Optimization of Process Control Parameters for Fully Mechanized Mining Face Based on ANN and GA
In the traditional optimization mathod, the process control parameters for fully mechanized mining face are determined by experts or technicians based on their own experience, which is lack of scientific basis, and need long production adjustment cycle. It is cause large loss, and not conducive to i...
Saved in:
| Published in: | Computational intelligence and neuroscience Vol. 2021; no. 1; p. 5557831 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Hindawi
2021
John Wiley & Sons, Inc |
| Subjects: | |
| ISSN: | 1687-5265, 1687-5273, 1687-5273 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | In the traditional optimization mathod, the process control parameters for fully mechanized mining face are determined by experts or technicians based on their own experience, which is lack of scientific basis, and need long production adjustment cycle. It is cause large loss, and not conducive to improving mine production efficiency. In order to solve this problem, the study proposes a process control parameter optimization method based on a mixed strategy of artificial neural network and genetic algorithm and uses a cross-entropy cost function to optimize an artificial neural network, which improves the learning speed and fitting accuracy of the neural network. Using the historical production data of a fully mechanized coal mining face, taking the pulling speed of the shearer, hydraulic support moving speed, chain speed of scraper conveyor, chain speed of stage loader, emulsion pump outlet pressure, and spray pump outlet pressure as the optimization objects and taking the value range of each process control parameter as a constraint condition to establish a mixed strategy optimization model of process control parameters for a fully mechanized mining face, each process control parameter is optimized with the output of coal per minute as the optimization goal. The results show that the method has high accuracy and short optimization process time and can effectively improve the production efficiency of the working face. |
|---|---|
| AbstractList | In the traditional optimization mathod, the process control parameters for fully mechanized mining face are determined by experts or technicians based on their own experience, which is lack of scientific basis, and need long production adjustment cycle. It is cause large loss, and not conducive to improving mine production efficiency. In order to solve this problem, the study proposes a process control parameter optimization method based on a mixed strategy of artificial neural network and genetic algorithm and uses a cross-entropy cost function to optimize an artificial neural network, which improves the learning speed and fitting accuracy of the neural network. Using the historical production data of a fully mechanized coal mining face, taking the pulling speed of the shearer, hydraulic support moving speed, chain speed of scraper conveyor, chain speed of stage loader, emulsion pump outlet pressure, and spray pump outlet pressure as the optimization objects and taking the value range of each process control parameter as a constraint condition to establish a mixed strategy optimization model of process control parameters for a fully mechanized mining face, each process control parameter is optimized with the output of coal per minute as the optimization goal. The results show that the method has high accuracy and short optimization process time and can effectively improve the production efficiency of the working face. In the traditional optimization mathod, the process control parameters for fully mechanized mining face are determined by experts or technicians based on their own experience, which is lack of scientific basis, and need long production adjustment cycle. It is cause large loss, and not conducive to improving mine production efficiency. In order to solve this problem, the study proposes a process control parameter optimization method based on a mixed strategy of artificial neural network and genetic algorithm and uses a cross-entropy cost function to optimize an artificial neural network, which improves the learning speed and fitting accuracy of the neural network. Using the historical production data of a fully mechanized coal mining face, taking the pulling speed of the shearer, hydraulic support moving speed, chain speed of scraper conveyor, chain speed of stage loader, emulsion pump outlet pressure, and spray pump outlet pressure as the optimization objects and taking the value range of each process control parameter as a constraint condition to establish a mixed strategy optimization model of process control parameters for a fully mechanized mining face, each process control parameter is optimized with the output of coal per minute as the optimization goal. The results show that the method has high accuracy and short optimization process time and can effectively improve the production efficiency of the working face.In the traditional optimization mathod, the process control parameters for fully mechanized mining face are determined by experts or technicians based on their own experience, which is lack of scientific basis, and need long production adjustment cycle. It is cause large loss, and not conducive to improving mine production efficiency. In order to solve this problem, the study proposes a process control parameter optimization method based on a mixed strategy of artificial neural network and genetic algorithm and uses a cross-entropy cost function to optimize an artificial neural network, which improves the learning speed and fitting accuracy of the neural network. Using the historical production data of a fully mechanized coal mining face, taking the pulling speed of the shearer, hydraulic support moving speed, chain speed of scraper conveyor, chain speed of stage loader, emulsion pump outlet pressure, and spray pump outlet pressure as the optimization objects and taking the value range of each process control parameter as a constraint condition to establish a mixed strategy optimization model of process control parameters for a fully mechanized mining face, each process control parameter is optimized with the output of coal per minute as the optimization goal. The results show that the method has high accuracy and short optimization process time and can effectively improve the production efficiency of the working face. |
| Audience | Academic |
| Author | Xu, Zhihai Pan, Tao Li, Qi Zhao, Hongze |
| AuthorAffiliation | 1 School of Energy and Mining, China University of Mining and Technology (Beijing), Beijing 100083, China 3 CHN Energy Information Technology Co., Ltd., Beijing 100011, China 2 State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China |
| AuthorAffiliation_xml | – name: 2 State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China – name: 1 School of Energy and Mining, China University of Mining and Technology (Beijing), Beijing 100083, China – name: 3 CHN Energy Information Technology Co., Ltd., Beijing 100011, China |
| Author_xml | – sequence: 1 givenname: Hongze orcidid: 0000-0003-2565-9648 surname: Zhao fullname: Zhao, Hongze organization: School of Energy and MiningChina University of Mining and Technology (Beijing)Beijing 100083Chinacumtb.edu.cn – sequence: 2 givenname: Zhihai orcidid: 0000-0002-1900-4656 surname: Xu fullname: Xu, Zhihai organization: School of Energy and MiningChina University of Mining and Technology (Beijing)Beijing 100083Chinacumtb.edu.cn – sequence: 3 givenname: Qi orcidid: 0000-0002-4991-3869 surname: Li fullname: Li, Qi organization: School of Energy and MiningChina University of Mining and Technology (Beijing)Beijing 100083Chinacumtb.edu.cn – sequence: 4 givenname: Tao orcidid: 0000-0002-6447-8893 surname: Pan fullname: Pan, Tao organization: CHN Energy Information Technology Co., Ltd.Beijing 100011China |
| BookMark | eNp9kV1vFCEUhompsR965w8g8cbErgUGBubGZLtxq0m_LvSaMMyZXRoGVpjRtL--rLupsYmGBE4Oz3nPgfcYHYQYAKG3lHykVIgzRhg9E0JIVdEX6IjWSs4Ek9XBU1yLQ3Sc8x0hQgrCXqHDilPGRMWOkL7ZjG5wD2Z0MeDY49sULeSMFzGMKXp8a5IZYISUcR8TXk7e3-MrsGsT3AN0-MoFF1Z4aSzgc5NLpujMr6-xCR2-mL9GL3vjM7zZnyfo-_Lzt8WX2eXNxdfF_HJmBaXjrJNVyxVnIEjVS94wrnplStwRKkG2lspWctm1HbdMgCibJaYjlpBWtUZWJ-jTTncztQN0Fsr0xutNcoNJ9zoap_--CW6tV_GnVrRumGBF4P1eIMUfE-RRDy5b8N4EiFPWTHAiy6c1dUHfPUPv4pRCeV6hKlkWVc0famU8aBf6WPraraie102tmlpyXqjTHWVTzDlB_zQyJXrrr976q_f-Fpw9w60bf3tXxJ3_V9GHXdHahc78cv9v8Qj26bM3 |
| CitedBy_id | crossref_primary_10_3390_pr10122747 |
| Cites_doi | 10.1177/1687814019850720 10.1109/TII.2019.2908989 10.1016/j.fuel.2020.118338 10.1016/j.jastp.2020.105328 10.1049/iet-smt.2016.0425 10.1016/j.ijheatmasstransfer.2020.12014 10.1016/j.ejor.2020.02.047 10.1061/(ASCE)HE.1943-5584.0001954 10.1016/j.chemosphere.2020.127081 10.1016/j.apacoust.2020.107490 10.1016/j.applthermaleng.2020.115794 10.1016/j.catena.2020.105114 10.13272/j.issn.1671-251x.2015.07.002 10.21278/TOF.451018520 10.1016/j.jhydrol.2020.125343 10.1016/j.measurement.2019.05.019 10.1016/j.foodchem.2020.127862 10.19769/j.zdhy.2019.09.032 10.1016/J.ASOC.2021.107318 10.1016/j.jclepro.2020.122189 10.3390/sym12040622 |
| ContentType | Journal Article |
| Copyright | Copyright © 2021 Hongze Zhao et al. COPYRIGHT 2021 John Wiley & Sons, Inc. Copyright © 2021 Hongze Zhao et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2021 Hongze Zhao et al. 2021 |
| Copyright_xml | – notice: Copyright © 2021 Hongze Zhao et al. – notice: COPYRIGHT 2021 John Wiley & Sons, Inc. – notice: Copyright © 2021 Hongze Zhao et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 – notice: Copyright © 2021 Hongze Zhao et al. 2021 |
| DBID | RHU RHW RHX AAYXX CITATION 3V. 7QF 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 7X7 7XB 8AL 8BQ 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU CWDGH DWQXO F28 FR3 FYUFA GHDGH GNUQQ H8D H8G HCIFZ JG9 JQ2 K7- K9. KR7 L6V L7M LK8 L~C L~D M0N M0S M1P M7P M7S P5Z P62 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PSYQQ PTHSS Q9U 7X8 5PM |
| DOI | 10.1155/2021/5557831 |
| DatabaseName | Hindawi Publishing Complete Hindawi Publishing Subscription Journals Hindawi Publishing Open Access CrossRef ProQuest Central (Corporate) Aluminium Industry Abstracts Ceramic Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Neurosciences Abstracts Solid State and Superconductivity Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Computing Database (Alumni Edition) METADEX Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Materials Science & Engineering ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central Technology collection Natural Science Collection ProQuest One Middle East & Africa Database ProQuest Central ANTE: Abstracts in New Technology & Engineering Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student Aerospace Database Copper Technical Reference Library SciTech Premium Collection Materials Research Database ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Civil Engineering Abstracts ProQuest Engineering Collection Advanced Technologies Database with Aerospace ProQuest Biological Science Collection Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) Biological Science Database Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China One Psychology Engineering Collection ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef Publicly Available Content Database Materials Research Database ProQuest One Psychology Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China Materials Business File ProQuest One Applied & Life Sciences Engineered Materials Abstracts Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) Engineering Collection ANTE: Abstracts in New Technology & Engineering Advanced Technologies & Aerospace Collection Engineering Database Aluminium Industry Abstracts ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Electronics & Communications Abstracts ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Ceramic Abstracts Biological Science Database Neurosciences Abstracts ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Solid State and Superconductivity Abstracts Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central Aerospace Database Copper Technical Reference Library ProQuest Health & Medical Research Collection ProQuest Engineering Collection Middle East & Africa Database Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies Database with Aerospace Civil Engineering Abstracts ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest SciTech Collection METADEX Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Medical Library Materials Science & Engineering Collection Corrosion Abstracts ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic CrossRef Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: RHX name: Hindawi Publishing Open Access url: http://www.hindawi.com/journals/ sourceTypes: Publisher – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Anatomy & Physiology |
| EISSN | 1687-5273 |
| Editor | Köker, Raşit |
| Editor_xml | – sequence: 1 givenname: Raşit surname: Köker fullname: Köker, Raşit |
| ExternalDocumentID | PMC8169252 A696896744 10_1155_2021_5557831 |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GrantInformation_xml | – fundername: National Key Research and Development Program grantid: 2018YFC0808301; 2017YFC0804307 – fundername: State Key Laboratory for GeoMechanics and Deep Underground Engineering grantid: SKLGDUEK1923 |
| GroupedDBID | --- 188 29F 2WC 3V. 4.4 53G 5GY 5VS 6J9 7X7 8FE 8FG 8FH 8FI 8FJ 8R4 8R5 AAFWJ AAJEY AAKPC ABDBF ABIVO ABJCF ABUWG ACGFO ACIWK ACM ACPRK ADBBV ADRAZ AENEX AFKRA AHMBA AINHJ ALMA_UNASSIGNED_HOLDINGS AOIJS ARAPS AZQEC BAWUL BBNVY BCNDV BENPR BGLVJ BHPHI BPHCQ BVXVI CCPQU CS3 CWDGH DIK DWQXO E3Z EBD EBS EMOBN ESX F5P FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HMCUK HYE I-F IAO ICD INH INR IPY ITC K6V K7- KQ8 L6V LK8 M0N M1P M48 M7P M7S MK~ O5R O5S OK1 P2P P62 PIMPY PQQKQ PROAC PSQYO PSYQQ PTHSS Q2X RHU RHW RHX RNS RPM SV3 TR2 TUS UKHRP XH6 ~8M 0R~ 24P 2UF AAMMB AAYXX ACCMX ACUHS AEFGJ AFFHD AGXDD AIDQK AIDYY ALUQN C1A CITATION EJD H13 IHR IL9 OVT PGMZT PHGZM PHGZT PJZUB PPXIY PQGLB UZ4 7QF 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 7XB 8AL 8BQ 8FD 8FK F28 FR3 H8D H8G JG9 JQ2 K9. KR7 L7M L~C L~D PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c511t-d73b4842e503f749248f8a03fd017e7bc17b747dbd4c25e5c25c0ad0c00b8ba73 |
| IEDL.DBID | M7P |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000669016600002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1687-5265 1687-5273 |
| IngestDate | Tue Nov 04 01:57:52 EST 2025 Sun Nov 09 11:16:37 EST 2025 Sat Nov 29 14:27:00 EST 2025 Tue Nov 11 10:56:26 EST 2025 Sat Nov 29 02:55:43 EST 2025 Tue Nov 18 20:55:52 EST 2025 Sun Jun 02 18:51:55 EDT 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c511t-d73b4842e503f749248f8a03fd017e7bc17b747dbd4c25e5c25c0ad0c00b8ba73 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Academic Editor: Raşit Köker |
| ORCID | 0000-0002-4991-3869 0000-0002-6447-8893 0000-0003-2565-9648 0000-0002-1900-4656 |
| OpenAccessLink | https://www.proquest.com/docview/2537373189?pq-origsite=%requestingapplication% |
| PMID | 34122532 |
| PQID | 2537373189 |
| PQPubID | 237303 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_8169252 proquest_miscellaneous_2540722596 proquest_journals_2537373189 gale_infotracmisc_A696896744 crossref_primary_10_1155_2021_5557831 crossref_citationtrail_10_1155_2021_5557831 hindawi_primary_10_1155_2021_5557831 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-00-00 |
| PublicationDateYYYYMMDD | 2021-01-01 |
| PublicationDate_xml | – year: 2021 text: 2021-00-00 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Computational intelligence and neuroscience |
| PublicationYear | 2021 |
| Publisher | Hindawi John Wiley & Sons, Inc |
| Publisher_xml | – name: Hindawi – name: John Wiley & Sons, Inc |
| References | e_1_2_9_10_2 e_1_2_9_21_2 e_1_2_9_20_2 e_1_2_9_12_2 e_1_2_9_11_2 e_1_2_9_7_2 e_1_2_9_6_2 e_1_2_9_5_2 e_1_2_9_4_2 e_1_2_9_3_2 e_1_2_9_2_2 e_1_2_9_1_2 e_1_2_9_9_2 e_1_2_9_8_2 e_1_2_9_14_2 e_1_2_9_13_2 e_1_2_9_16_2 e_1_2_9_15_2 e_1_2_9_18_2 e_1_2_9_17_2 e_1_2_9_19_2 |
| References_xml | – ident: e_1_2_9_3_2 doi: 10.1177/1687814019850720 – ident: e_1_2_9_9_2 doi: 10.1109/TII.2019.2908989 – ident: e_1_2_9_11_2 doi: 10.1016/j.fuel.2020.118338 – ident: e_1_2_9_13_2 doi: 10.1016/j.jastp.2020.105328 – ident: e_1_2_9_4_2 doi: 10.1049/iet-smt.2016.0425 – ident: e_1_2_9_15_2 doi: 10.1016/j.ijheatmasstransfer.2020.12014 – ident: e_1_2_9_16_2 doi: 10.1016/j.ejor.2020.02.047 – ident: e_1_2_9_18_2 doi: 10.1061/(ASCE)HE.1943-5584.0001954 – ident: e_1_2_9_12_2 doi: 10.1016/j.chemosphere.2020.127081 – ident: e_1_2_9_19_2 doi: 10.1016/j.apacoust.2020.107490 – ident: e_1_2_9_20_2 doi: 10.1016/j.applthermaleng.2020.115794 – ident: e_1_2_9_21_2 doi: 10.1016/j.catena.2020.105114 – ident: e_1_2_9_1_2 doi: 10.13272/j.issn.1671-251x.2015.07.002 – ident: e_1_2_9_8_2 doi: 10.21278/TOF.451018520 – ident: e_1_2_9_14_2 doi: 10.1016/j.jhydrol.2020.125343 – ident: e_1_2_9_2_2 doi: 10.1016/j.measurement.2019.05.019 – ident: e_1_2_9_10_2 doi: 10.1016/j.foodchem.2020.127862 – ident: e_1_2_9_5_2 doi: 10.19769/j.zdhy.2019.09.032 – ident: e_1_2_9_6_2 doi: 10.1016/J.ASOC.2021.107318 – ident: e_1_2_9_17_2 doi: 10.1016/j.jclepro.2020.122189 – ident: e_1_2_9_7_2 doi: 10.3390/sym12040622 |
| SSID | ssj0057502 |
| Score | 2.2182415 |
| Snippet | In the traditional optimization mathod, the process control parameters for fully mechanized mining face are determined by experts or technicians based on their... |
| SourceID | pubmedcentral proquest gale crossref hindawi |
| SourceType | Open Access Repository Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 5557831 |
| SubjectTerms | Algorithms Analysis Artificial neural networks Automation Chain conveyors Chains Coal mines Coal mining Conveying machinery Cooperation Cost function Efficiency Entropy (Information theory) Expected values Friction stir welding Genetic algorithms Hydraulics Industrial production Learning theory Mean square errors Mineral industry Mining industry Neural networks Optimization Process controls Process parameters Production capacity |
| SummonAdditionalLinks | – databaseName: Hindawi Publishing Open Access dbid: RHX link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1da9RAFL1oUfBFtFWMVplC2xcJJrOZnZnHWFz7YLdFVPZtmK_QhTZbulul_nrvTWaXRhElEBJmMhNy5uPeyblnAPYdolxJT9E4Fh0UXzS5U1bnjseGi0aPbBcL8-2TnE7VbKbPkkjS8s9f-DjbkXtevhMCmxbFS99Xgphbn49n6wEXDY6eWjjG_kJq72t--2_PDmaeNP4-PCfP98d8YF8O2ZF3ppvJE3ic7ERW98A-hXux3YadukUf-fKWHbKOudktie-AOcV-f5kCKtmiYYn9z456Hjo7s0TBIh1NhjYqI7fzlp1ECvqd_4yBnXS7RLCJ9ZG9x2ktMCynnk6ZbQP7WD-Dr5MPX46O87RxQu7RflrlQY5cpSoeRTFqZIUulmqUxeuAyETpfCkdIhRcqDwXUeDJFzYUviiccojOc9hqF218AUyXDddRSF0KtDWK0pGCffDWceuClEUGb9cf1fikKk6bW1yYzrsQwhAEJkGQwcEm91WvpvGXfLuEj6FOhqV5bPLe1KTjo8eyqjLYT7j9q5Q1qCb1zKXhYiTxKJXOYG-TTBUQ26yNixvKQ7Jx6BiOM5CDxrCpj1S5hynt_LxT51blWHPBX_7fO76CR3TbL-rswtbq-ia-hgf--2q-vH7TtfNfpeT0UQ priority: 102 providerName: Hindawi Publishing |
| Title | Optimization of Process Control Parameters for Fully Mechanized Mining Face Based on ANN and GA |
| URI | https://dx.doi.org/10.1155/2021/5557831 https://www.proquest.com/docview/2537373189 https://www.proquest.com/docview/2540722596 https://pubmed.ncbi.nlm.nih.gov/PMC8169252 |
| Volume | 2021 |
| WOSCitedRecordID | wos000669016600002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1687-5273 dateEnd: 20250131 omitProxy: false ssIdentifier: ssj0057502 issn: 1687-5265 databaseCode: P5Z dateStart: 20080101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1687-5273 dateEnd: 20250131 omitProxy: false ssIdentifier: ssj0057502 issn: 1687-5265 databaseCode: M7P dateStart: 20080101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1687-5273 dateEnd: 20250131 omitProxy: false ssIdentifier: ssj0057502 issn: 1687-5265 databaseCode: K7- dateStart: 20080101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 1687-5273 dateEnd: 20250131 omitProxy: false ssIdentifier: ssj0057502 issn: 1687-5265 databaseCode: M7S dateStart: 20080101 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: Middle East & Africa Database customDbUrl: eissn: 1687-5273 dateEnd: 20250131 omitProxy: false ssIdentifier: ssj0057502 issn: 1687-5265 databaseCode: CWDGH dateStart: 20080101 isFulltext: true titleUrlDefault: https://search.proquest.com/middleeastafrica providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1687-5273 dateEnd: 20250131 omitProxy: false ssIdentifier: ssj0057502 issn: 1687-5265 databaseCode: BENPR dateStart: 20080101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Health & Medical Collection customDbUrl: eissn: 1687-5273 dateEnd: 20250131 omitProxy: false ssIdentifier: ssj0057502 issn: 1687-5265 databaseCode: 7X7 dateStart: 20080101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1687-5273 dateEnd: 20250131 omitProxy: false ssIdentifier: ssj0057502 issn: 1687-5265 databaseCode: PIMPY dateStart: 20080101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVWIB databaseName: Wiley Online Library Open Access customDbUrl: eissn: 1687-5273 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0057502 issn: 1687-5265 databaseCode: 24P dateStart: 20070101 isFulltext: true titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3db9MwELfYBhIvfA1EYFRGGrygaIkT18kTyqaVIWiIxofKXiJ_RavE0rF2oPHXc-c4hTwAD6iS1ciW3ernO985d78jZFcByqnQmI0jwUHRUROqTOahYrZhvMkT6XJhPr0VZZnNZnnlL9yWPqyy14lOUZuFxjvyPcYTAZ84y1-efw2xahS-XfUlNDbIFrIkMBe6V_WaGCyRLuZwDIKENPB94Dvn6PPHe5zDfk3iwZHkFfONU3SJv88HhucwbPK3c2hy-3__wR1yy1ugtOi2zF1yzbb3yHbRgvd9dkWfUxcT6i7bt0n9DjTKmU_VpIuG-rwCetBFuNNKYnAXMnRSsH4pOrRXdGoxnXj-wxo6dfUn6ERqS_fhwDQU5inKksrW0FfFffJxcvjh4Cj0JRlCDZbZKjQiUWmWMsujpBEpOG9Zk0n4bgBzK5SOhQLsjTKpZtxyaHQkTaSjSGUKcH9ANttFax8SmscNyy0XeczBiolihdz4RkvFpDJCRAF50aNSa89XjmUzvtTOb-G8Rgxrj2FAnq1Hn3c8HX8Yt4MA1yi-MJsGYdJ1gQxB-VikaUB2PfD_mqWHuPYyv6x_4RuQp-tuXADj2Fq7uMQxSEgHLuc4IGKwm9brId_3sKednzre7ywe54yzR39f_DG5iT-1uybaIZuri0v7hFzX31bz5cWIbIiZcG02Ilv7h2V1DE9vRDhyouPa99BW_AT6q9fT6jM8HR_NfgL1Jx45 |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VAoILr4IIFDBSywVFTZx4nRwQCoWl1e6GHgrqLfgVdSWalO6WavlR_EZm8ljYA3DqAUWKIsWyneSb8Ywz8w3AlsavHEtD2TgKHRQTlL5OVOpr7kouyjRSTS7Mp7HM8-ToKD1Ygx99LgyFVfY6sVHUtja0R77DRSTxCJP09elXn6pG0d_VvoRGC4uRW1ygyzZ7tf8Wv-8258N3h7t7fldVwDdoXMx9KyMdJzF3IohKGaP_kZSJwmuL03ZSm1BqnL7VNjZcOIEnEygbmCDQicapY79X4GocJZLkaiT9XvOj5dPGOA5QcIl2vg-0F4L2GMIdIVA-onBlCewWguvH5IJfTFcM3dUwzd_WveHt_-2N3YFbnYXNslYk7sKaq-7BRlapeX2yYC9YE_Pa_EzYgOIDasyTLhWV1SXr8ibYbhvBzw4UBa8RAylD656Rw75gE0fp0tPvzrJJU1-DDZVx7A0aBJZhP1meM1VZ9j67Dx8v5VEfwHpVV-4hsDQseeqETEOBVloQauL-t0ZprrSVMvDgZY-CwnR87FQW5EvR-GVCFISZosOMB9vL1qctD8kf2m0SoApST9ibQWVhiowYkNKBjGMPtjqg_auXHlJFp9NmxS88efB8eZsGoDi9ytXn1IYI99ClHnggV9C7HI_4zFfvVNPjhtc8CQcpF_zR3wd_Bjf2DifjYryfjx7DTZp2uyW2Cevzs3P3BK6Zb_Pp7OxpI5wMPl82tn8CT-Rx6w |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VAhUXXgU1UMBILRcUbeLE6-SAUGhZqNouewBUcQl-RV2JJqW7pVp-Gr-OmTwWcgBOPaBIUSRbdpx8Hs_Y38wAbGn8y7E05I2j0EAxQeHrRKW-5q7gokgjVfvCfDyQ43FydJROVuBH5wtDtMpOJtaC2laG9sgHXEQSrzBJB0VLi5jsjl6efvUpgxSdtHbpNBqI7LvFBZpvsxd7u_ivtzkfvX6_89ZvMwz4BhWNuW9lpOMk5k4EUSFjtEWSIlH4bHEITmoTSo1DsdrGhgsn8GYCZQMTBDrROAxs9wpclWhjEp1wIj51qwBqQQ3fcYiTmELQd6R7IWi_IRwIgXMlCnvLYbsoXD8mc_xi2lN6-5TN39bA0a3_-evdhput5s2yZqrcgRVX3oX1rFTz6mTBnrGaC1sfMqxD_g4l6UnrosqqgrX-FGynYfaziSJSG0UmZaj1MzLkF-zQkRv19Luz7LDOu8FGyjj2ChUFy7CdbDxmqrTsTXYPPlzKUO_DalmVbgNYGhY8dUKmoUDtLQg15QSwRmmutJUy8OB5h4jctHHaKV3Il7y214TICT95ix8Ptpe1T5v4JH-ot0ngyklsYWsGhYjJM4qMlA5lHHuw1YLuX6108MpbWTfLf2HLg6fLYuqA-Hulq86pDgXiQ1N76IHsIXnZH8U575eU0-M63nkSDlMu-IO_d_4E1hDS-cHeeP8h3KC3bnbKNmF1fnbuHsE1820-nZ09rucpg8-XDe2fzbd7Dw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Optimization+of+Process+Control+Parameters+for+Fully+Mechanized+Mining+Face+Based+on+ANN+and+GA&rft.jtitle=Computational+intelligence+and+neuroscience&rft.au=Zhao%2C+Hongze&rft.au=Xu%2C+Zhihai&rft.au=Li%2C+Qi&rft.au=Pan%2C+Tao&rft.date=2021&rft.pub=Hindawi&rft.issn=1687-5265&rft.eissn=1687-5273&rft.volume=2021&rft_id=info:doi/10.1155%2F2021%2F5557831&rft_id=info%3Apmid%2F34122532&rft.externalDocID=PMC8169252 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1687-5265&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1687-5265&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1687-5265&client=summon |