The paper is aimed at discussing and fixing issues in providing a generalized approach to the simulation of sulfur recovery units (SRUs). The main goal is to get a simulation that is at the same time (i) reasonably detailed and robust to properly characterize SRUs and (ii) so generalized to provide a tool that is not only specific for the case in study. To achieve point (i), standard libraries belonging to commercial process simulators are coupled to specific heuristic relations coming from the industrial experience for modeling the thermal furnace and the catalytic Claus converters; this allows us to infer with a certain reliability those measures that are often missing or unavailable online in these processes. To achieve point (ii), a series of adaptive parameters are filled in the process simulation by making it more flexible and yet preserving all model details. The most recent techniques and numerical methods, to tune the adaptive simulation parameters, are implemented in Visual C++ and interfaced to PRO/II (by SimSci-Esscor) to obtain a robust parameter estimation solved by means of the BzzMath library. At last, the detailed and tuned adaptive simulation is validated along a period of 2 months on a large-scale SRU (TECHNIP-KTI SpA technology) operating in Italy.
Sulfur Recovery Units: Adaptive Simulation and Model Validation on Industrial Plant
MANENTI, FLAVIO;PIERUCCI, SAURO
2010-01-01
Abstract
The paper is aimed at discussing and fixing issues in providing a generalized approach to the simulation of sulfur recovery units (SRUs). The main goal is to get a simulation that is at the same time (i) reasonably detailed and robust to properly characterize SRUs and (ii) so generalized to provide a tool that is not only specific for the case in study. To achieve point (i), standard libraries belonging to commercial process simulators are coupled to specific heuristic relations coming from the industrial experience for modeling the thermal furnace and the catalytic Claus converters; this allows us to infer with a certain reliability those measures that are often missing or unavailable online in these processes. To achieve point (ii), a series of adaptive parameters are filled in the process simulation by making it more flexible and yet preserving all model details. The most recent techniques and numerical methods, to tune the adaptive simulation parameters, are implemented in Visual C++ and interfaced to PRO/II (by SimSci-Esscor) to obtain a robust parameter estimation solved by means of the BzzMath library. At last, the detailed and tuned adaptive simulation is validated along a period of 2 months on a large-scale SRU (TECHNIP-KTI SpA technology) operating in Italy.File | Dimensione | Formato | |
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