PTQ Q3 2022 Issue

Dry gas

Manipulated variable Controlled variable Objective function term

OBJ

PPs

OBJ

dC3

Reactor

Naphtha to NHT

Cold feed

OBJ

Riser

Main frac

LCO

BBs

OBJ

HVGO

dC4

HCO

Resid

OBJ

Alternative feed

Regen

Slurry

OBJ

Resid storage

Gasoline

OBJ

Figure 1 Schematic of FCC CLNN key variables and objective function terms. Key MVs, including total feed flow, feed preheat temperature and ROT, are used to control to key constraints, including regen bed temperature, regen pressure, slide valve dPs and wet gas compressor limits, and to optimise to the overall objective function, which is a function of feed and product yields

cycle running the LP solver and process engineering simu- lations to give operating orders to operations that use APC. It should also be noted that the FCC was fouling at an accelerated rate. The refiner measures success through an economic objec - tive function, classically calculating the difference between the value of products made minus the value of feed used. Every week, the planning and economics team runs its LP and makes a binary decision: either feed over conversion or conversion over feed. Based on market economics for the refiner, the LP typi - cally chose feed over conversion. The planning and econom- ics team then defines a total feed rate target for operations that slowly change the upper limit in APC to reach the total feed rate target. The planning and economics team felt it was most profitable to push resid into the FCC. To the best of their ability, process control engineers define a set of operating rules for the APC to drive towards • Maximise resid rate to the regen bed temperature limit • Use ROT to manage key constraints such as slide valves and wet gas compressor (WGC) limits. Knowing the APC programmed decisions, the process engineer and operators then change the ROT bounds and feed rate sources setpoints manually to manage con- straints, attempting to manage new limits associated with unit fouling and ensuring the APC will maximise feed to the upper bound. While manual execution of simplified rules attempts to execute the operating strategy, it unintention- ally destroys value. Every move made on critical handles on an FCC impacts critical constraints and the economic value by changing the operating orders. The rules are as follows: • Maximise total feed rate to the upper bound

yields. This problem gets exponentially complex when mul- tiple handles share common constraints and impact yields. For example, feed rate, ROT, and feed compositions all share relationships with the products made and the WGC suction limit. In the traditional approach, the LP models relationships between key handles and yields but does not account for constraints at the granularity of the APC. However, the APC does not understand the relationship between key handles and yields, relying on the LP targets and theoretical engineering models to give that information via targets and rules. To capture full value, a dynamic process model must model both yield and operating limit relationships with key handles in a holistic process model (see Figure 1 ). Dynamic process model reflecting reality Traditionally, online step-testing is performed for the key process relationships to build a steady-state gains matrix between independent and dependent variables within the APC. A gain is defined as the magnitude of change in the dependent variable relative to a unit change in the inde- pendent variable. The corresponding process model is the response of the dependent variable to the independent variable move. An experienced process control engineer assesses a dozen or so steps and uses their knowledge of the unit to define the gain and process model for the APC. The rela - tionships identified during step-testing usually produce a single gain that best generalises the relationship. The gains matrix also only includes the key relationships defined by a process control engineer, who is constrained by APC limi- tation of linear modelling and the ability to see clear, inde- pendent steps in the process data.

PTQ Q3 2022