Brenno C. Menezes,1 Ignacio E. Grossmann,1 Jeffrey D. Kelly,2 Faramroze Engineer3

Integration Strategies for Multi-Scale Optimization

in the Oil-Refining Industry

Goal: solve a multi-scale optimization involving process design synthesis,

supply chain coordination and refinery operations (in planning, scheduling

and RTO) considering simultaneous and decomposed strategies for

handling the hierarchy between the levels, the relationships among the

entities and the nonlinearity inherent to the oil-refining industry.

Figure 1. Proposed multi-layer and multi-entity integration.

1Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, United States. 2IndustrIALgorithms, Toronto, Canada. 3SK-Innovation, Seoul, South Korea.

Refinery Design Synthesis: a generalized capital investment planning in

an MILP model considers project stages using sequence-dependent

switchover formulation to represent the construction, commissioning and

correction stages of the revamp (expansion or installation), retrofit and

repair problems as repetitive maintenance tasks or activities that are

inserted between the "existing" and "expanded" unit-operations.1

The nonlinearities from processing and blending are calculated in an NLP

model by fixing the investment results, then new yields and rates are

updated iteratively in the MILP model until process design convergence.2

Data-Driven RTO: To optimize in real-time independent variables (IV) and

dependent variables (DV) of a network based on steady-state gains in an

LP model.

• On-line and Off-line boundary integrating scheduling to RTO.

• Demands steady-state detection, data reconciliation and gain estimation

techniques to improve data integrity.

• Manages multiple unit-operations collectively in a network as opposed

to optimizing a single unit-operation in isolation.

3. BC Menezes, JD Kelly, IE Grossmann, M Joly, LFL Moro, 2015, AICHE, Salt Lake City.

Figure 5. IV and DV using bias updating.

Figure 3. Fuels production in the three entities of the refinery site.

Refinery Operations: Coordinated fuels production considers three

entities: crude-oil management, crude-to-fuel transformation and blend-

shops as in Fig. 3, where smart process operations involving scheduling,

entity integration and real-time optimization are proposed.3

Multi-scale Integration:

Figure 2. Generalized capital investment planning example for expansion.

Crude Scheduling: partitioned in two problems: crude to tank assignment

(CTA) in MILP to define crude segregation rules and crude blend

scheduling optimization (CBSO) for crude diet, storage and feed tank

logistics and CDU operations in an iterative MILP + NLP decomposition.2

Figure 4. Proposed partition of the crude-oil scheduling problem.

2. BC Menezes, JD Kelly, IE Grossmann, 2015, Comp Aided Process Eng, 37.

1. BC Menezes, JD Kelly, IE Grossmann, A Vazacopoulos 2015, Comp Chem Eng, 80.

Future Work: the next step are planned for further development

• Strategic, tactical and operational planning integration (STRATACOP).

• Data-driven RTO generating key profitability indicators for scheduling.

• Parallel computing using MILP results (multiple nodes) from the logistics

problem to be run in the NLP problem (quality) for random search.

• Integrate multi-site refineries from operational planning (month) to

scheduling (days) in coordination and collaboration policies among sites.

• Define key indicators to link the decision levels over the entities.

cr crude (or time)cp yield or propertytk storage tank

Min = σcrσcpσtk

xcr,cp,tk

maxcr,cp prcr,cp −mincr,cp prcr,cp

−maxcr,cp prcr,cp ≤ xcr,pr,tk≤ maxcr,cp prcr,cp

−maxcr,cp prcr,cp ytk(cluster),cp ≤ xtk(cluster),cp≤ 0

prcrycr,cp ≤ xcr,cp≤ prcrycr,cp ∀ cr, cp, tk

xcr,pr,tk = xcr,cp= −xtk cluster ,cp ∀ cr, cp, tk

σtk ycr,cptk =1 ∀ cr, cp

14,753 continuous and 8,481 binary variables;

5,029 equality and 32,852 inequality constraints (DoF=18,205)

CPU: 7.2 min (CPLEX 12.6) and 3.6 min (GUROBI 6.5.0) (both in 8 threads)

UOPSS modeling, pre-solving, and parallel processing solved a

discrete-time formulation with 6 days/2h-step (72 periods) for a highly

complex refinery (38 crude, 23 storage tanks, 11 feed tanks, 5 CDUs).

7.4 min (CPLEX 12.6) and 8.4 min (GUROBI 6.5.0) (both in 1 thread)

Only logistics aspects in MILP

ytk(cluster),cp

ycr,cp

“k-means” clustering (KM) and “fuzzy c-means” clustering (FCM) algorithms found in Bezdek et. al. (1984).

Assign crude to only one cluster

Define one crude per time

x = continuous variables (flow f)

y = binary variables (setup su)

unit perimeter (sink, source)

tank

in-port (i)

out-port (j)

arrow (mode does not apply)

x = continuous variables (flow f)

Structural Programming Language (SPL) in IMPL using the UOPSS (unit-operation-port-state superstructure)

(shape+mode)

Storage Tanks

Feed or

Charging

Tanks

CTA

CTA CBSO

Cluster

Crude

Tanks(storage)

𝑥𝐿𝑦 ≤ 𝑥 ≤ 𝑥𝑈𝑦 ∀ u, arrow

mixers

splitters

𝑦𝑢 + 𝑦𝑢′ ≤ 2𝑦𝑎𝑟𝑟𝑜𝑤for u->(arrow)->u’ (links)

1

𝑥𝑢𝑈σ𝑗 𝑥𝑗𝑖 ≤ 𝑦𝑢 ≤

1

𝑥𝑢𝐿 σ𝑗 𝑥𝑗𝑖 ∀ (i, u)

1

𝑥𝑢𝑈σ𝑖 𝑥𝑗𝑖 ≤ 𝑦𝑢 ≤

1

𝑥𝑢𝐿 σ𝑖 𝑥𝑗𝑖 ∀ (u, j)

u = unit, perimeter and tank

∀ cr, cp, tk

∀ cr, cp, tk

yield or property “flows”naphtha-yield (NY)diesel-yield (DY) prcr

diesel-sulfur (DS) residue-yield (RY)

𝐱𝐜𝐫,𝐜𝐩,𝐭𝐤