We propose 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.

1. 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 property
tk storage tank
Min = σcr σcp σtk
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,cp
tk =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)
𝑥 𝐿
𝑦 ≤ 𝑥 ≤ 𝑥 𝑈
𝑦 ∀ u, arrow
mixers
splitters
𝑦𝑢 + 𝑦𝑢′ ≤ 2𝑦𝑎𝑟𝑟𝑜𝑤
for u->(arrow)->u’ (links)
1
𝑥 𝑢
𝑈 σ 𝑗 𝑥𝑗𝑖 ≤ 𝑦𝑢 ≤
1
𝑥 𝑢
𝐿 σ 𝑗 𝑥𝑗𝑖 ∀ (i, u)
1
𝑥 𝑢
𝑈 σ𝑖 𝑥𝑗𝑖 ≤ 𝑦𝑢 ≤
1
𝑥 𝑢
𝐿 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)
𝐱 𝐜𝐫,𝐜𝐩,𝐭𝐤