This paper presents a new modelling approach for a pH-process inthe wet end approaching systems in papermaking, which play a veryimportant role in the paper industry as the quality of finished paperdepends on the different types of added chemicals whose reaction arevery sensitive to pH values. pH control can be characterised by itssevere nonlinearity as reflected in the titration curve. By taking thestrong acid equivalent as the state variable in the reduced model, abilinear model of the system is established, which is connected by thesevere nonlinearity. The estimation of the equivalent titration curve isperformed via a B-spline neural network and algorithms for parameteridentification are developed
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