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Application of Neural Nets for Optimization of Vibro‐Fluidization Drying
Author(s) -
Woinaroschy A.,
Jinescu G.,
Tebrencu C.
Publication year - 2000
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/(sici)1521-4125(200002)23:2<130::aid-ceat130>3.0.co;2-b
Subject(s) - fluidization , mixing (physics) , fluidized bed , process (computing) , water content , fractional factorial design , factorial experiment , moisture , potassium persulfate , degree (music) , artificial neural network , process engineering , materials science , environmental science , mathematics , engineering , waste management , computer science , composite material , geotechnical engineering , physics , statistics , polymer , quantum mechanics , machine learning , acoustics , polymerization , operating system
In order to intensify the drying process of potassium persulfate the use of vibro‐fluidized bed with vertical vibrations was selected. Due to the high mixing degree of solid particles with air in the vibro‐fluidized bed, both superficial and internal moisture have been removed. The investigation of the process has been performed through a fractional factorial experiment, repeated for 13 values of the drying time. On the base of process simulation via neural net, a constrained non‐linear programming problem was solved: identification of the values of process variables that minimize the drying time, with restriction on the desired final moisture content.