CN-117405852-B - Coal quality index information determination method and device, electronic equipment and storage medium

CN117405852BCN 117405852 BCN117405852 BCN 117405852BCN-117405852-B

Abstract

The disclosure provides a method and a device for determining coal quality index information, electronic equipment and a storage medium. The method comprises the steps of obtaining a plurality of first coal samples and coal samples to be predicted, determining the first Gibbs flow degree, the first Offset full expansion degree, the first colloid layer thickness, the first bonding index, the first softening temperature, the first flowing temperature, the first solidifying temperature and the first plasticity temperature of each first coal sample, and then carrying out linear fitting processing on each of the first colloid layer thickness, the first bonding index, the first softening temperature, the first flowing temperature, the first solidifying temperature and the first plasticity temperature and the first Gibbs flow degree in sequence to obtain a coal quality index prediction model, and predicting target coal quality indexes of the coal samples to be predicted based on the coal quality index prediction model, so that the determining cost of coal quality index information can be effectively reduced, and the determining efficiency of the coal quality index information can be effectively improved.

Inventors

BAI XIAOYAN
CHEN WENKAI
HU ZIHAN
SHENG MING
XIONG YINWU
GUO HAOQIAN
LI XIAOLIANG

Assignees

煤炭科学技术研究院有限公司

Dates

Publication Date: 20260512
Application Date: 20230922

Claims (8)

1. A method for determining coal quality index information, the method comprising: acquiring a plurality of first coal samples and coal samples to be predicted; Determining a first Ji's fluidity, a first Osub full expansion degree, a first colloid layer thickness, a first bonding index, a first softening temperature, a first flowing temperature, a first curing temperature and a first plasticity temperature of each first coal sample; Sequentially performing polynomial regression processing on each of the first Osub full expansion degree, the first colloid layer thickness, the first bonding index, the first softening temperature, the first flowing temperature, the first curing temperature and the first plasticity temperature and the first Gibbs' fluidity to obtain a first coal index prediction model, a second coal index prediction model, a third coal index prediction model, a fourth coal index prediction model, a fifth coal index prediction model, a sixth coal index prediction model and a seventh coal index prediction model; The first coal quality index prediction model is used for predicting the Oya full expansion degree and is expressed as a+b= 12.248 (lgMF) 2-8.460 lgMF+14.988; the second coal quality index prediction model is used for predicting the thickness of a colloid layer and is expressed as Y=1.332 (lgMF) 2-1.490lgMF+9.763; The third coal quality index prediction model is used for predicting a bonding index and is expressed as G R.I = -2.694 (lgMF) 2+21.055lgMF+48.584; The fourth coal quality index prediction model is used for predicting softening temperature and is expressed as Ts= -1.096 (lgMF) 2-8.079 lgMF+443.395; The fifth coal quality index prediction model is used for predicting the flowing temperature and is expressed as T max = -0.966 (lgMF) 2+2.810 lgMF+454.660; The sixth coal quality index prediction model is used for predicting the curing temperature and is expressed as T f =0.455 (lgMF) 2+4.227 lgMF+473.109; the seventh coal quality index prediction model is used for predicting the plastic temperature and is expressed as DeltaT= 1.551 (lgMF) 2+12.307lgMF+29.714; the first coal quality index prediction model, the second coal quality index prediction model, the third coal quality index prediction model, the fourth coal quality index prediction model, the fifth coal quality index prediction model, the sixth coal quality index prediction model and the seventh coal quality index prediction model are used as coal quality index prediction models together; And predicting the target coal quality index of the coal sample to be predicted based on the coal quality index prediction model.
2. The method of claim 1, wherein predicting the target coal quality indicator for the coal sample to be predicted based on the coal quality indicator prediction model comprises: acquiring a second Gibbs fluidity of the coal sample to be predicted; substituting the second Gibbs fluidity into the coal quality index prediction model to determine a target coal quality index of the coal sample to be predicted, wherein the target coal quality index comprises a second Ownian full expansion degree, a second colloid layer thickness, a second bonding index, a second softening temperature, a second flowing temperature, a second curing temperature and a second plasticity temperature.
3. The method of claim 2, wherein each of the first coal samples has a corresponding coal sample type; After substituting the second Ji's fluidity into the coal quality index prediction model to determine the target coal quality index of the coal sample to be predicted, the method further includes: And determining the target coal sample type of the second coal sample according to the target coal quality index, the first Osub full expansion degree of the first coal sample type, the first colloid layer thickness, the first bonding index, the first softening temperature, the first flowing temperature, the first curing temperature and the first plasticity temperature.
4. The method of claim 3, wherein determining the target coal type for the second coal based on the target coal index, the first austempering degree of the first coal for a first coal type, a first gum layer thickness, a first bond index, a first softening temperature, a first flow temperature, a first curing temperature, and a first plasticity temperature comprises: The first Ownian full expansion degree, the first colloid layer thickness, the first bonding index, the first softening temperature, the first flowing temperature, the first curing temperature and the first plasticity temperature of the first coal sample type are weighted and summed to obtain a first sum value; Weighted summation is carried out on the second Osub full expansion degree, the second colloid layer thickness, the second bonding index, the second softening temperature, the second flowing temperature, the second curing temperature and the second plasticity temperature to obtain a second summation value; and under the condition that the difference value between the first sum value and the second sum value is smaller than a difference value threshold value, determining that the target coal sample type of the second coal sample is the first coal sample type.
5. A coal quality index information determining apparatus, comprising: the acquisition module is used for acquiring a plurality of first coal samples and coal samples to be predicted; The determining module is used for determining a first Gibbs flow degree, a first Ottsia full expansion degree, a first colloid layer thickness, a first bonding index, a first softening temperature, a first flowing temperature, a first curing temperature and a first plasticity temperature of each first coal sample; The processing module is used for sequentially carrying out polynomial regression processing on each item of the first Offwood full expansion degree, the first colloid layer thickness, the first bonding index, the first softening temperature, the first flowing temperature, the first curing temperature and the first plasticity temperature to obtain a first coal index prediction model, a second coal index prediction model, a third coal index prediction model, a fourth coal index prediction model, a fifth coal index prediction model, a sixth coal index prediction model and a seventh coal index prediction model, wherein the first coal index prediction model is used for predicting the Offwood full expansion degree, the first coal index prediction model is expressed as a+b= 12.248 (lgMF) 2-8.460 lgMF+14.988, the second coal index prediction model is used for predicting colloid layer thickness, the second coal index prediction model is expressed as Y=1.332 (lgMF) 2-1.49lgMF+9.763, the third coal index prediction model is used for predicting the third coal index, the fifth coal index prediction model is expressed as a (3735) 2-360.584, the fourth coal index prediction model is expressed as a (360.455) 2-360), the first coal index prediction model is expressed as a (360) 2-360.360), the second coal index prediction model is expressed as a (360.360) is expressed as a 5-360.96, the fourth coal index (360) is expressed as a 5-360.96), the second coal index (360.360), the second coal index (360) is expressed as a 5.360.360.96), the second coal index (360) is expressed as a 7.96, the seventh coal quality index prediction model is expressed as DeltaT= 1.551 (lgMF) 2+12.307lgMF+29.714, and the first, second, third, fourth, fifth, sixth and seventh coal quality index prediction models are used together as coal quality index prediction models; And the prediction module is used for predicting the target coal quality index of the coal sample to be predicted based on the coal quality index prediction model.
6. The apparatus of claim 5, wherein the prediction module is further to: acquiring a second Gibbs fluidity of the coal sample to be predicted; substituting the second Gibbs fluidity into the coal quality index prediction model to determine a target coal quality index of the coal sample to be predicted, wherein the target coal quality index comprises a second Ownian full expansion degree, a second colloid layer thickness, a second bonding index, a second softening temperature, a second flowing temperature, a second curing temperature and a second plasticity temperature.
7. An electronic device, comprising: at least one processor, and A memory communicatively coupled to the at least one processor, wherein, The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.
8. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-4.

Description

Coal quality index information determination method and device, electronic equipment and storage medium Technical Field The disclosure relates to the technical field of data processing, and in particular relates to a method and a device for determining coal quality index information, electronic equipment and a storage medium. Background The coal quality index information (such as Ji's fluidity, oya full expansion, gum layer thickness, bonding index, softening temperature, flowing temperature, curing temperature and plasticity temperature) of the coal sample can be used for evaluating the combustion performance and processing performance of the coal, can also be used for coke preparation process, and can also be used for evaluating the applicability and economic value of the coal in different application fields. In the related art, the coal quality index information of the coal sample needs to be determined by relying on a precise instrument and complicated experimental calculation. In this way, a great deal of cost is required for determining the coal quality index information of the coal sample, and the determination efficiency of the coal quality index information is low. Disclosure of Invention The present disclosure aims to solve, at least to some extent, one of the technical problems in the related art. Therefore, an object of the present disclosure is to provide a method, an apparatus, an electronic device, and a storage medium for determining coal quality index information, which can effectively reduce the cost of determining coal quality index information and effectively improve the efficiency of determining coal quality index information. The method for determining the coal quality index information provided by the embodiment of the first aspect of the disclosure comprises the steps of obtaining a plurality of first coal samples and the coal samples to be predicted, determining a first Gibby fluidity, a first Ownship total expansion degree, a first colloid layer thickness, a first bonding index, a first softening temperature, a first flowing temperature, a first curing temperature and a first plasticity temperature of each first coal sample, and carrying out linear fitting processing on each of the first Ownship total expansion degree, the first colloid layer thickness, the first bonding index, the first softening temperature, the first flowing temperature, the first curing temperature and the first plasticity temperature and the first Gibby fluidity in sequence to obtain a coal quality index prediction model, and predicting a target coal quality index of the coal samples to be predicted based on the coal quality index prediction model. The device for determining the coal quality index information provided by the embodiment of the second aspect of the disclosure comprises an acquisition module, a determination module, a prediction module and a processing module, wherein the acquisition module is used for acquiring a plurality of first coal samples and coal samples to be predicted, the determination module is used for determining a first Gibbs flow degree, a first Otsuba full expansion degree, a first colloid layer thickness, a first bonding index, a first softening temperature, a first flow temperature, a first curing temperature and a first plastic temperature of each first coal sample, and the processing module is used for carrying out linear fitting processing on each of the first Otsuba full expansion degree, the first colloid layer thickness, the first bonding index, the first softening temperature, the first flow temperature, the first curing temperature and the first plastic temperature and the first Gibbs flow degree in sequence to obtain a coal quality index prediction model, and the prediction module is used for predicting a target coal quality index of the coal samples to be predicted based on the coal quality index prediction model. An embodiment of a third aspect of the present disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor executes the program to implement a method for determining coal quality index information according to an embodiment of the first aspect of the present disclosure. An embodiment of a fourth aspect of the present disclosure proposes a non-transitory computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements a method for determining coal quality index information as proposed by an embodiment of the first aspect of the present disclosure. An embodiment of a fifth aspect of the present disclosure proposes a computer program product which, when executed by an instruction processor in the computer program product, performs a method for determining coal quality indicator information as proposed by an embodiment of the first aspect of the present disclosure. The method, the device, th