Scheduling of oil-refinery operations
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1 Felipe Díaz-Alvarado 2, Francisco Trespalacios 1, Ignacio Grossmann 1 1 Center for Advanced Process Decision-making. Carnegie Mellon University 2 Department of Chemical and Biotechnological Engineering. University of Chile March 2015
2 The problem Méndez, C.A., Grossmann, I.E., Harjunkoski, I., Kaboré, P. A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations. Computers & Chemical Engineering, 2006, 30 (4):
3 The problem Crude oil unloading and blending: current example Non-simultaneous load and unload Vessels can unload to any storage tank. Bounds for blend composition in Charging tanks. Concentration limits are specified for the products. Product yields are specified for each crude. Minimum product yields for CDU.
4 Priority-slot based formulation strategy A scheduling problem has two main decisions on time arrangement: Sequence of operations. Timing for each operation (start, duration, end). We use Priority-slot based formulation ( Time-slot) for continuous-time scheduling. Separated variables for: Priority (Z). Timing (S, D, E). Mouret, S., Grossmann, I., Pestiaux, P. A Novel Priority-Slot Based Continuous-Time Formulation for Crude-Oil Scheduling Problems. Industrial & Engineering Chemistry Research, 2009, 48 (18):
5 MINLP A scheduling problem strategy Mouret, S. Optimal Scheduling of Refinery Crude-Oil Operations. Ph.D. Thesis. Department of Chemical Engineering. Carnegie Mellon University
6 strategy A scheduling problem strategy with global solver (BARON) for MINLP. with two stages: MILP (CPLEX) + NLP (BARON or CONOPT). Loop: 20 cycles. Mouret, S., Grossmann, I., Pestiaux, P. A Novel Priority-Slot Based Continuous-Time Formulation for Crude-Oil Scheduling Problems. Industrial & Engineering Chemistry Research, 2009, 48 (18):
7 Time horizon: 8 days. A scheduling problem strategy 7 Priority Slots. 6 the problem is not feasible. 8 the problem is larger. (*) Time limit.
8
9 strategy
10 strategy
11 strategy
12 strategy
13 strategy
14 strategy
15 strategy
16 strategy
17 strategy
18 strategy
19 strategy
20 strategy
21 strategy
22 strategy
23 Partial conclusions and future work Partial conclusions: The two-stages strategy (MILP + NLP) gives a solution in a shorter CPU time than the MINLP with global solver. Why does this strategy work? Two hypothesis: Few blending operations where nonlinearities are active. Nonlinear constraint has no influence on priorities. Future work in this instance: Improve the algorithm. Decide the number of priority slots. Scale up. Decomposition.
24 Felipe Díaz-Alvarado 2, Francisco Trespalacios 1, Ignacio Grossmann 1 1 Center for Advanced Process Decision-making. Carnegie Mellon University 2 Department of Chemical and Biotechnological Engineering. University of Chile March 2015 felidiaz@ing.uchile.cl
25 Data (first obtained and best) (*) Time limit.
26 Data for the example A scheduling problem Data Time horizon: 8 days. Priority slots: 7
27 Data for the example A scheduling problem Data
28 Data for the example A scheduling problem Data
29 A scheduling problem Data General operating constraints Time in operations: S tw Sw lo z tw t T,w W (1) E tw lw up z tw t T,w W (2) E tw = S tw +D tw z tw t T,w W (3) Volume and flowrates in operations: Vw lo z tw V tw Vw up z tw t T,w W (4) V tw = ˆV twc c C t T,w W (5) F lo w D tw V tw F up w D tw t T,w W (6) xwk lo V tw xck 0 ˆV twc x up wk Vtw t T,w W,k K (7) c C
30 Data No overlapping constraints: z tw +z tw 1 t T,w W,w NO w,w,w > w (8) t 1 <t<t 2 E t1 w +E t1 w + t T (D tw +D tw ) S t2 w +S t2 w +H (1 zt 2w z t2 w ) t 1,t 2 T,w W,w NO w,w,w > w (9)
31 Data Constraints on blending and charging tanks Inventory definition: L tr = L 0 r + t <t t T w I r V t w t <t V t w t T w O r t T,r R S R C (10) ˆL trc = ˆL 0 rc + t <t t T w I r ˆV t wc t <t ˆV t wc t T w O r t T,r R S R C,c C (11) L tr = c C ˆL trc t T,r R S R C (12)
32 Data Blending constraints: ˆV twc L tr = V tw ˆL trc t T,r R S R C,w O r,c C (13) CD lo rc V tw ˆV twc CD up rc V tw t T,r R C,w W D,c C (14) Inventory bounds: L lo r L tr L up r t T,r R S R C (15) L lo r L 0 r + V tw V tw L up r r R S R C (16) t T w I r t T w O r
33 Data Constraints on CDUs Limit the number of discharge operations to CDUs: Nr lo z tw Nr up r R D (17) t T w I r Ensure constant operation: t T Product yield and properties in products: w I r D tw = H r R D (18) VP twp = c C PY 0 wcp ˆV twc t T,w W D,p P (19) ˆ VP twpk = c C PP 0 wcpk PY 0 wcp ˆV twc t T,w W D,p P,k K(20)
34 Data Limits on demands, product demands, and specifications in products: DD lo r V tw DD up r t T w I r r R D (21) PY lo rp V tw VP twp t T,r R D,w I r,p P (22) PP lo wpk VP twp ˆ VP twpk PP up wpk VPtwp t T,w W D,p P,k K (23) PDrp lo VP twp PDrp up t T w I r p P,r R D (24)
35 Data Complete unloading of Vessels: t T w O r V tw = L 0 r r R V (25)
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