Abstract
Hopper dredgers are massive ships that excavate sediments from the sea bottom while sailing. The excavated material is then transported and discharged at a specified location. The efficiency of this process is highly dependent on the detailed knowledge of the excavated soil. When the soil is composed mainly of sand, the parameter of the greatest importance is the average grain diameter. This, however cannot be directly measured by available sensors. Therefore, in this paper a particle filter is proposed to estimate the average grain diameter. The estimation is based on online measurements of the total height of the mixture in the hopper, total mass, the incoming mixture density and flow-rate and the height of a sand bed, together with estimates of the outgoing mixture density and flow-rate. The loading process is naturally decomposed into three phases and the filter is applied to the first two phases. In order to match different types of nonlinearities, a separate observer is proposed for each phase under consideration. This increases the modularity of the filter and makes tuning easier. The performance of the filter is evaluated in simulations and the results are encouraging.
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