Method for predicting unit calorific value carbon content based on bituminous coal quality industrial analysis

Abstract

The invention relates to the technical field of bituminous coal carbon content prediction, and discloses a bituminous coal coal quality industrial analysis-based unit calorific value carbon content prediction method, which comprises the following steps of 1, sampling a target coal type for industrial analysis, and detecting the total moisture Mt, the ash content Aar, the volatile component Var and the fixed carbon FCar content of the target coal type; measuring the calorific value of the sampled coal to obtain the lower calorific value Qnet, ar of the coal type; step 2, calculating a dry ash-free basis volatile component Vdaf and a lower calorific value Qnet, daf of the target coal type; 3, predicting the carbon content CQdaf of the dry ash-free basis unit calorific value of the target coal type; and step 4, calculating the unit calorific value carbon content CQar of the target coal type. According to the method, the unit calorific value carbon content of the bituminous coal can be accurately predicted, and coal purchase is guided; the coal carbon content is further calculated by using the unit calorific value carbon content calculated by the method, the prediction precision is very high, and the correlation coefficient can reach 1-10-10.

Description

Technical Field

[0001] This invention relates to the field of bituminous coal carbon content prediction technology, specifically to a method for predicting carbon content per unit calorific value based on industrial analysis of bituminous coal quality. Background Technology

[0002] Under a carbon emissions trading system, the carbon content per unit calorific value of coal directly affects its usage cost. Coal-fired power plants and other coal users need to assess carbon content to optimize their procurement strategies. However, in the current coal pricing mechanism, the available coal quality indicators are mostly limited to industrial analysis items (such as moisture, ash, volatile matter, fixed carbon, and sulfur). In contrast, elemental analysis is complex, expensive, and time-consuming. Introducing such testing at the pricing stage would drive up overall transaction costs.

[0003] Currently, there are some empirical models in the coal-fired power sector that can estimate the carbon content of coal based on industrial analysis results. For example, the formula proposed in "Coal Analysis Technology Q&A (Third Edition)" (see Formula 1 below) estimates the calorific value by performing linear regression on dry basis ash, volatile matter, sulfur content, and calorific value. This type of model performs excellently in calorific value prediction, with correlation coefficients often exceeding 0.99, mainly due to the close correlation between dry basis calorific value and carbon content. However, when carbon content is divided by calorific value to obtain the carbon content per unit calorific value, the aforementioned correlation is neutralized, leading to a significant decrease in the model's estimation accuracy for this indicator. Given that coal-fired unit operation focuses on target output rather than fixed coal consumption, the carbon content per unit calorific value is actually the core parameter determining combustion carbon emissions. Only by accurately predicting this value can carbon emission-based procurement be effectively supported.

[0004] Cd=35.411 - 0.341Ad - 0.199Vd - 0.412St,d + 1.632Qgr,d (1) In the formula, Cd is the carbon content of coal on a dry basis (%), Ad is the ash content of coal on a dry basis (%), Vd is the volatile matter of coal on a dry basis (%), St,d is the sulfur content of coal on a dry basis (%), and Qgr,d is the calorific value of coal on a dry basis (MJ / kg).

[0005] The linear structure of this formula limits the adjustment space for the coefficients, making it difficult to significantly improve accuracy. The main problem is that it equates ash content with volatile matter and linearly affects carbon content with calorific value. In reality, ash content only has a dilution effect on calorific value. If the influence of ash content is eliminated first, and the carbon content per unit calorific value is estimated using the proportion of dry, ash-free volatile matter and calorific value, and then ash and moisture are added to correct the calorific value, the reliability of the results can be significantly improved.

[0006] Therefore, there is a need for a method to directly predict the carbon content per unit calorific value using coal industry analysis data, in order to predict the carbon emission intensity of burning a specific type of coal and guide coal procurement when the elemental composition of coal is unknown. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention provides a method for predicting the carbon content per unit calorific value using coal industry analysis data.

[0008] The technical solution of the present invention is as follows: Step 1: Sample the target coal for industrial analysis, and test its total moisture (Mt) (%), ash (Aar) (%), volatile matter (Var) (%), and fixed carbon (FCar) (%). Measure the calorific value of the sampled coal to obtain the lower heating value (Qnet,ar) (MJ / kg).

[0009] Step 2: Calculate the dry ash-free volatile matter Vdaf (%) and lower heating value Qnet,daf (MJ / kg) of the target coal using Equations 2 and 3: Vdaf = Var * 100 / (100 – Mt - Aar) (2) Qnet,daf = (Qnet,ar – Mt * 0.025) * 100 / (100 – Mt – Aar) (3) Step 3, use Equation 4 to predict the carbon content CQdaf of the target coal on a dry ash-free basis (lower calorific value): CQdaf = (458.5 - 0.944 * Vdaf-5.20 * Qnet,daf) / 10000 (4) Step 4: Calculate the carbon content CQar per unit calorific value of the target coal using Equation 5: CQar = CQdaf / [Qnet,daf – Mt / (100 - Mt) / (1-Ad / 100) * 2.5] *Qnet,daf (5) Ad = Aar * 100 / (100 - Mt) (6).

[0010] The beneficial effects of this invention are as follows: This invention enables high-precision prediction of carbon content per unit calorific value (tC / GJ) using only conventional industrial analysis data, lower heating value, and total sulfur, without the need for expensive and time-consuming elemental analysis. While traditional empirical formulas can predict total carbon content relatively accurately (R>0.99), their accuracy drops significantly after conversion to carbon content per unit calorific value (typically R<0.6), failing to meet the demand for precise procurement guidance under carbon emission trading.

[0011] The formula structure of this invention overcomes the structural defects of traditional linear formulas, enabling it to achieve higher accuracy. Verification showed that, on 2858 independent coal samples, the correlation coefficient between the predicted carbon content per unit calorific value and the measured value reached 0.90, and the correlation coefficient for inferring the total carbon content reached 1-10. 9 Its accuracy far exceeds that of existing technologies.

[0012] This method is simple to operate and costs almost nothing, yet it can provide reliable carbon emission density data before trading, helping thermal power companies to select coal types "based on the lowest carbon emissions" and significantly reduce overall coal costs. It has outstanding substantive features and significant progress. Detailed Implementation

[0013] The present invention will be further described below with reference to the embodiments. Example

[0014] This invention is further explained by predicting the carbon content per unit calorific value of 2858 sets of known industrial analysis data and low-calorific-value coal, and comparing it with the carbon content per unit calorific value calculated from the measured elemental carbon content: Step 1: Obtain industrial analysis data for 2858 coal types: total moisture (Mt) (%), ash (Aar) (%), volatile matter (Var) (%), and fixed carbon (FCar) (%); obtain the lower heating value (Qnet,ar) (MJ / kg) for these coal types.

[0015] Step 2: Calculate the dry ash-free volatile matter Vdaf (%) and lower heating value Qnet,daf (MJ / kg) for all coal types using Equations 2 and 3: Vdaf = Var * 100 / (100 – Mt - Aar) (2) Qnet,daf = (Qnet,ar – Mt * 0.025) * 100 / (100 – Mt – Aar) (3) Step 3: Predict the carbon content (lower calorific value) CQdaf of all coal types on a dry ash-free basis using Equation 4: CQdaf = (458.5 - 0.944 * Vdaf-5.20 * Qnet,daf) / 10000 (4) Step 4: Calculate the carbon content CQar per unit calorific value for all coal types using Equation 5: CQar = CQdaf / [Qnet,daf – Mt / (100 – Mt) / (1 – Ad / 100) *2.5] *Qnet,daf (5) Ad = Aar * 100 / (100 - Mt) (6).

[0016] Step 5: Predict the carbon content of coal, Car = CQar * Qnet,ar. Obtain the measured elemental carbon content of the above coal types and compare it with the predicted value to obtain the correlation coefficient r = 1-10. -10 The carbon content per unit calorific value of these coal types was calculated using the measured elemental carbon content, and compared with the predicted value, resulting in a correlation coefficient r = 0.90.

[0017] The formula for the correlation coefficient is: Table 1 shows some of the coal quality data used to verify this formula, and Table 2 shows a comparison between the predicted results and the actual values.

[0018] Table 1. Coal quality data (partial) used to verify the prediction formula.

[0019] Table 2 Comparison of predicted and actual values ​​(partial)

[0020] This method accurately predicts the carbon content and carbon content per unit calorific value of coal. During the development of this method, the sulfur content of all coal types in the database was low (<2.0%), and no correlation was found between sulfur content and carbon content per unit calorific value. Based on the coal quality data used in the development of this method, the coal quality should meet the requirements of Table 3 below when using this method to predict the calorific value of coal: Table 3. Range of coal quality data used in the establishment of this method .

Claims

1. A method for predicting carbon content per unit calorific value based on industrial analysis of bituminous coal quality, characterized in that, Includes the following steps: Step 1: Sample the target coal for industrial analysis, and test its total moisture (Mt), ash (Aar), volatile matter (Var), and fixed carbon (FCar) content; measure the calorific value of the sampled coal to obtain the lower heating value (Qnet,ar) of the coal. Step 2: Calculate the dry ash-free volatile matter Vdaf and lower heating value Qnet,daf of the target coal type; Step 3: Predict the carbon content (CQdaf) of the target coal on a dry ash-free basis. Step 4: Calculate the carbon content (CQar) per unit calorific value of the target coal.

2. The method for predicting carbon content per unit calorific value based on industrial analysis of bituminous coal quality according to claim 1, characterized in that, Step 2 is described in detail below: The formula for calculating the dry ash-free volatile matter Vdaf is as follows: Vdaf = Var * 100 / (100 - Mt - Aar) (1) The formula for calculating the lower heating value Qnet,daf on a dry ash-free basis is as follows: Qnet,daf = (Qnet,ar + Mt * 0.025) * 100 / (100 - Mt - Aar) (2).

3. The method for predicting carbon content per unit calorific value based on industrial analysis of bituminous coal quality according to claim 1, characterized in that, Step 3 is described in detail below: The formula for predicting the carbon content (CQdaf) per unit calorific value of the target coal type on a dry ash-free basis is as follows: CQdaf =(458.5 - 0.944 * Vdaf-5.20 * Qnet,daf) / 10000 (3).

4. The method for predicting carbon content per unit calorific value based on industrial analysis of bituminous coal quality according to claim 1, characterized in that, Step 4 is as follows: The formula for calculating the carbon content (CQar) per unit calorific value of the target coal type is as follows: CQar = CQdaf / [Qnet,daf-Mt / (100-Mt) / (1-Ad / 100)*2.5]*Qnet,daf (4) In the formula, Ad is the dry basis ash content: Ad = Aar * 100 / (100 - Mt) (5).

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