CN-121993862-A - Intelligent ventilation system for airborne pollutants and control method thereof

CN121993862ACN 121993862 ACN121993862 ACN 121993862ACN-121993862-A

Abstract

The invention discloses an intelligent ventilation system for airborne pollutants and a control method thereof, belonging to the technical field of industrial ventilation and dust removal; the system collects dust concentration, wind speed and wind pressure data of each local ventilation and dust removal table in real time through a sensing monitoring module, and intelligent control is realized through a remote monitoring module; the invention is characterized in that algorithms such as dust concentration prediction, online parameter self-learning, multi-agent cooperative optimization and digital twin simulation are integrated, the required air quantity of each branch is dynamically calculated, the rotational speeds of a branch air valve and a main fan are synchronously regulated through a PLC, the accurate on-demand distribution of the air quantity and the global energy saving of a system are realized, and the invention changes passive response into prospective cooperative control, thereby improving the dust removal efficiency, the system safety and the energy utilization rate.

Inventors

HOU CHENG
Tong Linquan
ZHANG ZHEN
ZHANG ZHONGBIN
JIA XIN
SUN KAI
WANG JIAYING
LIU JIANHUA

Assignees

国家卫生健康委职业安全卫生研究中心(国家卫生健康委煤炭工业职业医学研究中心)

Dates

Publication Date: 20260508
Application Date: 20260311

Claims (10)

1. An intelligent ventilation system for airborne pollutants, comprising: The main ventilation module comprises an exhaust fan (1) for exhausting air and a fan supply (2) for supplying air; at least two local ventilation and dust removal tables (3), wherein each local ventilation and dust removal table (3) is connected with a main ventilation module through an independent ventilation branch (4), and a branch air valve (5) with adjustable opening is arranged on each ventilation branch (4); the sensing monitoring module comprises a dust concentration sensor (6), a wind speed sensor (7) and a wind pressure sensor (8) which are arranged on each local ventilation dust removal table (3) or a corresponding ventilation branch (4) thereof; The electric control module comprises a PLC (programmable logic controller) 9, the PLC 9 is electrically connected with the sensing and monitoring module, the branch air valve 5, the exhaust fan 1 and the air supply fan 2, and a remote monitoring module 10 is in communication connection with the PLC 9, Wherein the remote monitoring module (10) is configured to: receiving and displaying dust concentration, wind speed and wind pressure data from the sensing monitoring module in real time; Calculating the target air quantity required by each local ventilation and dust removal table based on a preset dust sparse formula according to the real-time dust concentration at each local ventilation and dust removal table (3); based on the calculated target air quantity and real-time air speed/air pressure data, PID control instructions are generated, and the opening degrees of the corresponding branch air valves (5) and the rotating speeds of the exhaust fan (1) and the air supply fan (2) are synchronously adjusted through the PLC (9), so that the actual air quantity of each ventilation branch (4) dynamically approaches to the corresponding target air quantity.
2. The intelligent ventilation system for airborne pollutants according to claim 1, wherein the dust sparseness formula is a formula that is fitted according to experimental data and describes a functional relationship between dust concentration and the air volume required to dilute it to a safety standard concentration.
3. The intelligent ventilation system for airborne pollutants according to claim 1 or 2, characterized in that the remote monitoring module (10) is an upper computer equipped with configuration software.
4. The intelligent ventilation system for airborne pollutants according to claim 1, characterized in that the remote monitoring module (10) further comprises a dust concentration prediction unit, the dust concentration prediction unit adopts a long-term and short-term memory network model based on an attention mechanism for prediction, and the calculation process is as follows: the model input is a dust concentration sequence of historical T time steps: ; Model output is future Predicted concentration sequence for each time step: ; the calculation formula of the model is as follows: ; Wherein, the Is a model parameter and is obtained through historical data training, and the remote monitoring module (10) is used for predicting concentration according to the predicted concentration And calculating the target air quantity in advance and generating a control instruction.
5. The intelligent ventilation system for airborne pollutants according to claim 1, characterized in that the remote monitoring module (10) further comprises an adaptive air volume calculation engine that dynamically updates the correction coefficients of the dust sparseness formula using an online learning algorithm The update rule is as follows: ; Wherein, the In order to update the correction coefficient after the update, As the current correction coefficient is to be used, In order for the rate of learning to be high, In order to actually measure the air quantity, For the air volume predicted according to the current formula, For the real-time dust concentration, Is a safe standard concentration.
6. The intelligent ventilation system for airborne pollutants according to claim 1, wherein the remote monitoring module (10) performs global air distribution by adopting a multi-agent collaborative optimization algorithm based on game theory, and regards each ventilation branch (4) as an agent, and the utility function is as follows: ; Wherein, the Is the first The utility of the individual agents is that, The air quantity distributed to the intelligent body, For the basic required air quantity of the air conditioner, , , The system determines the optimal air quantity of each branch by iteratively solving Nash equilibrium points 。
7. The intelligent ventilation system for airborne pollutants according to claim 1, characterized in that the remote monitoring module (10) further comprises a digital twin simulation unit which builds a virtual system based on computational fluid dynamics models, the control equations of which include the Navier-Stokes equation and the dust transport equation: ; ; Wherein, the In order to achieve an air density of the air, As a velocity vector of the velocity vector, In the case of a pressure force, the pressure, As a function of the stress tensor, In the form of a volumetric force, The mass fraction of the dust is given as the mass fraction of the dust, In order for the diffusion coefficient to be the same, The remote monitoring module (10) previews the control strategy in the digital twin environment and only issues the optimal strategy to the physical system.
8. The intelligent ventilation system for airborne pollutants according to claim 1, characterized in that said remote monitoring module (10) further comprises a safety margin assessment module that calculates in real time the safety margin of each point dust concentration from the Lower Explosion Limit (LEL) : ; When (when) < When the system is switched to the safety priority mode automatically, the energy-saving target is ignored, the maximum air quantity is adopted for ventilation, and an early warning signal is sent out.
9. A control method for an intelligent ventilation system for airborne pollutants, characterized by: the control method comprises the following steps: s1, collecting dust concentration values at each local ventilation dust removal table (3) in real time through each dust sensor (6); s2, inputting a real-time dust concentration value at each local ventilation and dust removal table (3) into a preset dust sparse formula, and respectively calculating target air quantity required by each local ventilation and dust removal table; s3, acquiring real-time wind speed and/or wind pressure data of each ventilation branch (4); s4, aiming at each ventilation branch (4), taking deviation between target air quantity and real-time air quantity as input quantity of PID control, calculating adjustment quantity of a branch air valve (5), and generating a first control instruction; s5, calculating the rotation speed adjustment quantity of the exhaust fan (1) and the air supply fan (2) through a PID algorithm according to the target air quantity sum of all the ventilation branches (4) and the pressure parameter of the main ventilation pipeline, and generating a second control instruction; S6, the first control instruction and the second control instruction are issued to the PLC (9), and the PLC (9) executes synchronous adjustment on each branch air valve (5), the exhaust fan (1) and the air supply fan (2) to realize dynamic on-demand distribution of the total air quantity and the branch air quantity of the system.
10. The control method according to claim 9, characterized in that in step S2, the dust-thinning formula is: ; Wherein, the Is the first The target air quantity required by the local ventilation dust removal platform, For the real-time dust concentration of the local ventilation dust removal platform, In order to set the safety standard concentration of the liquid, Is a correction factor related to the characteristics of the local ventilation and dust removal platform.

Description

Intelligent ventilation system for airborne pollutants and control method thereof Technical Field The invention relates to the technical field of industrial ventilation and dust removal, in particular to an intelligent ventilation system for airborne pollutants and a control method thereof. Background In environments with dust pollution in laboratories, industrial production workshops and the like, the local ventilation and dust removal system is a key device for guaranteeing air quality and personnel health. The traditional ventilation and dust removal system mostly adopts a mode of fixed air quantity or manually adjusting a branch air valve to operate, and has the obvious defects of 1, high energy consumption, low control precision, incapability of responding to the change of dust concentration in real time, possibility of excessive air suction when the dust concentration is low, possibility of insufficient suction when the concentration is high, health and safety hazards, 3, low automation level, dependence on manual experience adjustment, response lag and difficulty in realizing air quantity balance among multiple branches. In the prior art, some automatic control systems introducing sensors exist, but most of the automatic control systems only realize alarming or simply starting and stopping of a fan, cannot accurately and dynamically calculate and distribute the optimal air quantity required by each pollution point according to the dust concentration, and perform closed-loop PID control of the fan and a valve in a cooperative manner so as to realize the unification of energy conservation and high-efficiency dust removal. Disclosure of Invention The invention aims to overcome the defects of the prior art and provides an intelligent ventilation system for airborne pollutants and a control method thereof. In order to achieve the above purpose, the present invention provides the following technical solutions: The application provides an intelligent ventilation system for airborne pollutants, comprising: the main ventilation module comprises an exhaust fan for exhausting air and a fan supply unit for supplying air; at least two local ventilation and dust removal tables, wherein each local ventilation and dust removal table is connected with a main ventilation module through an independent ventilation branch, and a branch air valve with adjustable opening is arranged on each ventilation branch; The sensing monitoring module comprises a dust concentration sensor, an air speed sensor and an air pressure sensor which are arranged on each local ventilation dust removal table or a corresponding ventilation branch thereof; The electric control module comprises a PLC controller, and the PLC controller is electrically connected with the sensing monitoring module, the branch air valve, the exhaust fan and the air supply fan; the remote monitoring module is in communication connection with the PLC; Wherein the remote monitoring module is configured to: receiving and displaying dust concentration, wind speed and wind pressure data from the sensing monitoring module in real time; Calculating the target air quantity required by each local ventilation and dust removal table based on a preset dust sparseness formula according to the real-time dust concentration at each local ventilation and dust removal table; Based on the calculated target air quantity and real-time air speed/air pressure data, PID control instructions are generated, and the opening degrees of the corresponding branch air valves and the rotating speeds of the exhaust fan and the air supply fan are synchronously adjusted through the PLC controller, so that the actual air quantity of each ventilation branch dynamically approaches to the corresponding target air quantity. Further, the dust sparse formula is a formula which is obtained by fitting according to experimental data and describes a functional relation between dust concentration and air quantity required by diluting the dust concentration to a safety standard concentration. Further, the remote monitoring module is an upper computer provided with configuration software. Further, the remote monitoring module further comprises a dust concentration prediction unit, the dust concentration prediction unit predicts by adopting a long-term and short-term memory network model based on an attention mechanism, and the calculation process is as follows: the model input is a dust concentration sequence of historical T time steps: ; Model output is future Predicted concentration sequence for each time step: ; the calculation formula of the model is as follows: Wherein, the Is model parameters and is obtained through historical data training, and the remote monitoring module is used for predicting concentration according to the predicted concentrationAnd calculating the target air quantity in advance and generating a control instruction. Further, the remote monitoring module further comprises a self-adaptive air