A Probabilistic Optimization Approach for a Gas Processing Plant under Uncertain Feed Conditions and Product Requirements
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.
[1] 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.
[2] 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).
[3] Shapiro, A.(2008). Stochastic programming approach to optimization
under uncertainty. Math.Program., Ser.B 112: 183-220.
DOI10.1007/s10107-006-0090-4.
[4] Li, P., Wendt, M., & Wozny, G. (2004). Optimal production planning
for chemical processes under uncertain market conditions. Chemical
Engineering & Technology, 27, 641-651.
[5] Grossmann, I. E., & Floudas, C. (1987). Active constraint strategy for
flexibility analysis in chemical processes. Computers & Chemical
Engineering, 11, 675-693.
[6] Pistikopoulos, E. N., & Ierapetritou, M. G. (1995). Novel approach for
optimal process design under uncertainty. Computers & Chemical
Engineering, 19, 1089-1110.
[7] Rooney, W. C., & Biegler, L. T. (2003). Optimal process design with
model parameter uncertainty and process variability. AIChE Journal,
49,438-449.
[8] Petkov, S.B., & Maranas, C. (1997). Quantitative assessment of
uncertainty in the optimization of metabolic pathways. Biotechnology &
Bioengineering, 56, 145-161.
[9] Li, P., Arellano-Garcia, H., & Wozny, G. (2008). Chance constrained.
Programming approach to process optimization under uncertainty
Computers & Chemical Engineering, 32, 25-45.
[10] Rosenthal, R.E. (2006). A User-s Guide Manual: GAMS Development
Corporation, pp.55, Washington, DC, USA.
[11] Ben-Tal, A., & Nemirovski, A. (2000). Robust solutions of linear
programming problems contaminated with uncertain data. Mathematical
programming, 88, 411-424.
[12] Sen, S., & Higle, J.L.(1999). An introductory tutorial on stochastic linear
programming. Interfaces, 29, pp-33-61.
[13] 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.
[1] 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.
[2] 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).
[3] Shapiro, A.(2008). Stochastic programming approach to optimization
under uncertainty. Math.Program., Ser.B 112: 183-220.
DOI10.1007/s10107-006-0090-4.
[4] Li, P., Wendt, M., & Wozny, G. (2004). Optimal production planning
for chemical processes under uncertain market conditions. Chemical
Engineering & Technology, 27, 641-651.
[5] Grossmann, I. E., & Floudas, C. (1987). Active constraint strategy for
flexibility analysis in chemical processes. Computers & Chemical
Engineering, 11, 675-693.
[6] Pistikopoulos, E. N., & Ierapetritou, M. G. (1995). Novel approach for
optimal process design under uncertainty. Computers & Chemical
Engineering, 19, 1089-1110.
[7] Rooney, W. C., & Biegler, L. T. (2003). Optimal process design with
model parameter uncertainty and process variability. AIChE Journal,
49,438-449.
[8] Petkov, S.B., & Maranas, C. (1997). Quantitative assessment of
uncertainty in the optimization of metabolic pathways. Biotechnology &
Bioengineering, 56, 145-161.
[9] Li, P., Arellano-Garcia, H., & Wozny, G. (2008). Chance constrained.
Programming approach to process optimization under uncertainty
Computers & Chemical Engineering, 32, 25-45.
[10] Rosenthal, R.E. (2006). A User-s Guide Manual: GAMS Development
Corporation, pp.55, Washington, DC, USA.
[11] Ben-Tal, A., & Nemirovski, A. (2000). Robust solutions of linear
programming problems contaminated with uncertain data. Mathematical
programming, 88, 411-424.
[12] Sen, S., & Higle, J.L.(1999). An introductory tutorial on stochastic linear
programming. Interfaces, 29, pp-33-61.
[13] 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.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:53822", author = "G. Mesfin and M. Shuhaimi", title = "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.", keywords = "Butane, Feed composition, LPG, Productspecification, Propane.", volume = "4", number = "2", pages = "176-6", }