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Energy efficiency estimation based on data fusion strategy / Zhiqiang Geng in Industrial & engineering chemistry research, Vol. 51 N° 25 (Juin 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 25 (Juin 2012) . - pp. 8526-8534 Titre : Energy efficiency estimation based on data fusion strategy : Case study of ethylene product industry Type de document : texte imprimé Auteurs : Zhiqiang Geng, Auteur ; Yongming Han, Auteur ; Xiangbai Gu, Auteur Année de publication : 2012 Article en page(s) : pp. 8526-8534 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Energetic  efficiency Résumé : Data fusion is an emerging technology to fuse data from multiple data or information of the environment through measurement and detection to make a more accurate and reliable estimation or decision. In this Article, energy consumption data are collected from ethylene plants with the high temperature steam cracking process technology. An integrated framework of the energy efficiency estimation is proposed on the basis of data fusion strategy. A Hierarchical Variable Variance Fusion (HWF) algorithm and a Fuzzy Analytic Hierarchy Process (FAHP) method are proposed to estimate energy efficiencies of ethylene equipments. For different equipment scales with the same process technology, the HVVF algorithm is used to estimate energy efficiency ranks among different equipments. For different technologies based on HVVF results, the FAHP method based on the approximate fuzzy eigenvector is used to get energy efficiency indices (EEI) of total ethylene industries. The comparisons are used to assess energy utilization states of different equipments and technologies. It is helpful to decision-makers to identify quantitative energy consumptions and major influence factors to improve energy-saving chances. Furthermore, the proposed strategy can be used to evaluate energy efficiencies in other process units too. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26066779 [article] Energy efficiency estimation based on data fusion strategy : Case study of ethylene product industry [texte imprimé] / Zhiqiang Geng, Auteur ; Yongming Han, Auteur ; Xiangbai Gu, Auteur . - 2012 . - pp. 8526-8534. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 25 (Juin 2012) . - pp. 8526-8534 Mots-clés : Energetic  efficiency Résumé : Data fusion is an emerging technology to fuse data from multiple data or information of the environment through measurement and detection to make a more accurate and reliable estimation or decision. In this Article, energy consumption data are collected from ethylene plants with the high temperature steam cracking process technology. An integrated framework of the energy efficiency estimation is proposed on the basis of data fusion strategy. A Hierarchical Variable Variance Fusion (HWF) algorithm and a Fuzzy Analytic Hierarchy Process (FAHP) method are proposed to estimate energy efficiencies of ethylene equipments. For different equipment scales with the same process technology, the HVVF algorithm is used to estimate energy efficiency ranks among different equipments. For different technologies based on HVVF results, the FAHP method based on the approximate fuzzy eigenvector is used to get energy efficiency indices (EEI) of total ethylene industries. The comparisons are used to assess energy utilization states of different equipments and technologies. It is helpful to decision-makers to identify quantitative energy consumptions and major influence factors to improve energy-saving chances. Furthermore, the proposed strategy can be used to evaluate energy efficiencies in other process units too. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26066779

Rough set-based fuzzy rule acquisition and Its application for fault diagnosis in petrochemical process / Zhiqiang Geng in Industrial & engineering chemistry research, Vol. 48 N°2 (Janvier 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N°2 (Janvier 2009) . - p 827–836 Titre : Rough set-based fuzzy rule acquisition and Its application for fault diagnosis in petrochemical process Type de document : texte imprimé Auteurs : Zhiqiang Geng, Auteur ; Qunxiong Zhu, Auteur Année de publication : 2009 Article en page(s) : p 827–836 Note générale : chemical engineering Langues : Anglais (eng) Mots-clés : Petrochemical  Process Résumé : Data mining techniques can discover experience, knowledge, and operational rules from a large industrial data set to recognize process abnormal situations or faults, further improve production-level, and optimize operational conditions. In this paper, a rough set-based fuzzy rule acquisition approach and a fault diagnosis scheme of industrial process are studied in detail. A new heuristic reduct algorithm is proposed to obtain the optimum reduction set of decision information system. Moreover, a fuzzy discretization model for continuous data based on normal distribution of process variables is put forward to overcome the subjective of selecting fuzzy membership functions and decrease the sensitivity to noise signals. Furthermore, the proposed data mining algorithm and fault diagnosis scheme are applied into a petrochemical process. The validity of the proposed strategy is verified by application of a practical ethylene cracking furnace system, which can discover abnormal process situations and improve plant safety in petrochemical industry. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie071171g [article] Rough set-based fuzzy rule acquisition and Its application for fault diagnosis in petrochemical process [texte imprimé] / Zhiqiang Geng, Auteur ; Qunxiong Zhu, Auteur . - 2009 . - p 827–836. chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N°2 (Janvier 2009) . - p 827–836 Mots-clés : Petrochemical  Process Résumé : Data mining techniques can discover experience, knowledge, and operational rules from a large industrial data set to recognize process abnormal situations or faults, further improve production-level, and optimize operational conditions. In this paper, a rough set-based fuzzy rule acquisition approach and a fault diagnosis scheme of industrial process are studied in detail. A new heuristic reduct algorithm is proposed to obtain the optimum reduction set of decision information system. Moreover, a fuzzy discretization model for continuous data based on normal distribution of process variables is put forward to overcome the subjective of selecting fuzzy membership functions and decrease the sensitivity to noise signals. Furthermore, the proposed data mining algorithm and fault diagnosis scheme are applied into a petrochemical process. The validity of the proposed strategy is verified by application of a practical ethylene cracking furnace system, which can discover abnormal process situations and improve plant safety in petrochemical industry. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie071171g