At the current oil refinery, there is a unit called Fluid Catalytic Cracking. This unit has a very important role in an oil refinery. This unit converts crude oil into gasoline by cracking. in addition to producing gasoline, this unit also produces coke. This coke must be burned in the regenerator to restore the catalyst function so that it can be re-circulated. in this case, an Engineer wants to optimize the regenerator so that the amount of coke yield is always in the expected range. The expected target is the optimum operating conditions. Those parameters are Total Dry Air to Regenerator, HBW Flow, Regenerator Temperature, and Catalyst per Oil ratio.

Here, some variables of a Bioreactor to Produce Penicillin provided from a Pharmacy company. In Bioeactor, there are a lot of data produced by many sensors. But, not all sensors contribute for Bioreactor performance, furthermore, the data taken every second which means need a lot of storage to keep all data. Creating a model from all variables will spend a lot of time and long computation time. Hence, dimensionality reduction is the objective in this case.

At the current oil refinery, there is a unit called Fluid Catalytic Cracking. This unit has a very important role in an oil refinery. This unit converts crude oil into gasoline by cracking. in addition to producing gasoline, this unit also produces coke. This coke must be burned in the regenerator to restore the catalyst function so that it can be re-circulated. in this case, an Engineer wants to optimize the regenerator so that the amount of coke yield is always in the expected range. The expected target is the optimum operating conditions. Those parameters are Total Dry Air to Regenerator, HBW Flow, Regenerator Temperature, and Catalyst per Oil ratio

A main actuator of CO2 absorber found faulty due to broken stem and could not get fixed during online. Based on current history, the actuator still able to control flow of fluid, but opening of actuator sometime got higher and sometime got lower. On the other hand, the plant have schedule for Turn Around (Shutdown and do some maintenance) of several equipment. The dilemma is, if the actuator fail before planned Turn Around, the plant will shutdown for long time and costly. Hence, the prediction when this valve will fail became important to “decide” when the best time to schedule Turn Around. Hopefully, the plant could operate as maximum as possible to reach production target, delivery time for new set of actuator, manpower for maintenance but still in planned shutdown timeline. The objective of this analysis is to predict when the actuator will reach near 100% opening. At 100% the valve can not hold the fluid and all fluid drained from the absorber tower and plant control will trigger to trip/shutdown.

Consumer analysis of a retail company

Real-time Data-Driven Predictive “Digital Twin” of Propylene Glycol Plant

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