Hybrid training approach for artificial neural networks using genetic algorithms for rate of reaction estimation: Application to industrial methanol oxidation to formaldehyde on silver catalyst

Chemical Engineering Journal

Volumn 157, Issue 2-3, 2010, Pages 501-508

  Papes Filho, Antonio Carlos ^a   Maciel Filho, Rubens ^a  

^a UNIVERSITY OF CAMPINAS   (Brazil)

Author keywords

Artificial intelligence; Artificial neural networks; Formaldehyde; Genetic algorithms; Kinetic modeling; Methanol oxidation; Silver

Indexed keywords

ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORKS; CALCULATION PROCEDURE; DETERMINISTIC MODELS; EXPERIMENTAL DATA; HYBRID TRAINING; INITIAL WEIGHTS; KINETIC MODELING; KINETIC MODELS; METHANOL OXIDATION; NEURAL NET; NEURAL NETWORK TRAINING; OPERATING CONDITION; PLANT PROCESS; PROCESS DATA; RATE OF REACTION; REACTOR SIMULATOR; RESOURCE LIMITATIONS; RESPONSE TIME; SILVER CATALYST;

CATALYSIS; CATALYSTS; FORMALDEHYDE; GENETIC ALGORITHMS; IGNEOUS ROCKS; INDUSTRIAL PLANTS; KINETIC THEORY; METHANOL; OXIDATION; REACTION RATES; SILVER; SIMULATORS;

NEURAL NETWORKS;

EID: 76449093402     PISSN: 13858947     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cej.2009.12.045     Document Type: Article

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