An intelligent batching method and system for pretreatment of solid waste

Abstract

The present invention discloses an intelligent batching method for the pretreatment of solid waste. The intelligent batching method for the pretreatment of solid waste includes the following steps: implanting a variety of mathematical models in the intelligent batching system to construct an algorithm system for predicting the raw material composition of solid waste; using an interval-valued fuzzy system to establish an interval-valued fuzzy model to realize the prediction of the raw material composition interval; developing a strategy for intelligent control of the batching process and designing a corresponding batching fuzzy controller. The intelligent batching method and system for the pretreatment of solid waste solve the problems of low accuracy and stability of traditional manual batching, can automatically sample and batch, can effectively reduce the operation time of workers, and improve labor efficiency; use a variety of intelligent algorithms to automatically calculate and make adjustments according to the actual situation, can reduce the error rate, avoid production accidents, and increase the output.

Description

Technical Field

[0001] The present invention relates to intelligent control technology, and more specifically, to an intelligent batching method and system for the pretreatment of solid waste. Background Art

[0002] In the traditional metallurgical production process, a large amount of solid waste is generated, including ash in the production environment, ore raw material residues, and various solid wastes generated in the production process. These solid wastes often contain various metal elements that can be recycled. At the same time, due to the presence of some substances harmful to the human body and the environment in the solid waste, it cannot be randomly stacked and discarded. In order to achieve the purpose of resource recycling and reducing damage to the human body and the environment, it is crucial to dispose of various solid wastes generated in the production process in a timely manner. However, in the traditional solid waste disposal process, there are certain defects in aspects such as the reasonable dosage and ratio of waste raw materials. The manual calculation of the input amount and ratio of various raw materials has obvious lag, and it cannot be adjusted in real time according to production needs. Judgments are made only based on production experience, lacking a certain degree of scientificity, and the manual operation and calculation process lack stability, are more prone to errors, and may cause production accidents in severe cases, wasting time and resources. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent batching method and system for the pretreatment of solid waste, which solves the technical problems in the prior art that in the process of solid waste disposal, there are defects in the reasonable dosage and ratio of waste raw materials, the manual calculation of the input amount and ratio of various raw materials has obvious lag, cannot be adjusted in real time, lacks a certain degree of scientificity and stability, is more prone to errors, and may cause production accidents in severe cases, wasting time and resources. It can conveniently and efficiently proportion the input amount of raw materials, and can be quickly adjusted automatically according to the on-site production demand, saving costs such as manpower and time, improving production efficiency, and reducing safety risks.

[0004] To achieve the above purpose, the present invention adopts the following technical solutions:

[0005] As one aspect of the present invention, an intelligent batching method for the pretreatment of solid waste is provided, including the following steps:

[0006] S1. implant multiple mathematical models in the intelligent batching system to construct an algorithm system for predicting the raw material composition of solid waste;

[0007] S2. use the interval-valued fuzzy system to establish an interval-valued fuzzy model to realize the prediction of the raw material composition interval;

[0008] S3. develop a strategy for intelligent control of the batching process and design a corresponding batching fuzzy controller.

[0009] As an intelligent batching method for the pretreatment of solid waste in the above aspect of the present invention, the multiple mathematical models in S1 include neural networks, fuzzy systems, and least squares regression.

[0010] As an intelligent batching method for the pretreatment of solid waste in the above aspect of the present invention, the algorithm system for predicting the raw material composition of solid waste constructed in S1 includes the following steps:

[0011] S11. Predict the raw material composition to be put into use based on the composition data of raw materials of different historical solid wastes and the detection results of the finished product composition;

[0012] S12. Automatically calculate the raw material ratio that meets the production requirements.

[0013] As an intelligent batching method for the pretreatment of solid waste in the above aspect of the present invention, S11 includes the following steps:

[0014] S111. Predict the raw material composition to be put into use through the feedback results of the daily raw material composition detection data;

[0015] S112. Predict the raw material composition to be put into use through the industrial data obtained from production;

[0016] S113. Predict the raw material composition to be put into use through the feedback of the element content of the finished product quality detection data.

[0017] As an intelligent batching method for the pretreatment of solid waste in the above aspect of the present invention, S2 includes the following steps:

[0018] S21. Establish a prediction model associating a set of batching ratios with the test results of the finished product through weighted coefficient calculation;

[0019] S22. Repeatedly test the prediction model and continuously optimize the prediction model;

[0020] S23. Adopt fuzzy control in the actual batching process to achieve automatic control of different raw material bins.

[0021] As an intelligent batching method for the pretreatment of solid waste in the above aspect of the present invention, S21 includes the following steps:

[0022] S211. Obtain multiple ratio schemes through weighted coefficient calculation for selection according to the actual production requirements;

[0023] S212. Form a database based on the daily industrial production data and by detecting the composition of the types of conventional disposal raw materials.

[0024] As an intelligent batching method for the pretreatment of solid waste in the above aspect of the present invention, wherein S3 includes the following steps:

[0025] S31. Based on the analysis of the batching process mechanism, develop an intelligent control strategy for the batching process;

[0026] S32. Design a corresponding batching fuzzy controller, improve the flow adjustment algorithm, and improve the accuracy and stability of batching.

[0027] As an intelligent batching method for the pretreatment of solid waste in the above aspect of the present invention, wherein S32 includes the following steps:

[0028] S321. Select the types of raw materials for solid waste and input relevant variables;

[0029] S322. Adjust the flow rates of each bin through relevant variables to achieve the automatic proportioning of multi-variety materials.

[0030] As another aspect of the present invention, there is provided an intelligent batching system for the pretreatment of solid waste, including:

[0031] A raw material composition prediction unit for predicting the raw material composition in solid waste; and / or

[0032] A raw material ratio calculation unit for calculating the distribution ratio of raw materials in solid waste; and / or

[0033] A bin flow control unit for automatically controlling the flow rates of different raw material bins; and / or

[0034] A finished product test result feedback unit for providing real-time feedback on the test results of the finished product.

[0035] As an intelligent batching system for the pretreatment of solid waste in the above aspect of the present invention, the intelligent batching system is used in combination with an intelligent water distribution system, an intelligent mixing system, an intelligent drying system, and a finished product parameter intelligent feedback system respectively.

[0036] Adopting the above technical solutions, the present invention has the following advantages:

[0037] The present invention provides an intelligent batching method and system for the pretreatment of solid waste, which can automatically sample and batch, effectively reduce the operation time of workers, improve labor efficiency, and reduce safety risks; the intelligent batching system can automatically calculate and make adjustments according to the actual situation, can reduce the error rate, avoid production accidents, and increase production; the intelligent batching system can be used in conjunction with other intelligent systems, with an integrated process, and can quickly diagnose and correct faults after a failure, saving time and other costs. The intelligent batching system is simple to operate, has a low learning difficulty, and has a clear and easy-to-understand interface, greatly reducing the training and learning time of technicians and saving labor costs. The intelligent batching method for the pretreatment of solid waste has the advantages of high accuracy, simple operation, good stability, etc., and is of great significance for improving production efficiency, reducing production risks, and saving time and labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Other features, objects, and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments with reference to the following drawings.

[0039] Figure 1 is a schematic diagram of the design principle of the intelligent batching system for the pretreatment of solid waste;

[0040] Figure 2 is a flowchart of the intelligent batching method for the pretreatment of solid waste of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] The technical solutions of the present invention will be specifically described below in conjunction with the accompanying drawings of the specification. The detailed features and advantages of the present invention will be described in detail in the specific embodiments, and the content is sufficient to enable any person skilled in the art to understand the technical content of the present invention and implement it accordingly. According to the specification, claims, and drawings disclosed in this specification, those skilled in the art can easily understand the related objects and advantages of the present invention.

[0042] Figure 1 shows a schematic diagram of the design principle of the intelligent batching system for the pretreatment of solid waste of the present invention.

[0043] An intelligent batching system for the pretreatment of solid waste includes:

[0044] a raw material composition prediction unit for predicting the raw material composition in solid waste; and / or

[0045] a raw material ratio calculation unit for calculating the distribution ratio of raw materials in solid waste; and / or

[0046] a silo flow control unit for automatically controlling the flow of different raw material silos; and / or

[0047] The finished product test result feedback unit is used to provide real-time feedback on the test results of the finished product.

[0048] The schematic diagram of the design principle of the intelligent batching system for the pretreatment of solid waste in the present invention is specifically as Figure 1 shown. From Figure 1 it can be seen that the detection results of the finished product feed back the element content to the intelligent batching system, the flow rate is fed back to the production process, the raw material composition detection is fed back to the intelligent batching system in a timely manner, and the intelligent batching system can automatically adjust the ratio and flow rate of the raw materials according to the actual requirements of the production process, so as to ensure the normal progress of production.

[0049] An intelligent batching method for the pretreatment of solid waste is specifically as Figure 2 shown, including the following steps:

[0050] S1. Incorporate a variety of mathematical models into the intelligent batching system to construct an algorithm system for predicting the composition of raw materials for solid waste; among them, the variety of mathematical models in S1 include neural networks, fuzzy systems, and least squares regression.

[0051] The specific steps for constructing the algorithm system for predicting the composition of raw materials for solid waste in S1 are as follows:

[0052] S11. Predict the composition of raw materials to be put into use based on the composition data of raw materials and the test results of finished product compositions of different historical solid wastes;

[0053] Among them, S11 includes the following specific steps, specifically as Figure 1 shown:

[0054] S111. Predict the composition of raw materials to be put into use based on the feedback results of daily raw material composition detection data;

[0055] S112. Predict the composition of raw materials to be put into use based on the industrial data obtained from production;

[0056] S113. Predict the composition of raw materials to be put into use based on the element content feedback of the finished product quality detection data.

[0057] S12. Automatically calculate the raw material ratio that meets the production requirements.

[0058] S2. Use the interval-valued fuzzy system to establish an interval-valued fuzzy model to realize the prediction of the raw material composition interval;

[0059] Among them, S2 includes the following steps:

[0060] S21. Establish a prediction model that associates the batching ratio with the test results of the finished product through weighted coefficient calculation;

[0061] Among them, S21 includes the following steps:

[0062] S211. Through the calculation of weighting coefficients, the prediction of the raw material ratio can be realized, and multiple ratio schemes can be obtained within a short time for selection according to the actual production requirements.

[0063] S212. According to the daily industrial production data and by detecting the components of the types of conventional disposal raw materials, a sufficient database is formed.

[0064] S22. Repeatedly test the prediction model to continuously optimize the prediction model.

[0065] S23. Adopt fuzzy control during the actual batching process to realize the automatic control of different raw material bins.

[0066] S3. Develop a strategy for intelligent control of the batching process and design a corresponding batching fuzzy controller.

[0067] Among them, S3 includes the following steps:

[0068] S31. Based on the analysis of the batching process mechanism, develop an intelligent control strategy for the batching process.

[0069] S32. Design a corresponding batching fuzzy controller, improve the flow adjustment algorithm, and improve the accuracy and stability of batching.

[0070] Among them, S32 includes the following specific steps:

[0071] S321. Select the types of raw materials of solid waste and input relevant variables, such as parameters such as the expected disposal amount, expected output, belt length, and belt running speed, to realize the reasonable control of the raw material ratio.

[0072] S322. Adjust the flow rate of each bin through relevant variables to realize the automatic ratio of multi-variety materials.

[0073] In a specific embodiment, that is, during the daily production process, the elemental compositions of various raw materials are different from each other, and due to the deviation in the degree of material mixing, there are certain fluctuations in the elemental content of the materials entering the pelletizing system. When the fluctuation amplitude is too large, it may affect the final quality of the finished product and even damage the equipment. The intelligent batching method for the pretreatment of solid waste of the present invention accurately calculates the composition of the mixed materials according to the composition parameters of the raw materials and the finished products fed back in real time, makes timely adjustments, re-sets the distribution ratio of the raw materials, and thus controls the final quality of the finished product and reduces the probability of production accidents.

[0074] In yet another specific embodiment, during the normal production process, since certain failures may occur in each link of the transportation system, resulting in a certain change in the production rate. For example, when there is severe material blockage in the strong mixing or serious material accumulation on the belt, it may damage the machine. The intelligent batching system of the present invention can timely adjust the raw material ratio rate according to the output rate of the finished product and the feedback of the operation rates of various parts of the entire process flow, so as to achieve the normal operation of production.

[0075] Finally, it should be pointed out that although the present invention has been described with reference to the current specific embodiments, those of ordinary skill in the art should recognize that the above embodiments are only used to illustrate the present invention, rather than to limit the present invention. Various equivalent changes or substitutions can be made without departing from the concept of the present invention. Therefore, as long as the changes and modifications of the above embodiments are within the scope of the spirit of the present invention, they will fall within the scope of the claims of the present invention.

Claims

1. An intelligent batching method for the pretreatment of solid waste, characterized in that, It includes the following steps: S1. implant a variety of mathematical models in the intelligent batching system to construct an algorithm system for predicting the raw material composition of solid waste, including the following steps: S11. Predict the raw material composition to be put into use based on the composition data of raw materials of different historical solid wastes and the test results of finished product composition; S12. Automatically calculate the raw material ratio that meets the production requirements; S2. Use the interval-valued fuzzy system to establish an interval-valued fuzzy model to realize the prediction of the raw material composition interval, including the following steps: S21. Establish a prediction model that correlates the batching ratio with the test results of finished products through weighted coefficient calculation; S22. Repeatedly test the prediction model to continuously optimize the prediction model; S23. Adopt fuzzy control during the actual batching process to realize the automatic control of different raw material bins; S3. Develop a strategy for intelligent control of the batching process and design a corresponding batching fuzzy controller, including the following steps: S31. Based on the analysis of the batching process mechanism, develop an intelligent control strategy for the batching process; S32. Design a corresponding batching fuzzy controller, improve the flow adjustment algorithm, and improve the accuracy and stability of batching.

2. The intelligent batching method for solid waste pretreatment according to claim 1, wherein, The various mathematical models in S1 include neural networks, fuzzy systems, and least squares regression.

3. The intelligent batching method for the pretreatment of solid waste according to claim 1, wherein, The S11 includes the following steps: S111. Predict the raw material composition to be put into use through the feedback results of daily raw material composition detection data; S112. Predict the raw material composition to be put into use through the industrial data obtained from production; S113. Predict the raw material composition to be put into use through the feedback of the element content of the finished product quality inspection data.

4. The intelligent batching method for solid waste pretreatment according to claim 1, characterized in that, The S21 includes the following steps: S211. Obtain multiple batching schemes through weighted coefficient calculation for selection according to actual production requirements; S212. Form a database based on daily industrial production data and by detecting the composition of conventional disposal raw material types.

5. The intelligent batching method for solid waste pretreatment according to claim 1, wherein, The S32 includes the following steps: S321. Select the types of raw materials for solid waste and input relevant variables; S322. Adjust the flow rates of each bin through relevant variables to achieve automatic batching of multi-variety materials.

6. An intelligent batching system for the pretreatment of solid waste, characterized in that, It includes: A raw material composition prediction unit for predicting the raw material composition in solid waste; A raw material ratio calculation unit for calculating the distribution ratio of raw materials in solid waste; A bin flow control unit for automatically controlling the flow rates of different raw material bins; A finished product test result feedback unit for providing real-time feedback on the test results of finished products, The intelligent batching system is used in combination with an intelligent water distribution system, an intelligent mixing system, an intelligent drying system, and a finished product parameter intelligent feedback system respectively, The intelligent batching system for the pretreatment of solid waste is used to execute the intelligent batching method as described in any one of claims 1-5.

Images