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A Probabilistic Optimization Approach for a Gas Processing Plant under Uncertain Feed Conditions and Product Requirements
Abstract:This paper proposes a new optimization techniques for the optimization a gas processing plant uncertain feed and product flows. The problem is first formulated using a continuous linear deterministic approach. Subsequently, the single and joint chance constraint models for steady state process with timedependent uncertainties have been developed. The solution approach is based on converting the probabilistic problems into their equivalent deterministic form and solved at different confidence levels Case study for a real plant operation has been used to effectively implement the proposed model. The optimization results indicate that prior decision has to be made for in-operating plant under uncertain feed and product flows by satisfying all the constraints at 95% confidence level for single chance constrained and 85% confidence level for joint chance constrained optimizations cases.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1059707Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1067
 Diaz, S., Brignole, E.A., & Bandoni, A. (2002). Flexibility study on a dual mode natural gas plant in operation. Chemical Engineering Communications, 189, 623-641.
 Fleshman,J., Alderton, P., Bahnassi, E., & Khouri, A.R.(2005). Achieving product specifications for ethane through to pentane plus from NGL fractionation plants. Presented on AIChE annual meeting (Cincinnati, OH).
 Shapiro, A.(2008). Stochastic programming approach to optimization under uncertainty. Math.Program., Ser.B 112: 183-220. DOI10.1007/s10107-006-0090-4.
 Li, P., Wendt, M., & Wozny, G. (2004). Optimal production planning for chemical processes under uncertain market conditions. Chemical Engineering & Technology, 27, 641-651.
 Grossmann, I. E., & Floudas, C. (1987). Active constraint strategy for flexibility analysis in chemical processes. Computers & Chemical Engineering, 11, 675-693.
 Pistikopoulos, E. N., & Ierapetritou, M. G. (1995). Novel approach for optimal process design under uncertainty. Computers & Chemical Engineering, 19, 1089-1110.
 Rooney, W. C., & Biegler, L. T. (2003). Optimal process design with model parameter uncertainty and process variability. AIChE Journal, 49,438-449.
 Petkov, S.B., & Maranas, C. (1997). Quantitative assessment of uncertainty in the optimization of metabolic pathways. Biotechnology & Bioengineering, 56, 145-161.
 Li, P., Arellano-Garcia, H., & Wozny, G. (2008). Chance constrained. Programming approach to process optimization under uncertainty Computers & Chemical Engineering, 32, 25-45.
 Rosenthal, R.E. (2006). A User-s Guide Manual: GAMS Development Corporation, pp.55, Washington, DC, USA.
 Ben-Tal, A., & Nemirovski, A. (2000). Robust solutions of linear programming problems contaminated with uncertain data. Mathematical programming, 88, 411-424.
 Sen, S., & Higle, J.L.(1999). An introductory tutorial on stochastic linear programming. Interfaces, 29, pp-33-61.
 Henrion, R., Li, P., Möller, A., Steinbach, M., Wendt, M., & Wozny, In G.(2001). Stochastic optimization for chemical processes under uncertainty. Grötschel, et al.(Eds.), online optimization of large scale systems, pp. 455-476. Springer.