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To construct a prediction model for carbon emission from coal-fired power plants and address the problem of general lack of real-time elemental analysis for coal entering the furnace of coal-fired units, according to the in-furnace coal quality information of a million kilowatt unit in 2023, the low calorific value, volatile matter, and sulfur content were used as the basis for coal quality classification, K-means++ algorithm was used for clustering analysis, and correlation analysis was used to screen the input parameters of the carbon emission prediction model. The BP neural network suffered Bayesian optimization was used to construct carbon emission prediction models for each cluster data after clustering, and the models were tested for working conditions such as load increase and decrease. The results show that, the accuracy of the coal quality clustering model in predicting carbon emissions increases significantly. Compared with the non clustered model, the optimal cases of average root mean square error and average relative error reduce by about 53.4% and 49.2%, respectively. Especially under variable load conditions, the predicted results are more in line with the actual values. This indicates that the proposed method can not only effectively predict the carbon emissions of coal-fired power plants, but also maintain high accuracy in the case of complex and variable coal quality.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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