Dynamic modeling and optimization of chemical processes are currently accepted due to the broad range of benefits that can be derived from their application. However, the complex and large-scale nature of these problems has prevented their solution in real plant cases until the development of appropriate resolution techniques. Mandler (2000) has studied dynamic simulation of liquefied natural gas plants and air separation plants for control analysis and design in liquefaction processes. Diaz et al. (2003) analyzed optimal switching between operating modes in cryogenic distillation columns through the formulation of rigorous dynamic optimization models including predictions of carbon dioxide precipitation conditions at each stage in the column.

In this work we address the open and closed loop dynamic optimization of a large-scale natural gas processing plant through a control vector parameterization approach. Rigorous dynamic models have been developed for main units in the cryogenic sector, which includes cryogenic heat exchangers with partial phase change (Rodriguez and Diaz, 2007), high pressure separators, turboexpanders and demethanizing columns. Rigorous thermodynamic predictions are obtained with the Soave-Redlich-Kwong (SRK) equation of state (Soave, 1972) for equilibrium and carbon dioxide solubility calculations. The fact that countercurrent heat exchangers have been rigorously modeled gives rise to a partial differential equation system. These models render an optimization problem subject to constraints that constitute a partial differential algebraic equations (PDAE) system representing a highly integrated process. Plant controllers have been included in the model. The objective is to minimize the transient time between a current operating ethane recovery and a new set point value. The solution of the DAE optimization problem provides state and control variables profiles in space and time, as well as a detailed description of partial phase change in countercurrent heat exchangers. Numerical results have been obtained and compared to available plant data.

References

Diaz M.S., Tonelli S., Bandoni A., Biegler L.T., Dynamic optimization for switching between steady states in cryogenic plants. Foundations of Computer Aided Process Operations. 2003; 4: 601-04. Mandler J.A. (2000). “Modelling for control analysis and design in complex industrial separation and liquefaction processes”, J. Process Control, 10, 2, 167-175. Soave, G. (1972). “Equilibrium Constants for a Modified Redlich-Kwong Equation of State”, Chem. Eng. Sci., 27, 1197-1203. Rodríguez, M., M. S. Diaz, “Dynamic Modelling And Optimisation Of Cryogenic Systems”, 2007, Applied Thermal Engineering, 27, 1182-1190.

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