The mathematical model of mechanical pulp bleaching process
Applications of Large Scale Industrial Systems, Volume # 1 | Part# 1
Fisera Peter; Holler Manfred
Digital Object Identifier (DOI)
mechanical pulp,peroxide,chromophore groups,bleaching process,brightness,delignification,model predictive controller
The control task of the bleaching process is complicated by long retention time and strong process nonlinearity. The classical control tools are ineffective because of long retention time and new neuronal control systems have serious difficulties with process nonlinearity. After process change, they have to learn again to find parameters for the new working point. To support learning process of the multivariable controllers, the mathematical model of the bleaching process has been designed. The basic kinetic model is the reaction of dissociated HOO- molecules with chromophore groups. Ongoing reaction decreases HOO- concentration and changes bleaching conditions for following reactions. The NaOH creates conditions for peroxide dissociation and also attacks the lignin molecules, decreasing yield of bleaching process. The yellowing reaction due to high concentration of NaOH is included, as well. The peroxide decomposition process caused by high temperature or heavy metals is also part of the bleaching model. To simplify solution of complicated system of partial differential equations, the whole volume of the bleaching tower has been vertically divided to thin slices. The bleaching conditions in one slice are considered to be homogenous an constant during time t and t+? t. The chromophore are divided into the light, difficult bleachable and non bleachable groups with corresponding consumption of HOO- molecules. The supporting chemical reactions are also calculated in each slice separately creating conditions for next calculation step. The parameters of the chemical reactions have to be found using laboratory results. The model of peroxide bleaching process forecasts the bleaching results with an accuracy of ±1% taking into consideration, of course, that the main process variables are measured. Bigger deviation between calculated and measured results means significant changes in not measured process variables. This fact can be used as alarm. The model of the bleaching process integrated into multivariable model predictive controller enables setting of controller parameters in short time instead to learn controller from process. The model of bleaching process was tuned according to the results of laboratory bleaching trials. After setting of model parameters the model reached laboratory values for final brightness, pH, residual peroxide and COD. Model has been tuned to several different bleaching trials of mechanical pulps and also to results of laboratory bleaching of waste paper has been used to prove model universality. In all cases it was possible to tune model to meet bleaching results according to the used bleaching recipe. The model was also tested on-line in field-side reference HC Bleach Plant. Model parameters were tuned according to the laboratory results. Measured process values like pulp flow, consistency, charging of bleaching chemical and incoming brightness have been used as model variables. The Model calculated bleaching reactions and the out coming calculated brightness was over 90% of tested period very closed to the measured one (±1% ).
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