Tank Level Prediction Using Kalman and Lainiotis Filters
Tank level knowledge is very important in many applications, as in oil tank. The liquid in the tank can be static, filling or emptying, or sloshing, resulting to uncertain knowledge of tank level. In this work the tank level is predicted using prediction algorithms based on Kalman and Lainiotis filters. Time invariant and steady state prediction algorithms for static model and filling/emptying model are implemented. Time varying prediction algorithms for sloshing and filling/emptying and sloshing models are also implemented. The prediction algorithms’ behavior is examined concluding that the obtained predictions are very close to the real tank level. The calculation burdens of the prediction algorithms are derived, determining the faster prediction algorithm for each model.
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