The smart industrial revolution, the 4th after the mechanical, electrical, and digital ones, is a today’s process industry frontline research in terms of technology, but also in terms of the use of technology that implies in human behavior and resource issues to deploy new process-of-work. Smart operation makes use of new information and communication technologies (ICT) and advanced algorithms such as optimization[1], therefore there are requirements for high qualified and trained teams to handle such technologies[2]. Smart Process Manufacturing (SPM) also known as Industry 4.0 is an emerging field of research and refers to a design and operational paradigm involving the integration of measurement and actuation, safety and environmental protection, regulatory control, high fidelity modeling, real-time optimization and monitoring, and planning and scheduling[3]. It is the enterprise-wide application of advanced technologies, tools, and systems, coupled with knowledge-enabled personnel, to plan, design, build, operate, maintain, and manage process manufacturing facilities, where is expected reduced costs in inventories, manufacturing, logistics, maintenance, etc[4]. We present several applications and opportunities of smart operations to be explored in fuels industries. These operations improve the decision-making by (i) clustering crude oil arriving in the refineries as much as different they can be in terms of quality[5]; (ii) integrating cutpoint temperature optimization of the distillates (initial and final boiling points) with the final blending producing specified fuels[6] and (iii) hybrid real-time optimization considering steady-state gain in an LP modeling. (1) Thornhill NF, 2015. In https://workspace.imperial.ac.uk/smartops/Public/ENERGY-SMARTOPSOverview.pdf (2) Christofides PD, David JF, El-Farra NH, Clark D, Harris KRD and Gipson JN. Smart plant operations: vision, progress and challenges. Aiche Journal, 2007, 53 (11), 2734–2741. (3) Davis JF and Edgar TF. Smart Process Manufacturing – A Vision of the Future. In Book: Design for Energy and Environment. Ed. El-Halwagi MM, Linninger AA, 2008, 150-165. (4) David JF, Edgar T, Porter J, Bernaden J and Sarli M. Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Comput Chem Eng, 2012, 47, 145–156. (5) Industrial Algorithms LLC., 2015. In http://pt.slideshare.net/alkis1256/ctap-imf. (6) Kelly JD, Menezes BC, Grossmann IE. Distillation Blending and Cutpoint Temperature Optimization using Monotonic Interpolation. Ind Eng Chem Res, 2014, 52, 18324-18333.

1. Brenno C. Menezes,1 Jeffrey D. Kelly,2 Ignacio E. Grossmann,3 Lincoln F. L. Moro,4 Marcel Joly5
Smart Process Operations in Fuels Industries:
Applications and Opportunities
Goal: We present several applications and opportunities of smart
process operations in fuels industries.
The applications include (i) crude to tank assignment to minimize
crude-oil quality variation, (ii) integration of distillation towers’ cutpoint
temperature optimization in blend-shops and (iii) hybrid real-time
optimization (first-principles and data-driven) using steady-state gains.
The opportunities are in (i) data integration and integrity (data
reconciliation and parameter estimation), (ii) automated decision-
making (optimization) and (iii) parallel computing in real-time
scheduling, all in the “Automation-of-Things” (AoT) ideal.
Figure 2. Crude to tank assignment for improved schedulability.
1Center for Information, Automation and Mobility, Technological Research Institute, São Paulo, Brazil. 2IndustrIALgorithms, Toronto, Canada. 3Department of Chemical Engineering, Carnegie Mellon
University, Pittsburgh, United States. 4REVAP Refinery, PETROBRAS, São José dos Campos, Brazil. 5RECAP Refinery, PETROBRAS, Capuava, Brazil.
Figure 1. 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. 1, where smart process operations involving
scheduling, entity integration and real-time optimization are proposed.
Crude to Tank Assingment: To increase the polyhedral space of fuels’
amounts and properties variations improving the crude diet selection.
Each crude cr with q modeled qualities (yields and properties) is
transferred to receiving tanks minimizing their qualities variations.1
The arrows are binary variables y with q as coefficients in an MILP
model.
Cutpoint Temperature Optimization in Blend-shops: To integrate
blending of several streams’ distillation curves with also shifting or
adjusting cutpoints of distilled streams (i.e., initial and/or final boiling-
points, IBP and FBP) in order to manipulate their TBP curves in an
either off- or on-line environment in an NLP model.2
• Use daily experimental data converted to TBP instead of blending indices.
• Linear approximation in the front-end, middle and back-end regions.
• The optimized curve is renormalized considering new IBP and FBP.
• Reduce blender’s RTO efforts to get on-spec fuels, minimizing off-spec fuels.
Hybrid 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.3
• On-line and Off-line boundary integrating scheduling to RTO.
• Demands steady-state detection, data reconciliation and gain estimation
techniques to improve the data integrity.
• Manages multiple unit-operations collectively in a network as opposed to
optimizing a single unit-operation in isolation.
Opportunities:
Min cr,tk,q(ycr.qcr-ytk.qtk)2
Figure 5. Information and communication technology (ICT) in fuels production.
Figure 3. Actual, modeled and optimized TBP curve for naphtha.
2. JD Kelly, BC Menezes, IE Grossmann, 2014, Ind Eng Chem Res., 53, pg 15146-15156
1. Industrial Algorithms report, April, 2015.
4. BC Menezes, M Joly, LFL Moro, 2015, ESCAPE25, Copenhagen, DK.
3. Industrial Algorithms report, June, 2015.
Figure 4. IV and DV updating using bias.
Back-end:
Front-end:
New Temperature: NT
Old Temperature: OT
New Yield: YNT
YNT01 = 0.10 −
0.10 − 0.01
OT10 − OT01
OT10 − NT01
YNT99 = 0.90 +
0.99 − 0.90
OT99 − OT90
NT99 − OT90
Parallel Scheduling runs many similar scheduling problems called
“situations” or cases in parallel or concurrently on several CPU’s using
rules or inductions to reduce (not relax) the problem.
“Automation-of-Things”
(AoT)
Automated Data Integration = IT Development
Automated Decision-Making = Optimization
Automated Data Integrity = Data Rec./Par. Est.