LEADER 02342nam 22005293 450 001 9911044026503321 005 20250821173203.0 010 $a0-443-29059-8 035 $a(MiAaPQ)EBC32112581 035 $a(Au-PeEL)EBL32112581 035 $a(CKB)38791102100041 035 $a(OCoLC)1520914631 035 $a(EXLCZ)9938791102100041 100 $a20250311d2025 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine learning tools for chemical engineering $emethodologies and applications /$fFrancisco Javier Lo?pez-Flores, Rogelio Ochoa-Barraga?n, Alma Yunuen Raya-Tapia, Ce?sar Rami?rez-Ma?rquez, Jose? Maria Ponce-Ortega 205 $a1st ed. 210 $cElsevier Science [Imprint]$aSan Diego $cElsevier Science & Technology Books$aSaint Louis $cElsevier [Distributor]$aSaint Louis $cElsevier [Distributor] 210 1$aChantilly :$cElsevier Science & Technology,$d2025. 210 4$d©2026. 215 $a1 online resource (630 pages) 311 08$a0-443-29058-X 330 $aMachine Learning Tools for Chemical Engineering: Methodologies and Applications examines how machine learning (ML) techniques are applied in the field, offering precise, fast, and flexible solutions to address specific challenges.ML techniques and methodologies offer significant advantages (such as accuracy, speed of execution, and flexibility). 606 $aChemical engineering$xData processing 606 $aMachine learning$xIndustrial applications 606 $aGe?nie chimique$xInformatique 606 $aApprentissage automatique$xApplications industrielles 615 0$aChemical engineering$xData processing. 615 0$aMachine learning$xIndustrial applications. 615 6$aGe?nie chimique$xInformatique. 615 6$aApprentissage automatique$xApplications industrielles. 676 $a660.0285631 700 $aLo?pez-Flores$b Francisco Javier$01851466 702 $aRaya-Tapia$b Alma Yunuen 702 $aOchoa-Barragán$b Rogelio 702 $aRamírez-Márquez$b César 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ0 906 $aBOOK 912 $a9911044026503321 996 $aMachine learning tools for chemical engineering$94460823 997 $aUNINA