Dissertation
Dissertation > Industrial Technology > Metallurgical Industry > Nonferrous metal smelting > Light metal smelting > Aluminum

Caustic dissolution rate ratios based prediction model parameter optimization settings ingredients Bayer Research

Author TanHeJun
Tutor GuiWeiHua
School Central South University
Course Control Theory and Control Engineering
Keywords Original pulp ingredients Grey Model Fuzzy Identification Liquid-solid ratio Soft Measurement
CLC TF821
Type Master's thesis
Year 2005
Downloads 63
Quotes 2
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Alumina and alumina raw slurry ingredients is high pressure digestion Bayer alumina production two important process , their mutual contact and mutual influence . Original pulp ingredients are Bayer alumina production in the first process , the task is to prepare qualified high pressure digestion process raw pulp , can prepare to meet the production requirements of the original pulp, will directly affect the dissolution rate of this caustic dissolution ratios two important economic and technical indicators . But now thanks to manual calculation operation batching parameters , and does not reflect changes in working conditions dissolution , resulting in caustic dissolution rate ratios and unstable , unable to meet the actual control requirements. Therefore, this paper proposes a ratio based on the dissolution and dissolution rate parameters caustic ingredients prediction model parameter optimization setting model , effective solution to the problem formulation process parameter optimization settings . The main research results include: ( 1 ) analysis of high alumina dissolution process on the basis of the mechanism to determine the effect of caustic dissolution rate ratios of the main factors , ratios presented caustic dissolution rate mechanism model ; then presented based on the main multi- element analysis of the neural network model, in order to establish caustic dissolution rate ratios and mechanistic models and neural network intelligent integrated predictive models. ( 2 ) in the analysis of raw slurry blending process , based on the identified impact ore slurry to solid ratio of the main factors and the relationship between them , according to the material balance principle to establish a liquid to solid ratio of mechanistic models . Mechanistic model in which the material composition parameters using gray prediction model to solve the problem of parameter detection lag . ( 3 ) In order to solve the liquid-solid ratio mechanism model does not reflect changes in working conditions during the dissolution process defects in the analysis of the mechanism of the dissolution process , based on the theory of fuzzy identification data from a large number of factory floor to dig out the pulp caustic dissolution and dissolution rate ratio ore slurry to solid ratio of fuzzy expert rules , and according to the Bayer process caustic dissolution dissolution rate ratios soft sensor model predicted values ??, mechanistic model for liquid-solid ratio correction . Simulation results show that the use of caustic dissolution rate ratios intelligent integration of predictive models for batching liquid to solid ratio parameter optimization effect is good, stable production .

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