PTQ Q3 2022 Issue

FCC unit yield improvement with artificial intelligence How closed loop neural networks have improved FCC yields via direct control and continuous optimisation

Geraldine Hwang and Abishek Mukund Imubit

R efineries have 10 or fewer key operating strate - gies that, when executed efficiently, maximise plant value generation. Yield improvement on a fluidised catalytic cracking (FCC) unit is one of the most profitable operating strategies in a refinery by optimally converting low-value gasoil and sometimes resid into high-value gaso - line, olefins, and diesel. However, this operating strategy is also one of the most complex. The FCC process is highly dynamic, with an intri - cate energy and mass balance. Dynamics are also impacted by unmeasured disturbances such as catalyst activity and equipment fouling. Key mechanical limits, such as regener - ator (‘regen’) temperatures and slide valve differential pres - sures, and environmental limits, such as regen CO, need to be respected and controlled to ensure unit availability. In addition, the FCC is heavily integrated into the entire refinery system, facing impacts of feed composition changes from the crude vacuum tower as well as impact - ing downstream units and integrated utilities. While stay - ing within operating limits and balancing unit dynamics, decisions need to be made to generate the most profitable yields with the most economical handles, such as total feed rate and conversion by manipulating catalyst circulation. Plant experts understand the complex reality of the bespoke FCC description. However, traditional solutions cannot solve this problem entirely, forcing domain experts to break down the FCC operating strategy into distorted fragments of the true problem. Conventional strategies Common FCC operating strategies are defined based on an economic objective function or pushing to a downstream constraint such as alky feed operating limits. In either form, knowing where to set conversion handles, such as reactor overhead temperature (ROT) and feed rates in the dynamic environment with changing constraints, becomes critical to maximise profit. However, as domain experts do not have a solution that can take on the complexity of the FCC, experts are forced to break down the problem into components: economics, pro - cess, automation, and execution. Economics are managed by planning and economic groups, where they define the optimal targets for key handles such as feed and ROT on a weekly basis after running the linear program (LP) over several cases.

Process engineers focus on unit-specific engineering limits, helping to model unmeasured limits such as catalyst activity and manage local constraints with the use of first- principle simulations. Operators focus on safe and stable operation, using rules of thumb and heuristics to keep units steady while executing operating orders as a secondary objective. Finally, process control groups focus on automating the execution of operating orders while managing stable con - trol using technologies like advanced process control (APC). Each expert group is solely focused on their fraction of the objective, viewing the FCC through the lens of their siloed traditional approaches. Traditional approaches distort the true problem by imposing theory-based, linear, and strati - fied time assumptions. Capturing unrealised value Experts know that the FCC needs to encompass all these components to drive to optimal, so they attempt to recon - nect their sub-problems through communication and manual execution of operating orders in a weekly struggle. However, the cycle always ends with the FCC far from opti - mal. Plant expert groups waste time and resources, leaving millions of dollars of unrealised value potential. Proven in industry over the past several years, a spe - cific artificial intelligence (AI) process optimisation solu - tion called closed-loop neural networks (CLNN) has been able to model the entire FCC operating strategy to capture previously unrealised value for refineries. CLNN have three critical differentiators that enable plant experts to optimise operating strategies. First, CLNN holistically model the true dynamics of the FCC using deep learning trained on years of plant histori - cal data. Second, the CLNN model trains over millions of virtual simulations to master control and optimisation of the FCC, acquiring human-like intuition, which it applies dur - ing direct control of the plant once implemented on-site. Finally, the solution allows all plant experts to understand the model, representing a single view of the FCC operating strategy but encompassing each plant group’s perspective. Applying CLNN to an FCC A refiner with a 90-105 kbd FCC traditionally optimised their FCC operating strategy based on a weekly execution

PTQ Q3 2022