S104, Perform time axis offset processing on the predicted gas consumption sequence according to the lag time parameter to obtain the time-aligned gas consumption of each node; S105, summarize the time-aligned air consumption of each node to generate a total demand load curve, and generate frequency conversion adjustment commands and start / stop scheduling commands for the air compressor cluster based on the total demand load curve.

[0007] In a preferred embodiment, obtaining the operating status sequence of multiple distributed gas-using devices within the cement industry production area includes: Connect to industrial control programmable logic controllers; Read the action trigger cycle for each of the distributed gas-consuming devices from the industrial control programmable logic controller; Based on the action triggering cycle, time discretization mapping is performed within the future preset time period to generate the running state sequence consisting of a high-level indicator on state and a low-level indicator off state.

[0008] In a preferred embodiment, calculating the predicted gas consumption sequence for each of the distributed gas-consuming devices within a preset future time period based on the operating state sequence and the unit gas consumption includes: Identify the duration of the high level in the operating state sequence; The gas consumption per action is calculated by multiplying the high-level duration by the unit gas consumption. The gas consumption of a single action is concatenated into an array according to its corresponding trigger time in the operating state sequence to generate the predicted gas consumption sequence.

[0009] In a preferred embodiment, obtaining the pipeline topology from the gas supply side to the nodes where each of the distributed gas-consuming devices is located, and extracting the corresponding lag time parameters based on the pipeline topology, includes: Parse the physical cabling data in the pipeline network topology; Calculate the total length of the pipeline from the output end of the three-way valve on the gas supply side to the node where the target distributed gas consumption equipment is located based on the physical wiring data; Obtain the reference gas velocity within the pipeline network topology; The total length of the pipeline is divided by the reference gas flow rate to obtain the lag time parameter corresponding to the target distributed gas consumption device.

[0010] In a preferred embodiment, the step of performing time axis offset processing on the predicted gas consumption sequence based on the lag time parameter to obtain the time-aligned gas consumption at each node includes: Read the timestamp corresponding to each gas consumption value in the predicted gas consumption sequence; Subtract the lag time parameter from the timestamp to obtain the compensation timestamp; The original timestamp is replaced with the compensated timestamp, and the sequence after rearranging the timestamps is determined as the time-aligned gas consumption.

[0011] In a preferred embodiment, the step of summarizing the time-aligned gas consumption of each node to generate a total demand load curve, and generating variable frequency control commands and start / stop scheduling commands for the air compressor cluster based on the total demand load curve, includes: Under the same compensation timestamp, the time-aligned gas consumption of each node is numerically accumulated to generate the total demand load curve in the continuous time domain; Extract the base load range and peak load range from the total demand load curve; For the aforementioned basic load range, the number of fixed-frequency air compressors in operation is matched, and the corresponding start-stop scheduling command is generated. For the peak load range, calculate the compensation differential flow rate, and generate the frequency conversion adjustment command for the frequency converter air compressor based on the compensation differential flow rate.

[0012] In a preferred embodiment, after generating the frequency conversion control commands and start / stop scheduling commands for the air compressor cluster based on the total demand load curve, the method further includes: Collect the real-time gas pressure value at the main gas supply pipe; Calculate the first deviation between the real-time air pressure value and the preset target air pressure range; When the first deviation exceeds the fault tolerance threshold, an auxiliary correction coefficient is generated based on the proportional-integral-derivative control algorithm. The auxiliary correction coefficient is superimposed on the control quantity of the frequency conversion adjustment command for dynamic gain adjustment.

[0013] In a preferred embodiment, before obtaining the operating status sequence of multiple distributed gas-consuming devices within the cement industry production area, the method further includes: The operating parameters of each air compressor in the air compressor cluster are read to establish a device capability matrix that includes rated exhaust volume, frequency conversion response time and start-up preheating time. The step of generating variable frequency control commands and start / stop scheduling commands for the air compressor cluster based on the total demand load curve includes: matching and solving the total demand load curve with the equipment capacity matrix, such that the advance issuance time of the variable frequency control command is greater than or equal to the variable frequency response time.

[0014] To address the aforementioned problems, the present invention also provides a system for optimizing the compressed air demand and air compressor cluster control in cement production, the system comprising: The data acquisition module is used to acquire the operating status sequence of multiple distributed gas-consuming devices in the cement industry production area, and retrieve the unit gas consumption of each of the distributed gas-consuming devices. The sequence prediction module is used to calculate the predicted gas consumption sequence of each of the distributed gas-consuming devices in a future preset time period based on the operating status sequence and the unit gas consumption. The parameter extraction module is used to obtain the pipeline topology from the gas supply side to the nodes where each of the distributed gas-consuming devices is located, and to extract the corresponding lag time parameters based on the pipeline topology. The time compensation module is used to perform time axis offset processing on the predicted gas consumption sequence according to the lag time parameter to obtain the time-aligned gas consumption of each node. The instruction generation module is used to summarize the time-aligned air consumption of each node to generate a total demand load curve, and generate frequency conversion adjustment instructions and start / stop scheduling instructions for the air compressor cluster based on the total demand load curve.

[0015] To address the aforementioned problems, the present invention also provides a device for optimizing the compressed air demand and air compressor cluster control in cement production, the device comprising: A memory and a processor, the memory being used to store computer-readable instructions, and the processor being used to execute the computer-readable instructions.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention predicts the future gas consumption sequence of distributed gas-consuming equipment and compensates for time axis offset by combining the lag time parameters extracted from the pipeline topology. This achieves precise time alignment between the gas consumption on the demand side and the response on the supply side, solving the problem of terminal air pressure fluctuation caused by pipeline transmission lag and air compressor response lag in traditional pressure-controlled air pumping. The air pressure fluctuation amplitude of terminal pneumatic equipment is reduced, effectively avoiding production failures such as ash cleaning pulse valve failure and abnormal operation of pneumatic actuators caused by insufficient air pressure, and ensuring continuous and stable operation of all aspects of cement production.

[0017] 2. Based on the time-aligned total demand load curve, this invention distinguishes between base load and peak load, and specifically schedules the start-up and shutdown of fixed-frequency air compressors and the speed of variable-frequency air compressors. At the same time, it combines real-time pressure deviation dynamic correction control commands to avoid the high-pressure ineffective operation caused by increasing the main pipe set pressure to cover up lag defects in traditional control. This improves the operating efficiency of the air compressor cluster, reduces the energy consumption per unit of compressed air production, significantly reduces energy waste, and is suitable for the distributed and multi-condition air demand of the cement industry. Long-term operation can significantly reduce enterprise production costs. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a method for optimizing compressed air demand and air compressor cluster control in cement production, provided in an embodiment of the present invention. Figure 2 This is a functional block diagram of a cement production compressed air demand and air compressor cluster control optimization system provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0020] This application provides a method for optimizing the control of compressed air demand and air compressor clusters in cement production. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0021] Reference Figure 1 The diagram shown is a flowchart illustrating a method for optimizing compressed air demand and air compressor cluster control in cement production, according to an embodiment of the present invention. In this embodiment, the method includes: In some embodiments, the operating status sequence of multiple distributed gas-consuming devices within a cement industry production area is obtained, and the unit gas consumption of each distributed gas-consuming device is retrieved; based on the operating status sequence and the unit gas consumption, the predicted gas consumption sequence of each distributed gas-consuming device within a future preset time period is calculated; the pipeline topology from the gas supply side to the node where each distributed gas-consuming device is located is obtained, and the corresponding lag time parameter is extracted based on the pipeline topology; the predicted gas consumption sequence is time-axis offset according to the lag time parameter to obtain the time-aligned gas consumption of each node; the time-aligned gas consumption of each node is summarized to generate a total demand load curve, and frequency conversion adjustment commands and start / stop scheduling commands for the air compressor cluster are generated based on the total demand load curve.

[0022] Specifically, the process involves reading the switching control matrix from the programmable logic controller (PLC); this matrix represents the predetermined execution sequence of the production system's commands to the underlying hardware; extracting the start / stop array of actions for distributed gas-consuming equipment from the switching control matrix as the operating state sequence; the distributed gas-consuming equipment can be a pulse bag filter controlled by a lower-level machine or a pneumatic switch valve; calling the equipment nameplate attributes pre-stored in the database and determining the nominal exhaust index recorded in the nameplate attributes as the unit gas consumption; and performing a time-domain multiplication and addition operation on the operating state sequence and the unit gas consumption to generate a predicted gas consumption sequence; this predicted gas consumption sequence reflects the distribution relationship of purely theoretical gas consumption over time, independent of spatial location. Retrieve vectorized 3D drawings stored in industrial configuration software; extract the node connectivity matrix and pipe diameter coordinates from the vectorized 3D drawings as the pipeline topology; calculate the physical distance from the air compressor station outlet to the endpoint of the distributed gas consumption equipment based on the pipeline topology; calculate the physical time delay difference of gas conduction based on the physical distance and empirical gas flow rate and determine it as the lag time parameter; the lag time parameter quantifies the inherent time cost required for gas to flow in the pipeline; A subtraction operation is performed on each gas consumption time point recorded in the predicted gas consumption sequence; the specific deduction amount of the subtraction operation is equal to the lag time parameter of the corresponding node; the deducted time point is used as a new timestamp to remap and generate time-aligned gas consumption; the time-aligned gas consumption represents the time point and gas supply amount that the gas supply station must start supplying gas in advance in order to meet the gas demand of the end at a specific time. The gas consumption of all nodes under the production area is vector-summed according to the same time base. Based on the summation result, a total demand load curve mapping time and gas consumption is fitted. The integral area and slope change rate of the total demand load curve in the next control cycle are extracted. The integral area represents the required total gas supply. The slope change rate reflects the drastic degree of change in gas supply demand. Based on the integral area and slope change rate, the corresponding frequency conversion adjustment command and start-stop scheduling command are output from the table. The frequency conversion adjustment command is used to control the operating frequency of the frequency conversion motor. The start-stop scheduling command is used to control the contactor engagement action of the fixed frequency motor.

[0023] This solution abandons the traditional, reactive approach of replenishing gas only after pressure drops. By analyzing the control sequence of the front-end gas-consuming equipment and introducing a time lag parameter based on the pipeline topology for forward compensation of the time axis, the system can accurately predict the instantaneous demand at the current air compressor outlet due to pipeline delays. The air compressor cluster can receive frequency conversion adjustment commands in advance to accelerate gas production before the actual pressure drop, thus perfectly resolving the physical delay pain point caused by long-distance pipelines. This ensures that the gas supply curve and the actual gas consumption curve are highly consistent in the time dimension, completely eliminating the energy waste caused by excessive work done to maintain pressure.

[0024] In some embodiments, an industrial control programmable logic controller is accessed; the action triggering cycle for each of the distributed gas-consuming devices in the industrial control programmable logic controller is read; and time discretization mapping is performed on the action triggering cycle within the future preset time period to generate the operating state sequence composed of a high-level indicator on state and a low-level indicator off state.

[0025] Specifically, a standard industrial communication protocol connection is established with the lower-level control cabinet; this connection can be either Modbus TCP or OPCUA. The system listens for and reads the action trigger cycle parameters written in the memory address; these parameters characterize the periodic operating pattern of the automation system configuration. The system divides the future preset time period into fixed steps of milliseconds or seconds. Based on the action trigger cycle, the system determines the activation attribute of the distributed gas-consuming equipment within each step interval; the activated attribute is assigned a high level; the inactive attribute is assigned a low level; and the assignment results of all steps are concatenated in chronological order to generate a sequence of operating states. By digitally slicing and mapping the action trigger cycle, the black-box production business logic is transformed into a computer-readable digital level matrix, providing a solid and highly granular data foundation for subsequent high-frequency dynamic load calculations.

[0026] In some embodiments, the high-level duration in the operating state sequence is identified; the high-level duration is multiplied by the unit gas consumption to obtain the gas consumption for a single action; the gas consumption for a single action is concatenated into an array according to its corresponding trigger time in the operating state sequence to generate the predicted gas consumption sequence.

[0027] Specifically, the process involves traversing the data bits of the operating state sequence; retrieving the span value between the start and end bits of the high-level state; converting the span value into physical time units to determine the high-level duration; the high-level duration characterizes the absolute duration the solenoid valve is in the open / connected state; reading the flow rate scalar from the unit gas consumption; multiplying the flow rate scalar by the high-level duration to output the gas consumption for a single action; the gas consumption for a single action quantifies the absolute volume of air extracted from the pipeline network for a single process action; extracting the time scale corresponding to the start bit of the high-level state to determine the corresponding trigger time; placing each gas consumption for a single action on the corresponding trigger time coordinate in a coordinate system with time as the horizontal axis and concatenating them into an array; and outputting the predicted gas consumption sequence after array concatenation. This step transforms a pure level signal into a volumetric flow demand with physical meaning, and through a rigorous multiplication and concatenation process, objectively presents the complete picture of the independent load fluctuation of a single point device.

[0028] In some embodiments, the physical wiring data in the pipeline network topology is parsed; the total pipeline length from the output end of the three-way valve on the gas supply side to the node where the target distributed gas consumption device is located is calculated based on the physical wiring data; a reference gas flow velocity is obtained within the pipeline network topology; and the total pipeline length is divided by the reference gas flow velocity to obtain the lag time parameter corresponding to the target distributed gas consumption device.

[0029] Specifically, the process involves parsing the pipeline topology file; extracting the line segment node vector sequence from the file as physical wiring data; the physical wiring data reflecting the three-dimensional laying direction of the pipeline; accumulating the length scalar of each pipe section segment by segment along the line segment node vector sequence; accumulating until the node where the target distributed gas consumption equipment is located, and outputting the total pipeline length; reading the parameters of the flow meters and pressure transmitters configured in the main pipeline; calculating the reference gas velocity based on empirical formulas and historical cross-sectional flow rates; the reference gas velocity characterizing the average displacement velocity of the compressed medium in the pipe cavity under the current operating conditions; performing a division operation between length and velocity; establishing the calculated quotient value as the lag time parameter; and accurately capturing the spatial transmission delay variable in gas dynamics through mathematical modeling of the spatial pipeline, achieving a precise dimensionality reduction conversion from spatial distance to time offset.

[0030] In some embodiments, the timestamps corresponding to each gas consumption value in the predicted gas consumption sequence are read; the lag time parameter is subtracted from the timestamps to obtain the compensated timestamps; the original timestamps are replaced with the compensated timestamps, and the sequence after rearranging the timestamps is determined as the time-aligned gas consumption.

[0031] Specifically, elements representing the time dimension are extracted from the two-dimensional array of the predicted gas consumption sequence as timestamps; the timestamps record the actual time when the terminal equipment consumes gas; a subtraction operation is performed on the timestamps; the subtrahend of the subtraction operation is locked to the lag time parameter calculated above; the time node after subtraction is defined as the compensation timestamp; the compensation timestamp refers to the advance time when the air compressor station must issue gas production action in order to resist pipeline delay; the original timestamps in the array are removed and the compensation timestamps are written for data alignment; the gas consumption values ​​are bubble sorted or quick sorted according to the order of the compensation timestamps; the sorting result is output as the time-aligned gas consumption; this step is the core action to resolve the pain point of pipeline delay. Through an extremely objective time axis translation operation, the interference caused by physical space distance is smoothed out in the control data domain, ensuring that the time of issuing the gas supply control command and the actual time of gas arrival at the terminal are absolutely matched.

[0032] In some embodiments, under the same compensation timestamp, the time-aligned gas consumption of each node is numerically accumulated to generate the total demand load curve in the continuous time domain; the base load range and peak load range in the total demand load curve are extracted; for the base load range, the number of fixed-frequency air compressors in operation is matched to generate the corresponding start-stop scheduling command; for the peak load range, the compensation differential flow is calculated, and the variable frequency adjustment command for the variable frequency air compressor is generated based on the compensation differential flow.

[0033] Specifically, a global adder based on timestamp alignment is established; the gas consumption demand of all distributed nodes in the plant at the same moment is scalarly summed; the summation results of adjacent moments form a continuous envelope, which is the total demand load curve; the average lower limit of the amplitude of the total demand load curve is extracted using a sliding window algorithm to determine the base load range; the base load range represents the basic bottom-line gas consumption that is continuously present and fluctuates smoothly throughout the plant; the quotient of the base load range value divided by the rated discharge capacity of a single fixed-frequency air compressor is calculated and rounded up; based on the rounding result, the number of machines that need to be continuously powered is configured to generate start-stop scheduling instructions; and deviations from the base load range in the total demand load curve are extracted. The waveform data of the sudden change in load is used as the peak load range; the peak load range reflects the surge in transient demand caused by the superposition of high-frequency actions such as pulse dust removal; the compensation differential flow is obtained by subtracting the exhaust volume already covered by the fixed-frequency air compressor from the peak transient value; the target operating Hertz number is calculated by mapping the compensation differential flow with the V / F (voltage / frequency) control curve of the variable-frequency air compressor; the target operating Hertz number is issued as the variable frequency adjustment command; through strict separation logic of base quantity and variable quantity, the fixed-frequency unit bears a stable base load to maintain the highest volumetric efficiency, and the variable-frequency unit accurately shaving peaks and filling valleys through high-frequency adjustment, thereby achieving the ultimate energy efficiency ratio in the multi-machine cooperative state.

[0034] In some embodiments, after generating frequency conversion control commands and start / stop scheduling commands for the air compressor cluster based on the total demand load curve, the method further includes: collecting real-time air pressure values ​​at the main gas supply pipe; calculating a first deviation between the real-time air pressure value and a preset target air pressure range; when the first deviation exceeds a fault tolerance threshold, generating an auxiliary correction coefficient based on a proportional-integral-derivative control algorithm; and superimposing the auxiliary correction coefficient onto the control quantity of the frequency conversion control command for dynamic gain adjustment.

[0035] Specifically, the pressure transmitter current signal on the main output busbar is read through a hard-wired analog signal channel; the current signal is converted into standard physical units to determine the real-time air pressure value; the real-time air pressure value is compared with the preset target air pressure range set by the process engineer, and the difference is calculated; the absolute value of the output difference is used as the first deviation; the first deviation represents the closed-loop deviation of the feedforward prediction model caused by equipment aging and leakage or model calculation errors; it is determined that the first deviation is greater than the system's calibrated fault tolerance threshold; the fault tolerance threshold limits the acceptable reasonable pressure fluctuation boundary; the PID (proportional-integral-derivative) control operation module is triggered; a compensation factor is calculated as an auxiliary correction coefficient based on the current magnitude, cumulative amount, and trend of the error; the auxiliary correction coefficient is mixed into the original output frequency of the frequency converter regulation command using multiplication or addition logic; the control frequency is updated for dynamic gain adjustment. This step, as the "safety bottom line" of feedforward control, by introducing traditional pressure closed-loop feedback and setting a fault tolerance threshold, retains the high agility of feedforward reconstruction and compensates for unforeseen pipeline leakage attenuation through the auxiliary correction coefficient, ensuring the absolute robust operation of the system.

[0036] In some embodiments, before obtaining the operating status sequence of multiple distributed gas-consuming devices within the cement industry production area, the method further includes: reading the operating parameters of each air compressor in the air compressor cluster and establishing a device capacity matrix including rated exhaust volume, frequency conversion response time, and start-up preheating time; generating frequency conversion adjustment commands and start-up / stop scheduling commands for the air compressor cluster based on the total demand load curve includes: matching and solving the total demand load curve with the device capacity matrix, such that the advance issuance time of the frequency conversion adjustment command is greater than or equal to the frequency conversion response time.

[0037] Specifically, the underlying driver of the air compressor is accessed via the industrial bus; the mechanical and electrical delay parameters of each machine are read and recorded; the physical time required for the inverter to receive a given frequency and for the motor spindle to reach the target speed is extracted and determined as the inverter response time; the time required for the chiller to start up from power-on and for the star-delta switching to complete the establishment of pressure building and exhaust state is extracted and determined as the start-up preheating time; the above inherent parameters are integrated to construct an equipment capability matrix; the equipment capability matrix objectively outlines the hysteresis characteristics of the air supply hardware itself; constraints are introduced when generating inverter adjustment commands; the constraints force that the command issuance time point must be further backdated on the basis of the compensated timestamp; the backdated time span is not less than the inverter response time; this embodiment not only makes up for the delay of pipeline spatial transmission, but also fills the delay black hole of the electromechanical equipment's own electrical response, decouples and cancels multiple hysteresis factors from the global hardware and software, so that the on-demand air supply can truly be seamless.

[0038] like Figure 2The diagram shown is a functional block diagram of a cement production compressed air demand and air compressor cluster control optimization system provided in an embodiment of the present invention.

[0039] The cement production compressed air demand and air compressor cluster control optimization system 1 described in this invention can be installed in an electronic device. Depending on the functions implemented, the cement production compressed air demand and air compressor cluster control optimization system may include a data acquisition module, a parameter extraction module, a time compensation module, and an instruction generation module. The modules described in this invention can also be called units, referring to a series of computer program segments executed by a cement production compressed air demand and air compressor cluster control optimization device, capable of performing fixed functions, and stored in the memory of the electronic device.

[0040] In this embodiment, the functions of each module / unit are as follows: The data acquisition module is used to obtain the operating status sequence of multiple distributed gas-consuming devices in the cement industry production area and retrieve the unit gas consumption of each distributed gas-consuming device. The sequence prediction module is used to calculate the predicted gas consumption sequence of each distributed gas-consuming device in a future preset period based on the operating status sequence and unit gas consumption. The parameter extraction module is used to obtain the pipeline topology from the gas supply side to the nodes where each distributed gas consumption device is located, and extract the corresponding lag time parameters based on the pipeline topology. The time compensation module is used to perform time axis offset processing on the predicted gas consumption sequence based on the lag time parameter to obtain the time-aligned gas consumption of each node. The instruction generation module is used to summarize the time-aligned air consumption of each node to generate a total demand load curve, and generate frequency conversion adjustment instructions and start / stop scheduling instructions for the air compressor cluster based on the total demand load curve.

[0041] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0042] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0043] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0044] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0045] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing compressed air demand and air compressor cluster control in cement production, characterized in that, The method includes: S101, obtain the operating status sequence of multiple distributed gas-consuming devices in the cement industry production area, and retrieve the unit gas consumption corresponding to each of the distributed gas-consuming devices; S102, calculate the predicted gas consumption sequence for each of the distributed gas-consuming devices in a future preset time period based on the operating status sequence and the unit gas consumption; S103, obtain the pipeline topology from the gas supply side to the nodes where each of the distributed gas-consuming devices is located, and extract the corresponding lag time parameters based on the pipeline topology; S104, Perform time axis offset processing on the predicted gas consumption sequence according to the lag time parameter to obtain the time-aligned gas consumption of each node; S105, summarize the time-aligned air consumption of each node to generate a total demand load curve, and generate frequency conversion adjustment commands and start / stop scheduling commands for the air compressor cluster based on the total demand load curve.

2. The method according to claim 1, characterized in that, The acquisition of the operating status sequence of multiple distributed gas-using devices within the cement industry production area includes: Connect to industrial control programmable logic controllers; Read the action trigger cycle for each of the distributed gas-consuming devices from the industrial control programmable logic controller; Based on the action triggering cycle, time discretization mapping is performed within the future preset time period to generate the running state sequence consisting of a high-level indicator on state and a low-level indicator off state.

3. The method according to claim 1, characterized in that, The step of calculating the predicted gas consumption sequence for each of the distributed gas-consuming devices within a future preset time period based on the operating status sequence and the unit gas consumption includes: Identify the duration of the high level in the operating state sequence; The gas consumption per action is calculated by multiplying the high-level duration by the unit gas consumption. The gas consumption of a single action is concatenated into an array according to its corresponding trigger time in the operating state sequence to generate the predicted gas consumption sequence.

4. The method according to claim 1, characterized in that, The step of obtaining the pipeline topology from the gas supply side to the nodes where each of the distributed gas-consuming devices is located, and extracting the corresponding lag time parameters based on the pipeline topology, includes: Parse the physical cabling data in the pipeline network topology; Calculate the total length of the pipeline from the output end of the three-way valve on the gas supply side to the node where the target distributed gas consumption equipment is located based on the physical wiring data; Obtain the reference gas velocity within the pipeline network topology; The total length of the pipeline is divided by the reference gas flow rate to obtain the lag time parameter corresponding to the target distributed gas consumption device.

5. The method according to claim 1, characterized in that, The step of performing time axis offset processing on the predicted gas consumption sequence based on the lag time parameter to obtain the time-aligned gas consumption at each node includes: Read the timestamp corresponding to each gas consumption value in the predicted gas consumption sequence; Subtract the lag time parameter from the timestamp to obtain the compensation timestamp; The original timestamp is replaced with the compensated timestamp, and the sequence after rearranging the timestamps is determined as the time-aligned gas consumption.

6. The method according to claim 1, characterized in that, The process of summarizing the time-aligned gas consumption of each node to generate a total demand load curve, and generating variable frequency control commands and start / stop scheduling commands for the air compressor cluster based on the total demand load curve, includes: Under the same compensation timestamp, the time-aligned gas consumption of each node is numerically accumulated to generate the total demand load curve in the continuous time domain; Extract the base load range and peak load range from the total demand load curve; For the aforementioned basic load range, the number of fixed-frequency air compressors in operation is matched, and the corresponding start-stop scheduling command is generated. For the peak load range, calculate the compensation differential flow rate, and generate the frequency conversion adjustment command for the frequency converter air compressor based on the compensation differential flow rate.

7. The method according to claim 6, characterized in that, After generating variable frequency control commands and start / stop scheduling commands for the air compressor cluster based on the total demand load curve, the method further includes: Collect the real-time gas pressure value at the main gas supply pipe; Calculate the first deviation between the real-time air pressure value and the preset target air pressure range; When the first deviation exceeds the fault tolerance threshold, an auxiliary correction coefficient is generated based on the proportional-integral-derivative control algorithm. The auxiliary correction coefficient is superimposed on the control quantity of the frequency conversion adjustment command for dynamic gain adjustment.

8. The method according to claim 1, characterized in that, Before obtaining the operating status sequence of multiple distributed gas-consuming devices within the cement industry production area, the method further includes: The operating parameters of each air compressor in the air compressor cluster are read to establish a device capability matrix that includes rated exhaust volume, frequency conversion response time and start-up preheating time. The step of generating variable frequency control commands and start / stop scheduling commands for the air compressor cluster based on the total demand load curve includes: matching and solving the total demand load curve with the equipment capacity matrix, such that the advance issuance time of the variable frequency control command is greater than or equal to the variable frequency response time.

9. A system for optimizing the compressed air demand and air compressor cluster control in cement production, characterized in that, The system, used to implement the method for optimizing compressed air demand and air compressor cluster control in cement production as described in claim 1, comprises: The data acquisition module is used to acquire the operating status sequence of multiple distributed gas-consuming devices in the cement industry production area, and retrieve the unit gas consumption of each of the distributed gas-consuming devices. The sequence prediction module is used to calculate the predicted gas consumption sequence of each of the distributed gas-consuming devices in a future preset time period based on the operating status sequence and the unit gas consumption. The parameter extraction module is used to obtain the pipeline topology from the gas supply side to the nodes where each of the distributed gas-consuming devices is located, and to extract the corresponding lag time parameters based on the pipeline topology. The time compensation module is used to perform time axis offset processing on the predicted gas consumption sequence according to the lag time parameter to obtain the time-aligned gas consumption of each node. The instruction generation module is used to summarize the time-aligned air consumption of each node to generate a total demand load curve, and generate frequency conversion adjustment instructions and start / stop scheduling instructions for the air compressor cluster based on the total demand load curve.

10. A device for optimizing the compressed air demand and air compressor cluster control in cement production, characterized in that, include: A memory and a processor, the memory being used to store computer-readable instructions and the processor being used to execute the computer-readable instructions.

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