BioComp's Advanced Process Analytics Can Cut Product Variance in Half.

Product conformance to specification increased from 53% to 95%

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Reducing variance is, of course, essential to making quality products because your customers expect your products to conform to their specifications.  It is likely you have worked hard to tighten your product variance, but it is equally likely that there is still some room for improvement.

Variance creeps into your products from a variety of sources:

• Raw materials from suppliers
• Tolerances and variances in manufacturing processes
• External environmental factors that push and shove your process
• Variances and the quality of your process control
• Variances in testing and measurement

You can use our software to reduce variance, either through advanced predictive control or mere data analysis of causes of variance.  The case below gives one example of many where the customer substantially cut their product variance through the use of our products and services.  Perhaps we can do the same for you.

CASE SUMMARY:

Company		Sarawak Shell (Shell Malaysia, a subsidiary of Royal Dutch Shell)
Product		Crude Oil
Location		Bintulu MLNG Processing Facility, North Shore of Borneo, state of Sarawak
Situation		Oil and gas comes onshore from platforms and the oil is sent to four stabilization towers.  These stabilization units further remove gas from the raw, fizzy oil in preparation for storage and shipment by tanker to Japan.  If the oil is stored with too much dissolved gas, the gas will come out of solution and tilt storage tank roofs, creating a significant problem.  If intermediate hydrocarbons (light oil) are removed as a gas and sent to the gas processing facility, they are condensed and returned with a penalty processing fee.  Additionally, there is an economic advantage to sell the intermediate hydrocarbons in the oil, as the value of oil is higher than gas.  The feed rate and the composition to the towers is uncontrollable, at the whim of what comes from the ground out on the platforms.  The process is reasonably complicated, containing high and medium pressure separators, feed splits, reboilers, recycles, etc. The customer wishes to produce oil at a "spot on" vapor pressure, +/- 0.25 psi of target but could only produce 53% of the product within specification.
Objective		Produce oil at a specified Reid Vapor Pressure (RVP) +/- 0.25 psi.
Method		Predictive system behavior models were built using historical data.  These models predicted RVP ahead by 15 minutes using 14-20 process variables, including tower temperature, the selected control handle.  Predictions were put on-line to create virtual sensors.   Model-based optimization schemes were created to control each tower temperature, within constraints, simultaneously considering the dynamics of the 14-20 other process variables.   The optimization schemes were implemented in closed-loop process optimizers to achieve the desired RVP 15 minutes in the future in real-time (multivariate predictive control).  This temperature was sent to a Yokogawa distributed control system as a setpoint.
Result		Product conformance to specification increased from 53% to 95% as depicted in this graphic:
Reference Available?		Yes.