Hydrocracker Yield Improvement

$8-12 MM

Value Generation
per annum

6 months

Project Timeline
kick-off to closed-loop

2 hrs/week

Internal Resources
per client group
The Problem & Deep Learning Process Control® (DLPC) Scope

A client running a 115 kbd hydrocracker identified the opportunity to capture untapped value beyond existing Advanced Process Control (APC) through optimal yield management. Prior to DLPC, APC maximized feed and optimized backend fractionation, but Weighted Average Bed Temperature (WABT) targets were manually adjusted through changes to APC limits based on weekly runs of the economic Linear Program (LP) model. However, the changes were only performed on a daily, if
not weekly, basis, leaving significant value on the table. Imubit’s DLPC manipulates 1st and 2nd stage WABTs to optimize product yields while respecting key process constraints to maximize the objective function. The DLPC is integrated with the existing APC to manage lower-level constraints.

figure: Client’s system configuration around FCCU and Crude with DLPC Design

FIGURE 1 Client’s system configuration around FCCU and Crude with DLPC Design.

Value Generation

With DLPC running in closed loop, a total annualized value of $8-12 MM (at 80% engagement) was captured over two months, in comparison to a baseline period with similar operational constraints. With new WABT targets calculated dynamically, DLPC was able to increase volume swell by 0.6% which translated to an increase in incentive by almost $1.5/BBL, on top of the existing APC. The yield shift illustrates DLPC’s ability to understand the dynamics of the hydrocracker and create the most optimal yields based on prices defined in the objective function.

Hydrocracker Case Study Fig. 2 &3

FIGURE 2 DLPC shift in product yields with
respect to client baseline APC data.

FIGURE 3 Increase in conversion % with respect
to client baseline APC data..

Realtime Performance

Prior to DLPC, the client’s APC relied on operators to manually adjust WABT targets based on operating orders as defined per the weekly LP run. While APC managed backend fractionation, it could not optimize the yield output from the reactors. Once DLPC is engaged, the WABT targets are consistently adjusted to capture the optimal yield from the dynamic feed composition and therefore maximizing the objective function. The objective function in this window of time increased by $20k/day.

Hydro case study Fig 4

FIGURE 4 (1) Before DLPC is engaged, WABT targets are infrequently adjusted. (2) Once DLPC is engaged, WABT targets are consistently adjusting to increase product yield and maximize the objective function. (3) DLPC is responding to disturbances in the process, while still maximizing the objective function.

Resources & Timeline
hydro case study timeline

FIGURE 1 Client’s system configuration around FCCU and Crude with DLPC Design.

DLPC development, which includes Scoping, Data Cleaning, Model Training, and client approvals throughout the entire process. Client resources during development include defining scope, sending data over to Imubit, and approving DLPC models during various milestones throughout Imubit’s Project Workflow. Commissioning requires the most amount of support as it includes implementation, operations training, and building confidence with entire organization. Once DLPC is in Closed Loop and performing, Continuous Improvement effort required is minimal and can increase based on the desire of the client to be more involved in the process of maintaining DLPC models.

Unlock your plant’s Untapped Value.

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