http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114678080-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_4a50b4c9d3ea196fec12d8b31e1343d4 |
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C21C2300-06 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N7-01 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C21C7-064 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16C20-70 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16C20-30 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C21C5-28 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16C20-70 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16C20-30 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/C21C5-28 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N7-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/C21C7-064 |
| filingDate | 2022-03-28-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c16098b50db6d64347bd0b41a3f14268 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_658b36890a48a0ec06682ae9a06cd025 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_0139b890baf3dd5af7744b49bdcd78fa |
| publicationDate | 2022-06-28-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-114678080-A |
| titleOfInvention | Prediction model and construction method of phosphorus content at the end point of converter, and phosphorus content prediction method |
| abstract | The invention provides a phosphorus content prediction model and construction method at the end point of a converter, and a phosphorus content prediction method, belonging to the field of metallurgical control. The model building method includes: collecting historical data of relevant converter steelmaking, preprocessing the historical data, and obtaining clean historical data; from the clean historical data, determining factors affecting the phosphorus content at the end point, and determining factors affecting the phosphorus content at the end point according to the value of the factors. and the actual value of phosphorus content to construct training set, test set and validation set; construct at least two sub-models based on machine learning for predicting phosphorus content at the end of the converter, and use the training set to train multiple prediction sub-models, and based on Bayesian The algorithm couples the prediction results of multiple prediction sub-models, constructs a Bayesian weight network model, and together with multiple prediction sub-models constitutes a prediction model for the end-point phosphorus content of the converter. The invention makes up for the shortage of artificial experience and static model in applicability and hit rate, and improves the prediction accuracy and precision of the phosphorus content at the end point. |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115058555-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-116109012-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-116030900-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115456264-A |
| priorityDate | 2022-03-28-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 32.