<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml">One of the main toxic components of post quenching salts formed in large quantities during steel hardening processes is BaCl2. This dangerous ingredient can be chemically neutralized after dissolution in water by means of reaction crystallization with solid ammonium sulphate (NH4)2 SO4. The resulting size distribution of the ecologically harmless crystalline product - BaSO4 - is an important criteria deciding about its further applicability. Presence of a second component of binary quenching salt mixture (BaCl2-NaCl) in water solution, NaCl, influences the reaction-crystallization process kinetics affecting the resulting product properties. The experimental 39 input-output data vectors containing the information about the continuous reaction crystallization in BaCl2 - (NH4)2 SO4 - NaCl - H2 O system ([BaCl2]RM = 10-24 mass %, [NaCl]RM = 0-12 mass %, <italic>T</italic> = 305-348 K and τ = 900-9000 s) created the database for the neural network training and validation. The applicability of diversified network configurations, neuron types and training strategies were verified. An optimal network structure was used for the process modeling.</abstract>

One of the main toxic components of post quenching salts formed in large quantities during steel hardening processes is BaCl2. This dangerous ingredient can be chemically neutralized after dissolution in water by means of reaction crystallization with solid ammonium sulphate (NH4)2 SO4. The resulting size distribution of the ecologically harmless crystalline product - BaSO4 - is an important criteria deciding about its further applicability. Presence of a second component of binary quenching salt mixture (BaCl2-NaCl) in water solution, NaCl, influences the reaction-crystallization process kinetics affecting the resulting product properties. The experimental 39 input-output data vectors containing the information about the continuous reaction crystallization in BaCl2 - (NH4)2 SO4 - NaCl - H2 O system ([BaCl2]RM = 10-24 mass %, [NaCl]RM = 0-12 mass %, T = 305-348 K and τ = 900-9000 s) created the database for the neural network training and validation. The applicability of diversified network configurations, neuron types and training strategies were verified. An optimal network structure was used for the process modeling.

Kinetics of the continuous reaction crystallization of barium sulphate in BaCl2 - (NH4)2 SO4 - NaCl - H2O system - neural network model

Department of Chemical & Process Engineering, Silesian University of Technology, ks. M. Strzody 7, 44-101 Gliwice, Poland

Faculty of Chemistry, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

Polish Journal of Chemical Technology

Kinetics of the continuous reaction crystallization of barium sulphate in BaCl - (NH) SO - NaCl - HO system - neural network model

One of the main toxic components of post quenching salts formed in large quantities during steel hardening processes is BaCl2. This dangerous ingredient can be...

Kinetics of the continuous reaction crystallization of barium sulphate in BaCl₂ - (NH₄)₂ SO₄ - NaCl - H₂O system - neural network model

Publié en ligne: 08 janv. 2010

Pages: 13 - 19

DOI: https://doi.org/10.2478/v10026-009-0037-7

Mots clés
barium sulphate, sodium ions, used quenching salts, steel hardening, barium chloride, reaction crystallization kinetics, population density distribution, chemical neutralization, solid waste utilization, neural network model

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