Asphalt formula active optimization method and system based on intelligent algorithm

Abstract

The invention belongs to the field of asphalt formula optimization, relates to a data analysis technology, and aims to solve the problem that formula optimization can only be performed from the aspect of performance analysis in the prior art, in particular to an asphalt formula active optimization method and system based on an intelligent algorithm. The optimization analysis platform is in communication connection with a proportion distribution module, a performance analysis module, a formula optimization module and a database; the proportion distribution module is used for distributing the asphalt raw material proportion: marking a petroleum sample as a distribution object, randomly distributing an addition proportion for the distribution object, the addition proportion including a coal tar proportion and a shale oil proportion, and performing asphalt preparation according to the distribution object and the corresponding addition proportion to obtain a synthetic object; according to the method, the preparation proportion of the asphalt raw material can be optimized and analyzed, so that the accuracy of the addition proportion during subsequent asphalt preparation is improved, and users with different requirements can select proper optimization parameters according to own requirements.

Description

Technical Field

[0001] The present invention belongs to the field of asphalt formulation optimization and relates to data analysis technology, and specifically is an asphalt formulation active optimization method and system based on intelligent algorithms. Background Art

[0002] Asphalt is a dark brown complex mixture of hydrocarbons of different molecular weights and their non-metallic derivatives. It is a high-viscosity organic liquid that usually exists in liquid or semi-solid petroleum form. It has a black surface and is soluble in carbon disulfide and carbon tetrachloride. Asphalt is waterproof, moisture-proof and anti-corrosive, and is an organic cementitious material.

[0003] The invention patent with announcement number CN114358426A discloses a ceramic formula optimization method and system. The ceramic formula optimization method converts the optimal formula parameters into the actual addition amount of each formula component, and evaluates the comprehensive performance of the formula through experimental verification; however, the formula optimization method can only optimize the formula from the perspective of ceramic performance analysis, and its optimization method is relatively single, and it is unable to provide targeted optimization solutions for users with different needs.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide an active optimization method and system for asphalt formula based on intelligent algorithm, which is used to solve the problem that the existing technology can only optimize the formula from the perspective of performance analysis;

[0006] The technical problem to be solved by the present invention is: how to provide an asphalt formula active optimization method and system based on intelligent algorithms that can provide targeted optimization solutions for users with different needs.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An active asphalt formula optimization system based on intelligent algorithms, comprising an optimization analysis platform, wherein the optimization analysis platform is communicatively connected to a ratio distribution module, a performance analysis module, a formula optimization module and a database;

[0009] The ratio allocation module is used to allocate the ratio of asphalt raw materials: pre-process and evenly divide the petroleum to obtain a number of petroleum samples, mark the petroleum samples as allocation objects, randomly allocate addition ratios to the allocation objects, the addition ratios include coal tar ratio and shale oil ratio, and prepare asphalt according to the allocation objects and the corresponding addition ratios to obtain synthetic objects;

[0010] The performance analysis module is used to analyze the performance of the synthetic object: obtain the density data MD, viscosity data ND, soft temperature data RW and tensile data KL of the synthetic object and perform numerical calculation to obtain the performance coefficient XN of the synthetic object; perform numerical calculation on the performance coefficient XN of all synthetic objects to obtain the performance value and the performance deviation value, and judge whether the overall performance of the synthetic object meets the requirements through the performance value and the performance deviation value;

[0011] The formula optimization module is used to optimize the preparation ratio of asphalt raw materials.

[0012] Furthermore, the process of obtaining the density data MD, viscosity data ND, soft temperature data RW and tensile data KL of the synthetic object includes: obtaining the density value, viscosity value, softening temperature value and tensile strength value of the synthetic object, calling the density standard value, viscosity standard value, softening temperature standard value and tensile strength standard value of standard performance asphalt, marking the ratio of the density value of the synthetic object to the density standard value as density data MD, marking the ratio of the viscosity value of the synthetic object to the viscosity standard value as viscosity data ND, marking the ratio of the softening temperature value of the synthetic object to the softening temperature standard value as soft temperature data RW, and marking the ratio of the tensile strength value of the synthetic object to the tensile strength standard value as tensile data KL.

[0013] Furthermore, the process of obtaining the performance value and the performance deviation value includes: summing up the performance coefficients XN of all synthetic objects and taking the average value to obtain the performance value, and performing variance calculation on the performance coefficients XN of all synthetic objects to obtain the performance deviation value.

[0014] Furthermore, the specific process of determining whether the overall performance of the synthetic object meets the requirements includes: retrieving the performance threshold and the performance deviation threshold through the database, and comparing the performance value and the performance deviation value with the performance threshold and the performance deviation threshold respectively: if the performance value is greater than or equal to the performance threshold and the performance deviation value is less than the performance deviation threshold, then it is determined that the overall performance of the synthetic object meets the requirements, and a formula optimization signal is generated and sent to the formula optimization module; otherwise, it is determined that the overall performance of the synthetic object does not meet the requirements, and a preparation process optimization signal is generated and sent to the mobile terminal of the manager through the optimization analysis platform.

[0015] Furthermore, the specific process of the formula optimization module optimizing the analysis of the preparation ratio of asphalt raw materials includes: generating a performance sequence and an efficiency sequence, intercepting the top L1 analysis objects in the performance sequence and marking them as performance optimization objects; intercepting the top L1 analysis objects in the efficiency sequence and marking them as efficiency optimization objects, and generating performance optimization parameters and efficiency optimization parameters through the addition ratios of the performance optimization objects and the efficiency optimization objects respectively, and the performance optimization parameters and the efficiency optimization parameters constitute the optimized formula of asphalt, and the optimized formula of asphalt is sent to the database through the optimization analysis platform for storage.

[0016] Furthermore, the generation process of the performance sequence and the efficiency sequence includes: obtaining the preparation time of the synthetic object and marking it as an efficiency value, obtaining the efficiency threshold through the database, marking the synthetic object whose performance coefficient XN is not less than the performance threshold and whose efficiency value is less than the efficiency threshold as an analysis object, arranging the analysis objects in order of the performance coefficient XN values ​​from large to small to obtain a performance sequence; arranging the analysis objects in order of the efficiency values ​​from small to large to obtain an efficiency sequence.

[0017] Furthermore, the generation process of the performance optimization parameters includes: forming a coal tar performance optimization range by a maximum value of the coal tar ratio and a minimum value of the coal tar ratio of the performance optimization object, forming a shale oil performance optimization range by a maximum value of the shale oil ratio and a minimum value of the shale oil ratio of the performance optimization object, and forming a performance optimization parameter by the coal tar performance optimization range and the shale oil performance optimization range;

[0018] The generation process of the efficiency optimization parameters includes: the maximum value of the coal tar ratio and the minimum value of the coal tar ratio of the efficiency optimization object constitute the coal tar efficiency optimization range, the maximum value of the shale oil ratio and the minimum value of the shale oil ratio of the efficiency optimization object constitute the shale oil efficiency optimization range, and the coal tar efficiency optimization range and the shale oil efficiency optimization range constitute the efficiency optimization parameters.

[0019] An active optimization method for asphalt formulation based on intelligent algorithm comprises the following steps:

[0020] Step 1: Allocate the proportion of asphalt raw materials: pre-treat and equally divide the petroleum to obtain a number of petroleum samples, mark the petroleum samples as allocation objects, randomly assign addition ratios to the allocation objects, and prepare asphalt according to the allocation objects and the corresponding addition ratios to obtain synthetic objects;

[0021] Step 2: Analyze the performance of the synthetic object: obtain the density data MD, viscosity data ND, soft temperature data RW and tensile data KL of the synthetic object and perform numerical calculation to obtain the performance coefficient XN of the synthetic object;

[0022] Step 3: numerically calculate the performance coefficient XN of all synthetic objects to obtain performance values ​​and performance deviation values, and judge whether the overall performance of the synthetic objects meets the requirements through the performance values ​​and performance deviation values. If it meets the requirements, execute step 4;

[0023] Step 4: Optimize and analyze the preparation ratio of asphalt raw materials: obtain the preparation time of the synthetic object and mark it as the efficiency value, generate a performance sequence and an efficiency sequence according to the performance coefficient XN and the efficiency value respectively, intercept the performance sequence and the efficiency sequence to obtain the performance optimization object and the efficiency optimization object respectively, and generate an optimized formula according to the addition ratio of the performance optimization object and the efficiency optimization object.

[0024] The present invention has the following beneficial effects:

[0025] 1. The proportion of asphalt raw materials can be allocated through the proportion distribution module, the pre-treated oil can be evenly divided, and then the proportion of adding to the oil sample can be randomly allocated to prepare the asphalt;

[0026] 2. The performance analysis module can be used to analyze the performance of the synthetic object, collect and calculate multiple performance parameters of the synthetic object to obtain the performance coefficient, provide feedback on the material performance of a single synthetic object through the performance coefficient, and evaluate the overall performance of the synthetic object based on the generated performance value and performance deviation value;

[0027] 3. The formula optimization module can be used to optimize and analyze the preparation ratio of asphalt raw materials. The optimization analysis can be carried out from the perspective of performance optimization and preparation efficiency optimization. The optimized formula can be generated according to the screening results of the performance optimization object and the efficiency optimization object, thereby improving the accuracy of the addition ratio in the subsequent asphalt preparation. Users with different needs can also select appropriate optimization parameters according to their own needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 is a system block diagram of Embodiment 1 of the present invention;

[0030] Figure 2 This is a flow chart of the method of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0031] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] Embodiment 1: Figure 1 As shown, an active asphalt formula optimization system based on intelligent algorithms includes an optimization analysis platform, which is communicatively connected to a ratio distribution module, a performance analysis module, a formula optimization module and a database.

[0033] The proportion allocation module is used to allocate the proportion of asphalt raw materials: pre-treat and evenly divide the petroleum to obtain several petroleum samples, mark the petroleum samples as allocation objects, randomly assign addition ratios to the allocation objects, and the addition ratios include coal tar ratio and shale oil ratio. Asphalt is prepared according to the allocation objects and the corresponding addition ratios to obtain synthetic objects; the pre-treated petroleum is evenly divided, and then the addition ratios are randomly allocated to the petroleum samples to prepare the asphalt.

[0034] The performance analysis module is used to analyze the performance of the synthetic object: obtain the density value, viscosity value, softening temperature value and tensile strength value of the synthetic object, call the density standard value, viscosity standard value, softening temperature standard value and tensile strength standard value of the standard performance asphalt, mark the ratio of the density value of the synthetic object to the density standard value as density data MD, mark the ratio of the viscosity value of the synthetic object to the viscosity standard value as viscosity data ND, mark the ratio of the softening temperature value of the synthetic object to the softening temperature standard value as softening temperature data RW, mark the ratio of the tensile strength value of the synthetic object to the tensile strength standard value as tensile data KL; through the formula The performance coefficient XN of the synthetic object is obtained, wherein k1, k2, k3 and k4 are all proportional coefficients, and k1>k2>k3>k4>1. The performance coefficient XN is a value reflecting the performance of the synthetic object material. The larger the value of the performance coefficient XN is, the better the material performance of the synthetic object is. The performance coefficient XN of all synthetic objects is summed and averaged to obtain the performance value. The performance coefficient XN of all synthetic objects is calculated by variance to obtain the performance deviation value. The performance threshold and the performance deviation threshold are retrieved from the database, and the performance value and the performance deviation value are compared with the performance threshold and the performance deviation threshold respectively: If the performance value is greater than or equal to the performance threshold and the performance deviation value is less than the performance deviation threshold, it is determined that the overall performance of the synthetic object meets the requirements, and a formula optimization signal is generated and sent to the formula optimization module; otherwise, it is determined that the overall performance of the synthetic object does not meet the requirements, and a preparation process optimization signal is generated and sent to the mobile terminal of the manager through the optimization analysis platform; multiple performance parameters of the synthetic object are collected and calculated to obtain a performance coefficient, and the material performance of a single synthetic object is fed back through the performance coefficient, and the overall performance of the synthetic object is evaluated based on the generated performance value and performance deviation value.

[0035] The recipe optimization module is used to optimize and analyze the preparation ratio of asphalt raw materials: obtain the preparation time of the synthetic object and mark it as the efficiency value, obtain the efficiency threshold through the database, mark the synthetic object whose performance coefficient XN is not less than the performance threshold and whose efficiency value is less than the efficiency threshold as the analysis object, arrange the analysis objects in descending order according to the performance coefficient XN value to obtain a performance sequence, intercept the top L1 analysis objects in the performance sequence and mark them as performance optimization objects, L1 is a numerical constant, and the specific value of L1 is set by the management personnel; the maximum value and minimum value of the coal tar ratio of the performance optimization object constitute the coal tar performance optimization range, the maximum value and minimum value of the shale oil ratio of the performance optimization object constitute the shale oil performance optimization range, and the coal tar performance optimization range and the shale oil performance optimization range constitute the performance optimization parameters; the analysis objects are arranged according to the efficiency value by Arrange them in ascending order to get an efficiency sequence, extract the top L1 analysis objects in the efficiency sequence and mark them as efficiency optimization objects, the coal tar efficiency optimization range is composed of the maximum coal tar ratio and the minimum coal tar ratio of the efficiency optimization object, the shale oil efficiency optimization range is composed of the maximum shale oil ratio and the minimum shale oil ratio of the efficiency optimization object, the efficiency optimization parameters are composed of the coal tar efficiency optimization range and the shale oil efficiency optimization range, the performance optimization parameters and the efficiency optimization parameters constitute the optimized formula of asphalt, and the optimized formula of asphalt is sent to the database for storage through the optimization analysis platform; perform optimization analysis from the perspective of performance optimization and the perspective of preparation efficiency optimization in turn, generate an optimized formula according to the screening results of the performance optimization object and the efficiency optimization object, thereby improving the accuracy of the addition ratio in subsequent asphalt preparation, and users with different needs can also select appropriate optimization parameters according to their own needs.

[0036] Embodiment 2: Figure 2 As shown, a method for actively optimizing asphalt formulation based on an intelligent algorithm comprises the following steps:

[0037] Step 1: Allocate the proportion of asphalt raw materials: pre-treat and equally divide the petroleum to obtain a number of petroleum samples, mark the petroleum samples as allocation objects, randomly assign addition ratios to the allocation objects, and prepare asphalt according to the allocation objects and the corresponding addition ratios to obtain synthetic objects;

[0038] Step 2: Analyze the performance of the synthetic object: obtain the density data MD, viscosity data ND, soft temperature data RW and tensile data KL of the synthetic object and perform numerical calculation to obtain the performance coefficient XN of the synthetic object;

[0039] Step 3: numerically calculate the performance coefficient XN of all synthetic objects to obtain performance values ​​and performance deviation values, and judge whether the overall performance of the synthetic objects meets the requirements through the performance values ​​and performance deviation values. If it meets the requirements, execute step 4;

[0040] Step 4: Optimize and analyze the preparation ratio of asphalt raw materials: obtain the preparation time of the synthetic object and mark it as the efficiency value, generate a performance sequence and an efficiency sequence according to the performance coefficient XN and the efficiency value respectively, intercept the performance sequence and the efficiency sequence to obtain the performance optimization object and the efficiency optimization object respectively, and generate an optimized formula according to the addition ratio of the performance optimization object and the efficiency optimization object.

[0041] A method and system for actively optimizing asphalt formula based on intelligent algorithm. When working, petroleum is pre-processed and evenly divided to obtain several petroleum samples, the petroleum samples are marked as distribution objects, and the addition ratios are randomly assigned to the distribution objects. Asphalt is prepared according to the distribution objects and the corresponding addition ratios to obtain synthetic objects; the density data MD, viscosity data ND, soft temperature data RW and tensile data KL of the synthetic objects are obtained and numerically calculated to obtain the performance coefficient XN of the synthetic objects; the performance coefficients XN of all synthetic objects are numerically calculated to obtain performance values ​​and performance deviation values, and whether the overall performance of the synthetic objects meets the requirements is judged by the performance values ​​and the performance deviation values. If the requirements are met, the preparation time of the synthetic objects is obtained and marked as the efficiency value, and a performance sequence and an efficiency sequence are generated according to the performance coefficient XN and the efficiency value respectively, and the performance sequence and the efficiency sequence are respectively intercepted to obtain the performance optimization object and the efficiency optimization object, and an optimized formula is generated according to the addition ratio of the performance optimization object and the efficiency optimization object.

[0042] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0043] The above formulas are obtained by collecting a large amount of data and performing software simulation to select a formula close to the actual value. The coefficients in the formula are set by technicians in this field according to actual conditions; for example: Formula ; A technician in this field collects multiple groups of sample data and sets a corresponding performance coefficient for each group of sample data; Substitute the set performance coefficient and the collected sample data into the formula, any four formulas constitute a four-variable linear equation system, screen the calculated coefficients and take the average, and obtain the values ​​of k1, k2, k3 and k4, which are 4.05, 3.87, 3.22 and 2.79 respectively;

[0044] The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the preliminary setting of the corresponding performance coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantified value, such as the performance coefficient is proportional to the value of the density data.

[0045] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0046] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An active asphalt formulation optimization system based on intelligent algorithms, characterized in that: It includes an optimization analysis platform, which is communicatively connected with a ratio distribution module, a performance analysis module, a formula optimization module and a database; The ratio allocation module is used to allocate the ratio of asphalt raw materials: pre-process and evenly divide the petroleum to obtain a number of petroleum samples, mark the petroleum samples as allocation objects, randomly allocate addition ratios to the allocation objects, the addition ratios include coal tar ratio and shale oil ratio, and prepare asphalt according to the allocation objects and the corresponding addition ratios to obtain synthetic objects; The performance analysis module is used to analyze the performance of the synthetic object: obtain the density data MD, viscosity data ND, soft temperature data RW and tensile data KL of the synthetic object and perform numerical calculation to obtain the performance coefficient XN of the synthetic object; perform numerical calculation on the performance coefficient XN of all synthetic objects to obtain the performance value and the performance deviation value, and judge whether the overall performance of the synthetic object meets the requirements through the performance value and the performance deviation value; The formula optimization module is used to optimize the preparation ratio of asphalt raw materials.

2. The asphalt formula active optimization system based on intelligent algorithm according to claim 1 is characterized in that: The process of obtaining the density data MD, viscosity data ND, soft temperature data RW and tensile data KL of the synthetic object includes: obtaining the density value, viscosity value, softening temperature value and tensile strength value of the synthetic object, calling the density standard value, viscosity standard value, softening temperature standard value and tensile strength standard value of standard performance asphalt, marking the ratio of the density value of the synthetic object to the density standard value as density data MD, marking the ratio of the viscosity value of the synthetic object to the viscosity standard value as viscosity data ND, marking the ratio of the softening temperature value of the synthetic object to the softening temperature standard value as soft temperature data RW, and marking the ratio of the tensile strength value of the synthetic object to the tensile strength standard value as tensile data KL.

3. The asphalt formula active optimization system based on intelligent algorithm according to claim 2 is characterized in that: The process of obtaining the performance value and the performance deviation value includes: summing up the performance coefficients XN of all synthetic objects and taking the average value to obtain the performance value, and performing variance calculation on the performance coefficients XN of all synthetic objects to obtain the performance deviation value.

4. The asphalt formula active optimization system based on intelligent algorithm according to claim 3 is characterized in that: The specific process of determining whether the overall performance of the synthetic object meets the requirements includes: retrieving the performance threshold and the performance deviation threshold through the database, and comparing the performance value and the performance deviation value with the performance threshold and the performance deviation threshold respectively: if the performance value is greater than or equal to the performance threshold and the performance deviation value is less than the performance deviation threshold, then it is determined that the overall performance of the synthetic object meets the requirements, and a formula optimization signal is generated and sent to the formula optimization module; otherwise, it is determined that the overall performance of the synthetic object does not meet the requirements, and a preparation process optimization signal is generated and sent to the mobile terminal of the manager through the optimization analysis platform.

5. The asphalt formula active optimization system based on intelligent algorithm according to claim 4 is characterized in that: The specific process of the formula optimization module to optimize the analysis of the preparation ratio of asphalt raw materials includes: generating performance sequence and efficiency sequence, intercepting the top L1 analysis objects in the performance sequence and marking them as performance optimization objects; intercepting the top L1 analysis objects in the efficiency sequence and marking them as efficiency optimization objects, and generating performance optimization parameters and efficiency optimization parameters through the addition ratio of performance optimization objects and efficiency optimization objects respectively, and the performance optimization parameters and efficiency optimization parameters constitute the optimized formula of asphalt, and the optimized formula of asphalt is sent to the database through the optimization analysis platform for storage.

6. The asphalt formula active optimization system based on intelligent algorithm according to claim 5 is characterized in that: The generation process of the performance sequence and the efficiency sequence includes: obtaining the preparation time of the synthetic object and marking it as the efficiency value, obtaining the efficiency threshold through the database, marking the synthetic object whose performance coefficient XN is not less than the performance threshold and whose efficiency value is less than the efficiency threshold as the analysis object, arranging the analysis objects in the order of the performance coefficient XN value from large to small to obtain the performance sequence; arranging the analysis objects in the order of the efficiency value value from small to large to obtain the efficiency sequence.

7. The asphalt formula active optimization system based on intelligent algorithm according to claim 6 is characterized in that: The generation process of the performance optimization parameters includes: the maximum value of the coal tar ratio and the minimum value of the coal tar ratio of the performance optimization object constitute the coal tar performance optimization range, the maximum value of the shale oil ratio and the minimum value of the shale oil ratio of the performance optimization object constitute the shale oil performance optimization range, and the coal tar performance optimization range and the shale oil performance optimization range constitute the performance optimization parameters; The generation process of the efficiency optimization parameters includes: the maximum value of the coal tar ratio and the minimum value of the coal tar ratio of the efficiency optimization object constitute the coal tar efficiency optimization range, the maximum value of the shale oil ratio and the minimum value of the shale oil ratio of the efficiency optimization object constitute the shale oil efficiency optimization range, and the coal tar efficiency optimization range and the shale oil efficiency optimization range constitute the efficiency optimization parameters.

8. An active optimization method for asphalt formulation based on intelligent algorithm, characterized in that: The following steps are involved: Step 1: Allocate the proportion of asphalt raw materials; Step 2: Analyze the performance of the synthetic object; Step 3: Calculate the performance value and the performance deviation value, and determine whether the overall performance of the synthetic object meets the requirements through the performance value and the performance deviation value; Step 4: Optimize and analyze the preparation ratio of asphalt raw materials.

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