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The agitated slurry tank is widely used, given its versatility and relative ease of installation. This contribution explores the influence of agitation on the motion of solid aggregates that can be formed in a nickel oxide slurry suspended in iso-octane under bubbling nitrogen gas. Motion features of a relatively big particle, representing solid aggregates, are directly determined from Radioactive Particle Tracking measurements. A clear increase in the overall space occupied by tracer trajectory is observed with increasing stirring speed. The time series of instantaneous velocities can be calculated by time differentiation of successive positions from the tracer trajectories. Turbulence Kinetic Energy is mapped in three dimensions from the ensemble average correlation matrix obtained from Radioactive Particle Tracking data, enabling studying the influence of agitation on the turbulence levels distribution.

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The present work describes a method of automatic fault detection and identification based on a hybrid model (HM): First Principles – Neural Network. The FPM can simulate a wide range of situations while the NN corrects the model output using information from the historical data of the process. Operating conditions corresponding to different types of faults were simulated with the HM and saved with their description in a process state library. To detect a fault, the online measured data was compared with that corresponding to the operation under normal conditions. If a significant deviation was detected, the current state was compared with all the states stored in the process state library and it was identified as the one at the shortest distance. The method was tested with real data from a methanol-water industrial distillation column. During the studied period of operation of the plant, two faults were identified and reported. The proposed method was able to identify such failures more effectively than an equivalent model of first principles. The results obtained show that the proposed method has a great potential to be used in the automatic diagnosis of faults in refining and petrochemical processes.