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TitleImplementation and performance analysis of a model-based controller on a batch pulp digester
AuthorSandrock, Carl
SubjectModel based control
SubjectMonte carlo
SubjectPerformance analysis
SubjectBatch control
SubjectSulphite process
AbstractThe control of batch pulp digesters is hampered by insufficient measurements as well as nonlinearity and weak correlation between consecutive cooks. This makes a model-based approach to control attractive. Due to the age of the industry, many legacy controllers are in place on digesters around the world. The theoretical variance obtained by Monte Carlo modelling of a new controller is used as a benchmark for performance comparison between an old control system (S-factor) and a new model based controller developed by the University of Pretoria (the UP controller). This study covers the development of the controller, Monte Carlo modelling of the old and new controllers and in-situ testing of the UP controller on an operating digester. During Monte Carlo simulation, the UP controller outperformed the legacy controller, obtaining a theoretical overall variance of 3,07 (which will be used as the baseline for performance measurement) while also showing larger responses to tuning factors. The S-factor performed at 6,8 times the theoretical optimum variance during in situ testing, while the UP controller performed at 3,9 times the theoretical optimum (43% better than the S-factor controller). An average error 90% lower than that of the S-factor controller was obtained when using the UP controller. Additional benefits of the new controller include easy inclusion of new measurements and clear relations between the tuning parameters used and the conditions in the digester.
AbstractDissertation (MEng (Control))--University of Pretoria, 2003.
AbstractChemical Engineering
PublisherUniversity of Pretoria
IdentifierSandrock, C 2003, Implementation and performance analysis of a model-based controller on a batch pulp digester, MEng dissertation, University of Pretoria, Pretoria, viewed yymmdd