Abstract-Dynamic and mechanistic models of an Electrical Submersible Pump (ESP) lifted oil field are frequently used for understanding the process dynamics and the interactions occurring between the oil wells under varying operating conditions. They are also used to calculate the total fluid produced from the oil field. In this article, uncertainty and sensitivity analysis of such a model is studied. If the model is used for control and optimization of an oil field, it is important to see if the output calculated using the mathematical model lies within a confidence interval under the presence of uncertainties. It is also important to understand which parameters have strong/weak influence on the model output. The uncertainty analysis is performed by using the Monte Carlo simulation method. Morris method of elementary effect is used for input factor screening. To quantify the effect of the input factors on the model output, a variance based method of sensitivity analysis is used.
Index Terms-Model uncertainty, ESP lifted oil field, morris method, variance based sensitivity analysis
In this paper, a dynamic model for a rotary drum granulation loop with external product separator is developed. A population balance is used to capture dynamic particle size distribution in the 3-compartment rotary drum granulator model. Particle agglomeration along with particle growth due to layering are assumed as granulation mechanisms in the rotary drum. The model of the granulation loop includes models of the drum, screens and a crusher. Simulations using the developed model provide valuable data on dynamic fluctuations in the inlet and the outlet particle size distribution for the rotary drum. Simulation results showed that at smaller crusher gap spacings, the instabilities of the drum granulation loop occur, and damped oscillations are observed. Above the critical crusher gap spacing, sustained periodic oscillations are observed. The reason for oscillations is the off-spec particle flow that is recycled back to the granulator.
Few granulation plants are operated optimally. It is common to operate granulation plants below their maximum design capacity, and in many cases, periodic instabilities may also occur. From a process control and optimization point of view, it is desirable to develop a dynamic model that can show the dominating dynamics of a granulation process and can be used for design of optimal operation of the granulation plant. In this paper, a dynamic model of a drum granulator is developed using population balance (PB). Different simulation scenarios are used to analyze various granulation mechanisms that are characteristic to drum granulators. Simulation results show that for the drum granulator, the particle agglomeration has a greater impact on the change in particle size distribution (PSD) compared to the particle growth due to layering. In addition, coarser particles are produced when a sizedependent agglomeration kernel is used in the granulator model. For combined processes, i.e., processes where the particle growth due to layering and agglomeration are considered simultaneously, coarser particles with a wider PSD are obtained with the size-dependent agglomeration kernel.
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