AIChE Paper Number: 14a

Align Olefin Operations to Economics – Clifftent optimizes setpoints

Pierre R Latour

President CLIFFTENT Inc.

Prepared for Presentation at the 2007 Spring National Meeting Ethylene Producers Conference, American Institute of Chemical Engineers

Houston, Texas, April 23, 2007

AIChE and the EPC shall not be responsible for statements or opinions contained in papers or printed in its publications.

Align Olefin Operations to Economics – Clifftent optimizes setpoints

Pierre R Latour President

CLIFFTENT Inc.

Abstract: Olefin plant operation targets should be aligned directly with economics to maximize profit rate. The 1996 performance measure method for process control and IT that associates a profit tradeoff with each CV/KPI distribution allows olefin plant operators who know the financial consequences of violating limits to rigorously align operating conditions to economics, generating profit >0.005 $/lb ethylene. The risk optimization method, candidate applications, requirements and eleven important results are given.

Introduction.

The art of operating olefin plants most profitably includes proper setting of operating limits, specifications and constraints; the feasible operating region window. If the limits are set too tightly, the window is too small and profit is lost. If the limits are set too loosely, the window is so big that external forces dominate and profit is lost. Clifftent1, 2 provides a rigorous method for setting controlled variable or key performance indicator, CV/KPI, constraints and corresponding targets or setpoints just right, optimally.

Clifftent was disclosed in 19961, 2 to measure the financial value of improved dynamic performance of any process, plant, activity or system. The benefit source may be better measurement, timely and frequent data, better operators, better loop tuning, multivariable control, better models, better control valves, faster computers, integrated data bases, IT, CIM or training. The cause or tool is irrelevant to Clifftent analysis; but its cost can be compared to its performance benefit claim by Clifftent for appraisal of merit. Without a rigorous Clifftent performance measure of financial value added by components of CIM technology and solutions, suppliers and users cannot make rational investment, deployment and maintenance decisions, i.e. nobody knows the merit of what they are talking about3-11. So they naturally rely on intangibles, fads, faith theory and judgment11.

Flawed Benefit Analysis.

Clifftent showed the standard method of benefit analysis was fundamentally flawed. The standard method takes a given, arbitrary CV/KPI mean and variance, proposes to reduce variance, postulates the reduction adds no value (because they cancel out, it is hidden or intangible) but smoother operation is a prerequisite for moving the mean some arbitrary amount toward the more valuable limit for a corresponding steady-state gain (capacity, yield, utility saving) which, when multiplied by a benefit rate/gain factor estimates a financial benefit. The rigorous Clifftent steps reveal the flaws:

1. Original mean is never optimum, it should be set optimally first. This is easily done by Clifftent, providing a plant benefit on the order of all CIM claims at trivial cost. Optimize the setpoint first, to make money.

2. Variance reduction does indeed add value; it’s the same order of magnitude as the value from moving the mean. Making the variance reduction value tangible is the second benefit.

3. The new mean with reduced variance should be reoptimized and moved an optimum amount toward the profitable limit, determined rigorously by Clifftent, for a third benefit. This proves process control to reduce variance

integrated with Clifftent generates tangible, believable benefits twice those typically claimed from the standard method1.

Basis of Clifftent.

All Controllable response Variables and Key Performance Indicators, CV/KPI, in ethylene/propylene plants manifest financial tradeoffs in the neighborhood of their limits. For maximum limits there is a process credit realized by approaching the limit from below and an external penalty for exceeding it. For minimum limits there is a process credit realized by approaching it from above and an external penalty for going below it. This is true for flow, temperature, pressure, level, quality, speed, throughput, velocity, energy supply and emissions. Every olefin plant CV/KPI has an associated profit tradeoff, shaped like a tent, often with a discontinuous cliff near its limit, constraint or specification, because it matters, it is key, we care about its value, it affects profit, it has a Clifftent profit function.

Clifftent Method.

Every CV/KPI has an associated profit function that is shaped like a tent; often with a discontinuous cliff at the limit. The profit tradeoff connects dissimilar phenomena affected by operating conditions that impact long term expected value profit rate. Maximum theoretical profit when variance is zero is realized just at the peak of the profit function. But CV/KPI also vary, they are never perfectly controllable with zero variance. So holding them near the maximum steady-state profit point is risky business, Figure 1.

Clifftent requires two input functions of the CV, its distribution or histogram and its steady-state profit tent extending down from its limit peak in both directions. The mathematical technique is to integrate their product to obtain a number, the expected value (or weighted average) of profit rate. The integration is repeated for different distribution means throughout the range to get a smooth hill curve for average profit versus CV mean including variance uncertainty. The hilltop is easily located for the maximum profit setpoint. Multivariable controller CV weighting factors (DMC needs Equal Concern Errors) are easily derived from the smooth hill too.

CV/KPI Application Candidates.

Some olefin plant candidate Controlled Variables and Key Performance Indicators are given in Table 1. Many vapor velocity limits featured for oil refineries11 apply to olefin plants as well. They each exhibit tent shaped profit functions for their means, they each exhibit variability, they each offer opportunities for improved financial performance.

Profit Meters.

The Clifftent results constitute a profit meter10 for each CV/KPI. The pointer should be vertical when the CV setpoint is at its optimum hilltop. This long sought solution cannot be realized without modeling the penalties for limit violation and integrating risk management properly. Statistical process control to arbitrary limits like 95% confidence is replaced. Alarm systems with simple limit triggers are also replaced. The value of tuning is quantified.

Closed-Loop Real-Time Optimization.

The author worked for three application companies from 1980-96 offering online Closed-Loop Real-Time Optimization, CLRTO, of entire olefin manufacturing plants, using rigorous chemical engineering process models, (cracking chemistry, coking, distillation VLE, heat & component mass balances, compression, pressure drop, data reconciliation, etc.) and LP, SLP, CLP, QP or NLP profit optimizers. Observing eight of them in action, one quickly sees they are merely constraint set corner pickers, often selecting the same corner for long periods11-14.

And the corner is usually obvious to a good olefin plant engineer and operator. One CLRTO discovered the process gas compressor suction pressure should be minimum; discharge should be maximum, horsepower or speed should be maximum. This unremarkable discovery should be known to any olefin process engineer; hot side steam cracking is favored at low pressure; cold side condensing for distillation is favored at high pressure; maximizing the pressure increase is the purpose of the PG compressor. CLRTO has been a disappointment because it adds little value, costs a lot to implement and is expensive to maintain. It doesn’t do very much.

The important issue is how to set the dependent variable constraint values properly for an LP or NLP. Corner position is as important as corner selection. Since LP has no information about the financial consequences for exceeding its constraints, this issue is outside the realm of LP. Constraint setting is properly

done by Clifftent. Chemical engineer modelers neglected to go beyond their interior process models and optimizers to the interface of the plant with its surroundings (where value is created) when limits are exceeded and determine the tradeoff tent profit models for CV’s. One large solution supplier even disconnected its abnormal alarm situation management business from its established process centered control business throughout the 1990’s and, not surprisingly, could not quantify the value of either offering.

Requirements.

CV/KPI mean, variance and distribution data is readily available from control systems since the 1970’s; useful for operators to forecast near term variance.

Olefin producers have sound chemical engineering process models for the physical effects of changing CV/KPI average values; these are multiplied by economic sensitivities like differential product values to get the financial sensitivity of CV/KPI. Most producers also have long experience with the financial consequences of exceeding limits, abnormal situations, equipment wear and tare, customer dissatisfaction, emission noncompliance, Hazop, flaring, and safety.

Modern IT and CIM systems have, or should have, provided this information to process operators and managers since the early 1990’s. It should be captured in Clifftent profit sensitivity functions for every CV/KPI, forecast for near term guidance and used to properly, optimally set CV setpoints. Frequent resetting of setpoints by Clifftent ensures proper alignment of operating conditions to economics.

Cash Flow.

CLRTO benefit claims by 1990 were around 0.004 to 0.005 $/lb ethylene produced3, 4, 15, depending on plant and economic complexity and variability, but these could never be adequately verified, leading to great disappointment. Regularly resetting setpoints accurately with Clifftent delivers >0.005 $/lb ethylene profit (= benefit – cost) to operating companies who know what they are doing with little cost or risk. A typical cash flow15 is shown in Table 2. Notice even discounting the direct NPV with 0.50 probability of realization, the client profit NPV(30y, 10%) approaches $20 million. That probability can be higher.

Results.

Some important findings are offered to help run olefin plants right. 1. Set setpoints optimally with Clifftent and measure financial value of

change. Do this whenever any input changes. 2. Measure the financial value of variance reduction from CV measurement,

control and IT components and solutions with Clifftent. All they can do is modify their distribution position and shape. Variance reduction makes money. Abandon flawed conventional benefit analyses.

3. Specify the critical data needed for olefin plant operating decisions with Clifftent. Measure the financial value of accurate process and economic sensitivity data with Clifftent. Quantify the financial losses from using erroneous assumptions, data and models.

4. Never invest in instruments, controllers, optimizers, data bases, IT or CIM unless the solution supplier and customer concur on the measure of financial performance of such investments. Clifftent is the rigorous scorekeeper. This is an example of the adage – never engage in a competitive contest without understanding and concurrence on scorekeeping for success.

5. Set constraint values for LP planners and on-line, closed-loop, real-time optimizers with Clifftent.

6. Connect dissimilar models for the plant and its surroundings, like abnormal situations, safety alarms, maintenance, off-spec product quality, environment and customers by unifying operating credits with violation penalties using Clifftent for each CV/KPI.

7. Unify dissimilar objectives like yield, quality, energy, capacity, run length, maintenance, environmental compliance, safety and delivery to one objective to be maximized: expected net-present-value profit rate, with Clifftent.

8. Deploy best practice techniques like modeling, multivariable control, risk management, setpoint optimization, statistical quality control, alarm management, six – sigma quality and CIM comprehensively with Clifftent.

9. Build rigorous risk management into setting of all CV/KPI setpoints with Clifftent profit meters.

10. Use the rigorous Clifftent method for operating olefin plants for maximum expected net-present-value profit from near term forecasts of variance (operators), process behavior (engineering), limit violation penalties (specialists), economics (business), to selected CV/KPI (profit center management).

11. Recognize there is an optimum non-zero frequency of limit violations; confront, model and mitigate them. Learn from mistakes by improving Clifftent input data accuracy for improved future profit.

Clifftent is not a new paradigm for operation; it really is a rigorous method for operating as you do now, by breaking the problem into components using tested scientific principles of comprehensive mathematical modeling, accurate data, statistics, risk management and appropriate human values to optimize risky economic tradeoffs for best operating practice. It’s the best way for those who know how to run olefin plants to align operations to economics. Remember the risk fundamentals: Don’t be afraid to go out on a limb, that’s where the fruit is. I like my porridge not too hot and not too cold, but just right - Goldilocks. It’s always better to play it on the safe side - Greek philosopher, 430BC. If you don’t know how to keep score, don’t play that game. Sit on correct hilltops; locate them, go to the summit, relocate the hill when it moves, go to the top again. Keep up the good work to stay on top of your business.

References.

1. Latour, P.R., “Process control: CLIFFTENT shows it’s more profitable than expected”, Hydrocarbon Processing, V75, n12, December 1996, pp. 75-80. Republished in Kane, Les, Ed, “Advanced Process Control and Information Systems for the Process Industries”, Gulf Publishing, Co, 1999, pp. 31-37. 2. 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. 3. Latour, P.R., "Control in Petrochemical Industry", Article 180B-III-9, Encyclopedia of Systems and Control, M. Singh, Editor, Pergamon Press, Oxford, England, 1985. 4. Latour, P.R., "Petrochemical Industry: Process Control", pp. 3673-3680, Section in Madan G. Singh, Ed., Systems & Control Encyclopedia, Pergamon Press, Oxford, UK, 1987. 5. Sharpe, J.H. and Latour, P.R., "Calculating Real Dollar Savings from Improved Dynamic Control", Texas A&M University Annual Instruments and Controls Symposium, College Station, TX, January 23, 1986. 6. Latour, P.R., “Does the HPI do its CIM business right?” H P InControl 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. 7. Latour, P.R., “Decisions about risk reduction”, Letter to Editor, Hydrocarbon Processing, V80, n6, June 2001, p. 39. 8. Latour, P.R., “Quantifying financial values”, H P InControl Guest Columnist, Hydrocarbon Processing, V80, n7, July 2001, pp. 13-14.

9. Latour, P.R., “Why Invest in PROCESS CONTROL?”, CONTROL, Vol. XV, n5, May 2002, pp. 41-46. 10. Latour, P.R., “Why tune control loops? Why make control loops?”, Editorial, Hydrocarbon Processing, V81, n9, September 2002, pp. 11-12. 11. Latour, P.R., “Demise and keys to the rise of process control”, Hydrocarbon Processing, V85, n3, March 2006, pp. 71-80 and Letters to Editor, Process Control, Hydrocarbon Processing, V85, n6, June 2006, p. 42. 12. Latour, P.R., “Set vapor velocity setpoints profitably”, Hydrocarbon Processing, V85, n10, October 2006, pp. 51-56. 13. McMahon, T.K. (& Latour, P.R.), “CLIFFTENT For Process Optimization”, CONTROL, V17, n12, December 2004, p. 66. 14. Friedman, Y.Z. (& Latour, P.R.), “Dr. Pierre Latour’s views on APC”, HPIn Control editorial, Hydrocarbon Processing, V84, n11, November 2005, pp. 17-18. 15. Latour, P.R., “CIMFUELS”, bi-monthly contributing editorial, FUEL Reformulation, September 1995 - February 98.

Table 1. Olefin Plant Clifftent CV Candidates Hot Side Cracking - Coil Outlet Temperatures, Steam/hydrocarbon ratio, hydrocarbon feed rate, coil velocity, furnace draft, control valve positions. Compression, process gas & refrigeration - suction pressure, discharge pressure, horsepower, speed, surge, stall, driver load, bearing temperatures. Cold Side Recovery - Product qualities and loads: Cold box C1 in H2, H2 in C1 DC1 C2= in C1 fuel, C1 in C2=, pressure, flood C2 Splitter C2 in C2=, C2= in C2 recycle, pressure, flood DC2 C3= in C2, C2 in C3=, pressure, flood C3 Splitter C3 in C3=, C3= in C3 recycle, pressure, flood DC4 C5= in C4, C4 in C5=, pressure, flood C4 Splitter C4 in C4=, C4= in C4 recycle, pressure, flood Control valve positions, vapor velocities. Offsites - inventory, hydrogen, fuel gas, steam, power, atmospheric emissions, wastewater.

Table 2. Olefin Plant Clifftent Cash Flow, kk$/quarter Olefin Capacity: 0.88 billion lb C2=/yr or 400 KT/yr Benefit: 0.005 $/lb C2=; TVM, %/yr: 10.0 PRICE Install: 0.04, Maintenance: 0.02; COST: 0.01 (Inputs in bold) Client SupplierEnd Quarter Benefit PRICE* Net COST Net Q0 0.000 0.040 -0.040 0.030 0.010 Q1 0.000 0.040 -0.040 0.020 0.020 Q2 0.000 0.040 -0.040 0.020 0.020 Q3 0.330 0.040 0.290 0.020 0.020 Q4 0.661 0.040 0.621 0.010 0.030 NPV(1yr) 0.905 0.190 0.715 0.096 0.094 Q5 1.101 0.020 1.081 0.010 0.010 Q6 1.101 0.020 1.081 0.010 0.010 --- ---- ---- ---- ---- ---- Q40 1.101 0.020 1.081 0.010 0.010 NPV(10) 24.409 0.617 23.791 0.310 0.308 % Benefit 100.00 2.53 97.47 1.27 1.26 NPV(30) 39.177 0.885 38.291 0.444 0.442 % Benefit 100.00 2.26 97.74 1.13 1.13 Probability 0.50 0.95 0.49 0.98 0.92 Expect NPV 19.588 0.841 18.747 0.435 0.406 Exp % Ben 100.00 4.29 95.71 2.22 2.07 * Initially non-refundable. At risk

Figure 1. Clifftent Functions

CV/KPI Mean, like C3 in C3= (Spec = 1.0) Inputs: Top Unit Profit Cliff, Wide Distribution, Narrow Distribution Outputs: Wide Distribution Time Profit, Narrow Distribution Time Profit