Epoxy asphalt mixture production construction carbon emission real-time monitoring and regulation method

Abstract

The invention relates to the technical field of low-carbon road building material monitoring and control, and discloses an epoxy asphalt mixture production construction carbon emission real-time monitoring and regulation method. The system comprises a sensor network module, a data processing and transmission module, a cloud intelligent analysis module, an automatic adjustment module and a human-computer interaction interface module. Multiple types of sensor nodes are arranged on a production line and a construction site, CO2 concentration, fuel consumption, temperature and other parameters are collected in real time, and the parameters are preprocessed by a data processing and transmission module and then uploaded to a cloud system for intelligent analysis and threshold judgment. The system can automatically adjust the combustion device, the heating equipment and the mixing power according to the real-time emission state, and dynamic control over carbon emission and energy consumption optimization are achieved. The system can generate a real-time monitoring curve and a historical report, has abnormal early warning and intelligent decision support functions, and realizes accurate monitoring and intelligent management of carbon emission of the epoxy asphalt mixture in the whole process from production to construction.

Description

Technical Field

[0001] This invention relates to the field of road construction material monitoring and control technology, specifically to a method for real-time monitoring and control of carbon emissions from the production and construction of epoxy asphalt mixtures. Background Technology

[0002] As a high-performance pavement material, epoxy asphalt mixtures require high-temperature processes during their production and construction, including asphalt heating, aggregate drying, mixture mixing, and paving and compaction. These processes, accompanied by the combustion of fossil fuels and the thermal reaction of materials, are key stages in the generation of carbon emissions (mainly CO2 and CO).

[0003] Currently, carbon emission management for this process mainly relies on traditional methods. Firstly, it depends on periodic manual testing and offline sampling, followed by analysis and calculation in the laboratory. This method suffers from significant time lag; usually, by the time the analysis results are available, the corresponding production batch has already been completed, and carbon emissions exceeding limits have already occurred, failing to provide real-time guidance for the production process. Secondly, it employs macroscopic estimation methods based on material balance and emission factors. While this method can assess overall emission levels, it struggles to reflect the instantaneous emission dynamics caused by fluctuations in operating conditions on the production line (such as changes in fuel quality and equipment status fluctuations), lacking sufficient accuracy to pinpoint specific emission anomalies.

[0004] The common flaw in these traditional models lies in their inherent nature as "post-event accounting" and "passive monitoring." The entire process lacks a real-time data sensing network covering the entire production and construction process, making it impossible to obtain accurate carbon emission change curves at the second or minute level. More importantly, after detecting or calculating abnormal emissions, existing technological systems lack the ability to intervene immediately in conjunction with production equipment. They cannot automatically and intelligently adjust key process parameters such as heating temperature and fuel ratios in the early stages of an emission exceeding the standard, thereby suppressing excessive emissions at the source.

[0005] Therefore, there is an urgent need in this field for a carbon emission management method that can be implemented throughout the entire process of epoxy asphalt mixture production and construction, enabling real-time sensing, intelligent early warning, and dynamic control. This would overcome the lag and passivity of existing technologies and achieve a fundamental shift from "post-event accounting" to "in-process control." Based on this, a method for real-time monitoring and dynamic control of carbon emissions during the production and construction of epoxy asphalt mixtures is proposed to address the aforementioned problems. Summary of the Invention

[0006] The purpose of this invention is to provide a method for real-time monitoring and dynamic control of carbon emissions during the production and construction of epoxy asphalt mixtures, in order to solve the problems of lag, insufficient accuracy, and inability to intervene in the production process in a timely manner in traditional carbon emission management methods, which lead to high risk of carbon emission exceeding standards and difficulty in environmental compliance.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0008] A method for real-time monitoring and dynamic control of carbon emissions during the production and construction of epoxy asphalt mixtures includes the following steps:

[0009] S1. Delineate the key carbon emission processes in the production and construction of epoxy asphalt mixtures; key carbon emission processes include aggregate drying and heating, epoxy asphalt mixture mixing, epoxy asphalt mixture transportation, and epoxy asphalt mixture paving and compaction.

[0010] S2. Install sensors in key carbon emission processes;

[0011] S3. Construct a carbon emission prediction model, embed the carbon emission prediction model into the cloud processing system, and input the data collected by the sensor into the cloud processing system to calculate and predict carbon emissions.

[0012] S4. Compare the output carbon emissions with the preset carbon emission threshold. When the carbon emissions exceed the carbon emission threshold, adjust the process parameters of the key carbon emission processes.

[0013] As a preferred embodiment, in S2, the sensor deployment method is specifically as follows:

[0014] In the aggregate drying and heating process, n carbon dioxide sensors and n carbon monoxide sensors are installed on the flue gas emission pipe of the drum-type dryer, where n≥1;

[0015] In the epoxy asphalt mixture mixing process, temperature and humidity sensors are installed in the mixing tank to monitor the thermal state during the mixing process.

[0016] In the transportation process of epoxy asphalt mixture, a temperature sensor is installed inside the mixture insulation tank to monitor the temperature change of the mixture during transportation.

[0017] During the paving and compaction of epoxy asphalt mixtures, carbon dioxide sensors, carbon monoxide sensors, and temperature and humidity sensors are installed on the paver and roller respectively to enable mobile environmental monitoring at the construction site.

[0018] As a preferred embodiment, in S3, the data collected by the sensor is digitally processed, including signal filtering, dimension conversion, and data encapsulation.

[0019] As a preferred option, in S4, the controlled process parameters include the temperature setpoint of the heating system of the asphalt mixing plant during the production stage, the flow rate or speed of the fuel supply pipeline, and the feed ratio of aggregate to asphalt.

[0020] Operating speed, equipment power, engine speed, start-up and stop times, and temperature control parameters for pavers and rollers during the paving and compaction stages, as well as the operating paths and coordination methods of construction equipment.

[0021] As a preferred option, in S4, the control strategy is further optimized through a closed-loop control mechanism, specifically as follows:

[0022] The sensors re-collect environmental parameters and emission data after regulation and feed them back to the cloud processing system. The cloud processing system compares the changes in data before and after regulation, evaluates the control effect, and further optimizes the regulation strategy.

[0023] A real-time monitoring and dynamic control system for carbon emissions during the production and construction of epoxy asphalt mixtures, the system comprising:

[0024] Sensor network modules are used to collect environmental parameters and emission data;

[0025] The data processing and transmission module is used for data preprocessing and transmission;

[0026] The cloud-based intelligent analysis module calculates and predicts carbon emissions through a cloud-based processing system and generates instructions for adjusting process parameters.

[0027] The adjustment module is executed to regulate the process parameters.

[0028] As a preferred option, the data processing and transmission module adopts an industrial IoT gateway, which supports 4G / 5G wireless communication protocols and can ensure the stability of data transmission in complex industrial environments.

[0029] As a preferred option, it also includes a human-machine interface module, providing web and mobile access interfaces for real-time display of full-process carbon emission data, historical trends, equipment status and all control command records, and automatically generating compliance reports.

[0030] As a preferred option, the sensor network module, data processing and transmission module, and execution and adjustment module are characterized by high temperature resistance, dust resistance, shock resistance, and electromagnetic interference resistance, and can adapt to the harsh working conditions of high temperature and high dust in asphalt mixing plants.

[0031] As a preferred option, the cloud processing system employs a hierarchical decision-making logic.

[0032] When the real-time carbon emission rate exceeds the preset first threshold, the system generates a level one alarm and provides control suggestions;

[0033] When the predicted future short-term emissions or rate exceed a higher preset second threshold, the system generates a level-two alarm and a mandatory control command to regulate the production process.

[0034] The present invention has the following beneficial effects:

[0035] This invention collects raw carbon emission data through a sensor network deployed on the production line and on-site, uploads it to a data acquisition unit, and then transmits it to a cloud processing system for intelligent analysis and decision-making. Finally, an automatic adjustment execution unit precisely controls the production equipment. Compared to traditional methods that rely on manual detection and post-event accounting, this invention enables managers to monitor the dynamics of carbon emissions across the entire production line in real time and accurately. It also allows for automatic and timely intervention and adjustments to the production process based on predicted trends, thereby preventing excessive emissions at the source, ensuring environmental compliance in the production process, and achieving energy conservation and consumption reduction through optimized control. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the overall system of the present invention. Detailed Implementation

[0037] The present invention will now be described in further detail with reference to the accompanying drawings and specific preferred embodiments.

[0038] In the description of this invention, it should be understood that the terms "left side," "right side," "upper part," "lower part," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. "First," "second," etc., do not indicate the importance of the components, and therefore should not be construed as a limitation of this invention. The specific dimensions used in this embodiment are only for illustrating the technical solution and do not limit the scope of protection of this invention.

[0039] like Figure 1 As shown, a method for real-time monitoring and dynamic control of carbon emissions during the production and construction of epoxy asphalt mixtures includes the following steps:

[0040] S1. Delineate the key carbon emission processes in the production and construction of epoxy asphalt mixtures; key carbon emission processes include aggregate drying and heating, epoxy asphalt mixture mixing, epoxy asphalt mixture transportation, and epoxy asphalt mixture paving and compaction.

[0041] In this embodiment, epoxy asphalt mixture is a thermosetting material, and asphalt mixing plants are widely used as the core complete set of equipment for producing epoxy asphalt mixture during its production process.

[0042] An asphalt mixing plant includes a cold aggregate supply system, a drying and heating system, and a mixing system. The cold aggregate supply system delivers aggregates according to the gradation design; the drying and heating system includes a drum-type drying cylinder, which dries and heats the aggregates using hot air generated by a burner; the mixing system includes a mixing tank, which mixes the aggregates.

[0043] The mixed epoxy asphalt mixture is stored in an asphalt insulation tank, loaded onto trucks and transported to the construction site for paving and compaction.

[0044] This embodiment identifies the key carbon emission processes in the production and construction of epoxy asphalt mixtures based on the characteristics of the process flow. These key carbon emission processes include aggregate drying and heating, epoxy asphalt mixture mixing, epoxy asphalt mixture transportation, and epoxy asphalt mixture paving and compaction.

[0045] S2. Install sensors in key carbon emission processes.

[0046] In this embodiment, sensors are deployed at each critical carbon emission stage of epoxy asphalt mixture production and construction to form a sensor network. This sensor network is characterized by high temperature resistance, shock resistance, dust resistance, water resistance, strong anti-interference capabilities, and stable data transmission, making it adaptable to the high-temperature environment of production and paving. The sensors include carbon dioxide sensors, carbon monoxide sensors, and temperature and humidity sensors, etc., and their specific deployment is as follows:

[0047] In the aggregate drying and heating process, n carbon dioxide sensors and carbon monoxide sensors are installed on the flue gas emission pipe of the drum-type dryer, where n≥1. If n=1, they are placed at the outlet of the flue gas emission pipe; if n=2, they are placed at the outlet and the inlet; if n≥3, one is placed at the outlet and one at the inlet, and the rest are evenly distributed along the pipe.

[0048] In the epoxy asphalt mixture mixing process, temperature and humidity sensors are installed inside the mixing tank to monitor the thermal conditions during the mixing process.

[0049] In the transportation process of epoxy asphalt mixture, a temperature sensor is installed inside the mixture insulation tank to monitor the temperature change of the mixture during transportation.

[0050] During the paving and compaction of epoxy asphalt mixtures, carbon dioxide sensors, carbon monoxide sensors, and temperature and humidity sensors are installed at the four corners of the paver and roller to enable mobile environmental monitoring at the construction site.

[0051] In this embodiment, a mobile environmental monitoring system was deployed at the construction site based on the project characteristics and construction organization design. The system consists of 12 monitoring terminals, which are installed on 3 pavers, 4 double-drum rollers, and 5 pneumatic tire rollers, respectively, providing comprehensive monitoring of the entire construction area as the equipment moves. Each monitoring terminal integrates multiple sensors, including carbon dioxide, carbon monoxide, and temperature and humidity sensors, and is also equipped with a high-precision GPS positioning module and a 5G communication module. The monitoring terminal's casing has an IP67 protection rating, providing shockproof, dustproof, and waterproof characteristics, making it suitable for the harsh environment of the construction site.

[0052] S3. Construct a carbon emission prediction model, embed the carbon emission prediction model into the cloud processing system, and input the data collected by the sensors into the cloud processing system to calculate and predict carbon emissions.

[0053] In this embodiment, the sensor is connected to the data processing and transmission module in the central control room via an industrial-grade shielded cable. This module employs an industrial IoT gateway device, featuring multi-channel signal input, data preprocessing, and protocol conversion capabilities. The data processing and transmission module digitizes the data collected by the sensor, including signal filtering, dimension conversion, and data encapsulation. The processed data is then transmitted to the cloud server via industrial Ethernet and a wireless communication network. To ensure data transmission reliability, the system employs a dual network backup mechanism, automatically switching to the backup network in the event of a primary network outage.

[0054] Data collected by the monitoring terminal is transmitted in real time to the cloud processing system via a wireless network deployed at the construction site. To adapt to the complex electromagnetic environment of the construction site, the communication system uses industrial-grade wireless equipment with strong anti-interference capabilities to ensure stable and reliable data transmission.

[0055] The cloud processing system is built on a cloud computing platform and adopts a distributed architecture design, mainly including the following functional modules:

[0056] Real-time database module: responsible for receiving and storing massive amounts of monitoring data from various monitoring devices;

[0057] Streaming computing module: processes continuously incoming data streams in real time and calculates key indicators including instantaneous emission rate and cumulative emissions;

[0058] Predictive Analysis Module: It has a built-in carbon emission prediction model based on machine learning algorithms to calculate carbon emissions and predict emission trends for future periods by analyzing historical data sequences;

[0059] Intelligent decision-making module: compares real-time monitoring data and prediction results with preset carbon emission thresholds, and generates corresponding control strategies based on the comparison results.

[0060] S4. Compare the output carbon emissions with the preset carbon emission threshold. When the carbon emissions exceed the carbon emission threshold, adjust the process of key carbon emission processes.

[0061] In actual operation, the cloud-based processing system achieves precise control of the production process through continuous monitoring and intelligent analysis. For example, when the predictive analysis module determines that emissions levels may exceed a set threshold in the future, the intelligent decision-making module generates corresponding adjustment instructions. These instructions are sent to the asphalt mixing plant's control system via a secure network channel. Upon receiving the instructions, the control system drives the corresponding actuators, including the temperature setpoint of the asphalt mixing plant's heating system during the production stage, the flow rate or speed of the fuel supply pipeline, and the aggregate-to-asphalt feed ratio; the operating speed of the pavers and rollers during the paving and compaction stages, the equipment operating power or engine speed, start-up and shutdown times, paving temperature and compaction temperature control parameters, as well as the operating paths and collaborative scheduling methods of the construction equipment. This reduces carbon emissions per unit of operating time while ensuring construction quality, achieving optimized adjustment of production process parameters.

[0062] To ensure effective control, the cloud-based processing system establishes a complete closed-loop control mechanism. Environmental parameters and emission data after control are re-collected via a sensor network and fed back to the cloud system. The system compares the changes in data before and after control, evaluates the control effect, and further optimizes the control strategy as needed. This closed-loop control mechanism ensures that the system can continuously and stably control emission levels within the target range.

[0063] After receiving the field data, the cloud-based processing system uses spatial information processing technology to fuse the monitoring data with the construction location information, generating a visualized environmental quality distribution map of the construction site. This map can intuitively display the carbon emission levels and environmental quality status of different construction areas and time periods.

[0064] The cloud-based processing system also establishes a construction environment early warning mechanism. Through real-time analysis of monitoring data, when an anomaly is detected in an environmental indicator in a certain area, the system immediately activates the early warning procedure and sends warning information to on-site management personnel via mobile terminals. The warning information includes the location of the abnormal area, the type of abnormal indicator, an assessment of the severity of the anomaly, and handling suggestions, providing timely decision support for on-site management personnel.

[0065] Table 1. Statistics on the operational performance of asphalt mixing plant systems (based on 30 days of operational data)

[0066] Evaluation indicators Before implementation After implementation Improvement range Abnormal emission handling efficiency Manual handling, average response time 15 minutes Automatic processing, average response time 5 seconds Efficiency increased by 99.4% Emission concentration stability Fluctuation range ±3.5% Fluctuation range ±1.2% Stability improved by 65.7% Overall emission levels Baseline emissions 100% Reduced by 18-22% Significant emission reduction effect System automation rate Completely manual monitoring 87% of abnormal events are handled automatically. The degree of automation has been significantly improved.

[0067] Table 2 Analysis of the Monitoring Results at the Construction Site (Based on Data from a 1-Week Construction Period)

[0068] Monitoring indicators Improvement Effect description Data source Frequency of regional concentration exceeding standards Reduce by 75% The average number of times exceeding the standard per day decreased from 8 to 2. Monitoring terminal statistics Local high concentration emissions 40% decrease Achieved through construction path optimization Comparative Analysis Report Environmental quality compliance rate Increased to 90% The proportion of time that meets environmental standards Environmental protection department testing data Management response efficiency Increase by 70% Improved efficiency from problem discovery to resolution Construction log statistics fuel consumption Reduce by 15% Achieve this by optimizing equipment operating status. Equipment operation records

[0069] In addition, the cloud-based processing system also provides historical data query and statistical analysis functions. Managers can query environmental monitoring data for any time period and any construction area via a web interface or mobile device. The system automatically generates statistical analysis reports, including total emission statistics, emission intensity analysis, and trend analysis. These analysis results provide crucial data support for optimizing construction processes and improving construction organization.

[0070] This application also provides a real-time monitoring and dynamic control system for carbon emissions during the production and construction of epoxy asphalt mixtures. The system includes a sensor network module, a data processing and transmission module, a cloud-based intelligent analysis module, an execution and control module, and a human-machine interface module. It automatically monitors and intelligently controls carbon emissions throughout the entire production line. The specific process is as follows: the sensor network is deployed at key stages such as asphalt heating, mixing, and paving to collect real-time data on carbon emission concentration and process parameters such as temperature and flow rate. The data is uploaded to the data processing and transmission module via a wireless network and further uploaded to the cloud processing system. The system integrates and performs big data analysis on the data, calculates the emission rate and cumulative amount in real time, and predicts trends. When the prediction results indicate that emissions are about to exceed the standard, the system automatically generates control commands and sends them to the execution and control module on the production line to dynamically adjust key parameters such as heating temperature and fuel rate in real time, thus forming an intelligent closed loop of "monitoring-analysis-decision-execution" to ensure that carbon emissions are controlled throughout the entire process.

[0071] The implementation of this embodiment achieves refined management and intelligent control of the environmental quality at construction sites, ensuring environmental compliance during construction and improving the scientific level of construction management. Practical application shows that after adopting this system, environmental violations at construction sites have decreased by 80%, and overall construction efficiency has increased by approximately 15% through optimized construction organization.

[0072] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all fall within the protection scope of the present invention.

Claims

1. A method for real-time monitoring and dynamic control of carbon emissions during the production and construction of epoxy asphalt mixtures, characterized in that, Includes the following steps: S1. Delineate the key carbon emission processes in the production and construction of epoxy asphalt mixtures; key carbon emission processes include aggregate drying and heating, epoxy asphalt mixture mixing, epoxy asphalt mixture transportation, and epoxy asphalt mixture paving and compaction. S2. Install sensors in key carbon emission processes; S3. Construct a carbon emission prediction model, embed the carbon emission prediction model into the cloud processing system, and input the data collected by the sensor into the cloud processing system to calculate and predict carbon emissions. S4. Compare the output carbon emissions with the preset carbon emission threshold. When the carbon emissions exceed the carbon emission threshold, adjust the process parameters of the key carbon emission processes.

2. The method for real-time monitoring and dynamic control of carbon emissions during the production and construction of epoxy asphalt mixtures according to claim 1, characterized in that, In S2, the specific sensor deployment method is as follows: In the aggregate drying and heating process, n carbon dioxide sensors and n carbon monoxide sensors are installed on the flue gas emission pipe of the drum-type dryer, where n≥1; In the epoxy asphalt mixture mixing process, temperature and humidity sensors are installed in the mixing tank to monitor the thermal state during the mixing process. In the transportation process of epoxy asphalt mixture, a temperature sensor is installed inside the mixture insulation tank to monitor the temperature change of the mixture during transportation. During the paving and compaction of epoxy asphalt mixtures, carbon dioxide sensors, carbon monoxide sensors, and temperature and humidity sensors are installed on the paver and roller respectively to enable mobile environmental monitoring at the construction site.

3. The method for real-time monitoring and dynamic control of carbon emissions during the production and construction of epoxy asphalt mixtures according to claim 1, characterized in that, In S3, the data collected by the sensor is digitally processed, including signal filtering, dimension conversion, and data encapsulation.

4. The method for real-time monitoring and dynamic control of carbon emissions during the production and construction of epoxy asphalt mixtures according to claim 1, characterized in that, In S4, the process parameters to be controlled include, Temperature setpoints of the heating system of the asphalt mixing plant during the production stage, flow rate or speed of the fuel supply pipeline, and feed ratio of aggregate to asphalt. Operating speed, equipment power, engine speed, start-up and stop times, and temperature control parameters for pavers and rollers during the paving and compaction stages, as well as the operating paths and coordination methods of construction equipment.

5. The method for real-time monitoring and dynamic control of carbon emissions during the production and construction of epoxy asphalt mixtures according to claim 1, characterized in that, In S4, the control strategy is further optimized through a closed-loop control mechanism, specifically as follows: The sensors re-collect environmental parameters and emission data after regulation and feed them back to the cloud processing system. The cloud processing system compares the changes in data before and after regulation, evaluates the control effect, and further optimizes the regulation strategy.

6. A real-time monitoring and dynamic control system for carbon emissions during the production and construction of epoxy asphalt mixtures, using the real-time monitoring and dynamic control method for carbon emissions during the production and construction of epoxy asphalt mixtures as described in any one of claims 1-5, characterized in that, The system includes, Sensor network modules are used to collect environmental parameters and emission data; The data processing and transmission module is used for data preprocessing and transmission; The cloud-based intelligent analysis module calculates and predicts carbon emissions through a cloud-based processing system and generates instructions for adjusting process parameters. The adjustment module is executed to regulate the process parameters.

7. The real-time monitoring and dynamic control system for carbon emissions in the production and construction of epoxy asphalt mixtures according to claim 6, characterized in that, The data processing and transmission module adopts an industrial IoT gateway, which supports 4G / 5G wireless communication protocols and can ensure the stability of data transmission in complex industrial environments.

8. The real-time monitoring and dynamic control system for carbon emissions in the production and construction of epoxy asphalt mixtures according to claim 6, characterized in that, It also includes a human-machine interface module, providing web and mobile access interfaces for real-time display of full-process carbon emission data, historical trends, equipment status and all control command records, and automatically generating compliance reports.

9. A real-time monitoring and dynamic control system for carbon emissions in the production and construction of epoxy asphalt mixtures according to claim 6, characterized in that, The sensor network module, data processing and transmission module, and execution and regulation module are equipped with high temperature resistance, dustproof, shockproof and electromagnetic interference resistance, and can adapt to the harsh working conditions of high temperature and high dust in asphalt mixing plants.

10. A real-time monitoring and dynamic control system for carbon emissions in the production and construction of epoxy asphalt mixtures according to claim 6, characterized in that, The cloud processing system employs a hierarchical decision-making logic. When the real-time carbon emission rate exceeds the preset first threshold, the system generates a level one alarm and provides control suggestions; When the predicted future short-term emissions or rate exceed a higher preset second threshold, the system generates a level-two alarm and a mandatory control command to regulate the production process.

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