Coker advanced control and inferential modeling at BP Gelsenkirchen refinery

Transcription

1 1 Coker advanced control and inferential modeling at BP Gelsenkirchen refinery Volker Haseloff, BP Gelsenkirchen Sean Goodhart, AMT Y. Zak Friedman,

2 Coker unit overview 2 FEED FURNACE MAIN FRACTIONATOR Naphtha FEED LCGO HCGO COKE DRUMS COKER FURNACE

3 Coker APC challenge 3 Batch reactors (coke drums), integrated with a continuous distillation process. Two completely different modes of operation: low sulfur and high sulfur Drum switching every twelve hours or so After switching the unit is short of heat and light products. The challenge is to keep the unit under control during that time. Product separation optimization is a secondary challenge. Large product price differences provide incentive for precise product quality control.

4 Project objectives 4 Control drum switch procedure Warm up Cool down Throughput and inventories Control product qualities Keep within range during drum switches Optimize during steady operation Maximize feed

5 Design features 5 Foxboro IA instruments, Foxboro DMCplus Bridge interface DMC+ multi-variable predictive controller (MVPC) First principles inferential models (FPM) FPM s are coded on Aspen IQ platform Performance monitoring by Aspenwatch

6 Project highlights Implementation by AMT Functional design Step testing Coding FPMs Commissioning FPM by GCC model for the fractionator Coke drum outage model (in open loop) also acted as a client representative for the project Heavy participation by ROG Step testing Operator training Commissioning support Performance testing 6

7 DMC sub-controllers 7 Feed surge drum FEED Feed furnace BA160 Coker furnace BA101 Main fractionator FRAC Total size: about 90CVs, 30MVs, 14FFWs One set of DMC dynamic models satisfy both modes of operation

8 Feed management 8 SR TANK Surge drum VISB RESID F 0133 F 0165 L 0106 P 0173 HCO F 0135 F 0134 L 0173 F 0112 SL RESID BLOWDOWN DA-109 LCGO GA-177

9 Feed management 9 Feed surge drum Many feeds Feed surge drum LC slave can be selected to be any one of the feeds Recipe to be controlled Hydraulic constraints Feed management through drum switches and warm-ups

10 Coker Fractionator 10 LC PC Steam TC To HP drum To HP drum LCGO HGCO Drum effluent PC Feed Furnace L T To coker furnace

11 Fractionator control 11 Product quality control Responding to drum switches Column level a key CV Column bottom temperature Hydraulic constraints

12 Key inferences 12 Naphtha 90% point Heavy naphtha 5% point LCGO flash and 90% point HCGO 90% point Coke drum outage (in open loop)

13 Coker furnace 13 Many constraints Pass balancing by skin temperatures Throughput maximized to Furnace constraints in both furnaces Inventory constraints Fractionator constraints, when the fractionator sub-controller cannot handle them

14 230 Heavy naphtha 5 and 90% inference HN05_M HN90_L HN05_L HN90_M /30/04 0:00 9/18/04 0:00 11/7/04 0:00 12/27/04 0:00 2/15/05 0:00

15 500 LCGO and HCGO 90% inference HCG90_M LCGO90_M L90_L H90_L /30/2004 0:00 9/18/2004 0:00 11/7/2004 0:00 12/27/2004 0:00 2/15/2005 0:00

16 LCGO flash inference LFL_L FLLCGO_M /30/2004 0:00 9/18/2004 0:00 11/7/2004 0:00 12/27/2004 0:00 2/15/2005 0:00

17 Commissioning issues 17 Difficult to identify coker real constraints pertaining to downstream units. The noise pattern of certain flow readings impeded inferential model commissioning. Implemented validation routines and/or alternative measurements. Also applied custom validation for all CVs. Secondary constraints caused DMC to move furnace feed flow unnecessarily during drum switches DMC parameters were set to avoid throughput reduction unless no other route is possible. The application is now almost fully commissioned, and we will report performance data in another paper.