On-line Optimisation of an Integrated Petrochemicals Facility

1. On-line Optimisation of an Integrated Petrochemicals Facility Glyn Westlake, Emerson Mark Brewer, Emerson Tim Desmond, BP

2. Overview • The Grangemouth site. • On-line optimisation. • Types of optimisation. • Why use optimisation here? • The implemented system.

3. BP Grangemouth • Large integrated site. • Processing facilities for Forties pipeline system. • Approx 1 million barrels per day. • Major Fuels Refinery. • Annual capacity approx 10 million tonnes. • Major petrochemical producer. • Annual capacity approx 1.2 million tonnes. • Large energy user. • Steam. • Electricity.

4. Grangemouth Utilities Plant • 8 Boilers. • 7 Steam Turbines. • 1 Gas Turbine/Heat Recovery Steam Generator. • GT/HRSG run by separate company. • Contracts for exchange of fuel/steam between BP and “CHP”.

5. Fuel Gas Fuel Oil Blr 8 Blr 9 Blr 10 Blr 11 Blr 12 Blr 13 Blr 14 Blr 15 EHP Steam Users TA 5 TA 6 TA 7 TA 8 TA 9 TA 10 TA 11 MP Steam Users Electricity Users GT Natural Gas Electricity Grid Grangemouth Utilities Plant

6. Plant data entered manually. • Model tuned manually. • Results implemented manually. OPTIMISER Off-Line PLANT OPTIMISER • Plant data entered automatically. • Model tuned automatically. • Results implemented manually. Open-Loop PLANT OPTIMISER • Plant data entered automatically. • Model tuned automatically. • Results implemented automatically. Closed-Loop PLANT Types of Optimiser

7. Real-Time Optimisation - Criteria • Problem complexity • Is “the answer” obvious? • Can the system be simulated? • Degrees of freedom • Is there any scope for optimisation? • Will the optimiser generate a large number of small set point changes? • Amount of change in the problem • Can “the answer” be worked out once and then kept? • Can the system cope with process disturbances?

8. Why Use Optimisation Here? • Complexity • Individual unit performance can be found from historic data. • Units change performance gradually due to fouling etc. • Optimal operation requires trade off between different units • Degrees of freedom • Boiler rates. • Turbine rates. • Change • Steam demands. • Indigenous fuel availability. • Prices. • Unit performance.

9. Why Use Optimisation Here? • Site-wide push to reduce energy usage. • Desire to improve • Instantaneous operation. How should we currently be running to minimise costs? • Planning. What equipment configuration would be best during the expected plant start-up?

10. What does the optimiser do? • For the given total steam and power demand: • Minimise total operating cost (including CHP streams). • By • Choosing individual boiler steam generation rates. • Choosing individual T/A electricity generation rates.

11. Project Components On-line Optimiser Off-line Optimiser Control Room Offices

12. On-line/Off-line Optimiser • On-line optimiser • Results every few minutes displayed on DCS screens. • Operators use the advice to set up the plant for best economic operation. • Off-line Optimiser • Uses same model as on-line optimiser, tuned to current performance. • Allows office based staff to run operating scenarios to evaluate alternatives. • Automated comparison of different equipment configurations. • Used by Optimisation Engineers/Planners/Technical Engineers. • Intended for day by day use for short term planning.

13. System Components Models Optimisers Estimators Shared Data Data Conditioning DCS

14. Project Status/Conclusion • System successfully implemented. • Benefits audit ongoing. • On track to exceed guaranteed 1% benefits. • System intended to provide basis of closed-loop optimiser. • Good candidate for on-line optimisation.