Research on a semi-supervised soft sensor modelling method for complex chemical processes based on INGO-VMD-ESN Article Swipe
Qinghong Wang
,
Longhao Li
,
Naiqing Li
,
Fengpeng Sun
,
Xuefeng Liu
,
Shuang Wang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/ad71ea
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/ad71ea
The dynamic and non-linear nature of complex chemical processes often leads to low prediction accuracy of key quality variables by traditional soft sensors, thus affecting the overall system control accuracy and operational efficiency. Therefore, this paper proposes a semi-supervised soft sensor modelling method based on improved the northern goshawk optimization (INGO)-variable mode decomposition (VMD)-echo state network (ESN). Firstly, a new semi-supervised fusion method is proposed to address the problem of model training difficulty due to the scarcity of labelled samples and process dynamics, which reconstructs the sample dataset by fusing labelled and unlabelled samples into more representative new samples, improving the model’s generalization ability. Secondly, for the noise interference present in the reconstructed data, the input data is denoised using the VMD method to improve the quality of data. Then, a soft sensor model is built based on ESN. Additionally, the denoising and prediction performance of VMD and ESN is significantly affected by parameters, therefore the paper utilizes the INGO algorithm to achieve parameter rectification for VMD and ESN. Finally, the method is validated based on actual sulphur recovery data from a refinery. The results demonstrate that the method effectively mitigates the impact of dynamics and nonlinearity in the complex chemical process which enhances prediction accuracy.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6501/ad71ea
- https://iopscience.iop.org/article/10.1088/1361-6501/ad71ea/pdf
- OA Status
- hybrid
- Cited By
- 3
- References
- 40
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4401718775Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1361-6501/ad71eaDigital Object Identifier
- Title
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Research on a semi-supervised soft sensor modelling method for complex chemical processes based on INGO-VMD-ESNWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-21Full publication date if available
- Authors
-
Qinghong Wang, Longhao Li, Naiqing Li, Fengpeng Sun, Xuefeng Liu, Shuang WangList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6501/ad71eaPublisher landing page
- PDF URL
-
https://iopscience.iop.org/article/10.1088/1361-6501/ad71ea/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
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-
hybridOpen access status per OpenAlex
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https://iopscience.iop.org/article/10.1088/1361-6501/ad71ea/pdfDirect OA link when available
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Computer science, Biological system, Artificial intelligence, Materials science, BiologyTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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40Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.VMD | 122, 147, 167 |
| abstract_inverted_index.and | 3, 31, 81, 92, 143, 148, 168, 196 |
| abstract_inverted_index.due | 74 |
| abstract_inverted_index.for | 106, 166 |
| abstract_inverted_index.key | 17 |
| abstract_inverted_index.low | 13 |
| abstract_inverted_index.new | 60, 98 |
| abstract_inverted_index.the | 26, 47, 68, 76, 86, 101, 107, 112, 115, 121, 126, 141, 156, 159, 171, 188, 192, 199 |
| abstract_inverted_index.ESN. | 139, 169 |
| abstract_inverted_index.INGO | 160 |
| abstract_inverted_index.data | 117, 180 |
| abstract_inverted_index.from | 181 |
| abstract_inverted_index.into | 95 |
| abstract_inverted_index.mode | 52 |
| abstract_inverted_index.more | 96 |
| abstract_inverted_index.soft | 22, 40, 132 |
| abstract_inverted_index.that | 187 |
| abstract_inverted_index.this | 35 |
| abstract_inverted_index.thus | 24 |
| abstract_inverted_index.Then, | 130 |
| abstract_inverted_index.based | 44, 137, 175 |
| abstract_inverted_index.built | 136 |
| abstract_inverted_index.data, | 114 |
| abstract_inverted_index.data. | 129 |
| abstract_inverted_index.input | 116 |
| abstract_inverted_index.leads | 11 |
| abstract_inverted_index.model | 71, 134 |
| abstract_inverted_index.noise | 108 |
| abstract_inverted_index.often | 10 |
| abstract_inverted_index.paper | 36, 157 |
| abstract_inverted_index.state | 55 |
| abstract_inverted_index.using | 120 |
| abstract_inverted_index.which | 84, 203 |
| abstract_inverted_index.(ESN). | 57 |
| abstract_inverted_index.actual | 177 |
| abstract_inverted_index.fusing | 90 |
| abstract_inverted_index.fusion | 62 |
| abstract_inverted_index.impact | 193 |
| abstract_inverted_index.method | 43, 63, 123, 172, 189 |
| abstract_inverted_index.nature | 5 |
| abstract_inverted_index.sample | 87 |
| abstract_inverted_index.sensor | 41, 133 |
| abstract_inverted_index.system | 28 |
| abstract_inverted_index.achieve | 163 |
| abstract_inverted_index.address | 67 |
| abstract_inverted_index.complex | 7, 200 |
| abstract_inverted_index.control | 29 |
| abstract_inverted_index.dataset | 88 |
| abstract_inverted_index.dynamic | 2 |
| abstract_inverted_index.goshawk | 49 |
| abstract_inverted_index.improve | 125 |
| abstract_inverted_index.network | 56 |
| abstract_inverted_index.overall | 27 |
| abstract_inverted_index.present | 110 |
| abstract_inverted_index.problem | 69 |
| abstract_inverted_index.process | 82, 202 |
| abstract_inverted_index.quality | 18, 127 |
| abstract_inverted_index.results | 185 |
| abstract_inverted_index.samples | 80, 94 |
| abstract_inverted_index.sulphur | 178 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Finally, | 170 |
| abstract_inverted_index.Firstly, | 58 |
| abstract_inverted_index.ability. | 104 |
| abstract_inverted_index.accuracy | 15, 30 |
| abstract_inverted_index.affected | 152 |
| abstract_inverted_index.chemical | 8, 201 |
| abstract_inverted_index.denoised | 119 |
| abstract_inverted_index.dynamics | 195 |
| abstract_inverted_index.enhances | 204 |
| abstract_inverted_index.improved | 46 |
| abstract_inverted_index.labelled | 79, 91 |
| abstract_inverted_index.northern | 48 |
| abstract_inverted_index.proposed | 65 |
| abstract_inverted_index.proposes | 37 |
| abstract_inverted_index.recovery | 179 |
| abstract_inverted_index.samples, | 99 |
| abstract_inverted_index.scarcity | 77 |
| abstract_inverted_index.sensors, | 23 |
| abstract_inverted_index.training | 72 |
| abstract_inverted_index.utilizes | 158 |
| abstract_inverted_index.Secondly, | 105 |
| abstract_inverted_index.accuracy. | 206 |
| abstract_inverted_index.affecting | 25 |
| abstract_inverted_index.algorithm | 161 |
| abstract_inverted_index.denoising | 142 |
| abstract_inverted_index.dynamics, | 83 |
| abstract_inverted_index.improving | 100 |
| abstract_inverted_index.mitigates | 191 |
| abstract_inverted_index.modelling | 42 |
| abstract_inverted_index.model’s | 102 |
| abstract_inverted_index.parameter | 164 |
| abstract_inverted_index.processes | 9 |
| abstract_inverted_index.refinery. | 183 |
| abstract_inverted_index.therefore | 155 |
| abstract_inverted_index.validated | 174 |
| abstract_inverted_index.variables | 19 |
| abstract_inverted_index.(VMD)-echo | 54 |
| abstract_inverted_index.Therefore, | 34 |
| abstract_inverted_index.difficulty | 73 |
| abstract_inverted_index.non-linear | 4 |
| abstract_inverted_index.prediction | 14, 144, 205 |
| abstract_inverted_index.unlabelled | 93 |
| abstract_inverted_index.demonstrate | 186 |
| abstract_inverted_index.effectively | 190 |
| abstract_inverted_index.efficiency. | 33 |
| abstract_inverted_index.operational | 32 |
| abstract_inverted_index.parameters, | 154 |
| abstract_inverted_index.performance | 145 |
| abstract_inverted_index.traditional | 21 |
| abstract_inverted_index.interference | 109 |
| abstract_inverted_index.nonlinearity | 197 |
| abstract_inverted_index.optimization | 50 |
| abstract_inverted_index.reconstructs | 85 |
| abstract_inverted_index.Additionally, | 140 |
| abstract_inverted_index.decomposition | 53 |
| abstract_inverted_index.reconstructed | 113 |
| abstract_inverted_index.rectification | 165 |
| abstract_inverted_index.significantly | 151 |
| abstract_inverted_index.generalization | 103 |
| abstract_inverted_index.representative | 97 |
| abstract_inverted_index.(INGO)-variable | 51 |
| abstract_inverted_index.semi-supervised | 39, 61 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5024048284, https://openalex.org/A5049348791 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I119203015 |
| citation_normalized_percentile.value | 0.81388607 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |