Road construction waste recycling method and system

Abstract

The invention discloses a road construction waste recycling method and system, and relates to the field of construction waste recovery, and the method comprises the following steps: S1, carrying out on-site investigation and evaluation, determining the type, output and pollution degree of waste, and detecting the physicochemical properties of the waste; s2, waste materials are classified and collected, and the waste materials are sorted according to material types; s3, waste treatment planning is carried out, and a treatment scheme is selected by combining waste characteristics and utilizing scenes; step S4, crushing treatment: crushing the large materials by using a crusher; s5, detecting the performance of the waste material, namely detecting the performance of the waste material; and S6, regeneration treatment is conducted, specifically, different waste materials are recycled according to the types and performance of the materials. According to the method, the recovery process can be optimized, the resource utilization rate can be increased and the cost can be reduced by constructing the waste recovery model, analyzing and processing the waste data and generating the optimal recovery processing scheme and establishing the scientific data model.

Description

Technical Field

[0001] This invention relates to the field of construction waste recycling, and in particular to a method and system for recycling and utilizing highway construction waste. Background Technology

[0002] During highway construction, a large amount of waste is generated, including demolition waste, construction waste, and leftover materials. If these wastes are not handled properly, they may cause environmental pollution and waste of resources. Highway construction waste is usually recycled and reused. Common waste recycling operations often involve directly selecting the corresponding recycling method based on the type of waste, or recycling the waste as a whole without sorting it. However, due to the different characteristics of waste and application scenarios, such recycling methods are prone to improper recycling and processing, resulting in resource waste, reduced waste recycling efficiency, and increased recycling costs. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for recycling highway construction waste, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for recycling highway construction waste, comprising the following steps: Step S1: On-site investigation and assessment to determine the type, amount and degree of pollution of waste, and to test the physicochemical properties of the waste; Step S2: Waste sorting and collection, sorting waste according to material type, and marking hazardous materials; Step S3: Waste treatment planning, which involves weighing different types of waste and selecting a treatment plan based on the characteristics of the waste and the utilization scenario; Step S4: Crushing process, using a crusher to crush large pieces of material and screening the crushed waste; Step S5: Waste performance testing. By testing the performance of the waste, the waste is recycled and processed in accordance with environmental protection standards. Step S6: Recycling process, recycling different waste materials according to their material type and properties; Step S7: Data recording, recording the waste type, source, processing volume, uses and performance indicators of recycled materials.

[0005] Preferably, the waste treatment planning in step S3 includes the following steps: Step S31: Waste data collection, by weighing different types of waste to obtain data on the physical and chemical properties of waste, the performance of recycled materials, and environmental and economic data; Step S32: Establish a recycling model, construct a waste recycling model, and predict the performance and recycling efficiency of recycled materials; Step S33: Recycling scheme generation. By inputting the acquired waste data into the established recycling model, the optimal recycling and processing scheme is generated.

[0006] Preferably, the recycling model establishment in step S32 includes a prediction model, an optimization model, and a classification model. The prediction model uses regression models and neural networks from machine learning methods to predict the softening point of recycled asphalt and the compressive strength of concrete. The optimization model uses linear and nonlinear programming to calculate the upper limit of processing capacity, the performance standards of recycled materials, and the recycling cost. The classification model identifies the waste type through a camera and uses a decision system to automatically recommend processing methods based on the waste attributes.

[0007] Preferably, the crushing process in step S4 includes the following steps: Step S41: Coarse crushing process, using a jaw crusher and an impact crusher to crush large pieces of waste into smaller pieces; Step S42: Fine screening, using a vibrating screen to classify by particle size and remove impurities from small-sized fragments; Step S43: Magnetic separation, using a magnetic separator to separate steel bars and metal fragments from the crushed material; Step S44: Mechanical sorting to remove non-mineral waste such as wood and plastic from the scrap; Step S45: Debris cleaning, using high-pressure water rinsing to remove attached mud, sand and chemical residues.

[0008] Preferably, the waste performance testing in step S5 includes aggregate sampling testing and asphalt tar testing. The aggregate sampling testing involves sampling and mixing crushed aggregates to form concrete and testing the crushing value and water absorption rate of the concrete. The asphalt tar testing is used to detect the tar content in the recycled asphalt.

[0009] Preferably, the recycling process in step S6 includes wood recycling, steel bar recycling, concrete recycling, and asphalt recycling. The wood recycling process involves crushing the wood for use as biomass fuel and processing it into wood chips. The steel bar recycling process involves recycling steel bars and steel fibers and remelting them. The concrete recycling process includes using it in new concrete mixes and as roadbed material.

[0010] Preferably, the asphalt recycling process includes hot recycling technology and cold recycling technology. The hot recycling technology is used to heat and melt asphalt waste for recycling, and the cold recycling technology is used to crush and reuse asphalt waste.

[0011] A highway construction waste recycling system includes: A data storage module, which is used to record and store the properties of waste materials; A visual monitoring module is used to identify the type of waste and monitor the waste recycling operation; The processor module establishes a recycling model and processes information from the data storage module. It is used to plan the recycling and processing of waste materials. The processor module is connected to the data storage module and the visual monitoring module. Waste detection equipment, which is used to detect the characteristics of waste and recycled materials; Waste recycling equipment, which uses different mechanical devices to process and recycle different types of waste.

[0012] Preferably, the processor module predicts the performance and recycling efficiency of recycled materials based on machine learning methods, and the visual monitoring module consists of a 3D camera and a 2D camera.

[0013] Preferably, the waste testing equipment consists of a pressure testing machine, an ultrasonic rebound analyzer, a permeability coefficient meter, a penetration meter, and a softening point meter, and the waste recycling equipment consists of a crusher, a vibrating screen, a magnetic separator, and a high-pressure washer.

[0014] The technical effects and advantages of this invention are as follows: This invention weighs different types of waste and obtains data on the physicochemical properties of the waste, the performance of recycled materials, and environmental and economic data. It uses a predictive model and machine learning methods to predict the softening point of recycled asphalt and the compressive strength of concrete. A neural network calculates the complex mapping between waste components and the amount of recycling agent. Linear and nonlinear programming are used to calculate the upper limit of processing capacity, the performance standards of recycled materials, and the recycling cost. A camera identifies the waste type, and a decision-making system automatically recommends processing methods based on waste attributes. A waste recycling model is constructed, and the acquired waste data is input into the established recycling model to generate the optimal recycling solution. Establishing a scientific data model can optimize the recycling process, improve resource utilization, and reduce costs. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the waste recycling process of the present invention.

[0016] Figure 2 This is a schematic diagram of the waste treatment planning process of the present invention.

[0017] Figure 3 This is a schematic diagram of the crushing process of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] This invention provides, for example Figure 1-3 The method for recycling highway construction waste shown includes the following steps: Step S1: On-site investigation and assessment to determine the type, amount and degree of pollution of waste, test the physicochemical properties of waste, and conduct an initial assessment of waste recycling and treatment to facilitate subsequent operations; Step S2: Waste sorting and collection. Waste is sorted according to material type and marked as hazardous materials. By sorting and processing highway construction waste and marking asphalt materials, the environmental protection of waste recycling is ensured. Step S3: Waste treatment planning. By weighing different types of waste, and combining the characteristics of the waste with the utilization scenario, a treatment plan is selected. The weight of the recycled waste, waste characteristic parameters, performance data of recycled materials, and environmental and economic data are obtained. Combined with the waste recycling scenario, the optimal waste recycling plan is determined to ensure the efficiency of waste recycling and reduce the cost of waste recycling. Step S4: Crushing process, using a crusher to crush large pieces of material and screening the crushed waste; Step S5: Waste performance testing. By testing the performance of the waste, the waste is recycled and processed according to environmental protection standards. Recycling operations are carried out according to the environmental protection standards for different waste treatments. Step S6: Recycling process, recycling different waste materials according to material type and performance to maximize the recycling rate of waste materials; Step S7: Data recording, recording the waste type, source, processing volume, uses and performance indicators of recycled materials.

[0020] Step S3, waste disposal planning, includes the following steps: Step S31: Waste data collection. By weighing different types of waste, we obtain data on the physical and chemical properties of the waste, the performance of recycled materials, and environmental and economic data, providing a data foundation for the generation of recycling solutions. Step S32: Establish a recycling model, construct a waste recycling model, predict the performance and recycling efficiency of recycled materials, realize intelligent sorting of waste and recommendation of recycling paths, and ensure maximum economic benefits and minimum environmental impact; Step S33: Recycling plan generation. Based on the waste recycling model, the optimal recycling and processing plan is generated. Establishing a scientific data model can optimize the recycling process, improve resource utilization, and reduce costs.

[0021] The recycling model establishment in step S32 includes a prediction model, an optimization model, and a classification model. The prediction model uses regression models and neural networks from machine learning methods to predict the softening point of recycled asphalt and the compressive strength of concrete. It calculates the complex mapping between waste components and recycling agent dosage through neural networks. The optimization model uses linear and nonlinear programming to calculate the upper limit of processing capacity, performance standards of recycled materials, and recycling costs to ensure that costs are minimized and RAP utilization is maximized, while optimizing costs, carbon emissions, and the quality of recycled materials. The classification model identifies waste types through cameras and uses a decision system to automatically recommend processing methods based on waste attributes. It uses a rule-based system to achieve intelligent sorting of waste and recommendation of recycling paths.

[0022] Step S4, the crushing process, includes coarse crushing, which uses a jaw crusher and an impact crusher to crush large pieces of waste into smaller pieces; fine screening, which uses a vibrating screen to classify the particles by size and remove impurities from the smaller pieces; magnetic separation, which uses a magnetic separator to separate steel bars and metal fragments from the pieces; mechanical separation, which removes non-mineral waste such as wood and plastic from the pieces; and washing the pieces, which uses high-pressure water washing to remove attached mud and chemical residues.

[0023] The waste performance testing in step S5 includes aggregate sampling testing and asphalt tar testing. Aggregate sampling testing involves sampling and mixing crushed aggregates to form concrete and testing the crushing value and water absorption rate of the concrete. Asphalt tar testing is used to detect the tar content in the recycled asphalt. By testing the waste performance, a basis is provided for the recycling and treatment of waste, and the optimal waste treatment method is selected based on the waste performance.

[0024] The recycling process in step S6 includes wood recycling, steel bar recycling, concrete recycling, and asphalt recycling. Wood recycling involves crushing the wood for use as biomass fuel and processing it into wood chips. Steel bar recycling involves recycling steel bars and steel fibers and remelting them. Concrete recycling includes use in new concrete mixes and as roadbed material.

[0025] Asphalt recycling includes hot recycling and cold recycling technologies. Hot recycling involves heating and melting asphalt waste for reuse. It can be divided into plant-mixed hot recycling and in-situ hot recycling. Plant-mixed hot recycling involves transporting the old asphalt mixture to a mixing plant, crushing it, mixing it with new asphalt, aggregates, or recycling agents, and then repaving the road. In-situ hot recycling involves heating and loosening the old road surface, adding recycling agents or new materials, and then directly repaving it on-site. Cold recycling involves crushing and reusing asphalt waste. It can be divided into plant-mixed cold recycling and in-situ cold recycling. Plant-mixed cold recycling involves crushing the old material and mixing it with binders such as emulsified asphalt, foamed asphalt, and cement for use in the base or subbase layers. In-situ cold recycling involves milling the old road surface, adding stabilizers on-site, and directly compacting it as the base layer. The optimal recycling method is selected based on the waste recycling cost and actual use.

[0026] A highway construction waste recycling system includes a data storage module, a visual monitoring module, a processor module, waste detection equipment, and waste recycling equipment. The data storage module records and stores waste properties, including physicochemical characteristics, recycled material performance data, and environmental and economic data. The visual monitoring module identifies waste types and monitors recycling operations, using multiple 3D and 2D cameras for image recognition to facilitate waste sorting and recycling. The processor module establishes a recycling model and processes information from the data storage module. It plans waste recycling processes, constructing a recycling model based on collected waste data to predict recycled material performance and efficiency, and generating an optimal recycling solution. The processor module is connected to the data storage module and the visual monitoring module. The waste detection equipment detects the characteristics of waste and recycled materials. The waste recycling equipment processes and recycles different types of waste using various mechanical devices.

[0027] The processor module uses machine learning methods to predict the performance and recycling efficiency of recycled materials. It analyzes and processes waste data through a constructed model to determine the optimal waste recycling solution, thereby improving the efficiency of waste recycling and reducing recycling costs. The visual monitoring module consists of 3D and 2D cameras. Multiple 3D and 2D cameras are connected to the host to identify highway construction waste and monitor the recycling process.

[0028] The waste testing equipment consists of a pressure testing machine, an ultrasonic rebound hammer, a permeability coefficient meter, a penetration meter, and a softening point meter. The pressure testing machine tests the compressive and flexural strength of concrete waste and recycled concrete aggregates. The ultrasonic rebound hammer tests the strength of concrete waste and recycled concrete aggregates. The permeability coefficient meter tests the water permeability of recycled concrete. The penetration meter tests the penetration of recycled asphalt. The softening point meter tests the softening point of recycled asphalt. The waste recycling equipment consists of a crusher, a vibrating screen, a magnetic separator, and a high-pressure washer. The crusher crushes large volumes of waste into smaller pieces. The crushed waste is fed into the vibrating screen for multi-stage screening. The magnetic separator separates reinforcing bars and metal fragments from the fragments. The high-pressure washer removes adhering mud, sand, and chemical residues from the fragments.

[0029] How to use this invention: By weighing different types of waste and acquiring data on the physicochemical properties of the waste, the performance of recycled materials, and environmental and economic data, a predictive model using machine learning methods is employed to predict the softening point of recycled asphalt and the compressive strength of concrete. A decision-making system is then used to automatically recommend treatment methods based on the waste properties, constructing a waste recycling model to generate the optimal recycling solution. This model predicts the performance and recycling efficiency of recycled materials, enabling intelligent waste sorting and recycling path recommendation. This ensures maximum economic benefits and minimizes environmental impact. Establishing a scientific data model can optimize the recycling process, improve resource utilization, and reduce costs.

[0030] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for recycling highway construction waste, characterized in that, Includes the following steps: Step S1: On-site investigation and assessment to determine the type, amount and degree of pollution of waste, and to test the physicochemical properties of the waste; Step S2: Waste sorting and collection, sorting waste according to material type, and marking hazardous materials; Step S3: Waste treatment planning, which involves weighing different types of waste and selecting a treatment plan based on the characteristics of the waste and the utilization scenario; Step S4: Crushing process, using a crusher to crush large pieces of material and screening the crushed waste; Step S5: Waste performance testing. By testing the performance of the waste, the waste is recycled and processed in accordance with environmental protection standards. Step S6: Recycling process, recycling different waste materials according to their material type and properties; Step S7: Data recording, recording the waste type, source, processing volume, uses and performance indicators of recycled materials.

2. The method for recycling highway construction waste according to claim 1, characterized in that, The waste disposal planning in step S3 includes the following steps: Step S31: Waste data collection, by weighing different types of waste to obtain data on the physical and chemical properties of waste, the performance of recycled materials, and environmental and economic data; Step S32: Establish a recycling model, construct a waste recycling model, and predict the performance and recycling efficiency of recycled materials; Step S33: Recycling scheme generation. By inputting the acquired waste data into the established recycling model, the optimal recycling and processing scheme is generated.

3. The method for recycling highway construction waste according to claim 2, characterized in that, The recycling model establishment in step S32 includes a prediction model, an optimization model, and a classification model. The prediction model uses regression models and neural networks from machine learning methods to predict the softening point of recycled asphalt and the compressive strength of concrete. The optimization model uses linear and nonlinear programming to calculate the upper limit of processing capacity, the performance standards of recycled materials, and the recycling cost. The classification model uses a camera to identify the type of waste and uses a decision system to automatically recommend a treatment method based on the waste attributes.

4. The method for recycling highway construction waste according to claim 1, characterized in that, The crushing process in step S4 includes the following steps: Step S41: Coarse crushing process, using a jaw crusher and an impact crusher to crush large pieces of waste into smaller pieces; Step S42: Fine screening, using a vibrating screen to classify by particle size and remove impurities from small-sized fragments; Step S43: Magnetic separation, using a magnetic separator to separate steel bars and metal fragments from the crushed material; Step S44: Mechanical sorting to remove non-mineral waste such as wood and plastic from the scrap; Step S45: Debris cleaning, using high-pressure water rinsing to remove attached mud, sand and chemical residues.

5. The method for recycling highway construction waste according to claim 1, characterized in that, The waste performance testing in step S5 includes aggregate sampling testing and asphalt tar testing. The aggregate sampling testing involves sampling and mixing crushed aggregates to form concrete and testing the crushing value and water absorption rate of the concrete. The asphalt tar testing is used to detect the tar content in the recycled asphalt.

6. The method for recycling highway construction waste according to claim 1, characterized in that, The recycling process in step S6 includes wood recycling, steel bar recycling, concrete recycling, and asphalt recycling. The wood recycling process involves crushing the wood for use as biomass fuel and processing it into wood chips. The steel bar recycling process involves recycling steel bars and steel fibers and remelting them. The concrete recycling process includes using it in new concrete mixes and as roadbed material.

7. A method for recycling highway construction waste according to claim 6, characterized in that, The asphalt recycling process includes hot recycling technology and cold recycling technology. The hot recycling technology is used to heat and melt asphalt waste for recycling, while the cold recycling technology is used to crush and reuse asphalt waste.

8. A system for recycling highway construction waste, characterized in that, The method for recycling highway construction waste as described in any one of claims 1-7 includes: A data storage module, which is used to record and store the properties of waste materials; A visual monitoring module is used to identify the type of waste and monitor the waste recycling operation; The processor module establishes a recycling model and processes information from the data storage module. It is used to plan the recycling and processing of waste materials. The processor module is connected to the data storage module and the visual monitoring module. Waste detection equipment, which is used to detect the characteristics of waste and recycled materials; Waste recycling equipment, which uses different mechanical devices to process and recycle different types of waste.

9. A highway construction waste recycling system according to claim 8, characterized in that, The processor module predicts the performance and recycling efficiency of recycled materials based on machine learning methods, and the visual monitoring module consists of a 3D camera and a 2D camera.

10. A highway construction waste recycling system according to claim 8, characterized in that, The waste testing equipment consists of a pressure testing machine, an ultrasonic rebound analyzer, a permeability coefficient meter, a penetration meter, and a softening point meter. The waste recycling equipment consists of a crusher, a vibrating screen, a magnetic separator, and a high-pressure washer.

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