MULTILAYER PERCEPTRON AS THE TOOL FOR MODELING OF REACTION CRYSTALLIZATION OF BARIUM SULPHATE IN MSMPR CRYSTALLIZER
One of the most ecologically harmful industrial wastes are the post-processed, used quenching salts, especially rich in BaCl2. Original method of their neutralization is based on their dissolution in water followed by a complex reaction crystallization process (after solid (NH4)2SO4 addition) effecting in production of barium sulphate. The process regime, which determines the crystalline product quality, depends on many technological parameters which individually influence various partial processes in micro- and macroscale. Facing this intrinsic complexity of the process its reliable analytical model has not been elaborated up till now. Application of artificial neural networks (e.g. multilayer perceptrons) for thorough description of such complex systems is substantiated since these need only the scattered information incorporated within the raw experimental data. An alternative model of the system behavior - numerical but free of any simplifying assumptions - is thus possible. Neural network simulation effects concerning reaction crystallization of barium sulphate in a DT MSMPR crystallizer are presented and discussed.
Neural network model multilayer perceptron process effects prediction barium sulphate reaction-crystallization quenching salts neutralization ecology
KATARZYNA PENTOS KRZYSZTOF PIOTROWSKI JOANNA KORALEWSKA ANDRZEJ MATYNIA
Wroclaw University of Technology, Faculty of Electronics, Wroclaw, Poland Silesian University of Technology, Department of Chemical & Process Engineering, Gliwice, Poland Wroclaw University of Technology, Faculty of Chemistry, Wroclaw, Poland
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
3413-3417
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）