CIMFuels Editorials

CLIFFTENT Inc.: Process Control, Optimization, Scheduling, Performance
Dr. Pierre R. Latour, PE Consulting Chemical Engineer
CIMFuels Editorials
Pierre R Latour
FUEL Reformulation and
FUEL Technology & Management
July 1995 - January 1998
Hydrocarbon Processing
1997 - 1998
810 HERDSMAN DR, HOUSTON, TEXAS USA 77079-4203; Tel & Fax: 281-679-6709; clifftent@hotmail.com

Table of Contents
FUEL Reformulation
3 May 1995 Pierre Latour Added to Fuel Reformulation Advisory Board
4-5 July 1995 CIMFUELS: Computer-Integrated Manufacturing of Fuels
6-8 Sept 1995 Manufacturing Clean Fuels Doesn’t Have to be a Big Process Control
Problem
9-11 Nov 1995 Establish Performance Measures for Clean Fuels
FUEL Technology & Management (now World Refining)
12-15 Jan 1996 ECONOMICS – Local and Real Time
16-19 Mar 1996 INTEGRATION – Process, People, Computers, Information, Business
and R, D&A
20-23 May 1996 Scheduling = CLRTS
24-28 July 1996 Operations Optimization = CLRTO
29-33 Sept 1996 Advanced Dynamic Process Control = CMVPC
34-36 Nov 1996 Reconciliation, Learning, Improvement – RLI
37-40 Jan 1997 CIMFUELS: NPRA Computer Conference Continues to Grow
41-44 Mar 1997 CIMFUELS: Commercial Practice – Tools Vs. Solutions
45-47 May 1997 Time Cycle Management
48-50 July 1997 Intangible Benefits? Make Tangible!
51-52 Sept 1997 Data Management; Decision Support
53-56 Nov 1997 Benefit Potential > $1.00/bbl Crude
57-63 Jan 1998 Risk/Value: What’s Wrong With This Picture?
Hydrocarbon Processing
64 June 1994 Other ways to justify control and info. systems
65-68 July 1997 Does the HPI do its CIM business right?
69-70 Jan 1998 Decision-making and modeling in petroleum refining
71-74 June 1998 Optimize the $19-billion CIMFuels profit split
75 Author – Pierre R Latour
Copyright protected

Publisher for FUEL Reformulation, May/June 1995, p1 (3rd
column- Pierre Latour added)

CIMFUELS Editorial for FUEL Reformulation, July/August 1995, p17
CIMFUELS: Computer Integrated Manufacturing of Fuels
Dr. Pierre R. Latour
Co-founder, Vice-President (retired)
Setpoint, Inc.--Houston, Texas
While computers have played a basic but fragmented role in manufacturing fuels and
petrochemicals since the 1960's, the breadth, complexity, evolution, need and forecast of the role
for Computer Integrated Manufacturing of clean fuels in the 1990's and beyond 2000 is very
profound and challenging. In fact, it may be the biggest problem/opportunity which distinguishes
business performance and competitiveness worldwide.
Challenge and Opportunity
The profitable manufacture of clean gasolines and diesels, within the rules, is basically a
control problem, filled with economic modeling, optimization, scheduling, multivariable
predictive dynamic controls, integration, information management, accounting and decision
support activities. The scope is spreading from distillation columns to process units to refineries
to multi-site companies to third parties. Those who intend to compete and excel in manufacturing
fuels for profit must master these basic functions and activities with high skill and performance
for their processes, organizations and computer networks. The refinery margin benefit potential is
0.5 to 1.0 USD/BBL crude (depending on many things) with a sustained cost to capture of 10 to
50% (or more) of this gross benefit (depending on many other things). Some refiners have
captured much of this net potential, many have not.
Perspective
Fuels manufacturing management has four fundamental technology assets to lever influence
on business performance: processes, catalysts, humans (staff and organization) and CIM. Among
these, it now appears CIM is a strong differentiator among refineries because it is the most
difficult for managers to manage. The difficulties stem from hard to quantify intangible benefits,
high risk, unclear requirements and costs, rapid technology change and obsolescence,
controversial (political) functions and roles, short supply of expertise, lack of standards (make or
buy), fragmentation among inside staff and suppliers, unstable business practices (low cost
competitive bids versus strategic performance partnering) and at bottom disarray on
nomenclature, definitions, language and understanding for the new, always changing computer
field. These basic obstacles to success are falling as each company learns and matures from
CIMFUELS experiences.
CIMFUELS defines the intersection between the hydrocarbon processing industry and the
computerized information (Cyberspace, Internet?) industry, two of the biggest industries in the
world. There is universal consensus and obvious agreement in principle that some portions of
CIM ought to play a role in the manufacturing of the largest bulk chemical in the world: gasoline
and middle distillate transportation fuels. As quality/purity specifications (Octane, TOX, NOX,
SOX, RVP, CO, O2, etc.) explode in number, complexity and financial significance. The devil is
in the details; like what functions are performed, by whom (what system), on what basis, how
often, with what authority, for what purpose, with what benefit, at what cost, with what risk,
under what assumptions, with what options. What is the proper pace for broadening applications
and who is in charge of what?

While manufacturing cleaner fuels is technically feasible, the continuing problem is how to do
it well, efficiently, optimally, profitably and within the rules. CIM technology may have arrived
just in time to make clean fuels practically and profitably, but it is forcing business managers to
define and maintain clear descriptions for the objectives (performance measures) and the rules
(constraints, compliance measures, penalties). As modern computer networks (and staff and
processes) evolve in offices and plants, they should be viewed as tools and assets which
repeatedly inquire of business leaders: What is your/our objective and purpose for me, what can I
do to help? Use me better!
Modeling for Results
These ideas set the foundation for the modeling of everything: processes, control systems,
customers, environments, noncompliance, safety, reliability, flexibility, quality, speed, trade-offs
and decision-making. Sophisticated and effective CIMFUELS should not simply gather and
report mountains of flow, temperature, quality and financial data about the past. They should
provide natural functions, knowledge, memory, learning and discipline; complementary to people
and processes for planning, scheduling, optimization, control execution and auditing for
improvement of everything for future profit performance. These are the functions that make
money.
Where are we Going?
In future issues, this editorial column will report on CIMFUELS for business management.
We will build on history with definitions and descriptions, problems and solutions, performance
measures and tangible incentives, principles of planning, execution and maintenance, trends in
technology, debates and questions, explanations and achievements, and strategic visions. We will
point out management issues for consideration and action plus offer proven principles to guide
toward successful results.
It is my hope that the role and performance of CIMFUELS will be properly incorporated in
important planning and execution of business strategies for manufacturing future high quality
fuels. The companies with leadership and vision will prosper. The companies without leadership
and vision will not.
Look for a description of "the big control problem" in a future column. We intend to focus
later columns on performance measures, real time-local operator economics, integration,
scheduling, operations optimization, advanced dynamic process control, learning/improvement,
time cycle management, modeling, data management, decision support, tangible benefits
determination, how to identify/capture/sustain $1.0/bbl crude, blending and oil movements,
FCC/Alky/Ether/blending, CR/ISOM/H2/blending, managing carbon/hydrogen/sulfur,
transportation and logistics, government compliance, CIMGASOLINE, CIMDIESEL, CIMMID-
DIST, CIMPETROCHEM, CIMBOTTOMS, communications, data bases/MMI/client/server,
suppliers of technology/equipment/services/solutions, America/Europe/Asia activity, property
quality control and other CIMFUEL topics of interest to FUEL readers.
Stay tuned...

CIMFUELS Editorial for FUEL Reformulation, September/October 1995, p18, 20
Manufacturing Clean Fuels Doesn’t Have to be a Big Process Control
Problem
Vice President, Business Development
Dynamic Matrix Control Corporation
Houston, Texas
Manufacturing clean fuels commercially is, at bottom, a big process control problem.
Although new process, catalysts, and equipment have been developed to ensure the technical
feasibility of making reformulated gasoline and low sulfur diesel components, comprehensive
study of the US - CAAA90 illustrates a big control problem to operate plant, blender, delivery,
and compliance mechanisms efficiently for competitive profitability. How to make each batch of
RFG and LSD with all qualities (nine or so) exactly on specs, optimally, competitively, every
time?
It is wise to define the control problem carefully and completely, before devising and
installing methods and mechanisms to solve and operate it.
CAAA90 Control System
The Clean Air Act Amendments of 1990 formulated national goals and a control system (of
sorts) to 1) improve human health, by 2) cleaning air pollution, by 3) improving exhaust
emissions, by 4) improved fuel compositions, by 5) improving fuel components from 6) oil,
gas, and bio feedstocks... among other things. The Congress and President actually set some of
the controller setpoints (gasoline 02 > 2w%, RVP < 9PSI) as well as structures and mechanisms
for developing and operating this national distributed control system.
The system now has localized air quality attainment specs, evolving vehicle composition
specs, “corresponding” fuel composition specs like CARB2 in Jan ‘96, “corresponding” blend
component requirements, and consequences for crude oil and other feedstock quality/demand.
Process vs. Process Control
Chemical engineering tradition since 1965 has taught the distinction between process design
and process operation/control. The technology of advanced process control automation of major
plants focuses on adjusting operating conditions to optimize profits (yields, operating costs,
capacity) while safely meeting product quality targets with feeds and economics which differ
from design premises. These systems swap variability of qualities and constraints for valves, in
face of disturbances, by adjusting manipulated variables (MV) to hold dependent response
controlled variables (CV) at optimum setpoint targets (SP), in face of unmeasured disturbances
(DV) at the best combination of constraints (on CV and MV) to optimize a profit function.
Constraints are properly set at the intersection of credits against violation penalties.
Manufacturing vs. CIM
The information systems engineering tradition since 1980 has taught the distinction between
manufacturing equipment and the science/art of performance measures, data modeling,
scheduling, large scale optimization, and integration (of organizations, systems and businesses)
for computer integrated manufacturing. The 1980’s CIM is undergoing its first fundamental re-

engineering in the 1990’s. The power of CIM comes from using computers to perform useful,
meaningful, important, profitable functions like those to solve the big control problem, or major
segments of it.
Economics of Quality
Most now see the competing trade-offs between benefit (to people) and cost (to people
through manufacturers) for each quality. These must be modeled physically (chemically) and
financially (human values), in order to properly make critical decisions about the numerical
setting of targets, specs, limits and constraints. Further, many leaders now recognize the
importance of quantifying the net credit slope to approach each limit (quality give-away), as well
as the necessity to quantify the net penalty slope for violating each limit (cost of non compliance).
The latter is now known to be the quantitative source of major “hidden, intangible” benefits from
improved quality variance (reliability).
As future quality requirements tighten, these influences become increasingly nonlinear and
critical. That is, the marginal cost to achieve successive quality improvement steps naturally
increases, so quality giveaway costs become increasingly significant. Consequently, the value of
precise modeling and tight control becomes increasingly important.
The first step is to determine a quality setpoint assuming perfect control (zero variance) to
properly tradeoff violation penalties (from customer values) against manufacturing costs. The
second step is to properly determine the offset tolerance to account for real statistical variance
performance from uncertain analysis, inaccurate models, uncertain components, inaccurate
operations, and imperfect CIM. (Herein lays the incentive for powerful CIM.)
Blending Serves Marketing
Component blending for clean fuels has become a very complex business in the 1990’s, within
the refinery and downstream. Blending now ranks with fractionation and cracking as a major
refining process step. Customized RFG for regions and seasons, and the variety of diesels and
middle distillates have transformed in-line fuel blending into batch steps, oil movement
transactions, and refinery unit operating modes. In-line blends sometimes go directly to
transportation (pipeline, ship, rail, truck) and sometimes come directly from process units. This
requires sophisticated use of storage for components and finished fuel products.
New financial modeling of all these blending, oil movement, and inventory operations are
underway for control optimization and scheduling, i.e. CIM. This is one part of the “Big Control
Problem”.
Processes Serve Blending
Refinery and petrochemical processes make a host (8-12) of components for each
RFG/conventional gasoline blend. What are the proper (optimum) flow, quality and value of each
component needed for each blend? Obviously this depends on other components to be blended
(their flow, quality, value) and the finished fuel to be sold (flow, quality, value).
Traditionally, LP planning tools lump average (over a week or so) product qualities with
lumped crude types to determine aggregated/lumped/average flows and qualities for components
from processes to “pools”. This is now changing to customized manufacturing of many
components for each unique fuel blend batch, in concert and harmony with future plans (crudes,
shutdowns, products, price forecasts). This is another part of the “Big Control Problem”.

Economics of Cuts
Process control accepts the problem objectives in terms of proper (optimal) setting of each cut
along the processing chain to finished fuels. Some examples are splitting virgin naphtha between
isomerization and reforming, splitting FCC olefins among alkylation/ ethers/FCC gasoline
components, splitting iC4 between alky and gasoline components, stabilizing to split C4/C5
between component RVP and C5 in LPG. Each split decision, in real operating time, should
consider local operating costs and global economics and constraints.
Economics of Feeds
The value and quality of crudes, gases, and intermediate supplies are becoming more closely
coordinated along the scheduling chain of processes modes and blends to finished fuels, in order
to follow the principle: “what it is worth depends (critically) upon what you will do with it, what
you make of it, what you sell it for, when”.
Conclusion
Manufacturing clean fuels in the 1990’s is basically a “Big Control Problem”. There are many
operations, components, players, and interactions along the value added chain of steps from oil
and gas to the vehicle customer for combustion to the atmosphere.
The control problem to meet all fuel quality specs, for each batch blend, simultaneously,
optimally, on schedule, competitively, must be clearly understood before it can be solved and
mastered. The incentives to get it right with CIM can approach 1 USD/BBL crude (or the penalty
of regularly getting it wrong might approach 1 USD/BBL crude, as you wish).
Whether American (and foreign) political leaders designed a feasible or optimal control
system remains open. But CIM applications abound.
Look for ideas on Performance Measures in the next issue of Fuel Reformulation.

CIMFUELS Editorial for FUEL Reformulation, November/December 1995, p19 - 20
Establish Performance Measures for Clean Fuels
Vice President, Business Development
Houston, Texas
Improving and sustaining profitability is fundamental to a business’ success. There is a host of
secondary, yet useful and complementary performance measures (PMs), which indicate important
factors contributing to long term profitability with reduced risk.
Baseball is replete with statistical performance measures deemed to indicate success: winning
the World Series, winning pennants, winning games, hitting above 300, pitching below 2.00
ERA, committing no errors, stealing many bases, pitching many strike-outs, and hitting many
homeruns. These statistics relate to profits for the club owner in complex ways.
The strategic PM for the manufacturing of fuels is the present value of reliably forecasted
future profits taken over appropriate future periods and discounted for the time value of money.
One of the five primary functions of Computer Integrated Manufacturing of fuels, CIMFUELS, is
to model, measure, and report useful performance measures for keeping score, accounting,
learning, evaluating, and improving.
PM Examples
PMs for products include quality (giveaway/violations), delivery (early/late to inventory),
amount (over/under) and value. It is clear that product quality giveaway and violations for RFG,
LSD, and most future fuels is prohibitively expensive.
PMs for refineries include capacity (vs. yield), sulfur handling, carbon rejection, hydrogen
addition, crack spreads, inventory management, losses, energy consumption, emissions,
flexibility, reliability, responsiveness, cost of manufacture, safety, permit compliance, economic
margin and competitiveness benchmark.
PMs for process equipment includes the approach to limits for capacity (vs. yield) like
compressor speed, distillation flooding, furnace firing, tube rupture temperature, pressure relief to
flare or atmosphere, separator velocity, tank spills, reactor run-aways/reversals, heating limits,
cooling limits, valve positions, coking, corrosion, fouling, plugging.
PM Ingredients
Most PMs have six ingredients or components. These are mean (average), variance, limit
value, credit (yield, capacity, operating cost) for approaching the limit, penalty debit (safety,
customer, legal, emergency) for exceeding the limit and optimum target for the mean. The latter
can be determined from the preceding four ingredients. When the penalty debit exceeds the
approach credit, the best target is within the limit value. The Greeks were right when they advised
around 450 B.C. “it is better to play it on the safe side”. The amount of cushion depends on these
ingredients. In fact, it can be determined analytically. Also, when the approach credit exceeds the
penalty debit, the best target is beyond the limit value.

These six ingredients provide the basic means for quantifying improved performance of
CIMFUELS, which is manifested in:
1. proper determination of limit value, credit and debit for deviation,
2. assessment of mean and improvement of variance,
3. proper setting of target and mean at target,
4. increasing credit for approaching limit,
5. reducing penalty for exceeding limit.
Objective Functions
Each of the five CIMFUELS functions has an associated objective function for performance
measurement. Proper profit or cost function expressions are required for each major process step,
such as distilling, cracking, reforming, alkylation, etherification and blending. These functions
must be properly linked with the refinery profit or cost functions. Since no one is interested in
improving, let alone optimizing, the wrong objective function with incorrect economic incentives,
accurate objective functions are obvious PMs. They can be quite complex.
The second of five primary CIMFUELS functions, advanced multivariable predictive dynamic
control, compels careful attention to the financial purpose and performance of a process unit. The
third CIMFUELS function, closed-loop optimization of groups of processes and whole refineries,
compels careful attention to the financial purpose and performance of these larger groups and
plants. In fact, optimization starts with the expression of the profit function, or performance
measure to be optimized, subject to process relationships, rules, limits, and constraints. Modern
scheduling packages for the fourth primary CIMFUELS function highlight the requirement for a
scalar financial value measure of approach to JIT (Just In Time), which discriminates between
good (if only suboptimal) and poor schedules, all of which must be mechanically feasible. The
fifth primary CIMFUELS function, integration of information flow, CIM functions, people, and
process operations to the business should have a purpose and associated set of PMs.
CIMFUELS software platforms now make it easy to insert models of plants, plans and rules of
operation. Often a major human task for successful CIMFUELS is to sort out and quantify what
we want the plants and CIMFUELS systems to accomplish. Focus on PM is very useful to start
building CIMFUELS right.
CIMFUELS Performance
Performance measures can also be devised specifically for CIMFUELS systems. The long
sought methods for justifying information systems and quantifying the value of information are
now at hand, because these ill-posed questions are now related to business functions with PMs.
They turn out to be comparison appraisals (base case/delta case, without/with, off/on) of
improved business performance (yield, operating cost, capacity, PV profit), just like any other
business component such as process equipment, staff group, support contract. CIMFUELS
benefits start with analysis of the five primary functions which CIMFUELS can improve: PMs,
planning and scheduling, optimization, control and integration. If improvements in these
CIMFUELS functions can enhance fuel manufacturing performance, this performance benefit can
be weighed against the cost to identify, capture and sustain benefits.
If the business operation is simple, static, low risk and well known, the value added by PMs
and CIMFUELS is naturally rather low. If the business is complex, dynamic, controversial and
risky, PMs are critical and CIMFUELS value added is substantial. For complex fuel and
petrochemical plants, the net return from comprehensive CIMFUELS systems is 0.5 to 1.0
USD/barrel of crude. Invariably, most capacity constraints can be safely pushed 2 - 4%.

It pays to know what you are doing and why. PMs tell that story. CIMFUELS provide PMs for
fuel manufacturing, and PMs provide the benefits for CIMFUELS. The James Dunlap (Texaco)
article “Meeting the challenges of Global Competition” in the July issue of FUEL illustrates how
he sees integration of the information revolution principles and technology for CIMFUELS. This
is another form of “REFORMULATION” for fuels.

CIMFUELS Editorial for FUEL Technology & Management, January/February 1996, p16, 18
Economics - Local and Real Time
CIMFUELS is Beginning to Revamp the Methods for Determining Manufacturing
Economics for Fuels and Petrochemicals
Vice President
Dynamic Matrix Control Corp.
Houston, Texas
……………………………………………………………………………………………………….
Closed-loop real-time optimization incorporates business objective functions, rigorous
process models, flexible open-equation software formats, large scale non-linear
successive quadratic programming optimization techniques and reconciled data fitted
with plant parameters.
……………………………………………………………………………………………………….
Every restaurant menu shows the customer the benefit and cost for meal selections. Most track
volume, costs, value added and profitability of each item and each restaurant. Every clothing and
grocery store shows the customer the benefit and cost for product selections. Each tracks volume,
costs, value added and profitability of individual items, departments and stores along the supply
chain. Does every debutanizer (or ACU, FCC, alky, blender) operator track the volume, quality,
costs, value added and profitability of his products (overhead C5, bottom C4) to his customers
(blenders), based on feed price and reboiler/condenser costs, hourly?
Situation – Credits and Penalties
Operating economics for processing hydrocarbons to manufacture fuels and petrochemicals
are complex, highly interactive, volatile, nonlinear, uncertain and hence controversial. There are
sell prices, production values and buy costs. Each may be average or marginal for a particular
stream. Each may be contracted, spot, future, offered, or forecasted. There are production amount
values/barrel and quality values/property. There may be credits for improved quality within
specifications and penalties for quality specification violations. There may be incentives for
product uniformity and timely delivery, and penalties for variability and unreliability.
Accounting policies may impose ad hoc rules for allocating fixed cost overhead among
processing steps and profit centers such as head count, capital deployed, throughput or "activity
based". These methods are used rather than allocating overhead proportional to value added/profit
generated because determining the profit independently of overhead allocation has been
computationally unwieldy.
Intermediate stream transfer prices for flows and qualities, particularly across manufacturing
business profit center interfaces, for major recycles (e.g., H2) and environmental emissions
remains notoriously difficult and controversial.
Tracking Plant Profit Margins
Tracking value added and sources of plant profit margin contributions remains difficult.
Assuming a refinery margin is 3 USD/BBL crude for average crude and product prices and
variable operating costs, what is the contribution among the major products: gasolines (each

grade), middle distillates, black products, aromatics, oxygenates and sulfur? What is the
contribution from each process: ACU, FCC, CR, alky, blender, utilities, sulfur? What is the
profit contribution from each debutanizer, boiler, storage tank and sour water stripper? How are
such things determined? If refinery margin is negative, how is the loss distributed among
products and processes?
Buy/sell decisions for intermediate streams (e.g., virgin naphtha, FCC feed, isobutylene,
MTBE, RBOB, raffinate) need clear assessment of existing transfer prices (average and
marginal), variance and causes/consequences of these values.
Economic Information Handicaps
Every process (e.g., distillation) has an optimum feed rate at the trade-off point where the
incremental value of products (marginal yield times marginal prices) equals incremental cost of
feed (marginal feed flow/quality times marginal cost). Yet there are very few shift operators of
crude units, FCCU’s, blenders with such current, accurate information. Marketing information
about price forecasts is often poorly synchronized with current (let alone future) crude processing
and unit modes. Customer (dis)satisfaction and preferences are rarely quantified or
accommodated analytically. Economic planners and schedulers often issue production and quality
targets to operations, without in-depth business information about financial objectives, economic
sensitivities, assumed constraints, prices or penalties.
As a result, the economic consequences of adjusting cut points, refluxes, recycles, pressures,
temperatures and flows are often not available to the operator or his advanced control system.
Operations planning continues to be impeded by LP tools which employ averaging (over 30 days
or so), preset multiple periods, lumping, pools, linear model segments, inaccurate shadow prices
and artificial constraints on dependent variables. Scheduling blend batches, oil movements,
inventory, receipts/shipments and unit modes/maintenance continues to be limited to the short
term because CIMFUELS scheduling technology for mechanical feasibility, financial appraisal
and improvement algorithms remains in its infancy.
While it is remarkable how well plants are operated under these economic information
handicaps, most observers agree the lack of accurate, localized, real-time economic information
for hourly operating decisions is a fundamental weakness of fuel and petrochemical
manufacturing practice worldwide. It is a basic barrier to improved performance.
The Solution - CLRTO
At bottom, CIMFUELS forces people to think more analytically and quantify objectives,
models, communications and decisions.
CLRTO in particular is currently progressing rapidly to provide closed-loop real-time
optimization of major processes (ACU, FCC, olefins, blending) and entire refineries and
manufacturing complexes.
CLRTO incorporates business objective functions, rigorous process models, flexible open-
equation software formats, large scale nonlinear SQP optimization techniques and reconciled data
fitted with plant parameters (i.e., efficiency, fouling, hydraulics). Solution periods are shrinking
to less than hourly, while the number of multiple periods is increasing.
One useful result of the plant-wide global optimum solution allows arbitrary interface
envelopes defining sub plants of interest to obtain intermediate stream transfer prices, which have
already been optimally negotiated among the units. If the CLRTO results are routinely and

reliably implemented to the plant, integrally involved in affecting its performance (usually
through comprehensive dynamic multivariable predictive controllers), these local, real-time
economics finally become accurate, meaningful, reliable and useful for other business purposes.
CLRTO of a refining complex provides the basic link of CIMFUELS between marketing and
operations. It includes allocation of fixed overhead costs based on value added among suitably
defined operating profit centers.
The Expectation - 0.3 $/B
Trend plots of local, real-time economics start with forecasts for product and feed prices. Next,
process shutdown/maintenance/catalyst performance trends are developed.
CLRTO uses price forecasts to optimize operating mode conditions. As a consequence, it
provides reconciled local, real-time cash flow economics of all streams and process steps of
interest, with trends plotted at least hourly. Value added tracking and profit margin allocation
among interacting process steps is simultaneously determined. Primary active constraints are
identified down to heat exchangers, compressors and valves. Performance of process profit
centers and product profit centers is measured. Buy/sell decisions can be made for any stream.
All stream values and process profits are determined for each batch of gasoline/distillate, for each
property of each product batch, for each crude, product slate and processing mode/configuration.
People need to focus on modeling, constraint trade-offs, objectives and performance
monitoring, along with how to get computers to act on these human creations.
Local, real-time economics, deployed with CLRTO and business decisions, can generate net
benefits of 0.3 to 0.5 USD/BBL of crude for most refineries. For 200 MBPD, the present value
profit over 20 years at 10%/y is 173 - 288 MMUSD.
Landmark Paper - HCU
Sunoco, at Sarnia, Canada recently reported the first commercial CLRTO with MVC on a
HCU - SMR plant (1). The work began in 1985. By 1994, CLRTO and MVC were generating
0.17 plus 0.23 = 0.4 USD/BBL feed times 20 MBPD for NPVP (20y, 10%) of 23 MMUSD. This
success was recognized by a Smithsonian Institute award.
Stepping from a HCU to an entire refinery is now at hand, one of the major current activities
of CIMFUELS.
Table 1. CLRTO Captures Economic Opportunities Otherwise Lost
Tactical Opportunities
One of the most lucrative opportunities for real-time optimization is in coordinating setpoints to
take advantage of trade-offs that are outside the scope of the individual controllers.
Strategic Opportunities
Market opportunities identified by planning and scheduling can be captured as soon as updated
economics are available.
Refinery-Wide Optimization
Provides the necessary platform through which refinery-wide optimization can be implemented.

Reference
1. Pederson, C.C., et al, "Closed Loop Real Time Optimization of a Hydrocracker Complex," paper CC-95-121, NPRA
Computer Conference, Nashville, TN, Nov 6, 1995.

CIMFUELS Editorial for FUEL Technology & Management, March/April 1996, p14, 16
INTEGRATION - Process, People, Computers, Information, Business
and R, D&A.
Vice President
Houston, Texas
……………………………………………………………………………………………………….
CIM technology highlights the arbitrary definition of jobs and groups and the
interfaces among them for business purposes. Since the amount, quality and value of
any person/group work depends on how it is used, modeling of human organization
work flow network integration is vital to manufacturing decision success.
……………………………………………………………………………………………………….
One of the five principal active money making functions of Computer Integrated
Manufacturing of fuels and petrochemicals is INTEGRATION. Integration of what? Processes,
people, computers, information, business(es) and research/development/application. Why? To
increase profitability (read profit growth). This is the most complex and critical of the five CIM
functions.
Process Integration
Processes for manufacturing operations are traditionally defined as steps like reaction,
conversion, separation, recovery, blending and auxiliary utilities. Processes must be segregated
and integrated simultaneously for analysis, operation, control, optimization, scheduling and
accounting.
Current CIM technology shows how to manage series/parallel networks of processes with
recycles for multifeed/multiproduct plants. Most processes are connected to most other processes.
The proper operation of any process is intimately related to the operation of processes to which it
is connected. Each process is a client/customer of its supplier processes, and is in turn
simultaneously a server/supplier to its product processes. Integration is
upstream/downstream/sidestream/recycle stream.
CIM technology highlights the arbitrary definition of envelopes for processes and their
interfaces to other processes, for business purposes. Since the amount, quality and value (average
and marginal) of any stream depends on where it goes and what happens to it, modeling
knowledge of process integration and interfaces is vital to manufacturing decision success. CIM
is the enabling technology for actively operating integrated processes in integrated ways for
improved global performance.
People Integration
People and organizations for manufacturing operations are traditionally defined in terms of
functions, teams and skills for performing required work. People must be segregated and
integrated simultaneously for analysis, operation, control, optimization, scheduling and
accounting work.

Current CIM technology shows how to manage networks of people and organizations for
multifunction/multidiscipline/multiobjective plants. All people/groups are connected to most
other people/groups. (It seems everything is connected to everything else!) The proper role,
responsibility and authority (RRA) of any person or group is intimately related to the RRA of
people or groups to which they are connected. Each person/group is a client/customer of its
supplier persons/groups and is in turn simultaneously a server/supplier to its customer
people/groups. Integration is up/down/across/recycled through the organization.
CIM technology highlights the arbitrary definition of jobs and groups and the interfaces
among them for business purposes. Since the amount, quality and value of any person/group work
depend on how it is used, modeling of the human organization work flow network integration is
vital to manufacturing decision success.
CIM is the enabling technology for actively managing integrated people/groups in integrated
ways for improved global performance.
Computer Integration
Networks of digital equipment for manufacturing operations are traditionally defined as
mainframes, work stations, personal computers, distributed control systems, LANS and WANS of
client/servers. System components must be segregated and integrated simultaneously for analysis,
operation, control, optimization, scheduling and accounting.
Current CIM technology shows how to build computer networks for real-time continuous and
batch operation. Most computers are connected to most other computers. Proper operation of any
component is intimately related to the components to which it is connected. Each component is a
client/customer of its supplier components, and is in turn simultaneously a server/supplier to its
product components. Integration is up/down/side/ring wise.
CIM technology highlights the arbitrary definition of network components and interfaces for
business purposes. The size, speed and value of any component depend on its role in the network;
modeling knowledge of network integration is vital to manufacturing computer performance.
CIM is the enabling technology for actively designing and operating integrated computer
networks in integrated ways for improved global performance.
……………………………………………………………………………………………………….
CIM technology highlights the definition of information functions and interfaces.
Since the amount, quality and value of any information and software depends on how
it is used, modeling knowledge of information integration and software performance is
vital to manufacturing success.
……………………………………………………………………………………………………….
Information Integration
Manufacturing businesses make money by supplying products and information competitively.
Information generation, communication and management are the glue at the interface between
physical and financial events. Information must be segregated and integrated simultaneously in
efficient, meaningful and timely ways for analysis, operation, control, optimization, scheduling
and accounting of processes by people and computerized functions.

Current CIM technology shows how to manage series/parallel information networks for multi-
process/multigroup plants. It provides the software and communications to achieve valuable
results. The proper management of any information network is intimately related to the processes
and organizations to which it is connected. Each information processing step (software package)
is a client/customer of its suppliers and is in turn simultaneously a server/supplier to its
customers. Information integration is up/down/sideways/recycled.
CIM technology highlights the definition of information functions and interfaces. Since the
amount, quality and value of any information and software depends on what is done with it (how
it is used), modeling knowledge of information integration and software performance is vital to
manufacturing success.
CIM is the enabling technology for actively building and managing integrated information
flows in integrated ways for improved global performance.
Business Integration
Businesses and profit centers for manufacturing are traditionally defined as value added
activities manifested in arbitrary ways like processes, products, plants, sectors, regions, markets,
customers. Businesses must be clearly defined as to what is offered for sale, to what market, with
what assets, for what purpose, within what rules, with what authority. Businesses must be
segregated and integrated simultaneously at different levels for analysis, operation, control,
optimization, scheduling and accounting.
Current CIM technology shows how to integrate series/parallel businesses with interactions.
Most profit centers are connected to most other profit centers. The proper management of any
profit center is intimately related to the management of profit centers to which it is connected.
Each profit center is a client/customer of its supplier profit centers, and is in turn simultaneously a
server/supplier to its customer profit centers. Integration is up/down/sideways/recycled.
CIM technology highlights the arbitrary definition of envelopes for profit centers/businesses
and their interfaces to other profit centers/businesses for business/financial purposes. Since the
amount, quality and value of any business depend on what it does, for whom to create value,
modeling knowledge of business value added integration is vital to manufacturing business
success.
CIM is the enabling technology for actively managing integrated fuel manufacturing profit
centers for improved global performance.
R&D&A Integration
Integration of CIM systems research, development and application is fostered by university,
industry and vendor interaction. Integration of chemical engineering research and education is
fostered by process engineering, process dynamics and control engineering, process optimization
and scheduling engineering interaction. Integration of chemical engineering, chemistry, computer
science, systems engineering and mathematics fosters CIMFUELS technology. Many conferences
and consortia are promoting this integration around the world. The Chemical Process Control
Conference V at Tahoe City, NV on January 7-12, 1996, sponsored by CACHE Division of
AIChE every five years brought together 120 experts from academia and industry for the fifth
time since 1976 for assessment and new directions in constrained multivariable predictive
dynamic control (CMVPC), closed-loop real-time optimization (CLRTO), plant-wide modeling
and scheduling for chemical engineering systems. Here is the foundation for CIMFUELS like
RFG and LSD. Commercialization is now expanding due to improved R&D&A integration.

CIM Integration
Notice any parallelism, repetition and commonality among these six sectors? Beyond
integration within each sector: processes, people, computers, information, businesses and
R&D&A is the integration of all six sectors together.
Sectors must be segregated (as above) and integrated simultaneously for analysis, operation,
control, optimization, scheduling and accounting. Each sector must be defined, justified, planned,
designed, built, used, managed, maintained, improved, audited and integrated. Current CIM
technology is beginning to show how this can be done profitably for manufacturing fuel and
petrochemicals. Incentives approach 1.0 USD/BBL crude. The real issue is how to identify,
capture and sustain such performance, net after costs. 1996 looks like an interesting year for
CIMFUELS - INTEGRATION.

CIMFUELS Editorial for FUEL Technology & Management, May/June 1996, p21 - 23
Scheduling = CLRTS
Vice President
Aspen Technology, Inc.
Houston, Texas
……………………………………………………………………………………………………….
The purpose of scheduling is to ensure mechanical feasibility of move sequences,
within the rules, for good (prefer best) profitability.
……………………………………………………………………………………………………….
One of the five principal active money making functions of Computer Integrated
Manufacturing of fuels and petrochemicals is SCHEDULING. Scheduling of what? Processes,
shipments, receipts, movements, purchases, sales, deliveries, people, computers, information,
business(es) and research/development/application. Everything. Why? To increase profitability
(read profit growth) of course. Scheduling is fundamental to all our activities and interactions.
This editorial is confined to Closed-Loop Real-Time Scheduling (CLRTS) of manufacturing
operations, one of the five active CIM functions: performance measures, multivariable control,
rigorous optimization and integration. CLRTS resides at the center of CIMFUELS.
Setting the Scene
Discrete product manufacturing, batch chemical processing and batch fuel blending operations
have been scheduled by people for a long time. The first refinery I worked for in 1966 had an
Economics & Scheduling Department (E&S) reporting directly to the Refinery Manager. It had
been in existence since around 1946; it ran the place. Every refinery has a refinery scheduler.
Refinery operations scheduling is the (human) activity of accepting and adjusting crude
arrivals, process operating modes, blend batches, product shipments, planned unit shutdowns and
other important refinery operating events prior to their occurrence. The purpose of scheduling is
to insure mechanical feasibility of move sequences, within the rules, for good (prefer best)
profitability.
Scheduling provides the primary interface link between refinery internal operations and
external suppliers and customers. Furthermore, scheduling provides the primary link between
longer term (monthly) aggregated operating Linear Program Plans (LPP), which are not
guaranteed to be mechanically feasible over time, and daily process control and unit optimization
(CMVPC & CLRTO), which may not be well represented by the scheduler’s models. The
scheduler is in the middle of these horizontal and vertical entities (some schedulers call them
crosshairs). If there is a conflict between LPP and CLRTS, the latter usually rules. If there is a
conflict between CLRTS and CMVPC, the latter usually rules. If there is a conflict between
CLRTO and CLRTS, the latter usually wins the negotiation. If there is a conflict between CLRTS
and crude supplier or product purchaser the negotiation is about who pays how much when.
Inputs to scheduling are the desired long term commitments and specific requests or demands
arising at any time from many places. Outputs from scheduling are commitments to requestors
and timing/sequencing orders to operations (CLRTO & CMVPC) to accommodate the
commitments.

Naturally the better the scheduler can evaluate the consequences (feasibility and profitability)
of each commitment the easier it is to make and honor good, fast commitments. The schedule
shows receipt, production, shipment and inventory profiles into the future. Rescheduling occurs
whenever newly revised request/demand information is received/accepted. The schedulers’ orders
specify crude receipts, product shipments, oil movements between tanks and units, process
modes, product types, flow routings from sources to destinations, blend recipes and sequences (all
with exquisite timing).
Problems/Opportunities
Recent changes to a myriad of mogas products (RFG, CARB2, RBOB, REG, PREM, OXY
etc), diesel products, jet fuels, mid distillates, fuel oils, petrochemicals, and lubes have
transformed continuous refineries into “continuous batch” manufacturing plants. Merchant profit
center refineries competing in the spot markets with a worldwide ocean based crude diet, diverse
fragmented markets (geographical, seasonal, commercial, commodity, niche, political) have
significantly more scheduling activity (headache or opportunity?) than a land locked refinery near
a secure billion barrel reservoir servicing a captive stagnant fixed price market.
Savvy scheduling of a modern, complex, dynamic competitive and exciting refining and
trading business for sophisticated clean fuels, petrochemicals and lubes is not so easy. There are
situations today where potential profit from improved scheduling exceeds 0.3 USD/bbl crude
purchased (not necessarily refined).
Status
CIMFUELS is helping in a big way. Modeling for a purpose is the name of the game.
Modeling mechanical steps to simulate an entire refinery for scheduling (tank transfers, receipts,
shipments) is very easy. Assembling inputs (requests, demands, prices, and plans) is also easy.
Object oriented expert system software platforms with relational data bases, powerful graphical
user interfaces, and extensive system interface connects is widespread and inexpensive. Data
management, hardware and communications are powerful and no issues.
However, CLRTS, the Closed-Loop Real-Time Scheduler for the entire refining chain to
deliver (by manufacture or purchase) fuels and petrochemicals to customers is rare, even
controversial.
……………………………………………………………………………………………………….
Faithful implementation of precise schedule output orders remains spotty, a common
weak link with human interpretation and execution heavily involved.
……………………………………………………………………………………………………….
Barriers
What’s holding things up? As usual for CIM, it is hard to confidently predict the financial
performance of CLRTS in order to justify it. Computers and software can do the job all right,
provided we (folks) can tell them WHAT we want to schedule, with what rules (limits), WHY
(objectives) and what happens when the rules are broken.
1. PM. The first barrier is inattention to the financial performance measure, PM, to allow us
(and CLRTS) to distinguish between good/great/profitable/desirable schedules and poor/bad/
loser/difficult schedules. Business models of logistics, customer penalties for off spec or late
products, price forecasting, time value of money for optimum (not minimum) inventory
management which accounts for customer late penalties to optimize JIT policies, demurrage and
operating penalties for rapid sequence switching with low fidelity process control systems are all

fundamental ingredients to appraisal of candidate schedules. Optimization should always be out
of the question if the proper objective function cannot be formulated and computerized. The
objective function should be the expected value of NPV(X mos, Y%). What is the “best” time
horizon for the scheduling period X? Best time resolution for each order, minutes? Uncertainties
and reliability of the inputs to the future schedule should be classified, modeled and accounted
for. Some classifications of types of future information are: contracted, forecasted, proposed,
futures market, tentative, offered and rejected.
2. Methodology. Many seem to insist on optimizing the schedule or nothing in spite of the fact
there is abundant old mathematical proof that no general purpose rigorous algorithm can ever be
devised for combinatorial explosive, nonlinear, mixed integer, generalized scheduling problems.
Since time marches on, if the whole rescheduling problem ever were solved it would be too late.
So the methodology of scheduling will remain partly art as long as humans determine the value
and beauty of things. A better approach is to deploy methods and tools for improving scheduling
within the time, information and capability available. Scheduling research offers many algorithms
(like time based SQP) that can be customized to the particular nature of problems like refinery
scheduling which help find better answers. Expert systems have proven to offer a fruitful tool.
Several heuristics have proven useful:
1) insure mechanical feasibility first.
2) consider financial consequences of every move, decision or change: it costs lots of
money to clean a tank overfill spill or put black oil in a white oil tank.
3) revise schedule as soon as new information or opportunities appear, and decisions are
made.
4) feedback comparison of past orders with results, then continually attempt to reconcile by
improving models, definitions, methodology, communications, analyses, instructions,
safety margins, (everything).
3. Segmentation & Interfaces. Theory and practice have not yet clarified how CLRTS
activities should be segmented to be manageable and solvable: crude receipts, product shipments,
blend sequences, unit modes, whole refinery onsite and offsite together, several connected
refineries and chem plants together? How does the refinery CLRTS negotiate with crude supply
CLRTS and with product distribution logistics CLRTS? Across horizontal profit centers? Who
decides who takes a loss to reconcile discrepancies? Perhaps the greatest potential for CLRTS is
forcing people to resolve these long standing issues.
4. Output. Faithful implementation of precise schedule output orders remains spotty, a
common weak link with human interpretation and execution heavily involved. The connecting
link for closing the scheduler to actuators, control systems and optimizers is gaining attention.
Many offsite tank farms have invested heavily in motorizing remote valves to bring CLRTS to
reality.
5. Gap Closure. Scheduling resides in a technology gap between operations planning
(monthly, quarterly by LP), and operation execution, which includes control (CMVPC) and
rigorous unit optimization (CLRTO). Technical developments are underway to reconcile and
harmonize these rather distinct problems and technologies, in order to close the major
CIMFUELS gap. New CLRTS techniques allow us to devise and implement comprehensive
revised schedules which reconcile the planning - execution gap to meet new situations quickly,
accurately, reliably, and profitably because they act in real-time, closed-loop. That is the key to
gathering rather important money in the gap. Some have found 0.3 USD/bbl crude x 200 MBPD
= 21 kk$/y = NPV (20y, 10%) = 179 kk$/ refinery.

Outlook
Refinery scheduling is a growing proportion of the CIMFUELS business because the
incentives are high for good rescheduling. Clear integrated connections (2 way) downward to
advanced control promises high returns. Clear integrated connections upward to planning (2 way)
provide high returns as well.
The interesting developments are extending CLRTS activity horizontally across traditional
organizational and profit/cost center lines from crude and intermediates supply to
products/components manufacturing and/or trading.
The sector of CIMFUELS called CLRTS can do the job when people can communicate to
CLRTS the job to be done: WHAT to schedule, with what rules, WHY, HOW. Also define the
results to be communicated properly to its CIMFUELS system partners for control, optimization,
planning in an integrated way to enhance appropriate performance measures.
Remember what Woody Allen said: “time is just nature’s way of keeping everything from
happening all at once”. Are you good at scheduling? Does it matter? Do you know for sure?
Worth your while to improve? Know how much your customers value your excellence at
scheduling? Your shareholders? Do you need to check out CLRTS? Sure you know the
benefit/cost/risk? Could it be critical for profitability? Survival? Ask any airline. Follow trade
journals and NPRA. Stay tuned, the refining business is getting trickier every year.

CIMFUELS Editorial for FUEL Technology & Management, July/August 1996, p16, 18, 19
Operations Optimization = CLRTO
Vice President
Houston, Texas
One of the five principal active money-making functions of Computer Integrated
Manufacturing of Fuels and petrochemicals is CLOSED-LOOP, REAL-TIME OPTIMIZATION
of process operations. Optimization of what? The steady-state operating conditions for each mode
that determine production rates, yields, qualities and utilities. Why? To maximize current profits
in harmony with future plans. This editorial is directed to nonlinear steady-state optimization of
rigorous process models for current (minute-to-minute, hour-by-hour) operating modes (1). We
do not include simplified, lumped, pooled, off-line, LP-based monthly planning optimization.
Description
On-line, closed-loop, real-time optimization of process operations first requires formulation of
the profit function representing the financial purpose of the process to be maximized. Next, the
process model of its chemical and physical behavior must be formulated. The specific
independent variables to be manipulated or adjusted (MV) are identified. These are
predominately flow controller setpoints which adjust valves, or feedback control system setpoints
like temperatures, pressures and qualities which reset flows. Independent disturbance variables
(DV) that are not manipulated because they are set by other means (such as ambient conditions or
some feedstock compositions) are identified. Then, dependent variables (DV) that have limits or
specifications and economic importance and which can be measured or inferred are identified.
The number of degrees of freedom that can be optimized equals the number of available MV’s.
Formulating comprehensive profit objective function expressions which properly represent the
purpose and financial performance of the process operation, all significant trade-offs and real-
time price/cost economics remains a challenging yet essential step for CLRTO (not to mention
management of fuels and petrochemicals manufacturing in general). We must tell CLRTO why
we want to run the process.
Rigorous process models include material, energy and momentum balances of chemical
engineering. Component mass, molar and volume (mass/density) material balances include
kinetics and equilibria. Energy balances for heat transfer, reactions and separations are
fundamental. Momentum balances for hydraulics and pressure profiles are basic. Many of these
rigorous steady-state models incorporate differential equations which are integrated through
distance (length and even radially) inside equipment. Accurate prediction of tower flooding,
compressor surge, pump cavitation, separator entrainment carryover, fouling, plugging, corrosion,
metal fatigue, coke deposits and catalyst deactivation are also essential. Neural networks of
combined rigorous and empirical models of hitherto intractable phenomena (e.g. visbreaker
residue stability, combustion NOX emissions, diesel cetane, asphalt penetration) have recently
been successfully applied commercially. We must tell CLRTO how the plant works.
Constraint bounds are placed on the range of feasible values for the independent variables.
Specification and limit values are placed on the range of acceptable values for the dependent

response variables. These usually represent basic trade-offs between process yield credits and
risks of damage, reprocessing, customer dissatisfaction or noncompliance. (In fact, setting these
limits properly is a commonly unmodeled optimization opportunity not covered here.) We must
tell CLRTO the rules (and penalty consequences for violating the rules). Penalty modeling should
receive vigorous attention in CLRTO commercial practice to connect maintenance, safety and
environmental permit compliance to production, yield and quality results.
Statistically-based calculated risks abound. A major US refiner reported (2) that it experienced
“Unscheduled shutdowns and other refinery operating problems increased operating expenses .....
That is why incident-free operations are now the number one priority”. An ASM (Abnormal
Situation Management) Consortium of Amoco, BP, Chevron, Mobil, Shell, Texaco, Novacor and
several suppliers believe the impact on the US economy exceeds $30 billion and $20 billion cash
be eliminated (3). A US refiner experienced $60 million/year expenses from unforeseen
occurrences and abnormal incidents. The adjective “unforeseen” causes greater concern than the
noun “$60 mil”. Well calculated risks are far superior to uncalculated ones.
Some think of MEMM modeling for CLRTO: Mass, Energy, Momentum and Money
balances.
Optimization solver algorithms like sequential LP and Reduced Gradient searchers are being
replaced by large-scale, open equation, sparse matrix and quadratic programming methods
solving more than 300,000 equations simultaneously, as frequently as hourly.
Span of Processes
CLRTO has been successfully applied to run distillation trains, crude units, hydrocrackers,
fluid catalytic crackers, catalytic reformers, RFG blenders, and whole steam cracker olefin plants.
Programs are underway to handle whole refineries and petrochemical complexes with CLRTO.
The number of MV (designated DOF, degrees of freedom) for common processes are: SGP (10-
15), ACU/VAC (25-30), HCU (20-30), FCC (30-40), CCR (10-15), RFG Blend (10-16), eight
furnace olefin plant (50-60) and fuels refinery (150-300).
Functions
The four main functions of rigorous CLRTO systems are parameter fitting reconciliation,
future operation and equipment revamps simulation, intermediate stream transfer pricing (ISTP)
and optimization of operating conditions. Reconciliation of vast amounts of plant measurement
data with fundamental model relationships is the standard method for fitting empirical efficiency
factors, which may change in unpredictable ways, to provide the most accurate process model of
commercial plants. Optimized simulations of plant performance with hypothetical future feed
types, rates, product qualities, economics or equipment modifications provide the best method for
related decision making.
Routine CLRTO of the operating DOF makes money directly by securing the best process
operation. Further, it verifies the accuracy and fidelity of the models, empirical factors and
associated economics. When these all work together routinely for the whole processing train
connected to suppliers, customers and the environment, we gain assurance that each component is
valid.
The real optimum solution may be with all DOF at some combination of DV limits and MV
constraints. For such a fully constrained solution at a corner in DOF-space, LP solver techniques
are usually suitable. LP solutions are necessarily at a constraint corner where the sum of the
number of limiting DV’s plus the number of constrained MV’s equals DOF. The real optimum

solution may find some DOF at unconstrained interior smooth hilltops and others at limit corners.
Mixed solutions can only be found by nonlinear solvers such as QP. In practice, while the
preponderance of process operation DOF optima are found at limit/constraint corners, some
smooth interior point optima may occur for recycles, refluxes, reactor severities, yield/capacity
trade-offs, parallel flow splits, recoveries and intermediate qualities.
With 90% of DOF optima at limits and constraints, profit improvements from CLRTO are
principally determined by proper setting of DV limit values. If they are set too tightly, they
restrict solution movements and little gain is realized. If they are set too loosely, solutions can
move outside the domain of model validity and experience where nonlinear penalties arise and
risk of damage increases. The current art of successful CLRTO takes great care to set DV limits
properly for maximum safe performance.
Plausible and useful transfer prices for intermediate stream flows and qualities (ISTP) are
notoriously difficult to obtain from simplified, linearized, lumped, averaged LP modeling. Large-
scale, global, plant-wide CLRTO solutions and local process unit CLRTO solutions suitably
connected to other processes for global results provide the proper method for determining ISTP.
These prices are marginal, average and even functions (principally of production rates). ISTP are
used with CLRTO profit functions for value added tracking (VAT) through the plant-wide
processing train to reveal sources of profit generation and loss. Value added determined by
CLRTO provides the rigorous method for allocation of fixed accounting costs rather than
common ad hoc methods such as head count, capital employed or “activity-based” guidelines.
This allows easier definition of profit centers by process units or product lines. In addition, ISTP
provide the proper basic information for buy/sell decisions on intermediate streams and blend
components. Plant-wide CLRTO strengthens profit estimating for crude oil purchase selection
and cost decisions as well as for product slate menus and pricing.
Augment Scheduler
Beyond CLRTO of current operating condition DOF, rigorous optimization of postulated
future feeds, products, modes and economics along a planned schedule sequence can trim and
enhance the estimates from simplified scheduler process models for improved accuracy and
profitability. Important work is now underway to provide people with easy linkage between
CLRTS (scheduling) and CLRTO; further, these two basic functions promise to soon become
computationally combined.
Augment CMVPC
The conventional, primary control systems of most processes lack sufficient dynamic
performance to directly accept targets from steady-state CLRTO. Current technology for
Constrained MultiVariable Predictive dynamic Controllers (CMVPC) provides this necessary
capability for closing the complete optimization loop. Some have steady-state LP or QP
optimizers built in for every move step, minute-to-minute, using necessarily simplified steady-
state models in conjunction with comprehensive dynamic process models for all interactions.
These controllers find and hold a constrained, steady-state optimum solution (just like CLRTO
finds) at a combination of limited DV plus constrained MV summing to DOF. When the CLRTO
solution does not have many interior points, these powerful CMVPC’s capture the main process
performance improvement by reducing variance and holding the process in the neighborhood of
the critical constraint set, which maximizes profit. They provide the fundamental protection to
keep the process transients within the safe operating region at all times. In these common
situations, rigorous CLRTO takes a secondary role as a limiting DOF point selector which
verifies and corrects the limiting points determined by the simpler CMVPC.

When the CLRTO solution is mixed, with significant DOF at numerous interior smooth hilltop
points, rigorous CLRTO finds them more effectively than CMVPC. Flat, hilltop interior profit
optima prove that incentives for tight control of dynamic variance are diminished and the CLRTO
value added dominates that of CMVPC.
In any case, CLRTO is no longer seen as simply “setting optimum steady-state setpoints” for
the basic process control systems (for flow, pressure, temperature, level and quality). Their
automatic, closed-loop connections through CMVPC are more sophisticated as they work
together in closely coordinated partnership. CLRTO may even provide profit functions of DV
values as input to proper statistical selection of DV limits and CMVPC targets to optimize
calculated risk trade-offs fundamental to profitability of any plant.
Barriers
Computational speed limits for plant-wide CLRTO are being addressed by segmenting and
distributing subproblems among parallel workstation/personal computers with proper executive
linking for the global solution. Formulating accurate profit objectives with correct economics
remains a challenge. Modeling the consequences and financial penalties for violating limits and
specifications needs much greater attention. Quantifying the plant characteristics and
environments that are conducive for CLRTO and the performance contribution that it can deliver
will foster its proper commercialization in CIMFUELS. Predicting financial benefits from
CLRTO requires special expertise.
Performance
CLRTO has been generating benefits for manufacturing fuels and petrochemicals since the
1970’s (1). Benefits of 0.002 to 0.003 $/LB of C2= have been generated from a number of olefin
plants since the late 1980’s. They optimize severity against coking. They optimize C3 cocracking
against crack spreads for C2= and C3=. They maximize use of process gas and refrigeration
compressors. They optimize recycles, operating conditions and profits from cracking C2 through
gas oil.
CLRTO can optimize FCC conversion and selectivity for olefins, mogas and distillate against
fresh feed rate (when feed price is well known and rate can be adjusted). They optimize heat
balance, pressure profile and recoveries. They generate 0.05 to 0.1 $/bbl feed. Similar results are
obtained from CLRTO on HCU. Reoptimizing CR severity to customize octane for each blend,
BTX production and H2 yield can generate 0.05 to 0.1 $/bbl naphtha feed in complex refineries.
Crude distillation units are candidates for CLRTO when yield of low value AR increases with
crude rate and marginal economics of a trade-off are clear, strong and variable. While most
refineries do not experience this situation, optimization of pressure and fractionation against heat
recovery merits optimization on large units with strong and volatile product price differentials.
Refinery-wide or plant-wide CLRTO promises to generate 0.1 to 0.2 $/bbl crude in dynamic
competitive economic environments, and substantially more when ISTP and VAT are highly
significant.
Outlook
Standardized, rigorous modeling and CLRTO of operating plants for clearly defined profit
purposes and widespread use within operating companies for all relevant decisions will accelerate
through the remaining 1990’s. As one of the five pillars of CIMFUELS, it will enhance the
competitiveness of fuels and petrochemical manufacturing at sites around the world.

References
1. Latour, P.R., “Online computer optimization 1: What it is and where to do it”, and “2: benefits and implementation”,
Hydrocarbon Processing, Jun & Jul 79.
2. Refiner Profile, Chevron, Octane Week, vX, n43, 6 Nov 95.
3. Companies Team Up To Tackle Control and Software, Chemical Engineering Progress, May 96, p 10.

CIMFUELS Editorial for FUEL Technology & Management, September/October 1996, p17 - 20
Advanced Dynamic Process Control = CMVPC
Vice President
Houston, Texas
We identified the five basic active CIMFUELS functions that make money: Performance
Measures (PM), Information Integration (IT), Scheduling (CLRTS), Operations Optimization
(CLRTO) and Advanced Dynamic Process Control (ADPC or recently Constrained MultiVariable
Predictive Control - CMVPC). The first four were described in recent issues.
This editorial will cover Constrained MultiVariable Predictive Control. Since the late 1980’s
CMVPC has become almost synonymous with Advanced Dynamic Process Control (ADPC).
This is the automatic execution function of CIMFUELS, which ensures that CIMFUELS
technology truly affects change. It moves the plant directly and automatically, while people
watch, check, approve, audit, learn and maintain. ADPC enables CIMFUELS to take charge, take
control, manage - really implement decisions to actively integrate computers with manufacturing.
Some would add ADPC protects the process from awkward CIM, infeasible CIM, incorrect CIM,
and dangerous CIM. Further, good ADPC provides feedback to other CIM functions on
inaccuracies, errors and infeasibilities.
What Does it Do?
The job of ADPC is to adjust or manipulate the primary operating condition settings (every 30
to 60 seconds) for flow, pressure, temperature, level and quality on single-loop controllers, which
in turn adjust primary actuators (every 0.1 second) such as valves and motors. This activity has
traditionally been done by board operators in control rooms. ADPC must safely adjust and protect
the process to achieve some purpose (e.g., production rate, quality and efficiency), while adhering
to the rules (operating limits and procedures). Inputs include operating condition limits (max and
min) on all controlled variables of interest, quality specifications, equipment limitations and
adjustment range bounds. It must employ some economic objective function to guide its trade-off
actions and performance. It should incorporate information about the consequences and penalties
for violating specifications, exceeding limits, breaking the rules.
The function of ADPC is to accept input commands and desires from people and other CIM
functions and execute them as well as possible, i.e., accurately, promptly, safely and optimally.
What is the Problem?
Fuel and petrochemical processes are inherently constrained and limited. Independent
manipulated variables are constrained and dependent controlled response variables are limited.
Fuel and petrochemical processes are inherently multivariable and interacting: each flow
affects other flows, each heat affects other heats, flows affect heats, heats affect flows, heats and
flows affect compositions and qualities, heats and flows affect pressure, most adjustments affect a
variety of limits and economic performance in different ways. Multivariable interactions must be
accounted for to operate processes and make products.

Unmeasured disturbances abound. Feed composition, ambient conditions, catalyst activity and
equipment malfunctions are notable. One might also include economic incentives.
Fuels and petrochemical processes are inherently dynamic. Transient lags and dead times can
exceed several hours. Recycle changes around alkylation plants, olefin plants and hydrocrackers
have long settling times to reach the new steady state. Initial responses are often opposite the
ultimate direction to final steady state. Many responses are oscillatory, with multiple frequencies.
Some are highly nonlinear and not fully reproducible. Response speed increases when production
rates are low. The nature of dynamic responses can differ significantly when portions of the basic
control system are disabled or restructured.
An interesting FCC had it’s preheat furnace and feed temperature control off for a valid
economic reason. Main fractionator pumparound heat to feed affected the riser, which affected
the regenerator and main fractionator, which subsequently affected the riser and main fractionator
again. Uncontrolled transients were detected from regenerator stack CO through the C3 recovery
absorber with several oscillations after 90 minutes, inhibiting the ability to approach limits.
The financial objectives of processes can change modes significantly. FCC can switch from
mogas liquid to olefins to serve conventional and RFG summer blends, then to middle distillates
for winter heating oil or kerosene. Olefin plants change severity to follow ethane-propane co-
cracking spreads. HCU changes from mogas to jet modes are significant. Catalytic reformer
economic objectives can swing from octane to BTX to H2.
How does CMVPC Work?
These controllers are built on a dynamic model of the response of each dependent controlled
variable (CV) to all independent manipulated variables (MV). Most include a steady-state model,
profit objective and optimizer to determine the best feasible final steady-state targets within the
constraint or limit region. They also retain a prediction of how the process is destined to respond
in the near term (its dynamic horizon) based on known prior manipulated inputs and disturbances
currently propagating through the process.
At each control interval (usually every 30 to 60 seconds), the CMVPC devises a sequence of
feasible future moves that will drive CV’s to the desired limiting steady-state targets with
minimum variance along the way for maximum dynamic performance within all imposed limits
and constraints. The first move of this sequence is implemented because it is deemed to be
optimal based on all of the best information available (in a future dynamic as well as a steady-
state sense, i.e., an optimal path to an optimal destination). Then, one time interval later, with new
process feedback measurements available (which invariably differ from predictions) and perhaps
new objectives, the entire prediction, steady-state optimization and minimum variance sequence
is recalculated to determine the new best move sequence and next move.
This creates a robust, high fidelity, high performance, dynamic control system for operating
big fuel manufacturing plants at their proper economic limits, provided the dynamic model fairly
represents the true process dynamics. These models may be rigorous first principles differential
equations for simpler processes, but are more commonly developed empirically from carefully
executed process testing and comprehensive data collection and analysis for complex commercial
processes.

CMVPC technology, developed separately by Shell Oil in the US and Adersa Gerbios in
France during the late 1970’s, is very basic and profound systems theory. The scenario
techniques, rapid inversion of large nonsquare matrices, and identification of process dynamics
by experimental testing of plants with 20 to 30 MV’s and 30 to 50 CV’s on small
microprocessors is remarkable.
What are CV’s?
Not all imaginable dependent response variables relating to a process are candidate controlled
variables. The weight fraction of C41 normal paraffin in crude distiller tray 49 downcomer is not
a CV because it is of no interest or consequence. CV’s are variables we select to control. CV’s
represent phenomena and characteristics we care about. They are important, they may have
imposed limits, they can have financial consequences, and we can assign an economic value to
them that depends upon their magnitude. CV’s must be measurable - directly or indirectly. They
must also be controllable - sufficiently influenced by one or more independent manipulated
variables.

Span of Processes
CMVPC has been successfully applied to run distillation trains, crude units, hydrocrackers,
fluid catalytic crackers, catalytic reformers, RFG blenders and whole steam cracker olefin plants.
Programs are underway to handle larger process combinations in refineries and petrochemical
complexes with CMVPC. The number of MV’s for common processes are: SGP (10-15),
ACU/VAC (15-25), HCU (15-20), FCC (20-40), CR (10-15), RFG Blend (10-16), eight furnace
olefin plant (50-60) and fuels refinery (150-300).
How Well does it Perform?
Dynamic variance is routinely reduced 50 to 90% over basic operator based control. The
financial benefit is critically dependent upon the importance of reduced variance and proper
setting of CV limits. If limits are set too narrowly, the controller (or human operator for that
matter) cannot move the plant much or improve profits. If limits are set too widely, the controller
(or human operator) may move the plant beyond the validity of the model outside the domain of
its expertise into uncharted, dangerous regions, placing profit at risk and perhaps even inducing
its decline. Setting CV limits properly is the key to successful CMVPC application. In practice
these settings are integrally linked to dynamic variance performance, profit trade-off profiles and
statistically based calculated risk taking (which is known to be superior to uncalculated risk
taking) to lessen unforeseen loss incidents and increase overall long term profits.
Benefits in $/bbl throughput of major feed or product that can be identified, captured and
sustained for common fuel and petrochemical processes are typically: ACU/VAC (0.10), FCC
(0.25), HCU (0.25), CR (0.20), DCU (0.30), ALKY (0.15), ether (0.15), mogas blend (0.10), RFG
blend (0.20), middist blend (0.06), aromatics recovery (0.20), lubes (0.5) and entire refinery (0.5).
Olefin plants provide about 0.002 $/lb C2=. Costs to obtain these benefits in the worldwide HPI
were reported in 1995 (2) to be less than 50% of these figures. Many cases have been reported
where costs are less than 10% of benefits.
What is Required?
Clear economic objectives, knowledge of the process, commercially proven software tools,
knowledgeable and experienced appliers, satisfied operator users and financially driven
instrument - computer - software sustaining support are all essential for success. Commercial
software tools, applications know-how technology and capable attention to sustained performance
are available within some large operating companies, from some control system vendors and a
few specialist suppliers. Profit oriented outsourcing business arrangements are growing in
significance.
How does it Fit CIM?
As a basic CIM function, ADPC makes money by itself. It makes even more money when it is
harmoniously connected to the other CIM functions (PM, IT, CLRTS, CLRTO) and used for their
execution to achieve their unified objectives. It can make even more money if realistic results
from CMVPC are regularly fed back to the other four CIM functions so they can modify their
models and behavior to more closely match the true plant (and its associated control systems,
including ADPC) characteristics.
Watching CMVPC in action on processes such as FCC, HCU, DCU, ACU, OLEF and RFG
blenders in the control room with operators and supervisors, in conjunction with scheduling and
optimization, is a 30 year dream of many practitioners now coming true. One must turn it off to
see how much money is lost and to appreciate its true value, because people only learn to value
things properly after they are deprived of them.

What is the Current Status?
By late 1995, there were over 2233 commercial CMVPC installations (1) from the five main
suppliers, with 1500 of these in oil refining and another 483 in petrochemicals and chemicals.
Virtually every type of process unit has been controlled by CMVPC. Universities teach this
technology, professors continue active research, books have been written, papers frequently
report performance of commercial successes, conferences are held regularly worldwide, short
courses are offered widely, most DCS vendors offer some tools and algorithms and several
technology suppliers have growing businesses licensing products and working commercial
application solutions.
Trends
Applications are trending to larger single controllers (30 MV’s x 60 CV’s) on multiple
connected processes, coordination among several subcontrollers (for a whole olefin plant), new
methods for quantifying financial value, tighter integration with operations optimization
(CLRTO) and outsourcing of implementation and long term on-site maintenance support with
financially sound partnerships between operating companies and selected suppliers. Sound
commercial shared risk - shared reward (SR)2 arrangements are useful (probably essential) to
sustain profit performance from CMVPC over the life of the process operation.
Applications in the US, Europe and Japan are often revamps of older classical ADPC with
CMVPC. Applications are spreading to existing process units throughout the world. Grassroots
plants normally provide for CMVPC shortly after startup. Environmentally driven quality
specifications for fuels are compelling applications of CMVPC and broader CIMFUELS
technology.
Once dynamic performance claims for capacity, yield and operating costs (by closer approach
to limits) are widely accepted, CMVPC will influence process design tolerances and sizing of
new plants. The broad Chemical Engineering connection between process design and process
control will strengthen.
The next NPRA Computer Conference, Nov 11-13, 1996 in Atlanta, will feature a half day
session on Process Control Megatrends, with speakers from four oil companies. The 650 expected
attendees will find out more about achievements, problems and trends from this vital technology.
References
1. Qin, S. Joe, Badgewell, Thomas A., “An Overview of Industrial Model Predictive Control Technology”, AIChE
Chemical Process Control - V Conference, Tahoe City, CA, 11 Jan 96.
2. HPI Market Data, Hydrocarbon Processing, Gulf Publishing Co., 1994 & 95.

CIMFUELS Editorial for FUEL T & M, November/December 1996, p12, 14
Reconciliation, Learning, Improvement - RLI
Vice President
Houston, Texas
Since inauguration of CIMFUELS editorials in the Jul - Aug 95 issue of FUEL, we have
attempted to describe its role and contribution to competitive manufacturing of fuels and
petrochemicals, particularly clean fuels like RFG, CARB2 and LSD. Recent editorials described
the five basic money making functions of CIMFUELS: Performance Measures (PM), Integration
(IT), Advanced Dynamic Process Control (ADPC), Operations Optimization (OOPT), and
Scheduling (SCH).
Now we turn from technical areas to some deeper principles of good management practice that
are employed with successful CIMFUELS. This issue will focus on RLI - Reconciliation,
Learning and Improvement.
Reconciliation
As reconciliation is basic to human relationships, the scientific method and checkbook
balancing, it is also a basic ingredient for useful CIMFUELS.
Plant data (the facts) is full of discrepancies, errors, inconsistencies, redundancies,
inaccuracies, conflicts, transients, misunderstandings and lies. Things are suspect, they don’t add
up, check out, make sense, jive, seem right, match experience, correlate well, go in the right
direction, or fit models. Plant people spend a significant portion of their time verifying and
reconciling facts and data into something believable, accurate, reliable, meaningful, truthful and
useful, which then becomes what we call information.
The basic reconciliation idea is to devise methods and policies to deploy mathematical
techniques of CIMFUELS as a tool to do data reconciliation work easily to create information.
Large scale open equation SQP solvers for profit optimization by operating condition adjustment
are equally useful for data reconciliation by parameter adjustment. However, we should
remember the basics of the scientific method (the Greeks, 4th century BC) at work here:
hypothesis of theory - model, experimental tests to verify or refute the theory - model and
analysis to accept, reject or improve the theory - model. Do analysis properly before synthesis.
The scientific method has not yet been fully computerized (even with AI - neural nets). Human
thought (art and/or science?) will remain a critical ingredient of data reconciliation as long as
people set the objectives and values of the inquiry endeavor.
There are well established methods for adjusting massive amounts of raw, inexact
measurements to satisfy complex relationships humans choose to impose for some reason or
belief, such as mass balance closure for weight flows, volume balance closure and density
properties for volume flows, kinetics and equilibria for component balances, energy balances for
temperatures, momentum balances for pressures and optimization money balances for
intermediate transfer prices and value added. In each case, definition and adjustment of empirical
factors (rate constants, mass/heat transfer coefficients, efficiencies, resistances, polynomial

regression coefficients, neural net weights, functional forms and limit values) requires some
human involvement (art and/or science?).
Automatic adaptation practice for reconciliation remains rather ad hoc. People must select the
relationships we wish to impose upon the data in order to convert it into something we are willing
to value and use as information. Reconciliation for its own sake has little or no value.
Reconciliation to create information should strengthen understanding for sound decisions (by
people and CIMFUELS functions), and clearly relate to (nay impact) consensus among people for
business (= financial) success.
The mathematical power to reconcile data according to any rules and relationships we wish to
impose is now at hand, but people have difficulty knowing what to reconcile, why they should
reconcile, what relationships to honor and how to determine the value in order to justify
reconciling in the first place. One reason for this situation is that people do not connect
reconciliation to higher purposes well; they do not learn from reconciliation.
Learning
CIMFUELS learning is manifested in its 1) models and 2) model improvements.
We must model 1) how the process works, 2) what the financial purpose of the process is, 3)
what the rules and limits are and 4) what the consequences and penalties will be for breaking the
rules or violating the limits. Plant people have always modeled plants, improved these models and
learned from them. They have also specified the purpose of the plant and set rules and limits upon
it. Some have experienced the penalties for violating the rules. However, people forget, change
and depart.
CIMFUELS provides the means for the permanent plant to model 1) how the process behaves
(not necessarily how or why it works), 2) what the financial objective is, 3) what the rules and
limits are and 4) the penalties for limit violation. As plant people learn more about the details,
accuracy and significance of these model components, they should encode them in CIMFUELS
document storage and retrieval, and better yet, into the active CIMFUELS functions (PM, IT,
ADPC, OOPT, SCH). This allows the permanent plant CIM to become the repository of know-
how and experience, which becomes smarter over time and is regularly used.
Remember, memory is part of learning. CIM should remember plant performance with past
feeds, catalysts, modes, economic situations, discoveries and mishaps. Since it is possible to learn
much from mistakes (probably the only merit of a mistake is what we learn from it and the only
way to really learn is from mistakes), there is value in tight linkage between errors and mistakes
and CIMFUEL learning.
Further, reconciliation by people and CIMFUELS provides a powerful means for rational
model improvement. This is how the permanent plant becomes a learning system, with a
permanent built in capability to learn. This capability requires active human leadership and
involvement using reconciliation technology.
People still have difficulty knowing how to use CIMFUELS learning well and how to justify
it. One reason is that they do not improve from their knowledge. Learning for its own sake is
appropriate for academia and personal leisure, but not for business. Business learning must serve
a business purpose and be deployed for improvement, because corporations are instituted to
create profits.

Improvement
The quality revolution of Dr. W. Edwards Deming and Dr. J. M. Juran in the 1980’s taught the
importance of continuous improvement in order to make more money and even to survive.
Improvement must be regular and pervasive. Plants must improve their products, processes,
procedures, control systems, people, models, CIM systems, customer satisfaction and competitive
performance every day.
Consider one example: the US industry effort to comply with CAAA90 to make RFG without
degrading the remaining conventional mogas pool below the 1990 baseline. That’s quality
improvement! Clearly manufacturing improvement by adaptation is a fundamental requirement
for business success. The basic way to adapt properly for improvement is to align learning with
risk for optimum expected financial performance. Of course this is just as true for CIMFUELS as
it is for people.
So now we have RLI, with or without CIMFUELS. Success and survival require performance
improvement. Performance improvement (I) is based on relevant learning (L) that starts with
good reconciliation (R) of the data into information. So RLI is the link between data and success.
Business leaders should study the role of CIMFUELS for the RLI activity in their
manufacturing operations. Why bother? What is the incentive? Reconciliation alone is worthless.
Reconciliation for learning alone is worthless. However, proper use of RLI throughout the
CIMFUELS functions in concert with people and decision making can capture and sustain net
benefits exceeding 0.1 USD/bbl crude, and might even approach 0.2 for some refiners! Might
even be essential for long term survival! Recommend you connect R to L to I well. Then you can
connect data to profits. RLI provides the venue to guide the proper specification of model
relationships to be imposed when upgrading inexpensive raw data into valuable information. That
would be useful reconciliation.

CIMFUELS Editorial for FUEL Technology & Management, January/February 1997, p14 - 15
NPRA Computer Conference
Consulting Engineer & Vice President
Houston, Texas
……………………………………………………………………………………………………….
New techniques to determine the financial performance of computer systems
technology are emerging.
……………………………………………………………………………………………………….
The National Petroleum Refiners Association provides the premier annual international
conference on CIMFUELS and CIMCHEM technology and business. The 38th
NPRA Computer
Conference was held November 11 - 13, 1996 in Atlanta. (The first was held in 1958!) There
were 550 attendees this year from oil refineries, petrochemical plants and suppliers/vendors
around the world. Since attending this conference for the first time in 1972 and joining the NPRA
Computer Application Committee with 19 operating companies and 17 suppliers in November
1995, I have been privileged to see this group in action, growing and maturing significantly. The
leaders of CIMFUELS are involved in this conference.
The centerpiece of this conference is the selected papers and presentations by operating
company representatives, often co-authored by suppliers these days, on technical and business
accomplishments, experiences and needs.
Process Control Megatrends.
Process computer control has been a bedrock topic of NPRA Computer Conferences for many
years. Traditionally this has included basic and advanced dynamic control, multivariable control
(CMVPC), on-line optimization (CLRTO), scheduling, online integration and performance
measures. These are the five active functions of CIMFUELS that make money. Lately some have
excluded scheduling and integration from “process control” but as they go closed loop they
become basic functions of “process control”. This year the committee decided to invite four
speakers to take a broader view and report on megatrends in this burgeoning area. They came
from Ultramar, BP, Sunoco and Mobil; all experiencing profound changes: 1) merger, 2)
acquisition/shutdown, 3) public spin-off and 4) sell off/acquire/consolidation. This half day
session proved to be the highlight of the conference.
In 1982, John Naisbitt wrote MEGATRENDS, gave ten; nine were right, one remains open. In
1990, John Naisbitt & Aburdene wrote MEGATRENDS - 2000, gave ten new; three were right,
seven remain open. I offered these Process Control Megatrends:
1. Fast - cheap computers
2. Fabulous software
3. Strict quality & environmental compliance
4. Multivariable dynamics
5. Rigorous profit optimization
6. On-line scheduling
7. Real-time process unit economics
8. Integration: people, processes and computers for the business purpose

9. Profit performance
10. CIMFUELS an established, distinct, mature business
Ultramar, BP, Sunoco and Mobil presented compelling descriptions of their process control
activities, accomplishments and plans which illustrate the megatrends to the trained observer.
BP (1) described their “Vision for Optimal Commercial Refining” developed since 1994 for
the next ten years with input from 23 designated suppliers and consensus from eight BP refinery
managers worldwide. The reason for this vision in their belief the “average potential maximum
benefit through process control and optimization is 0.3 - 0.5 USD/bbl crude and through decision
support is an additional 0.1 to 0.2 USD/bbl crude”. This confirms previous reports (2, 3, 4).
Sunoco (5) described “Design and Integration Issues for Dynamic Blend Optimization” that
shows how constrained multivariable predictive control and nonlinear multiperiod optimization of
a complicated mogas blending operation connects process operation for components with product
tankage and marketing logistics. Tangible benefits of about 0.06 USD/bbl product (0.08
CDN/bbl) were realized. Sunoco claimed “economics drives the production of each blend.” This
simple statement remains an elusive goal for many mogas & middist blenders. This was another
breakthrough paper from this leader in applying CMVPC and CLRTO for financial gain, high
Solomon benchmark ranking and a Smithsonian Institute Award for technology innovation (6).
Mobil (7) described results to date from their CIMFUELS master plan at Jurong Refinery,
Singapore, built upon CMVPC of all major processes and progress toward CLRTO. They
revealed what they are doing, why and how without compromising their proprietary position.
The audience detected some big trends underway. Old barriers to quality measurement are
falling. New techniques to determine the financial performance of computer systems technology
are emerging. In order to harness computers to do our bidding to run a refinery we people must
tell the computer:
1) how the plant works = process model,
2) purpose and objective of the process = profit performance model,
3) rules = limits,
4) consequences for breaking the rules = penalty model for violating limits.
If we do these well, it will work well; if we do not, it will not. Computer technology has taught us
humans that we have not always done things the best or proper way, so we reengineer our
methods and work processes to get them right before we can deploy CIMFUELS well. Often we
do not have our act together, do not have sufficient consensus on our values and goals. Humans
set values, not computers. That portion of process control will always remain an art. If things do
not work well, it is never the computer’s fault. Pogo told us “we have met the enemy and he is
us”. Those who know their enemy are securing big victories.
……………………………………………………………………………………………………….
Computer technology has taught us that we have not always done things the best or
proper way, so we re-engineer our methods and work processes to get them right before
we can deploy CIMFUELS well.
……………………………………………………………………………………………………….

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