Business – In the early 1990s it became apparent the instrumentation, process control and IT businesses selling products, services, models, technology and projects were failing to identify, capture and sustain significant profits for the HPI and themselves8,9.
Chemical engineering has not provided a comprehensive method for aligning process control system setpoints to economics3, 6. Information technology hasn't either. Process control reduces variance, but its value could not be properly quantified until Clifftent integrated it with optimum mean setting in 19966
Consequences – It is now clear that current decision-making practice
- Cannot be assured that it is operating at its best,
- Cannot measure operating performance relative to its base case or perfection,
- Cannot reconcile disconnected performance measures,
- Cannot learn to improve operation well from imprecise decision methods,
- Cannot quantify that any deviations from optimum operation loses money2, 7
- Cannot quantify the value of reduced variance and improved dynamic performance of systems6, 8
- Cannot properly determine the value of data, information, components, models, controllers or solutions,
- Cannot manage risk consistently
How should the HPI operate?
The HPI should operate as it always has, but determine operating setpoints with a rigorous mathematical method, using accurate data, with a unified purpose; that is, Clifftent6
Solution – Shoring up the foundation of and repairing the disconnected contributions to HPI operations, the HPI must adopt best operating practices. This means defining the decisions to be made and the proper method for determining them. Then determine the required input data, establish procedures for assuring input accuracy and ensure faithful deployment and use. Analyze past performance for sustained improvement. Decision support requirements are clear and value measurable.
Re-optimize risky tradeoffs rigorously whenever any change occurs. Adopt a mathematically sound method with correct inputs for a proper unified purpose. Since 1996, Clifftent6 has offered a rigorous method for setting CV means. It provides a way to take into account risk factors; i.e., accounting for the chances of violating constraints versus the consequence of violating them. It is built on the steady state profit as a function of the controlled variable mean, which is always a tent- shaped trade-off, sometimes with a discontinuous cliff at the constraint peak. It is based on the discovery that every CV has a financial tradeoff, not too high and not too low, just right. An optimum always exists. Of course these business 101 truisms are well-known. That is why Clifftent fits so naturally into solution businesses offering profit performance.
Method – Here is how to optimize risky tradeoffs.
- Establish a unified objective such as expected value profit rate over the near term.
- Specify a base case CV mean, usually its current value.
- Determine the location and size of any profit cliff in the neighborhood of the limit and maximum theoretical profit at the limit.
- Set process model requirements for credits as each CV approaches its limit and the damage when it is exceeded.
- Specify economic gain sensitivity to approach the limit and loss when exceeding the limit.
- Determine the steady-state profit rate vs. CV/KPI mean. This is the financial sensitivity function. It is always shaped like a tradeoff tent, often with a discontinuous cliff in the neighborhood of a limit value.
- Determine the uncertainty in each CV/KPI; forecast its near term statistical distribution (some are not Gaussian) and variance. This is a primary duty of operators, before resetting setpoints.
- Calculate the expected value profit hill function vs. CV mean and locate the maximum hilltop CV and corresponding profit. Calculate best move size, expected gain and new expected percentage of spec violation.
- Study best mean change and corresponding profit gain from the suboptimum base to the optimum settings for reasonableness and merit. Specify the specific IT information needed for setpoint decisions, optimization of risky tradeoffs and holding HPI operations at maximum profit rate.
- Alter any input assumptions and resolve for a new optimum mean and corresponding profit change. Implement the change and confirm the results
Uses – Here are important applications.
- Deploy a comprehensive, holistic method for tracking correct information (the truth) for every significant CV/KPI.
- Set operating conditions to maximize risky tradeoff expected value profit.
- Several applications have been solved: low sulfur fuel oil sulfur6, vessel vapor velocity13, alky DIB nC4in top iC414, olefin plant7 C3 in C3=, ACU cuts and FCC regenerator slide valve pressure drop.
- Design and use profit meters for every CV/KPI of interest in operating management dashboards. Align HPI operations to its economics. This is worth >$1/barrel crude for oil refineries2, 8, 12, 13.
- Learn from your history to forecast near-term CV variance to manage risks mathematically.
- Determine the incentive and value of IT with the mathematical proof that acting on correct data always maximizes profit; acting on incorrect data always results in a quantifiable loss. This was first done for AGO pour point2.
- Provide a clear justification tool for components such as instruments, analyzers, laboratories, control algorithms, models, computers, actuators, valves, and alarm shutdown systems, that can specify their performance contribution to CV performance above their competitor or existing components. Control components must reduce CV variance or improve accuracy. IT components must improve tradeoff profit function accuracy.
- Measure financial performance of installed solutions and alarm systems so they can be licensed fairly based on shared-risk and shared-reward business partnerships to maximize expected value profit to both operating company user and technology solution sustainer.