Double-waste intelligent cooperative processing system based on industrial interconnection

Abstract

The invention belongs to the technical field of waste gas and waste water treatment, and particularly relates to a double-waste intelligent cooperative treatment system based on industrial interconnection, which comprises a waste gas treatment module, a waste water treatment module, a sensing layer data acquisition module, a treatment layer and a management and control layer, and a monitoring-collaboration-processing-management and control integrated solution is formed. Real-time monitoring, intelligent regulation and control and remote management of the waste gas and wastewater treatment process are achieved through the Internet of Things technology, the waste gas and wastewater treatment efficiency and quality are improved, the operation cost is reduced, and intelligence and high efficiency of environment-friendly treatment are achieved. Advanced treatment technologies such as activated carbon and a reverse osmosis membrane are adopted, and the Internet of Things technology is combined, so that the system can adapt to waste gas and waste water treatment requirements under different working conditions, and digitization of waste gas and waste water treatment is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of waste gas and wastewater treatment, and particularly relates to a dual-waste intelligent collaborative treatment system based on industrial interconnection. Background Art

[0002] With the acceleration of the industrialization process, the emissions of waste gas and wastewater have increased exponentially, causing continuous and multi-dimensional severe damage to the ecological environment. In the traditional governance system, the VOCs waste gas and wastewater treatment systems have been operating independently for a long time, and the problems of resource misallocation and waste are prominent. The lag of the regulation mechanism has become the core obstacle to improving the governance efficiency. Existing systems generally rely on manual inspections and empirical judgments, making it difficult to cope with the high-frequency dynamic changes in pollutant concentrations. At the same time, the parameter adjustment of different treatment units lacks linkage, and the insufficient depth of data value mining further exacerbates the governance dilemma. Although a large number of sensors are deployed in the traditional monitoring system, the phenomenon of data islands is serious: the waste gas monitoring parameters and wastewater treatment parameters belong to different databases, and there is a lack of correlation analysis between the equipment operation data and environmental parameters.

[0003] In terms of technical bottlenecks, there are three major structural defects in the existing process system: First, the lack of a multi-medium collaborative mechanism leads to the breakage of the energy and material cycles, and potential resources such as the heat energy generated by waste gas treatment and the biogas generated by wastewater treatment are not effectively utilized; Second, the application of the Internet of Things stays at the data collection level, lacking intelligent decision-making capabilities based on deep learning. Third, the method of compliance determination is single, only using the concentration at the emission outlet as the assessment index, ignoring the impact of parameter fluctuations during the treatment process on the final result. These technical shortcomings seriously restrict the refined and intelligent development of environmental governance and urgently require systematic innovation and breakthroughs. Summary of the Invention

[0004] The present invention provides a dual-waste intelligent collaborative treatment system based on industrial interconnection, which can solve the problems pointed out in the background art.

[0005] A dual-waste intelligent collaborative treatment system based on industrial interconnection includes a waste gas treatment module, a wastewater treatment module, a perception layer data acquisition module, a processing layer, and a management and control layer; The perception layer data acquisition module is used to obtain the component concentration, working condition temperature, pressure, and air volume data in VOCs in real time and to collect the water quality index, reaction tank liquid level, and biochemical reaction potential in real time; The processing layer includes a cross-media collaborative management and control module. The cross-media collaborative management and control module combines the perception layer data acquisition module to collect various heterogeneous data of the waste gas treatment module in real time through distributed edge computing nodes, and establishes a pollutant cross-media migration model to realize the dynamic matching of the waste gas adsorption cycle and the wastewater aeration intensity; thereby calculating the compliance situation of the waste gas and wastewater, and the calculation formula is as follows: , In the above formula: Pollutant concentration and standard-related parameters: C i represents the real-time monitored concentration of the i-th pollutant (covering waste gas and waste water). The unit of waste gas is (mg / m³), and the unit of waste water is (mg / L), which intuitively reflects the actual content of various pollutants in the current treatment unit; S i is the national regulated emission standard concentration corresponding to the i-th pollutant, and the unit is the same as that of C i which is the core benchmark for measuring whether the pollutant emission complies with the standard. The ratio of the two reflects the degree to which the actual pollutant concentration deviates from the standard; Removal efficiency-related parameters: E i is the theoretical removal efficiency of the i-th pollutant in this treatment unit, presented in percentage form, determined based on the system design principle and ideal working conditions, and characterizing the ideal treatment capacity of the system for pollutants; e i is the removal efficiency of the i-th pollutant under the current actual operating conditions, also expressed in percentage, reflecting the treatment effect during the actual operation of the system; Equipment operating parameters: For waste gas treatment, T represents the equipment operating temperature (unit: °C), T0 is the optimal operating temperature of the equipment, P is the equipment operating pressure (unit: kPa), P0 is the optimal operating pressure. For waste water treatment, pH is the waste water acidity and alkalinity index, pH0 is the optimal acidity and alkalinity, V represents the waste water treatment equipment flow rate (unit: (m³ / h)), and V0 is the optimal flow rate.

[0006] Weight coefficients: K1 to K6 are weight coefficients, and satisfy K 1+ K 2+ K 3+ K 4+ K 5+ K6 = 1. By adjusting these coefficients, according to different working conditions and industry characteristics, the importance of each factor in the determination index can be flexibly changed; When the calculated I ≤ 1, it is determined that the waste gas and waste water treatment meet the standards; when I > 1, it is determined that the treatment does not meet the standards.

[0007] Preferably, the waste gas treatment module includes a pretreatment unit, an adsorption and purification unit, and a catalytic combustion unit connected in sequence; The pretreatment unit preliminarily filters the waste gas, the adsorption and purification unit adsorbs the pollutants in the waste gas, and the catalytic combustion unit combusts and decomposes the pollutants in the waste gas.

[0008] Preferably, the waste water treatment module includes a grille filtration unit, an adsorption filtration unit, and a reverse osmosis membrane treatment unit arranged in sequence; The grid filtration unit includes an automatic backwashing grid, a bag filter, and a security filter arranged in sequence to remove suspended solids step by step and intercept solid impurities of corresponding sizes in the wastewater; The adsorption filtration unit is used for the adsorption filtration of wastewater.

[0009] Preferably, the control layer includes an equipment health management module, a multi-level alarm module, an Internet of Things dual-purity intelligent control platform, and a mobile application. The equipment health management module and the multi-level alarm module are respectively connected to the cross-media collaborative control module, and the Internet of Things dual-purity intelligent control platform and the mobile application are respectively connected to the multi-level alarm module.

[0010] Preferably, the data acquisition module of the perception layer includes a gas chromatograph sensor, an infrared gas detector, an integrated temperature, pressure, and flow transmitter, and a pressure sensor located in the waste gas treatment module to obtain the component concentration, operating temperature, pressure, and air volume data in VOCs in real time; It also includes a COD on-line monitor, an ammonia nitrogen sensor, a pH electrode, and a dissolved oxygen probe located in the wastewater treatment module to collect water quality indicators, reaction tank liquid level, and biochemical reaction potential in real time.

[0011] Beneficial effects: The present invention provides a dual-waste intelligent collaborative treatment system based on industrial Internet, having the following beneficial effects: 1. Innovation in the multi-media collaborative treatment architecture; constructing an integrated waste gas and wastewater treatment system, realizing the efficient coupling of energy and matter through cross-media resource recycling technology, and breaking through the traditional single-media independent operation mode; 2. Deep purification by advanced processes. The waste gas treatment adopts a combined process of "Venturi spray + honeycomb activated carbon adsorption + catalytic combustion", with a particulate matter removal rate exceeding 95% and a VOCs purification rate ≥ 99%; the wastewater treatment adopts multi-stage treatment of "grid filtration + activated carbon adsorption + reverse osmosis membrane" to effectively remove suspended solids, organic matter, and dissolved impurities, realizing the deep purification of dual wastes; 3. Internet of Things intelligent control system. Constructing a three-layer architecture of "perception - network - application": deploying high-precision sensors in the perception layer to collect waste gas temperature, pressure, humidity, wastewater COD / pH data at each point, and equipment status parameters in real time; the network layer realizes data synchronization and equipment control through a 5G private network and a PROFINET bus; the application layer develops a reinforcement learning algorithm for dynamic regulation, combines an LSTM early warning model and a genetic algorithm to optimize energy efficiency, constructs a closed loop of "monitoring - analysis - decision - execution", and realizes parameter self-adaptive optimization and preventive maintenance; 4. Innovatively propose a multi-dimensional dynamic compliance judgment model, breaking through the limitations of a single concentration indicator, integrating the three dimensions of pollutant concentration ratio, removal efficiency deviation, and equipment operating parameter deviation, and dynamically calculating the judgment index through an adaptive weight matrix; 5. It can greatly improve the efficiency of abnormal handling, reduce the frequency of manual inspections, and improve operation and maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is a logical schematic diagram of the present invention, Figure 2 This is a schematic diagram of waste gas and wastewater treatment according to the present invention. Description of reference numerals: Numbers in the figure: exhaust gas treatment module 1, pretreatment unit 11, adsorption purification unit 12, catalytic combustion unit 13, wastewater treatment module 2, grille filtration unit 21, activated carbon adsorption filtration unit 22, reverse osmosis membrane treatment unit 23, perception layer data acquisition module 3, gas chromatograph sensor 31, infrared gas detector 32, temperature and pressure flow integrated transmitter 33, pressure sensor 34, COD online monitor 35, ammonia nitrogen sensor 36, pH electrode 37, dissolved oxygen probe 38, treatment layer 4; cross-media collaborative management and control module 41; management and control layer 5; equipment health management module 51; multi-level alarm module 52; IoT dual-clean intelligent control platform 53; mobile application 54. DETAILED DESCRIPTION

[0013] A specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific implementation.

[0014] Example: Figure 1 As shown, an embodiment of the present invention provides a dual-waste intelligent collaborative treatment system based on industrial interconnection, including a waste gas treatment module 1, a wastewater treatment module 2, a perception layer data acquisition module 3, a processing layer 4 and a management and control layer 5; In the industrial interconnected dual-waste intelligent collaborative treatment system, the waste gas treatment module 1 is composed of a pretreatment unit 11, an adsorption purification unit 12 and a catalytic combustion unit 13 connected in sequence. The pretreatment unit 11 adopts a Venturi spray tower with a built-in 5μm metal wire mesh demister and a pH sensor. In actual operation, the unit has a stable removal rate of more than 95% for particles larger than 5μm. The acidic waste gas is neutralized by NaOH solution, and the spray liquid is circulated to the wastewater regulating tank to effectively achieve acid-base neutralization and preliminarily filter large particle impurities in the waste gas. For example, in a certain chemical production scenario, the waste gas contains a large amount of acidic gas and dust. After being treated by the pretreatment unit 11, the acidic gas is effectively neutralized and the dust content is greatly reduced, which reduces the burden on subsequent treatment.

[0015] The adsorption and purification unit 12 adopts a three-bed parallel honeycomb activated carbon adsorption device, filled with modified honeycomb activated carbon, and equipped with a VOCs concentration gradient sensor inside. Through the pressure swing adsorption process, VOCs can be concentrated by 10 - 20 times. The saturated activated carbon is regenerated by humidifying with reverse osmosis concentrated water of wastewater, and it can efficiently adsorb organic pollutants in the waste gas. Taking the waste gas treatment of a certain painting workshop as an example, the adsorption efficiency of this unit for organic pollutants such as benzene series and esters reaches more than 98%, significantly reducing the pollutant concentration in the waste gas.

[0016] The catalytic combustion unit 13 adopts a horizontal RCO reactor, configured with an infrared temperature matrix sensor. In actual tests, the VOCs removal rate is stable ≥ 99%. The combustion tail gas transports heat to the wastewater MVR evaporation system through a plate heat exchanger to realize the recovery and utilization of energy. For example, in the waste gas treatment project of a certain pharmaceutical factory, the waste gas after being treated by the catalytic combustion unit 13 meets the discharge standards, and at the same time, the waste heat generated provides energy for the evaporation of wastewater, saving a large amount of energy costs.

[0017] The wastewater treatment module 2 is sequentially provided with a grid filtration unit 21, an activated carbon adsorption and filtration unit 22, and a reverse osmosis membrane treatment unit 23. The grid filtration unit 21 adopts a combination of an automatic backwashing grid → bag filter → security filter to gradually remove suspended solids and effectively intercept larger solid impurities in the wastewater. In actual applications, after being treated by this unit, the removal rate of suspended solids in the wastewater can reach more than 99%.

[0018] The activated carbon adsorption and filtration unit 22 adopts an upflow fixed bed, filled with shell activated carbon and equipped with an on-line TOC monitor inside. The activated carbon is used to remove the color, odor, organic matter, residual chlorine, etc. in the wastewater. After testing, the removal rate of organic matter in the wastewater by this unit reaches more than 80%, effectively improving the water quality of the wastewater.

[0019] The reverse osmosis membrane treatment unit 23 adopts a spiral wound RO membrane module. The concentrated water is reused for waste gas humidification after ozone catalytic oxidation. The reverse osmosis membrane is used to remove impurities such as dissolved salts, small molecule organic matter, bacteria, and viruses in the water to obtain purified water. In the wastewater treatment project of a certain electronics factory, after being treated by the reverse osmosis membrane treatment unit, the conductivity of the wastewater is significantly reduced, and the removal rate of dissolved salts reaches more than 95%, meeting the reuse standard of production water.

[0020] The data acquisition module 3 of the perception layer is built on the distributed architecture of the industrial Internet of Things, and realizes the real-time and accurate acquisition of the whole process parameters of waste gas and wastewater treatment through multi-type intelligent sensors. For the waste gas treatment module 1, gas chromatographic sensors 31, infrared gas detectors 32, integrated temperature, pressure and flow transmitters 33 and pressure sensors 34 are deployed to obtain the concentration of VOCs components (such as benzene series, esters, etc.), operating temperature, pressure and air volume data in real time. For the wastewater treatment system, COD online monitors 35, ammonia nitrogen sensors 36, pH electrodes 37 and dissolved oxygen probes 38 are configured to collect water quality indicators, reaction tank liquid level and biochemical reaction potential and other parameters in real time.

[0021] This module adopts an edge computing node to integrate a distributed sensor network, improves the data acquisition accuracy through anti-interference shielding design and signal conditioning circuits, uses a spatio-temporal alignment algorithm to eliminate asynchronous errors of multi-source data, and realizes high-speed and stable data transmission in combination with industrial communication protocols (OPC UA, Modbus TCP). At the same time, it has a built-in self-calibration algorithm and a fault diagnosis mechanism to periodically self-correct sensor zero drift, response delay, etc., to ensure the reliability and integrity of data acquisition. The multi-dimensional data collected is preprocessed and then connected to the system data center, providing real-time and accurate basic data support for the cross-media collaborative management and control module, realizing the full-element digital mapping of the waste gas and wastewater treatment process, effectively improving the system operation status monitoring ability and intelligent decision-making response speed, and laying a data foundation for subsequent collaborative optimization control; In practical applications, the accuracy and stability of the data acquisition of this module are verified by analyzing the monitoring data of multiple industrial scenarios. For example, in the waste gas and wastewater treatment project of a dyeing factory, the monitoring error of the VOCs concentration in the waste gas by the data acquisition module 3 of the perception layer is controlled within ±2%, and the monitoring error of the wastewater quality indicators is controlled within ±3%, providing reliable data guarantee for the stable operation of the system.

[0022] The processing layer 4 includes a cross-media collaborative management and control module 41. The cross-media collaborative management and control module 41 innovatively constructs a "gas-liquid-solid" multiphase coupling treatment system based on the industrial Internet of Things. Specifically, refer to Figure 2 the double-waste process flow chart, combined with the data acquisition module 3 of the perception layer, and through distributed edge computing nodes, 23 types of heterogeneous data such as the zeolite wheel adsorption efficiency of the waste gas treatment module 1, the RTO combustion chamber temperature, and the membrane flux of the wastewater module are collected in real time. An 8-class pollutant cross-media migration model such as benzene series is established by combining the improved material flow analysis method. This module develops a multi-constraint NSGA-II genetic algorithm, adopts an adaptive crossover rate and an elite retention strategy, takes energy consumption, chemical agent cost, VOCs and CO2 emissions as optimization objectives, constructs a four-dimensional Pareto solution set, and realizes the dynamic matching of the waste gas adsorption cycle and the wastewater aeration intensity; and calculates the compliance of the waste gas and wastewater based on this.

[0023] For example, in the actual application in a chemical industrial park, when the toluene concentration > 200 ppm, the system synchronously adjusts the PID parameters of the aeration fan and the opening of the catalytic combustion gas valve, predicts the mass transfer kinetic process through the digital twin model, and dynamically switches between the "adsorption - catalytic oxidation" and "incineration - evaporation" process modes. After switching the process mode, through actual monitoring, the toluene concentration in the waste gas rapidly drops below the emission standard. At the same time, the operating efficiency of the wastewater treatment system is improved, and the energy consumption is reduced by 15%. This module constructs an energy - material double - cycle system, uses the waste heat of the waste gas to drive the evaporation of wastewater, and intelligently regulates the makeup water volume of the scrubber and the evaporation desalination volume through an online turbidity meter and an ORP sensor, forming a closed - loop cycle of absorbent - biochemical sludge - catalyst. In a project of an electroplating factory, through this energy - material double - cycle system, the efficient utilization of waste heat in the waste gas is realized, the energy cost required for wastewater evaporation is reduced by 30%, and at the same time, the usage amount of chemical agents is reduced, and the treatment cost is lowered. This module combines with a multi - level alarm module to develop a graph neural network fault diagnosis model, analyzes the cross - system fault propagation path in real time, and triggers a redundant unit compensation mechanism in combination with fuzzy logic. During the actual operation process, when the system detects an abnormality in a certain processing unit, it can quickly locate the fault point and start the redundant unit for compensation to ensure the stable operation of the system. For example, in the waste gas and wastewater treatment system of a food processing factory, when a fault of membrane flux decline occurs in the reverse osmosis membrane treatment unit, the system promptly starts the standby membrane module. At the same time, by analyzing the fault propagation path, it is found that it is caused by the saturation of the activated carbon in the front - end activated carbon adsorption and filtration unit 22, and the activated carbon is replaced in time to avoid system shutdown.

[0024] When determining whether the waste gas and wastewater meet the standards, the formula , In the actual experiment, 5 representative pollutants (benzene, toluene, xylene in the waste gas, COD, ammonia nitrogen in the wastewater) were selected for monitoring and analysis. Set K1 = 0.3, K2 = 0.2, K3 = 0.15, K4 = 0.1, K5 = 0.15, K6 = 0.1. During the experiment, the optimal operating temperature T0 of the waste gas treatment equipment = 350 °C, the optimal operating pressure P0 = 100 kPa; the optimal pH value pH0 of the wastewater treatment equipment = 7, the optimal flow rate V0 = 50 m³ / h.

[0025] At a certain moment, the real - time concentration of benzene in the waste gas, C 苯 = 10 mg / m³, the national regulated emission standard concentration S 苯 = 50 mg / m³, the theoretical removal efficiency E 苯 of benzene by the system = 98%, the actual removal efficiency e 苯 = 96%; the real - time concentration of toluene C 甲苯= 20 mg / m³, emission standard concentration S 甲苯 = 80 mg / m³, theoretical removal efficiency E 甲苯 = 97%, actual removal efficiency e 甲苯 = 95%; real - time concentration C of xylene 二甲苯 = 15 mg / m³, emission standard concentration S 二甲苯 = 70 mg / m³, theoretical removal efficiency E 二甲苯 = 96%, actual removal efficiency e 二甲苯 = 94%. At the same time, the operating temperature T of the waste gas treatment equipment = 340 °C, and the operating pressure P = 98 kPa.

[0026] Real - time concentration C of COD in wastewater COD = 50 mg / L, national regulated emission standard concentration S COD = 100 mg / L, theoretical removal efficiency E of the system for COD COD = 90%, actual removal efficiency e COD = 85%; real - time concentration C of ammonia nitrogen 氨氮 = 10 mg / L, emission standard concentration S 氨氮 = 15 mg / L, theoretical removal efficiency E 氨氮 = 85%, actual removal efficiency e 氨氮 = 80%. The pH value of the wastewater treatment equipment is pH = 6.8, and the flow rate V = 48 m³ / h.

[0027] Substitute the above data into the formula for calculation: , Since I = 0.218 ≤ 1, it is determined that the waste gas and wastewater treatment meet the standards at this moment. Through verification with multiple experimental data, this formula can comprehensively and accurately evaluate the operation effect and compliance of the dual - waste treatment system.

[0028] In the described dual-waste intelligent collaborative processing system for industrial Internet, the equipment health management module is closely linked with the perception layer and the cross-media collaborative control module 41 to form a closed-loop management system of "monitoring - diagnosis - optimization". The module uses various types of sensors in the perception layer to collect real-time data such as the temperature of the RCO reactor in the waste gas treatment module 1, the pressure of the activated carbon adsorption device, the transmembrane pressure difference of the reverse osmosis membrane in the wastewater treatment module 2, and the vibration spectrum of the water pump. After preprocessing through edge computing, the fuzzy neural network is used to analyze the equipment operation parameters to quickly locate the root cause of anomalies. For example, when it is monitored that the temperature of the RCO reactor rises abnormally, the system automatically correlates data such as catalyst activity and waste gas concentration for diagnosis. If it is found that the decrease in catalyst activity due to too high waste gas concentration leads to the increase in temperature, the system will promptly adjust the waste gas inlet volume and regenerate the catalyst. Linked with the cross-media collaborative control module 41, when an anomaly occurs in the equipment, such as a decrease in the desalination rate of the reverse osmosis membrane, the module sends a signal to it to trigger dynamic adjustment of process parameters. The cross-media collaborative control module 41 re-matches the waste gas adsorption and wastewater treatment parameters according to the four-dimensional Pareto solution set, switches the process mode, and balances the system operation load. In terms of predictive maintenance, the module uses LSTM combined with the grey prediction model to estimate the life of key equipment components. For example, according to the prediction of the activated carbon regeneration cycle, the reverse osmosis concentrated wastewater system is linked to optimize the regeneration process. Through long-term monitoring and analysis of the activated carbon regeneration cycle, a relationship model between the activated carbon regeneration cycle and the operation parameters of the reverse osmosis concentrated wastewater system is established. When it is predicted that the activated carbon is about to reach saturation, the operation parameters of the reverse osmosis concentrated wastewater system are adjusted in advance to ensure that the activated carbon can be regenerated in time and the service life of the equipment is extended. At the same time, an equipment health file is established to record faults and collaborative optimization solutions, providing a basis for the continuous optimization of the system and ensuring the efficient and stable operation of the dual-waste treatment system.

[0029] The management and control layer 5 includes a device health management module 51, a multi-level alarm module 52, an Internet of Things dual-clean intelligent control platform 53, and a mobile application 54; the multi-level alarm module 52 deeply integrates the information resources of the data acquisition module 3 in the perception layer, the cross-media collaborative management and control module 41, and the device health management module 51 to build a three-dimensional alarm system of "hierarchical early warning - intelligent diagnosis - linkage disposal" to ensure the rapid response and effective handling of system anomalies. This module receives in real time the full-process data of waste gas and wastewater treatment collected by the data acquisition module 3 in the perception layer, including key indicators such as pollutant concentration and equipment operation parameters. Based on preset thresholds and dynamic algorithms, the alarm levels are divided into three levels: general warning, serious warning, and emergency warning. For example, when the data in the perception layer shows that the VOCs concentration in the waste gas exceeds 80% of the emission standard, a general warning is triggered; if the reverse osmosis membrane pressure in the wastewater treatment module 2 suddenly rises and the water production suddenly drops, a serious warning is issued; when the temperature of the RCO reactor gets out of control or there is a severe acid-base imbalance in the wastewater treatment system, an emergency warning is immediately triggered. In close cooperation with the cross-media collaborative management and control module 41, after the multi-level alarm module 52 triggers an alarm, it automatically calls the cross-media migration model and the multi-constraint optimization algorithm to conduct intelligent diagnosis and impact assessment of abnormal situations. For example, after the data of the TOC online monitor in the wastewater treatment module 2 exceeds the standard and triggers an alarm, the system quickly analyzes the cross-media migration path of pollutants, judges whether it has an impact on the waste gas treatment link, and predicts the fault diffusion trend through the digital twin model. At the same time, based on the four-dimensional Pareto solution set, an emergency strategy including process parameter adjustment and equipment load allocation is generated, and the cross-media collaborative management and control module 41 is linked to execute gas-liquid linkage control, such as adjusting the opening degrees of the aeration fan and the catalytic combustion gas valve, or switching to the standby treatment process. Linking with the device health management module 51, when the alarm information involves equipment failures, the multi-level alarm module 52 will immediately retrieve the device health status evaluation data and the results of the predictive maintenance model to accurately locate the root cause of the failure. For example, when it is detected that the wastewater treatment grille motor vibrates abnormally and triggers an alarm, the system combines the device health file and the LSTM prediction data to judge whether it is caused by mechanical wear or excessive load, and associates with the similar fault case library to push a targeted maintenance plan. At the same time, through the graph neural network, the propagation path of the fault between systems is analyzed, and the redundant device automatic compensation mechanism is triggered in combination with fuzzy logic to ensure the uninterrupted operation of the system.

[0030] In addition, the multi-level alarm module 52 also supports real-time push to multiple terminals. Through the Internet of Things dual-purification intelligent control platform 53 and the mobile application 54, the alarm information and disposal suggestions are synchronously sent to the operation and maintenance personnel in the form of sound and light prompts, text messages, pop-up windows, etc., to ensure that abnormal situations are promptly responded to and processed. In the actual application of a chemical enterprise, when the multi-level alarm module 52 detects that the temperature of the catalytic combustion unit 13 of the waste gas treatment module 1 rises abnormally, the alarm information is pushed to the mobile phone of the operation and maintenance personnel within 10 seconds, and detailed fault analysis and treatment suggestions are provided. The operation and maintenance personnel take timely measures to avoid equipment damage and production interruption.

[0031] The above are only several specific embodiments of the present invention disclosed. However, the embodiments of the present invention are not limited thereto, and any changes that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A dual-waste intelligent collaborative processing system based on industrial Internet of Things, characterized in that: It includes an exhaust gas treatment module (1), a wastewater treatment module (2), a perception layer data acquisition module (3), a processing layer (4), and a control layer (5); The perception layer data acquisition module (3) is used to obtain the component concentration, working condition temperature, pressure, and air volume data in VOCs in real time and to collect water quality indicators, reaction tank liquid level, and biochemical reaction potential in real time; , In the above formula: Pollutant concentration and standard-related parameter: C i represents the real-time monitored concentration of the i-th pollutant (covering waste gas and wastewater). The unit of waste gas is (mg / m³), and the unit of wastewater is (mg / L), which intuitively reflects the actual content of various pollutants in the current treatment unit; S i is the national regulated emission standard concentration corresponding to the i-th pollutant, and the unit is the same as that of C i which is the core benchmark for measuring whether the pollutant emission complies with the standard. The ratio of the two reflects the degree to which the actual concentration of the pollutant deviates from the standard; Removal efficiency related parameter: E i is the theoretical removal efficiency of the i-th pollutant in this treatment unit, presented in percentage form, determined based on the system design principle and ideal operating conditions, and characterizing the ideal treatment capacity of the system for pollutants; e i is the removal efficiency of the i-th pollutant under the current actual operating conditions, also expressed as a percentage, reflecting the treatment effect during the actual operation of the system; Equipment operation parameters: For exhaust gas treatment, T represents the equipment operation temperature (unit: °C), T0 is the optimal operation temperature of the equipment, P is the equipment operation pressure (unit: kPa), P0 is the optimal operation pressure. For wastewater treatment, pH is the wastewater acidity and alkalinity index, pH0 is the optimal acidity and alkalinity, V represents the wastewater treatment equipment flow rate (unit: (m³ / h)), and V0 is the optimal flow rate; Weighting coefficients: K1 to K6 are weighting coefficients, and satisfy K 1+ K 2+ K 3+ K 4+ K 5+ K6 = 1. By adjusting these coefficients, according to different working conditions and industry characteristics, the importance of each factor in the determination index can be flexibly changed; When it is calculated that I ≤ 1, it is determined that the exhaust gas and wastewater treatment meet the standards; when I > 1, it is determined that the treatment does not meet the standards.

2. The dual-waste intelligent collaborative processing system based on industrial Internet according to claim 1, wherein: The exhaust gas treatment module (1) includes a pretreatment unit (11), an adsorption purification unit (12), and a catalytic combustion unit (13) connected in sequence; The pretreatment unit (11) preliminarily filters the exhaust gas, the adsorption purification unit (12) adsorbs the pollutants in the exhaust gas, and the catalytic combustion unit (13) burns and decomposes the pollutants in the exhaust gas.

3. A dual-waste intelligent collaborative treatment system based on industrial Internet according to claim 1, characterized in that: The wastewater treatment module (2) includes a grid filtration unit (21), an adsorption filtration unit (22), and a reverse osmosis membrane treatment unit (23) arranged in sequence; The grid filtration unit (21) includes an automatic backwashing grid, a bag filter, and a security filter arranged in sequence to gradually remove suspended solids and intercept solid impurities of corresponding sizes in the wastewater; The adsorption filtration unit (22) is used for the adsorption filtration of wastewater.

4. A dual-waste intelligent collaborative treatment system based on industrial Internet according to claim 1, characterized in that: The control layer (5) includes an equipment health management module (51), a multi-level alarm module (52), an Internet of Things dual-clean intelligent control platform (53), and a mobile application (54). The equipment health management module (51) and the multi-level alarm module (52) are respectively connected to the cross-media collaborative control module (41), and the Internet of Things dual-clean intelligent control platform (53) and the mobile application (54) are respectively connected to the multi-level alarm module (52).

5. A dual-waste intelligent collaborative treatment system based on industrial Internet according to any one of claims 1-4, characterized in that: The perception layer data acquisition module (3) includes a gas chromatograph sensor (31), an infrared gas detector (32), a temperature, pressure, and flow integrated transmitter (33), and a pressure sensor (34) located in the exhaust gas treatment module (1) to obtain the component concentration, working condition temperature, pressure, and air volume data in VOCs in real time; It also includes a COD on-line monitor (35), an ammonia nitrogen sensor (36), a pH electrode (37), and a dissolved oxygen probe (38) located in the wastewater treatment module (2) to collect water quality indicators, the liquid level of the reaction tank, and the biochemical reaction potential in real time.

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