A chance constrained approach for a gas processing plant with uncertain feed conditions

In this work, we propose a new optimization model for gas processing plant under uncertain feed flow rate and composition. Deterministic model has initially being formulated; subsequently, linear steady state chance constrained models with time-dependent uncertainties under single and joint chance constraints have been developed. The chance constrained models have then been converted to their equivalent deterministic form so as to be solved. Case study has been taken for real plant to effectively implement the proposed model. The solution from the optimization provides prior-decision for the in-operation plant to produce the desired product at certain confidence level by satisfying the process constraints. Sensitivity analysis has been made to see the effect of each uncertain feed component flows. Final computational results ratify the need of taking optimal decisions using the chance constrained approach which comprises the profitability and reliability of the process compared to deterministic, “worst case”, and two-stage programming approaches.