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ACHEMAACHEMA 2006, which takes place from May 15–19, 2006 in Frankfurt am Main, Germany, is one of the leading exhibitions for the process industries. As a leading international event for equipment suppliers to the HPI, ACHEMA in 2006 will present technology developments, offer worldwide contacts and new business networks. The event is expected to attract some 4,000 exhibitors and 200,000 visitors from all over the world.

This event, held every three years, will be covered in 2006 with a special supplement in the May issue of Hydrocarbon Processing. This supplement will continue over 18 years of coverage on ACHEMA by Hydrocarbon Processing and will offer the unique country-by-country wrap up of industry activity. Special rates do apply. Please contact your representative for full details.

Closing date for advertising is April 5, 2006.

THE DEMISE AND KEYS TO THE RISE OF PROCESS CONTROLOccasionally we get a “landmark” article that we know will be referenced for decades to come —this article is one of them. The author, Dr. Pierre Latour, is an acknowledged world expert on, and one of the originators of, modern advanced process control. Currently president of CLIFFTENT, Inc., he was a cofounder of Biles & Associates and Setpoint, and has worked for DuPont, Shell Oil, DMCC and AspenTech. Dr. Latour holds BS and PhD degrees in chemical engineering from Virginia Polytechnic Institute and Purdue University respectively. Following the practices he outlines in this report will substantially improve the value of process control.

Closing date is February 5, 2006.

DESIGNER DIESELEurope wants diesel badly. It wants the efficiency of diesel, and it wants fuels that can be manufactured from renewable resources. But because of concerns about microscopic diesel emissions, it expects future diesel to be cleaner than ever.

Designer diesels are a blend apart. Refiners will face a major blending challenge. How Europe’s refiners, catalyst manufacturers, process licensors, additives vendors and engineering/constructor companies respond to this challenge will dictate in large part how successful Europe is in meeting its goal to protect the planet’s climate, while protecting its city populations from airborne pollution.

From the unparalleled editorial team at Hydrocarbon Processing, Designer Diesel 2005–2010, examines the policy aims, the market drivers and the work-a-day challenges that face Europe’s process industry in providing abundant supply, on-spec for the demands of the coming five years.Circulation: EuropeClosing: December 2005Distribution: January 2006

HPI Construction Boxscore 2005

Supplement to

Photo courtesy of, GASTECH 2005

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umerous articles, editorials and ads in the past few years described trouble in the commercial practice of process control and suggested remedies like better quality infer-

entials, basic loop tuning, operator training, process knowledge, algorithms, models, engineers, instruments, integration and management faith. The business is fragmented, floundering and declining. There is no consensus on the cause or remedy, but some suggestions to improve the industry are provided.

The full business promise of process control, instrumentation, automation, closed-loop optimization, information technology (IT) integration and HPI computer integrated manufacturing (CIM) in 1990 has not been and will not be realized because of:

• Inability to measure financial performance of system solu-tions

• Confusion between products and solutions businesses• Failure to license solutions based on shared risk-shared

reward (SR2) performance relationships.

In the beginning. Chemical process systems engineering for dynamic analysis and control was inaugurated in universities and large operating companies like Shell, Esso, Mobil, DuPont, Phillips, Texaco and Monsanto in the 1960s. Chemical process operation was deemed the third arena of chemical engineering, after product development and manufacturing plant design. Con-trol systems engineering was created for mechanical, electrical, aerospace, agricultural, chemical and business systems.1–29 Analog instruments and early digital computers were used for feedback and feedforward control of flow, temperature, pressure, level and product qualities.

Advanced process control was developed and commercialized for the HPI in the 1970s by several advanced control suppliers using general-purpose computers with real-time Unix operating systems connected to basic pneumatic, electric and new digital controllers. Investments were initially justified on cost savings like manpower reduction, which operators despised, or on the notion that everybody else is doing it because computers are good so we must keep up-“the faith theory.” Soon process yield and utility credits were seen.

In the 1980s, CIM expanded to distributed control systems (DCSs), multivariable control, online optimization, scheduling, plantwide control and IT integration throughout the worldwide HPI. Investments were justified by smoother operation that made no direct money but allowed average targets to be moved some-

Demise and keys to the rise of process controlModeling the penalty for violating constraints as well as modeling the credit for approaching them, and shared-risk shared-reward business practices are the solutions

P. R. LATOUR, Clifftent Inc., Houston, Texas

N LITERATURE REVIEWA host of papers have attempted to identify the reasons

for the demise of the automation business. A comprehensive study of numerous publications indicates the confusion. Some say it’s technology; some say it’s relationships. Practitioners seek performance, suppliers promote results, customers com-plain about invisible benefits. Kane provided insights from a computer conference full of ideas but lacking sound financial support to sustain quantified benefits.71, 72

TechnologyFlaws. Many have cited technical flaws. Friedman pushes

better inferential measurements73–78 but has no way to quan-tify the financial consequences and he fears the process control industry has completely collapsed. Qualities were first commer-cially inferred from simple measurements in 1971, when crude unit side-draw TBP cutpoints were inferred from temperatures, pressures, internal refluxes and steam rates.219–224 King decries poor engineering practice and shows 14 ways to lose money with improper property inferentials but does not quantify the losses rigorously.79, 80 Smith has long proposed knowing how the process works but never relates process operation to its associated economics and profit generation.81, 82 Hill regularly writes on the value of good operators but cannot quantify their financial performance.83–87 A supplier’s survey88 reported the biggest challenges: limited budget for integrating software with business performance, 35%. (Budgets should never limit compelling profit generation.)

Six Sigma management. Doble reports 13 Six Sigma myths, confirming it has floundered due to lack of proper dynamic financial performance measurement,89 as reported by Clif-ford.90 Welch had the same difficulty.91–94 Stewart reports on CEO Larry Carter’s use of up-to-date information to run Cisco,95 which could probably be standardized with clifftent.42

Limit setting. Hartmann’s refinery LP work clearly shows the financial importance of setting constraint values properly,96 as proposed in 1996.42, 45 Kalis has an excellent description of the importance of knockout drum demisters to protect compres-sors,97 a large profit opportunity for statistically optimal gas velocity setting42, 45; Golden confirms.98

Continued

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what in a more profitable steady-state direction.30–33 A major DCS vendor’s ads featured King Cole surrounded by piles of gold. The faith theory prevailed, but savvy management continued to inquire: Please show me where the money comes from and how much is net; we want secure, sustained profits.

At the peak. By 1990, CIM was poised to profoundly con-tribute to profitable manufacture of clean fuels like reformulated gasoline (RFG), enhancing refinery performance by $1/bbl crude and refining plus downstream petrochemicals by $2/bbl crude. The technology was known, experienced staff was affordable, hardware was capable and improving, and economic drivers were clear.34–37

Throughout the 1980s, universities were busy teaching process control methods, developing algorithms, writing papers, present-ing short courses and analyzing commercial multivariable control algorithms. The National Petroleum Refiners Association (NPRA) began separate annual process control conferences attracting 300 worldwide. The Instrument Society of America (ISA) had large annual conferences and exhibits. The American Institute of Chemical Engineers (AIChE) was active with papers and confer-ences. Instrumentation suppliers were busy. Major HPI magazines published articles on developments and accomplishments in every issue. Articles invariably touted a method, algorithm, controller, model, system, project or product that generated a payout in <12 months. Books were published. Control systems engineering was recognized as the technology for profitably mitigating risk, reduc-ing uncertainty, improving quality and satisfying customers.

Suppliers offered hardware, software, services, studies, projects, experience, algorithms, models, databases, technology, instru-ments, analyzers, control systems and a host of other components; all products, on trust, without guarantees or refunds. Commercial risk was not properly aligned with technical know-how. Customer operating companies accepted all the risk for success without proper knowledge; solution providers took no risk because they really did not know how to measure the value of their offerings. Investments were justified on simple, ad hoc, steady-state change predictions in process performance allowed by smoother control. (All processes are dynamic; steady-state is a mathematical figment of human imaginations.) Users pushed project implementers to leave to cut costs rather than entice them to stay to sustain profits. Project implementers were anxious to leave to avoid maintenance rather than stay to sustain profits. There were no performance standards; confusion reigned; the faith theory prevailed.

Quality control gained visibility from Deming, Crosby, Juran28 and Peters. ISO 9000 quality procedures were inaugurated. Ford’s Job1 was announced to compete with Japanese car quality. Quality circles and Six Sigma were promoted. Arthur D. Little pioneered hazardous operations (HAZOP) analysis. IT went commercial, system and business integration was the key. CIM became reen-gineering, then plantwide control, then value-added supply chain management. They are all subsets of process control that generate intangible benefits but suffered due to lack of a rigorous method for quantifying financial value from improved dynamic perfor-mance, i.e., reduced variance. Financial verification was impos-sible; faith theory was victorious.

IT was described in Harvard Business Review and business schools. Large systems engineering theories and nonlinear opti-mization methods were published. Risk management was pro-moted. The mantra was to apply good ideas and post-audit for

Lemmers shows the importance of limit setting for com-pressor surge and stonewall99 without a method for setting them optimally or quantifying the control system financial value. Amrouche gives tank limit setting standards100 which could be optimized for uncertainty and financial conse-quences.42, 45

SafetyRisk management. A risk management feature101 lacks

any sound performance measure. Ghosh claims improved critical condition management can add at least 5% to prof-its102 but lacks the proper method to measure and prove this claim. Mannan promotes the Mary Kay O’Conner Process Safety Center,103 long handicapped by lack of a method for connecting limit violation penalties to process credit tradeoffs statistically and financially to quantify the value of safety management, as provided by clifftent. Ayral pro-motes critical situation management,104 but does not include the power of clifftent risk management to avoid pitfalls to financial success.

Abnormal situations. Some seek the link between process operation and abnormal situation reliability management; between process control and safety alarms. Two magazine editors see the opportunity that clifftent resolves: Gonzalez describes the reliability challenges and opportunities in refin-ing105 and Rosenzweig describes the unsolved opportunity for abnormal situation management in the chemical indus-try.106 Alford struggles with the value of alarm management using ad hoc alarm ranges107 that was superseded by clifftent in 1996.42 Brown gives alarm management guidelines108 but lacks a quantitative measure of financial merit. Hill describes the importance of alarm management84 but does not quan-tify its value. Grosdidier offers a path forward for DCS alarm management,109 but does not quantify its financial value. Goble offers a risk-reduction approach to manage the safety life cycle,110 recognizing the need to properly connect to financial value, as already solved.42 Nimmo’s alarm manage-ment feature111 lacks any way to measure the financial value of his proposals.

Plant maintenance. Many seek to connect process opera-tion to equipment wear and tear; operating credits against maintenance costs. Clifftent risk tradeoffs abound. FIATECH reported on plant operations and maintenance information needs and problems of financial justification.112

RelationshipsSuccessful business partnerships are an HPI hallmark.Engineering and construction (E&C). Valot has pleaded

for more value-oriented owner/contractor relationships with properly aligned commercial risk and reward,113, 114 as pro-vided by SR2 licensing of CIM solutions. Moore writes about enterprise resource planning (ERP) maintenance contracting with questions touching on value-added aligning of risk and reward.115 Cunic shows the importance of performance-based E&C contracting,116 claiming “key to successful performance contracting depends on understanding (should add measur-ing) the net effect, impact or benefit that a contracted scope of work will have on business results long term.” Transmar

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value—not to first seek quantifiable value and deploy what is necessary to capture and sustain that value to maximize expected value profit. With the Internet on the horizon, remote mainte-nance was born.

Paradigm shift proposed. By 1990, the fragmented prod-ucts/components process control business, armed with power-ful technology and skilled implementers, was poised and ready to mature into a performance-based solutions business. A few recognized that the technology slogan and hype bubble could not continue. They realized that failing to follow the scientific method (measure results to confirm theories) leads to chaos and failure.38 The question remained: how to measure the financial performance of dynamic system improvements to prove the value added and properly guide use, investments and maintenance?

The proposed new mission was to identify, capture and sustain significant economic benefits for clients and suppliers from prop-erly integrated CIM solutions.34–37, 39–50 Rather than continue to offer to install products and projects that payout handsomely in six months and generate large profits henceforth without main-tenance and upgrades in the face of overwhelming evidence that this approach invariably led to disappointment and failure, the new proposal was for much more careful attention at the start to process economics, clear measurement of process plant financial performance improvement and the business requirements for assembling technology solutions to make money for those who accepted commercial risk. The HPI has had too many six-month payoffs by intangible and transitory benefits.

Performance measure technology. CIM technology had fatal flaws related to lack of a method for measuring the financial value of improved dynamic performance, or reduced variance, until the concept of clifftent was published in a seminal 1996 paper42 and subsequently extended.43– 45, 47–50 It provided the long-sought method for constructing profit meters for each control variable (CV) and key performance indicator (KPI). Control engineers have worked excessively on reducing variance without being able to quantify the value of such improvement. They wrongly assumed the limit and target mean for CVs and KPIs are properly given, or optimal, when they never are, and when the value of setting them optimally is easy42, 43, 45 and just as rewarding as variance reduction by all CIM, i.e., $1/ bbl crude refined.

They failed to model the steady-state profit function for each CV/KPI, which is invariably shaped like a tent, defining the ben-efit tradeoff, often with a discontinuous cliff at the most profitable limit point. They failed to appreciate all process performance improvement is embodied in modifying the shape and position of the distribution function of CVs and KPIs. That is all one can do mechanically to change process performance (other than trivial cost reductions that do not impair process performance). Proper connection of the distribution function to the steady-state profit tradeoff tent function for each CV/KPI provides the rigorous way to measure financial benefit from variance reduction and resetting targets and limits. This is rigorous risk management for maximum expected profit rate. The consequences for modeling process profit are profound and illustrate the disconnects that permeated the CIM business for 40 years since 1965.

People wrongly assume reduced variance alone has no quan-tifiable financial value. In fact, the 1996 discovery42, 45 showed it was equivalent to simple conventional claims for moving the new mean closer to a spec or limit by some arbitrary amount after

describes the perennial E&C industry contracting troubles and calls for performance-based strategic alliances.117

CIM. Since CIM performance is easier to measure42–45 than E&C performance, SR2 licensing is much easier for the CIM solutions business. Cobb has published comments on the demise of IT by accounting firms and suggests the need for the right value proposition for sustained results.118, 119 Hill and Walker see Dow collaboration as key to value chain opera-tional excellence,120 while seeking a rigorous method for mea-suring its financial contribution. Bullemer writes on the role of the operator to push processes to their optimal limits and seeks a rigorous method to benchmark the value of improved performance,121 as reported in 1996.42, 45 Bullemer endorses the Abnormal Situation Management Consortium’s highlights that the plant operator’s role needs a new profit paradigm,122 realizing the need for a proper method for measuring finan-cial performance. Mohrmann shows how to include the field work force in the automation loop123 but needs a way to quantify the financial value.

PractitionersA few control engineers recognize the demise and have

published ideas to reinvigorate process control. Quantify benefit. White noted the benefit problem and

offered an ad hoc statistical method to value information and decisions,124, 125 which is handled rigorously by easier means.42,

45 Martin published his understanding of control benefits with an ad hoc statistical approach126 that does not rigorously incorporate economics.42, 45 Grosdidier offers seven tips for APC project success and a value proposition for oil account-ing, claiming $1.4 million/yr without proof127, 128 but excludes financial performance and misstates the source of value of APC (APC does not push constraints, it only allows others, like clifftent, to attempt it). Grosdidier did utilize and reference clifftent work to analyze blend giveaway economics.129

Measure pleas. Fiske frequently promotes the connec-tion between IT and operations and shows process variability causes underperformance130–133 without realizing his problem is solvable.42, 45 Woll also promotes proper organizational roles for performance131, 134 –136 without a proper measure to keep financial score. Mowat, Woertz and O’Malley confirm the need to quantify the intangible benefits (soft stuff) to create value.68 Beautyman assesses profitability of real-time optimization (RTO) with pre- and post-audits137 but does not provide the rigorous financial performance measure to quantify profit creation. Pandit’s profitability of optimization letter in response to Beautyman138 hints at a weakness of RTO that emphasizes excessive model rigor for behavior inside the constraints where the plant should not operate, but unrealistic modeling at the constraints where it should operate, while improperly setting the constraints—which was revealed in 1996.42 Pitt described the need for measuring financial value for refinery supply chain management and human operators well.139, 140 Powley described the need and requirements for financial performance measure for MPC and KPIs very well.141 Lang describes the use of process KPIs but does not relate them properly to financial value.142 Canney tries to explain the dearth of benefit from 6,000 APC installations by claim-

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variance was reduced. The 1996 discovery of how to integrate the CV distribution to its associated profit function (shaped like a clifftent) to give the profit hill vs. CV mean also gave the optimum setpoint move size and corresponding profit gain, providing addi-tional value at trivial cost. It proved the proper distance between limit and mean is never six standard deviations or Six Sigma.

It showed how to set limits and targets properly to mitigate risk. It transformed arbitrary limits for statistical process control and alarm management into economic optimum limits. It showed the weakness of rigorous online optimizers that were merely con-straint corner pickers because they had no model of the physical and financial consequences of breaking dependent CV constraints. It showed that the benefit of process control cannot be properly determined without the economic sensitivities and discontinui-ties associated with each CV embodied in its clifftent function. It showed the financial value of better-performing solution compo-nents: control valves, analyzers, instruments, models, algorithms, tuners, databases, computers and maintainers.

Once engineers and managers certify the five economic sen-sitivity parameters, clifftent gives operators a new paradigm for running plants by optimizing risk management: forecast CV/KPI near-term variance. That’s it, the basic human input.

Process control benefits can be double traditional claims, but they must be visible, tangible, current, accurate and accepted. Clifftent connects the statistical properties of dynamic systems to financial value, like string theory connects quantum mechanics to relativity,38 but with more immediate benefit to people.52, 53 It optimizes tradeoffs under uncertainty, as basic as setting the speed of your car near the posted limit. It revealed the need to model the penalty for violating limits to be as important as modeling the credit for approaching them. Many have sought this mathematical modeling method to reconcile and optimize clifftent tradeoffs.54,

55 The only significant performance claim to quantify financial benefit for control/IT is CV/KPI variance reduction.

Koppel’s elegant, comprehensive ISBEN approach to quantify information system benefits from all associated business activities is particularly noteworthy.56 Koppel has long advocated greater attention to proper performance metrics for control and IT sys-tems.11, 14

Lack of proper performance measurement cannot be over-emphasized. The situation is like 100 million football fans ready to view the Super Bowl kickoff when one team claims a touchdown is worth five points and the other says six. As they argue back and forth, the fans say they don’t particularly care whether it’s five or six, but they really care that the opponents agree on one value and agree the team with the most points fairly scored after 60 minutes is the game winner. The Olympics are viable when all competitors and viewers understand and agree on the performance scoring measures. Without that, there is no reason to participate.

As long as CIM practitioners fail to adopt the proper financial performance measure standard to maximize expected value of net present value (NPV) profit over a long term, say 30 years, fairly discounted, they remain unable to prove the value of their work and solutions to properly serve their customers and themselves. As long as process control publications continue to describe some method or tool and claim <12 month payout henceforth and forever, the CIM demise will continue henceforth and forever. Financial analysis of CIM solutions should be like a 30-year home mortgage or a lifetime annuity: economic value added (EVA) with realization probabilities.

ing 15 statements are myths, attempting to refute them and summarizing with “truth.”143 Actually only seven were real myths; Canney refutes six. His three claimed truths illustrate the reason for the demise.

SuppliersResults. Many solution providers promote performance

and results but cannot license based on their rightful per-centage because they cannot measure their solution financial value-added properly. One database software supplier has an IT monitor for network performance and enterprise per-formance management144 –146 but investigation revealed it does not relate physical statistics to profit.42, 45 A DCS vendor claims its “solution” or product and its KPI manager improve plant profitability, promising quick, no-risk payoffs,147–149 without any rigorous method for proving such claims. A large system supplier recognizes the performance oppor-tunity with its “Prove It” ads 150–152 but might gain with a rigorous financial measure and solutions performance licensing. (If they could really prove it with an unambiguous, rigorous financial measure of their solutions’ performance, they would undoubtedly offer it privately to selected clients rather than publicly advertising that their customers want them to prove it.)

Confusion. Another DCS vendor mixes solutions and prod-uct components,153–156 with no measure of financial merit. An experienced control engineer offers fast operator advi-sor software, but has no method to quantify its financial value.157 A consulting firm often advertises Optimum Plant Performance.158, 159 A supplier offers free public literature on performance optimization benefits rather than private profit sharing sustained solutions.160 A large system vendor offers a results-driven automation World Conference & Exhibition161 without a rigorous method for measuring the financial value of improved dynamic solutions. It also offers software to run plants smarter162 without quantifying financial gain.

A compressor controls company has promoted the benefit of compressor controls with proper attention to limit and tar-get setting but lacks a rigorous way to quantify the important financial value.163, 164 A software firm promotes MPC control-ler performance141, 165, 166 but does not claim rigorous financial value. A large CIM solutions, products and control provider offers fast polymer-grade changes for six-month paybacks167 with no method to properly quantify their financial value. Further, they broadly claim increasing ROI rather than EVA for customers168 without offering a rigorous way to quantify and sustain benefits, profits or ROI, when its technology is pow-erful enough to allow it to offer a risk-free, zero-cost annu-ity and infinite (hence, meaningless) ROI to their customers. (Getting the scorekeeping right is important.) Another report describes the widespread confusion about ROI as the proper performance measure for e-business.169

Strategic offers. The major IT platform vendor has long struggled with measuring the financial value of its supply chain software platform.170, 171 An expanded control solution that boosts production performance is actually two software products that do noble things that are not financially quanti-fied.172, 173 An HPI operating company solutions provider offers

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Products vs. solutions. Process control offers confuse the normal commercial distinction between products and solutions; the difference is profound. Know your customer; know your offer. Align commercial risk with know-how. Process control and IT businesses are infected with a fatal disease of confused mixing of products and solutions businesses.

Products, tools, components, software, algorithms, models, databases, platforms, valves, instruments, projects and services should only be sold to customers seeking product components (that they presumably will integrate into sustainable profit-gen-erating solutions). Products are sold on features, capabilities and cost; often competitively bid for low price. The customer takes the risk of generating value from them because he or she has the know-how to do so. Products are sold in grocery stores, lumber yards, pharmacies, bookstores and Websites.

CIM installation solutions that generate sustaining value-added are sold to customers seeking solutions. Solutions combine hardware, software, technology and appropriate people to gener-ate sustainable process profits for the plant customer and supplier. Solutions are sustained by performance, results and value added; never competitively bid for low cost. The supplier takes the risk of generating value because it has the know-how. Solutions are offered by restaurants, realtors, physicians and colleges.

Never sell products to customers seeking solutions; it’s a com-mon disconnect. Never sell solutions to customers seeking prod-ucts; it’s a common disconnect. Never buy products based on sustained financial performance; it’s a disconnect. Never buy solutions on low-cost competitive bidding; it’s a disconnect. (People never select a computer, college, home, suit, car, physi-cian, attorney, vacation, restaurant or much of anything on low-cost competitive bid. They buy on value; cost is only one of many considerations.)

Operating company customers want CIM solutions that gen-erate maximum expected NPV profit streams sustained over long periods, like 30 years. They do not want maximum benefits that may be costly, minimum costs that may have no benefit, six-month payouts that are not sustained. They do not want to risk capital or spend money. They really don’t care how the solution works, just how much money it makes. They want clear, large, sustained, easy, legal, ethical, no-risk profits. They want large variable annuities, for free. CIM has offered this potential since 1990.

Licensing performance solutions. The CIM business failed to mature to a sound solutions business, where experienced sup-pliers knew enough about the financial value of their numerous installations integrated into operating plants and how to identify, capture and sustain their value. Had they done so in 1990 they would have evolved naturally to SR2 licensing arrangements that endure. Their sales costs would have evaporated.43, 44

Since profit-oriented CIM investments are so attractive, sup-pliers could have easily offered free net revenue streams or free variable annuities from $1/bbl profits with expected NPV (30 yr, 8%) = $875 million for a 200-Mbpd refinery. This provides comprehensive solution suppliers about 25% of that amount.43, 44 Experience established the existence of an optimum benefit split between the operating company customer and its CIM solution supplier to sustain performance over say 30 years to realize such value.43, 44 This explains why ROI, touted as the performance measure by one supplier for years,168 becomes meaningless; invest-ment is zero, ROI is infinite. It’s the size of the zero-risk profit stream EVA that matters. No-risk SR2 offers are most appropriate

services and technology to improve margins by squeezing to make every last drop count174 without a sound method for measuring and proving financial value. A chemical operating company global service provider offers simulation tools, per-formance monitoring, manufacturing economics and supply chain optimization175 without a rigorous financial perfor-mance method for sustaining solutions. Another chemical operating company offers dynamic benchmarking of plant performance in physical terms176 without standardizing on a rigorous method for financial value.

A tank terminal automation firm offers flexible solutions to optimize operation177 without a way to measure financial value. A controller tuning expert firm promotes the impor-tance of proper loop tuning178, 179 but suffers from inability to connect reduced variance to money.42, 45 A major computer firm recommends “the HPI harvest a return on IT investments by managing risk in terms of individual elements of risk, and the combined business and operating risk to strike the best balance between optimization and predictable production of products to achieve refining excellence,”180 which is easily done with clifftent on every CV and KPI.42, 45

An accounting method is offered to define a plant’s true profit potential, generating 20% to 40% profit increases,181 but it is not clear if process behavior and statistical risk man-agement of dynamic performance are covered. (Accounting is designed to measure profit after-the-fact, not forecast, manage and optimize it in real time.) ISA has offered a train-ing course that includes justifying automation projects182 that lacks a proper method for measuring financial performance of automation and control systems.

Poor benefits. Since the 1970s, many authors reported interesting technical successes with weak financial gains. Recent articles continue the practice. Rotava reports an inter-esting application of MVC and RTO (commercialized in the early 1980s) on a South American crude unit with no discus-sion on value.183 McFarlane describes improved crude unit optimization156 with benefits from fast project completion in six months but no proven, quantified process economic benefits. Bonavita offers a step-by-step approach to APC184 that neglects proper scorekeeping at the outset and sustained financial performance thereafter. Chang offers a well-known graphic display wheel to capture process control benefits but cannot quantify the value of his contribution.185 Barsamian has promoted inline blending analyzers and control without any way to quantify their financial merit.186 Sanz & Papon tout traditional MVC to a batch reactor but cannot determine its value, relying on faith theory.187 Mandal does a nice job describing improved desalter and level control but cannot quantify their value.188, 189 Galante describes a molecule man-agement technique to improve real-time, process unit optimi-zation tools with no method for quantifying its value.190 Kelly has published extensively on IT techniques like reconciliation, scheduling and models without a sound method for quantify-ing financial merit.191–195

Moore relates process control to environmental impacts and H2 management but cannot quantify the financial value.196, 197 Sivaraman recognizes the critical nature of proper control limit setting for loss control but his statistical method

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for the doubting, skeptical, risk-adverse, inexperienced operat-ing companies. Sharing a percentage, a piece of the action, is an old, proven way to higher profitability for both parties. It affects behavior in meaningful ways. Think about the Super Bowl and Olympic games. Refinery control engineers who add value by specifying clifftent input factors should earn 2% to 3%. Supplier employees should earn 3% to 4%. Researchers should earn 3%.

SR2 licensing eliminates the need for the solution-supplier sales staff. Rather, supplier executives manage and expand each client SR2 relationship as his or her profit center, integrating and deploying appropriate products to sustain the engineered solution. Advertising and publications are useful for product offers, not sustaining CIM solutions.

SR2 licensing eliminates the need for operating company tech-nology-product appraisal staff. Rather, operating company execu-tives focus on economic factors like margins, differential values and consequences for breaking limits as input to clifftent for optimizing KPI s. They forecast risk (in terms of variance based on historic vari-ance) to optimize sensitive tradeoffs, operate plants as profit centers with CIM solutions and are delighted to share meaningful benefits to their risk-taking solution providers because they understand sustaining success depends on fair, mutual profit and risk sharing.

No interest. Performance measurement and solution licensing know-how was offered to all HPI CIM solution providers and major operating companies—privately and publicly—during the early 1990s,34–37, 39–50 but the obsolete faith theory products features business paradigm prevailed.

Situation today. What happened since 1990? What is the situation in 2006? What’s next?

Academia no longer teaches, researches or publishes chemical process control or CIM. Process control and instrument engineers at operating companies and suppliers have left their profession.57 Association conferences like NPRA, ISA, AIChE and JACC have declined dramatically in content, attendance and interest. The NPRA plant automation conference declined from over 300 attendees in the 1980s to fewer than 100 in September 2004 and was combined with process engineering in September 2005. Literature has declined. Few books are written. Magazine articles are repetitious and unconvincing, still superficially touting meth-ods with <12 month payouts. Quillin wrote about some “critical issues” for CIM in the HPI58 but repeated old hoopla and cannot prove the magnitude of any financial value from his assertions.

Suppliers are offering the same familiar tools and products while creating insufficient profit for their shareholders and facing declining revenues, layoffs and consolidations in shrinking mar-kets. They struggle with ill-conceived mergers and acquisitions. Suppliers are not very profitable. One stock with no dividends should have grown from $40/share in July 1997 to 40(1.3)**8 = $326/share by June 2005, yet it has languished below $10/share since July 2002 with weak competition; a massive destruction of shareholder wealth. (Analysts attribute this to the sum of prof-its over a decade < 0.) Most CIM suppliers have failed to create shareholder wealth. Recently, a Western European oil movements CIM specialist since 1980 disclosed it lost a contract for a Viet-namese refinery to a new group in Eastern Europe which was under-bidding engineering services at $25/hour. This is a natural consequence of failure to offer assured performance with no risk. At a time when blending for boutique RFG and low-sulfur diesel (LSD) provides large incentive to get it right, this is a tragedy.

does not connect to economics.198 A control systems supplier offers polymer process advanced control and optimization after acquiring an advanced control company and its powerful controller, but has no way to determine its financial value.199 Wilson reports statistical control of an FCC with classic statis-tical quality control (SQC) limits because he does not have a rigorous way to convert dynamic improvement into money.200 Mathur features superfractionator MVC without plant tests and significant CV variance reduction201 but is not able to convert his accomplishment properly into dollar profit. Martín offers a new control scheme for an SRU, reducing standard deviation by five and sulfur emissions approximately 250 mt/y,202 describing the air/gas clifftent tradeoff without properly determining the value of standard deviation reduction or optimally setting the setpoint.42, 45

Sharpe justifies tools and services on cost reduction203 rather than rigorous process performance improvement, which is an order of magnitude larger but impossible to prove if you don’t know how. Cheng offers another algorithm for distilla-tion control204 without showing any superior profit genera-tion. Sayyar-Rodsari reports on MPC for nonlinear processes205 without showing any superior profit generation. Deshpande claims turbocharging a constrained model predictive control-ler with fuzzy logic allows application to nonlinear systems,206 but cannot quantify the financial merit of his idea.

Novak writes on SCADA’s support of business processes and supply chains,207 without quantifying financial value. Kern implements expert online operator advisors,208 without quantifying its profit generation. Valleur describes optimiz-ing refinery scheduling comprehensively but omits any mea-sure of financial performance; benefits are listed as words.209 Gilbert writes on implementing an environmental, health and safety management information system210 with ten tips, none of which include a measure of financial performance. Karagoz claims a breakthrough in polymer control improves the business cycle211 but does not rigorously convert statisti-cal improvement to profit. Hall extols the importance of MVC plant testing and that recognized big money comes from pushing constraints properly,212 but he cannot quantify the value of his proposals.

Singh describes business-to-manufacturing integration using XML throughout the supply chain,213 which can col-lect data input to KPI s.42 Moore offers several ways to cap-ture value from supply chain management and ERP,214, 215 which require a rigorous method to determine financial performance.42, 45 Catalyst monitoring software216 would be strengthened if it incorporated a rigorous method for quantifying the financial value of dynamic performance and optimizing tradeoffs.42, 45

Sim claims optimizing asset utilization through process engineering improves ROC up to 4%217 with no way to prove it without a rigorous method relating risk abatement to profit.42, 45 Sim also reports on advanced cubic equations of state218 but offers no way for his customers to determine their worth or value over his price; clifftent tradeoffs to optimize limits and setpoints require process models and can quantify comparisons with less rigorous approaches to guide when model fidelity is sufficient.42, 45 HP

DEMISE/RISE OF PROCESS CONTROL BONUSREPORT

Suppliers of alarm management, abnormal situation manage-ment, HAZOP analysis and safety shutdown still struggle with financial performance measurement of their systems because they do not connect to process operation control. Plants risk unplanned atmospheric emissions and catastrophes like BP’s Texas City isomerization unit raffinate splitter explosion in 2005.59

Operating companies continue to complain about process control performance—the inability to see tangible benefits. They are disappointed; they have lost interest. Control engineers blame management for lack of vision and budgets; management still says show me the money first. Operating limit violations cause unplanned damage like Environmental Protection Agency (EPA) settlements60 : Valero $705 million, Motiva $550 mil-lion and ConocoPhillips $525 million. Company CEO William Wise reported El Paso lost a suit charging they withheld natural gas pipeline capacity from California, raising prices, because “EPNG did not consistently operate at or near its maximum allowable operating pressure on a continuous basis.”61– 63 Had EPNG understood and deployed the principles of clifftent,42 they would have a mathematically sound legal defense. The US Supreme Court ruled that economic factors could not be used by the EPA in setting air quality standards because it “could simply complicate the procedure without improving it.”51, 64– 66 Justices Scalia, Ginsberg, Breyer, Souter and Rehnquist were seeking a sound basis for economically optimizing limits and targets, but the American Chemistry Council, EPA and Solicitor General were unaware that clifftent provided a simple mathematical method to do it. The relief valve sizing standards67 do not properly optimize risk tradeoffs.

Refinery and petrochemical margins reached historic highs in August 2005; a golden age for HPI profits has arrived.68 ARC forecast the process industries automation market in 2003 to grow 4.7% to $58 billion and fieldbus adoption was growing rapidly.69, 70

President G.W. Bush and the Saudi Oil Minister have publicly agreed fuel availability in the US will be limited by oil refinery capacity rather than producing field capacity. If so, refinery mar-gins will grow. Then proper determination and setting of equip-ment limits and targets as well as product quality limits and targets with clifftent becomes the main operating challenge for produc-tion and profit. The CIM benefit potential in 1990 of $1–$2/bbl crude has undoubtedly increased to >$3/bbl by July 2005. (See the literature review sidebars for insights into the reasons for the demise of the automation business and the confusion.)

Outlook. While control and modeling technology is sound, instruments work and computers are fast and cheap, the whole computer application to manufacturing field, CIM, suffers from failure to adopt a definitive, comprehensive, rigorous, standard method42 for measuring the financial value of dynamic systems performance.

Once solution suppliers and their customers agree on that, solutions can be offered based on proper SR2 profit sharing.43, 44 In most cases, the cost of generating compelling value is so low the supplier can offer selected plant clients a variable annuity, for free. Once clifftent optimizing modules and CV/KPI profit meters are standard components in commercial databases, every CV and KPI setpoint will be optimized based on forecast vari-ance, and the financial consequences of variance changes will be quantified.

Process control and CIM businesses are in a long decline, failing to realize promised benefits, because they do not properly measure the financial value of dynamic performance of installed solutions, commercially confuse products and solutions, and remain unable to pursue performance-based licensing of sustain-ing solutions.

With so much time lost, so many mistakes made, such wide-spread confusion, so many competent and experienced con-trol engineers retired or gone, so few newcomers and so much damaged infrastructure, the demise of process control and CIM appears destined to continue. Is anybody delighted?

HPI profit increase potential exceeding $2– $3/bbl crude refined 85 million bpd worldwide will remain unrealized until the CIM demise is reversed. The main lesson is never invest or embark on an automation activity without a rigorous measure of financial value from improved dynamic system performance. Never. Don’t gamble on a game that does not have a clear method of scoring either. Don’t neglect the scientific method; it works for business too. Ignoring these is a recipe for failure, perhaps disaster. HP

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1961. 6 Pontryagin, L. S., V. G. Boltyanskii, R. V. Gamkrelidze, and E. F. Mishchenko,

The Mathematical Theory of Optimal Processes, John Wiley, 1962. 7 Zubov, V. I., Mathematical Methods for the Study of Automatic Control Systems,

Macmillan, 1963. 8 Gibson, J. E., Nonlinear Automatic Control, McGraw-Hill, 1963. 9 Shinners, S. M., Control System Design, John Wiley, 1964. 10 Harriott, P., Process Control, McGraw-Hill, 1964. 11 Coughanowr, D. R., and L. B. Koppel, Process Systems Analysis and Control,

McGraw-Hill, 1965. 12 Lapidus, L., R. Luus, Optimal Control of Engineering Processes, Blaisdell, 1967. 13 Athans, M., P. L. Falb, Optimal Control, McGraw-Hill, 1966. 14 Koppel, L. B., Introduction to Control Theory with Applications to Process Control,

McGraw-Hill, 1968. 15 Gould, L. A., Chemical Process Control: Theory and Applications, Addison-

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1971. 19 Douglas, J. M., Process Dynamics and Control, Vol 1 Analysis of Dynamic Systems,

Prentice-Hall, 1972. 20 Douglas, J. M., Process Dynamics and Control, Vol 2 Control System Synthesis,

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Continuous Distillation Units, Elsevier, 1975. 25 Fisher, D. G., and D. F. Seborg, Multivariable Computer Control, North-

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Hill, 1979. 28 Juran, J. M., Juran on Leadership for Quality —An Executive Handbook, The Free

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78 I MARCH 2006 HYDROCARBON PROCESSING

Orleans, LA, November 12, 1981 and Senior Control Seminar, Chemical Engineering Dept., University of Texas, Austin, TX, February 25, 1982.

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33 Latour, P. R., “Petrochemical Industry: Process Control,” pp. 3673-3680, Section in Madan G. Singh, Ed., Encyclopedia of Systems and Control, Pergamon Press, Oxford, UK, 1987.

34 Latour, P. R., “Advanced Computer Control of Oil Refineries —Where We Are, Where We Are Going,” Paper 21, Petroleum Refining Conference, Tokyo, Japan, 1988, The Japan Petroleum Institute, October 21, 1988, and Paper 6A, Fourth Refinery Technology Conference, Center for High Technology, Indian Oil Corporation Ltd., Vadodara, India, June 9, 1989.

35 Latour, P. R., “Modeling Intangible, Hidden Benefits from Better Product Quality Control,” International Conference on Productivity and Quality in the Hydrocarbon Process Industry, Hydrocarbon Processing/Coopers and Lybrand, Houston, TX, February 27, 1992, Hydrocarbon Processing, V71, n5, May 1992, p. 61.

36 Latour, P. R., “Role of RIS/APC for Running Refineries in the 1990s,” Fuel Reformulation, V2, n2, March 1992, p. 14.

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38 Greene, B., The Fabric of the Cosmos, Knopf, 2005. 39 Latour, P. R., “Role of RIS/APC for Manufacturing RFG/LSD,” National

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40 Latour, P. R., “Mission: Plan to Use RIS/APC for Manufacturing RFG/LSD,” Fuel Reformulation, V4, n4, July/August 1994, p. 20.

41 Latour, P. R., “Benefits of Modern Refinery Information Systems for Manufacturing Cleaner Fuels,” API Reformulated Fuels Conference, American Energy Week 1995, George R. Brown Convention Center, Houston, TX, January 31, 1995.

42 Latour, P. R., “Process control: CLIFFTENT shows it’s more profitable than expected,” Hydrocarbon Processing, V75, n12, December 1996, pp. 75-80.

43 Latour, P. R., “CIMFUELS,” bi-monthly contributing editorial, Fuel Reformulation, September 1995–February 1998.

44 Latour, P. R., “Does the HPI do its CIM business right?” HPIn Control guest columnist, Hydrocarbon Processing, V76, n7, July 1997, pp. 15-16 and “Optimize the $19-billion CIMfuels profit split,” V77, n6, June 1998, pp. 17-18.

45 Latour, P. R., “CLIFFTENT: Determining Full Financial Benefit from Improved Dynamic Performance,” Paper C01, Third International Conference on Foundations of Computer-Aided Process Operations, Snowbird, Utah, July 5-10, 1998. Proceedings published in AIChE Symposium Series No. 320, V94, 1998, pp. 297-302.

46 Latour, P. R., “Decisions about risk reduction,” letter to editor, Hydrocarbon Processing, V80, n6, June 2001, p. 39.

47 Latour, P. R., “Quantifying financial values,” HPIn Control guest columnist, Hydrocarbon Processing, V80, n7, July 2001, pp. 13-14.

48 Latour, P. R., “Why Invest in Process Control?,” Control, Vol. XV, n5, May 2002, pp. 41-46.

49 Latour, P. R., “Why tune control loops? Why make control loops?,” editorial, Hydrocarbon Processing, V81, n9, Sept 2002, pp. 11-12.

50 McMahon, T. K., “CLIFFTENT For Process Optimization,” Control, V17, n12, Dec 2004, p. 66.

51 Latour, P. R., “What is the optimum U.S. mogas sulfur content?,” Hydrocarbon Processing, V81, n11, Nov 2002, pp. 45-50.

52 Ryan, M., “What’s Really Risky?,” Parade Magazine, 15Jun97, p. 12. 53 Ross, J. F., “What’s Really Risky?,” Parade Magazine, 11Apr99, pp. 12-13. 54 DeHaven, J., “Gamblers’ secret of success: managing risk,” Houston Chronicle,

10Feb03. 55 Vendantam, B., “The fear factor: High anxiety over low risks,” Houston

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V74, n6, June 1995, pp. 41-56. 57 Boyes, W., “How Can We Save ISA?,” Control, Jul05, pp. 19. 58 Quillin, D. “Processing at a critical point,” Petroleum Technology Quarterly,

Spring 04, p. 5. 59 Belli, A., “BP must fix safety culture, board says,” Houston Chronicle, 18Aug05. 60 Cappiello, D., “Oil refiner Valero must make upgrades,” Houston Chronicle,

17Jun05. 61 Wise, W.A., “Open Letter from El Paso CEO,” Houston Chronicle, 9Oct02. 62 Davis, M., “CEO Wise begins swan song, prepares to exit El Paso,” Houston

Chronicle, 12Feb03. 63 Goldberg, L., “El Paso’s CEO Wise out early, Troubled company names new

leader,” Houston Chronicle, 13Mar03. 64 Hogue, C., “Clean Air Act’s Day in Court,” Chemical and Engineering News,

13Nov00, p. 9; “Health-based rules endorsed,” Chemical and Engineering News, 5Mar01, p. 9.

65 Cole, C., “Court hints at striking Clean Air Act; broad legal questions at issue. Court overturns EPA’s revised ozone and PM standards,” Octane Week, 24May99, pp. 1, 4-5.

66 Cone, M., “Scientists are debating whether EPA is moving too fast on air pollu-tion,” Houston Chronicle, 15Jun97.

67 Summers, A.E., “Consider an Instrumented System for Overpressure Protection,” Chemical Engineering Progress, Nov00, pp. 65-68.

68 Mowat, D., Woertz, P., O’Malley, T. “Hard and soft stuff both important to HPI value generation,” Hydrocarbon Processing, Apr04, pp. 25-27.

69 ARC report, “Automation market in process industries forecast for $58 billion,” Hydrocarbon Processing, Oct03, p. 21.

70 ARC Advisory, “Fieldbus adoption ‘skyrocketing’,” Hydrocarbon Processing, Sep03, p. 7.

71 Kane, L. A., “Insights from the ERTC Computer Conference,” Hydrocarbon Processing, Sep03, p. 132; “Include control loops in asset management,” Hydrocarbon Processing, Jun03, p. 138; “Consider other control system benefits,” Hydrocarbon Processing, May99, p. 21; “Other ways to justify control and info systems,” Hydrocarbon Processing, Jun94, p. 23.

72 Kane, L. A., “2002 ISA show: hot topics, ongoing trends,” Hydrocarbon Processing, Dec02, p. 102.

73 Friedman, Y. Z., “What is advanced process control?” Pt 1, Hydrocarbon Processing, May05, p. 15; Pt 2, Hydrocarbon Processing, Jun05, p. 114; Pt 3, Hydrocarbon Processing, Jul05, pp. 19-20.

74 Friedman, Y. Z., “Website provides useful advanced process control informa-tion,” Hydrocarbon Processing, Mar05, pp. 17-18.

75 Friedman, Y. Z., “More about inferential control models,” Hydrocarbon Processing, Feb05, pp. 17-18.

76 Friedman, Y. Z., “Has the advanced process control industry completely col-lapsed?,” Hydrocarbon Processing, Jan05, pp. 15-16.

77 Friedman, Y. Z., “First-principles inference model improves deisobutanizer col-umn control,” Hydrocarbon Processing, Mar03, pp. 43-47.

78 Friedman, Y. Z., “APC, modeling and optimization insights,” Hydrocarbon Processing, Mar03, p. 158. “Dr. Pierre Latour’s views on APC,” Hydrocarbon Processing, Nov05, pp. 17-18.

79 King, M. J., “How to lose money with inferential properties,” Hydrocarbon Processing, Oct04, pp. 47-52.

80 King, M. J., “How to lose money with basic controls,” Hydrocarbon Processing, Oct03, pp. 51-54.

81 Bras, A. and Smith, C. L., letters, “Enhance control performance,” Hydrocarbon Processing, Jan04, pp. 36-37; Smith, C.L., “Enhance process control perfor-mance,” Hydrocarbon Processing, Oct04, p. 57.

82 Smith, C. L., “Consider process control a specialty within chemical engineering,” Part 1, Hydrocarbon Processing, Sep02; Part 2, Hydrocarbon Processing, Oct02, pp. 75-83; Part 3, Hydrocarbon Processing, Dec02, pp. 71-75.

83 Hill, D, “Operator training practices revealed,” Hydrocarbon Processing, Jun05, p. 17; “Can metrics reveal automation as a ‘profit maker’?,” Hydrocarbon Processing, Sep05, p15.

84 Hill, D., “Alarm management: more important than ever,” Hydrocarbon Processing, Oct04, p. 19.

85 Hill, D., “Issues continue with IT in manufacturing,” Hydrocarbon Processing, Jan04, p. 17.

86 Hill, D., “Is performance being managed in real time?,” Hydrocarbon Processing, Sep03, pp. 19-20.

87 Hill, D., “Ten questions for your process automation system migration partner,” Hydrocarbon Processing, Apr03, pp. 15-16.

88 Chromolox, “Trends in control system enhancements,” Hydrocarbon Processing, Jun05, p. 9.

89 Doble, M., “Understanding the myths surrounding Six Sigma,” Hydrocarbon Processing, Mar05, pp. 80-82.

90 Clifford, L., “Why You Can Safely Ignore Six Sigma,” Fortune, 22Jan01, p. 140.

91 Welch, J., “To Share Owners —Six Sigma Quality,” General Electric 1999 Annual Report, p. 5.

92 Deutsch, C.H., “Intuition’s Out, 6 Sigma’s In at GE,” Herald Tribune, 13Jun00.

93 Deutsch, C.H., “GE uses Six Sigma method to get closer to quality control solu-tion,” Houston Chronicle, 20Dec98.

94 Deutsch, C.H., “A Boardroom Theology—Executives Embrace Quest for Flawlessness,” Herald Tribune, 9Dec98.

95 Stewart, T., “Making Decisions In Real Time,” Fortune, 26Jun00, pp. 332-334. 96 Hartmann, J. C. M., “Ramp up LP models with audits and transparent report-

ing methods,” Hydrocarbon Processing, Sep04, pp. 91-96; “Decision-making and modeling in petroleum refining,” Hydrocarbon Processing, Nov97, pp. 77-81.

97 Kalis, B., “Cure liquid carryover from compressor suction drums,” Hydrocarbon Processing, Oct04, pp. 77-84.

98 Barletta, T. and S. W. Golden, “Centrifugal compressor operations,” Petroleum Technology Quarterly, Summer 04, pp. 113-119.

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HYDROCARBON PROCESSING MARCH 2006 I 79

99 Lemmers, S. P. B., “Segregated and integrated process and antisurge compressor control systems,” Hydrocarbon Processing, Aug04, pp. 47-54.

100 Amrouche, Y. et al., “General Rules for Tank Design and Operation,” Chemical Engineering Progress, Dec02, pp. 54-58.

101 Williams, D., “Risk Management, Convergence within the Enterprise,” Fortune, 19Feb01, pp. S1-S10.

102 Ghosh, A., “Improve your bottom line with critical condition management,” Hydrocarbon Processing, Feb03, p19.

103 Mannan, M. S., “The Mary Kay O’Conner Process Safety Center - the first ten years,” Hydrocarbon Processing, Mar05, pp. 65-71.

104 Ayral, T. E., “Critical situation management—avoid the pitfalls,” Hydrocarbon Processing, Oct02, pp. 37-40.

105 Gonzalez, R. G., “Reliability Challenges,” Petroleum Technology Quarterly, Q3 05, p. 3.

106 Rosenzweig, M., “Plants get smarter,” Chemical Processing, Jun05, pp. 16-21. 107 Alford, J. S., et al., “Alarm Management for Regulated Industries,” Chemical

Engineering Progress, Apr05, pp. 25-30. 108 Brown, N., “Effective alarm management,” Hydrocarbon Processing, Jan04, pp.

65-71. 109 Grosdidier, P et al., “A path forward for DCS alarm management,” Hydrocarbon

Processing, Nov03, pp. 59-64. 110 Goble, W., “Using the safety life cycle,” Hydrocarbon Processing, Jul03, p. 96. 111 Nimmo, I., “Alarm Management,” Chemical Engineering Progress, Nov02, pp.

30-38. 112 FIATECH, “Owners seek better data delivery to operations functions,”

Hydrocarbon Processing, May04, p. 27. 113 Valot, D., “Owner/contractor relationships,” Hydrocarbon Processing, Dec04, p.

7. 114 Valot, D., “ECCs, owners exchange views at annual meeting,” Hydrocarbon

Processing, Dec02, pp. 19-21. 115 Moore, J., “Negotiating ERP Maintenance Contracts,” Chemical Engineering

Progress, May04, p. 16. 116 Cunic, B., “Performance-based contracting,” Hydrocarbon Processing, Dec03,

pp. 43-46. 117 Transmar offer, “Strategies needed to rebuild weakened ECC industry,”

Hydrocarbon Processing, Jul03, p. 19-21. 118 Mullin, R, & C. Cobb, “Calculating a Comeback,” Chemical and Engineering

News, 15Aug05, pp. 17-22. 119 Cobb, C. B., “A forward look at the refinery of the future,” Petroleum Technology

Quarterly, Spring 2004, pp. 21-27. 120 Hill, D., M. Walker, “Dow sees collaboration as the key to operational excel-

lence,” Hydrocarbon Processing, May03, pp. 17-18. 121 Bullemer, P., et al., “Shaping a New Role for the Operator,” Chemical Engineering

Progress, May04, pp. 42-46. 122 ASM Consortium offer, “Process plant operator’s role needs new paradigm,”

Hydrocarbon Processing, Mar03, pp. 23-25. 123 Mohrmann, C., “Include your field work force in the automation loop,”

Hydrocarbon Processing, Oct02, pp. 61-62. 124 White, D. C., “Determining the true economic value of improved plant infor-

mation,” Hydrocarbon Processing, Dec04, pp. 53-58. 125 White, D. C., “Creating the ‘smart plant’,” Hydrocarbon Processing, Oct03, pp.

41-50. 126 Martin, G. D., “Understand control benefits estimates,” Hydrocarbon Processing,

Oct04, pp. 43-46. 127 Grosdidier, P., “Improve APC project success,” Hydrocarbon Processing, Oct04,

pp. 37-41. 128 Grosdidier, P., “Value proposition for oil accounting,” Part 1, Hydrocarbon

Processing, Apr03, pp. 85-88; Part 2, Hydrocarbon Processing, May03, pp. 105-109.

129 Grosdidier, P., “Economics of blend giveaway,” Hydrocarbon Processing, Nov97, pp. 55-60.

130 Fiske, T., “Collaboration between IT and operations leverages common tech-nology and enhances performance,” Hydrocarbon Processing, May05, p. 13.

131 Fiske, T, and Woll, D., “Unifying operations to improve business performance,” Hydrocarbon Processing, Dec04, p. 15.

132 Fiske, T., “Optimizing asset effectiveness through performance monitoring,” Hydrocarbon Processing, May04, p. 19.

133 Fiske, T., “Real-time performance management unlocks hidden value in assets,” Hydrocarbon Processing, Aug03, p. 15.

134 Woll, D., “Time to rethink process plant roles and responsibilities,” Hydrocarbon Processing, Sep04, pp. 15-16.

135 Woll, D., “Increased performance dictates more effective plant human assets,” Hydrocarbon Processing, Apr04, pp. 17-18.

136 Woll, D., “Effective operations management,” Hydrocarbon Processing, Sep02, pp. 17-18.

137 Beautyman, A. C., “Assessing profitability of real-time optimization,” Hydrocarbon Processing, Jun04, pp. 39-42.

138 Pandit, H., “Profitability of process optimization,” letter to the editor, Hydrocarbon Processing, Sep04, p. 18.

139 Pitt, R., “The intelligent refinery— or is it?” Hydrocarbon Processing, Dec03, p. 11.

140 Pitt, R., “IT makes further headway into the refinery,” Hydrocarbon Processing, Jun03, p. 17.

141 Powley, G., “Performance visibility and the road to sustainable profitability,” Hydrocarbon Processing, Dec03, p. 96.

142 Lang, L, and Gerry, J., “Universal key performance indicators for process plants,” Hydrocarbon Processing, Jun05, pp. 83-85.

143 Canney, W. M., “Are you getting the full benefits from your advanced process control systems?,” Hydrocarbon Processing, Jun05, pp. 55-58.

144 OSIsoft offer, “Real-time insights into operational metrics,” Hydrocarbon Processing, Apr03, pp. 29-30.

145 OSIsoft offer, “Optimize inventory management,” Hydrocarbon Processing, Dec02, p. 89.

146 Kennedy, J. P., “Executive interface for the process industry,” Petroleum Technology Quarterly, Summer 02, pp. 113-117.

147 Honeywell offer, “Comprehensive simulation solution for processing plants,” Hydrocarbon Processing, May05, p. 98.

148 Honeywell ad, “Honeywell automation quickly pays for itself,” Hydrocarbon Processing, Sep03, p. 133; Hydrocarbon Processing, Aug03, pp. 2, 30-31.

149 Honeywell offer, “KPI Manager,” Gulf Publishing Company Software Reference, Fall 03, p. 10.

150 Emerson ad, “Prove it,” Hydrocarbon Processing, Oct04, p. 48 and Hydrocarbon Processing, Sep03, p. 21.

151 Emerson offer, “Model predictive control software application,” Hydrocarbon Processing, Oct03, p. 105.

152 Emerson ad, “Prove it,” Petroleum Technology Quarterly, Summer 04, p. 68.

153 Jackson, K. M., “Invensys solution components,” Hydrocarbon Processing, Jan05, p. 31.

154 SimSci-Esscor offer, “Improved advanced process control solution,” Hydrocarbon Processing, Jan05, p. 88.

155 Invensys ad, “ArchestrA technology,” Hydrocarbon Processing, Sep03, pp. 8-9. 156 Ranganathan, S., A. Offerman, J. Cijntje, R. McFarlane, V. Lough, “Improved

optimization of a refinery crude unit,” Petroleum Technology Quarterly, Spring 2003, pp. 59-67.

157 Cutler Tech ad, “Operator Advisor,” Hydrocarbon Processing, Apr05, p. 67. 158 KBC ad, “Optimum Plant Performance,” Hydrocarbon Processing, Jun05, p. 8. 159 HP/KBC poster, “2005 Refining Advanced Process Control—Profit. Ability,”

Hydrocarbon Processing, Jan05. 160 Performance Plus offer, “Performance optimization services,” Hydrocarbon

Processing, Mar05, p. 85. 161 ABB ad, “Results-Driven Automation,” Hydrocarbon Processing, Feb05, p. 71. 162 ABB offer, “Data Acquisition and Analysis Software,” Chemical Engineering

Progress, Feb03, p. 53. 163 CCC offer, “Control system to increase olefins production,” Hydrocarbon

Processing, Dec04, p. 25. 164 CCC ads, “Turbomachinery Control Profit Enhancement,” Hydrocarbon

Processing, Aug03, pp. 29, 31. 165 Matrikon offer, “Operational excellence in controller performance,” Hydrocarbon

Processing, Apr04, p. 32. 166 Matrikon offer, “Software solution purchased,” Hydrocarbon Processing, Aug03,

p. 91. 167 AspenTech offer, “Resin Producers’ New Ally in Fast Grade-Change Transitions,”

Chemical Engineering Progress, Aug03, p. 10. 168 AspenTech ad, “ROI,” Hydrocarbon Processing, Apr04, p. 55 and Mar04, p.

20. 169 Aberdeen Group Ad Section. “Growing Technology ROI,” Fortune, 24Nov03,

pp. S1-18. 170 SAP ad, “If we can’t afford the solution, then it’s not a solution,” Fortune,

24Nov03, p. S7. 171 SAP offer, “Adaptive supply chain network solution,” Hydrocarbon Processing,

Aug03, p. 97. 172 Pavilion offer, “Expanded control solution boosts production performance,”

Hydrocarbon Processing, Jan04, p. 83. 173 Pavilion offer, “Pavilion Technologies Acquires Business Threads,” Chemical

Engineering Progress, Feb03, p. 23. 174 Shell Global Solutions ad, “Squeeze. Make every drop count,” Petroleum

Technology Quarterly, Autumn 03, p. 87. 175 Bayer offer, “New company transforms HPI ideas into reality,” Hydrocarbon

Processing, Jul03, p. 29. 176 DuPont offer, “Dynamic benchmarking of plant performance,” Hydrocarbon

Processing, Mar03, pp. 28-29. 177 Enraf ad, “Terminal Automation solutions,” Petroleum Technology Quarterly,

Autumn 03, p. 88. 178 ExperTune offer, “Process control consultant accessible in a box,” Hydrocarbon

Processing, Dec03, p. 25. 179 ExperTune offer, “Loop Performance Monitor Mines Knowledge From Data

Historians,” Chemical Engineering Progress, Jun03, p. 32.

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80 I MARCH 2006 HYDROCARBON PROCESSING

180 IBM offer, “Refiners: Manage key functions for success,” Hydrocarbon Processing, Aug03, p. 17.

181 Hagen offer, “Accounting approach defines plant’s true profit potential,” Hydrocarbon Processing, Oct02, p. 31.

182 Two day course, “Planning, Justifying, and Executing Automation and Control Projects,” ISA Training Courses, Houston, 22Oct03.

183 Rotava, O., and A. C. Zanin, “Multivariable control and real-time optimiza-tion—an industrial practical view,” Hydrocarbon Processing, Jun05, pp. 61-71.

184 Bonavita, N., et al., “A step-by-step approach to advanced process control,” Hydrocarbon Processing, Oct03, pp. 69-73.

185 Chang, E., “Pattern recognition displays capture advanced process control ben-efits,” Hydrocarbon Processing, Jun05, pp. 93-96.

186 Barsamian, A., “Consider near infrared methods for inline blending,” Hydrocarbon Processing, Jun05, pp. 97-100.

187 Sanz, A, J. Papon, et al., “Model-based predictive control increase batch reactor production,” Hydrocarbon Processing, May05, pp. 61-65.

188 Mandal, K. K., “Improve desalter control,” Hydrocarbon Processing, Apr05, pp. 77-81.

189 Mandal, K. K., “New level control techniques,” Hydrocarbon Processing, Oct04, pp. 71-74; “Optimize surge vessel control,” Hydrocarbon Processing, Mar03, p. 41.

190 Galante, E. G., “ExxonMobil’s molecule-management,” Hydrocarbon Processing, Apr05, p. 17.

191 Kelly, J. D., “Improve accuracy of tracing production qualities using successive reconciliation,” Hydrocarbon Processing, Apr05, pp. 65-72.

192 Kelly, J. D., “Improve yield accounting by including density measurements explicitly,” Hydrocarbon Processing, Feb05, pp. 93-95.

193 Kelly, J. D., “Crude oil blend scheduling optimization: an application with multimillion dollar benefits” Part 1, Hydrocarbon Processing, Jun03, pp. 47-53; Part 2, Hydrocarbon Processing, Jul03, pp. 72-79.

194 Kelly, J. D., “Modeling Production-Chain Information,” Chemical Engineering Progress, Feb05, pp. 28-31.

195 Kelly, J. D., “Formulating Production Planning Models,” Chemical Engineering Progress, Jan04, pp. 43-50.

196 Moore, I., “Reducing CO2 emissions,” Petroleum Technology Quarterly, Q2 05, pp. 97-103.

197 Moore, I. et al., “Hydrogen optimization at minimal investment,” Petroleum Technology Quarterly, Spring 03, pp. 83-90.

198 Sivaraman, S. et al., “Determining system capability for loss control and custody transfers,” Hydrocarbon Processing, Mar05, pp. 45-51.

199 PAS/DOT offer, “Polymer process advanced control and optimization,” Hydrocarbon Processing, Jan05, p. 28.

200 Wilson, J. W., “Statistical process control in FCC operations,” Petroleum Technology Quarterly, Summer 04, pp. 69-73.

201 Mathur, U., R. J. Conroy, “Successful multivariable control without plant tests,” Hydrocarbon Processing, Jun03, pp. 55-65.

202 Martín, R. G., et al., “New control scheme improves SRU control,” Hydrocarbon Processing, Sep03, pp. 101-106.

203 Sharpe, P., and J. Rezabek, “Embedded APC tools reduce costs of the technol-ogy,” Hydrocarbon Processing, Oct04, pp. 53-56.

204 Cheng, G. S., “Model-free adaptive technology improves distillation column chain control,” Hydrocarbon Processing, Oct04, pp. 57-62.

205 Sayyar-Rodsari, B. et al., “Model predictive control for nonlinear processes with varying dynamics,” Hydrocarbon Processing, Oct04, pp. 63-69.

206 Deshpande, P. B. et al., “Control and Optimize Nonlinear Systems,” Chemical Engineering Progress, Feb03, pp. 63-73.

207 Novak, R., “SCADA’s new capabilities support business processes and supply chain,” Hydrocarbon Processing, Aug04, pp. 17-18.

208 Kern, A. G., “Implementing expert online operator advisors,” Hydrocarbon Processing, Jun04, pp. 43-45.

209 Valleur, M and J. L. Grue, “Optimize short-term refinery scheduling,” Hydrocarbon Processing, Jun04, pp. 46-49.

210 Gilbert, J. B., “Implementing an EHS Management Information System,” Chemical Engineering Progress, May04, pp. 28-32.

211 Karagoz, O., Versteeg, J., Mercer, M., P. Turner, “Advanced control methods improve polymers’ business cycle,” Hydrocarbon Processing, Apr04, pp. 45-49.

212 Hall, J., “Benefits of plant testing for multivariable controllers,” Hydrocarbon Processing, Mar04, p. 92.

213 Singh, S., “Business-to-manufacturing integration using XML,” Hydrocarbon Processing, Mar03, pp. 62-65.

214 Moore, J., A. Gonzalez, “The Supply Chain Cornerstone,” Chemical Engineering Progress, Nov02, p. 29.

215 Moore, J., “The Big Squeeze is on in ERP,” Chemical Engineering Progress, Sep02, p. 30.

216 CRI/Pavilion offer, “Real-time catalyst monitoring software— CATSCAN,” Hydrocarbon Processing, Dec02, p. 27.

217 Sim, W., et al., “Engineering to Business: Optimizing Asset Utilization Through Process Engineering,” Chemical Engineering Progress, Sep02, pp. 54-63 and Roberts, W. T., letters, Chemical Engineering Progress, Feb03, p. 8.

218 Sim, W. D., C. H. Twu, V. Tassone, “Getting a Handle on Advanced Cubic Equations of State,” Chemical Engineering Progress, Nov02, pp. 58-65.

219 Latour, P. R., “Process Design Decisions Related to Process Control,” Paper 60b, 74th National Meeting, AIChE, New Orleans, LA, March 1973.

220 Latour, P. R., “Process Computer as a Tool for Golden Eagle Refinery Energy Conservation,” 68th Annual Meeting, AIChE, Los Angeles, California, November 18, 1975 and Chemical Engineering Progress, V72, n4, April 1976, pp. 76-81.

221 Latour, P. R., “Process Computer Monitors, Reduces Energy Use,” Oil & Gas Journal, February 23, 1976, p. 92.

222 Van Horn, L. D. and Latour, P. R., “Crude Distillation Computer Control Experience,” ISA-76 Conference, Houston, TX, October 12, 1976.

223 Van Horn, L. D. and Latour, P. R., “Computer Control of a Crude Still,” Instrumentation Technology, V23, n11, November 1976, p. 33.

224 Latour, P. R., “The Hidden Benefits from Better Process Control,” ISA-76 Conference, Houston, TX, October 13, 1976 and Cleveland ISA 3rd Annual Conference and Exhibit, Ohio, May 16, 1977.

Pierre R. Latour is an independent consultant specializing in measuring financial performance of HPI information and process control systems (Clifftent) to support shared risk–shared reward (SR2) licensing of profit-sustaining solutions. He has been a vice president of engineering, marketing, business development, proj-

ect implementation and consulting at Aspen Technology, Dynamic Matrix Control Corp, Setpoint and Biles & Associates. Dr. Latour cofounded the last two firms and held senior positions at Shell Oil and DuPont. He has worked on contracts for 50 HPI companies worldwide on most processes like ACU, FCC and olefins. Dr. Latour holds a BS degree, ChE from Virginia Tech and a PhD degree, ChE from Purdue. He served as Captain U.S. Army, NASA, Houston. Dr. Latour has authored 60 publications. He was cimfuels editor for FUEL and is a registered PE in Texas and California. He resides in Houston, Texas. E-mail: clifftent@hotmail.com.

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