A Deep Learning-Based Parameter Prediction Method for Coal Slime Blending Circulating Fluidized Bed Units Article Swipe
Jiyu Chen
,
Hong Feng
,
Mingming Gao
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app12136652
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app12136652
Coal slime blending can effectively improve the utilization rate of fossil fuels and reduce environmental pollution. However, the combustion in the furnace is unstable due to the empty pump phenomenon during the coal slurry transport. The combustion instability affects the material distribution in the furnace and harms the unit operation. The bed pressure in the circulating fluidized bed unit reflects the amount of material in the furnace. An accurate bed pressure prediction model can reflect the future material quantity in the furnace, which helps adjust the operation of the unit in a timely fashion. Thus, a deep learning-based prediction method for bed pressure is proposed in this paper. The Pearson correlation coefficient with time correction was used to screen the input variables. The Gaussian convolution kernels were used to implement the extraction of inertial delay characteristics of the data. Based on the computational theory of the temporal attention layer, the model was trained using the segmented approach. Ablation experiments verified the innovations of the proposed method. Compared with other models, the mean absolute error of the proposed model reached 0.0443 kPa, 0.0931 kPa, and 0.0345 kPa for the three data sets, respectively, which are better than those of the other models.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app12136652
- https://www.mdpi.com/2076-3417/12/13/6652/pdf?version=1656921513
- OA Status
- gold
- Cited By
- 3
- References
- 38
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://doi.org/10.3390/app12136652Digital Object Identifier
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A Deep Learning-Based Parameter Prediction Method for Coal Slime Blending Circulating Fluidized Bed UnitsWork title
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articleOpenAlex work type
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enPrimary language
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2022Year of publication
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2022-06-30Full publication date if available
- Authors
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Jiyu Chen, Hong Feng, Mingming GaoList of authors in order
- Landing page
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https://doi.org/10.3390/app12136652Publisher landing page
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https://www.mdpi.com/2076-3417/12/13/6652/pdf?version=1656921513Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/12/13/6652/pdf?version=1656921513Direct OA link when available
- Concepts
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Coal, Combustion, Process engineering, Petroleum engineering, Computer science, Environmental science, Waste management, Engineering, Chemistry, Organic chemistryTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 1, 2024: 1, 2023: 1Per-year citation counts (last 5 years)
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.and | 12, 45, 183 |
| abstract_inverted_index.are | 193 |
| abstract_inverted_index.bed | 51, 57, 69, 101 |
| abstract_inverted_index.can | 3, 73 |
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| abstract_inverted_index.for | 100, 186 |
| abstract_inverted_index.kPa | 185 |
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| abstract_inverted_index.deep | 96 |
| abstract_inverted_index.kPa, | 180, 182 |
| abstract_inverted_index.mean | 171 |
| abstract_inverted_index.pump | 28 |
| abstract_inverted_index.rate | 8 |
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| abstract_inverted_index.with | 112, 167 |
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| abstract_inverted_index.Thus, | 94 |
| abstract_inverted_index.data. | 138 |
| abstract_inverted_index.delay | 134 |
| abstract_inverted_index.empty | 27 |
| abstract_inverted_index.error | 173 |
| abstract_inverted_index.fuels | 11 |
| abstract_inverted_index.harms | 46 |
| abstract_inverted_index.helps | 83 |
| abstract_inverted_index.input | 120 |
| abstract_inverted_index.model | 72, 150, 177 |
| abstract_inverted_index.other | 168, 199 |
| abstract_inverted_index.sets, | 190 |
| abstract_inverted_index.slime | 1 |
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| abstract_inverted_index.which | 82, 192 |
| abstract_inverted_index.0.0345 | 184 |
| abstract_inverted_index.0.0443 | 179 |
| abstract_inverted_index.0.0931 | 181 |
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| abstract_inverted_index.future | 76 |
| abstract_inverted_index.layer, | 148 |
| abstract_inverted_index.method | 99 |
| abstract_inverted_index.paper. | 107 |
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| abstract_inverted_index.screen | 118 |
| abstract_inverted_index.slurry | 33 |
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| abstract_inverted_index.timely | 92 |
| abstract_inverted_index.Pearson | 109 |
| abstract_inverted_index.affects | 38 |
| abstract_inverted_index.furnace | 21, 44 |
| abstract_inverted_index.improve | 5 |
| abstract_inverted_index.kernels | 125 |
| abstract_inverted_index.method. | 165 |
| abstract_inverted_index.models, | 169 |
| abstract_inverted_index.models. | 200 |
| abstract_inverted_index.reached | 178 |
| abstract_inverted_index.reflect | 74 |
| abstract_inverted_index.trained | 152 |
| abstract_inverted_index.Ablation | 157 |
| abstract_inverted_index.Compared | 166 |
| abstract_inverted_index.Gaussian | 123 |
| abstract_inverted_index.However, | 16 |
| abstract_inverted_index.absolute | 172 |
| abstract_inverted_index.accurate | 68 |
| abstract_inverted_index.blending | 2 |
| abstract_inverted_index.fashion. | 93 |
| abstract_inverted_index.furnace, | 81 |
| abstract_inverted_index.furnace. | 66 |
| abstract_inverted_index.inertial | 133 |
| abstract_inverted_index.material | 40, 63, 77 |
| abstract_inverted_index.pressure | 52, 70, 102 |
| abstract_inverted_index.proposed | 104, 164, 176 |
| abstract_inverted_index.quantity | 78 |
| abstract_inverted_index.reflects | 59 |
| abstract_inverted_index.temporal | 146 |
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| abstract_inverted_index.verified | 159 |
| abstract_inverted_index.approach. | 156 |
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| abstract_inverted_index.segmented | 155 |
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| abstract_inverted_index.extraction | 131 |
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| abstract_inverted_index.correlation | 110 |
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| abstract_inverted_index.environmental | 14 |
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| abstract_inverted_index.learning-based | 97 |
| abstract_inverted_index.characteristics | 135 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5100369155 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I153473198 |
| citation_normalized_percentile.value | 0.58378945 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |