Hydrocarbon Processing - February 2021 - 36

Digital Transformation

Months
Weeks
Economic objective function

Enterprise
Real-time product prices

Days
Controlled operational
variables
Hours
Manipulated
variables
Minutes

Seconds

Facility
Unit operational
constraints
Plant
Unit
Manipulated
variables
Loop

FIG. 2. Closed-loop interconnecting DNN.

A closed-loop DNN for interconnecting process optimization. There exists only one type of AI model capable of

capturing these tens of millions of dollars of lost annual margin
per plant: a closed-loop deep neural network (DNN) (FIG. 2).
The DNN receives controlled operational and economic variables, real-time product prices and operational constraints,
and then directly manipulates the critical variables. This DNN
is a closed-loop interconnection between planning and economics, process engineering, process control and operations.
Over the past decade, DNNs have shattered through stateof-the-art benchmarks in areas such as imaging, text and
speech. Recently, Google's DeepMind has used DNNs to
solve the 50-yr fundamental computational biology mystery
of protein folding prediction-a medicine breakthrough for
mankind, potentially enabling drugs for diseases that include
Alzheimer's, Parkinson's, diabetes, cancer and more. Applying DNNs in closed-loop control-a field called deep reinforcement learning (DRL)-exceeded researchers' expectations in 2016 by defeating the world champion in the board
game known as " Go. " Self-driving car control, one of the most
challenging and complicated control problems, is also being
solved by DRL.
The closed-loop process optimization DNN manipulates a
small number of key process parameters at the APC and PID
layers. It controls critical process constraints and optimizes to
economic objectives. This translates to millions of dollars per
major refining process unit or chemical plant.
The DNN uses historical process data to learn subtle nonlinear dynamics between selected variables. In many cases,
several process vessels reside between the DNN input and
output variables. Each relationship between these key variables is not consistent or self-contained. These relationships
are each affected by dozens of other plant variables that the
DNN must account for.
The DNN can compensate for the lack of real-time information about feed composition by using pressure, temperature, flow and level indications throughout process units.
Slight disturbances in the (unmeasured) feed composition
36 FEBRUARY 2021 | HydrocarbonProcessing.com

create subtle patterns across these process variables. These
patterns are picked up and learned by the DNN. They are then
used in real time to determine how key handles should be manipulated to optimize economic objectives while accounting
for feed composition changes.
Aligning and interconnecting stakeholders through AI.
The closed-loop DNN crosses process unit and plant area
boundaries. It flows through the traditional layers and interconnects all the various teams. This allows everyone to speak
the same language and to drive toward a common economic
goal. The DNN does not necessarily need thousands of input
and output variables. It manipulates carefully selected key
variables by interconnecting real-time product prices with
process constraints and economically critical properties.
Instead of each team managing its own model in a discrete
stack layer, all the various disciplines interface with the closedloop DNN. Planning and economics feed real-time product
and feedstock prices directly into the neural network. Optionally, LP model outputs are also fed into the DNN. Process
engineers input true unit operational constraints, along with
known process relationships. Process control engineers manage the interactions between the DNN and the existing APC
and DCS. Operators interact with the DNN on a 24/7 basis,
constraining it to their desired bounds and limits in real time
to allow for a safe and compliant closed-loop operation.
Takeaway. A stack of simplified layers has formed over decades to tackle the considerable complexity of refinery and
chemical plant control and optimization. This traditional
stack makes thousands of ongoing decisions to run the plant
in a safe and stable manner. However, tens of millions of dollars in annual margins are being unrealized due to suboptimally operating the 10-15 most economically critical handles. To
capture these lost opportunities, a new closed-loop process
optimization model must work across the entire traditional
stack, interconnect different layers and different teams, capture nonlinear relationships, and compensate for missing
compositional information. Only a closed-loop DNN that
bridges silos and links disciplines (such as planning and economics, process engineering, process control and operations)
can accomplish this mission. This closed-loop DNN not only
has the input/output simplicity to focus on critical key variables, but also the internal complexity to capture severe nonlinear dynamics and to use subtle data patterns to compensate
for missing composition measurements. This interconnecting
closed-loop process optimization DNN is how AI is truly leveraged to capture the incremental margin opportunity of optimal continuous operation of key plant handles.
GIL COHEN is the CEO and Co-founder of Imubit. He has
led Imubit's product development and market adoption
by leading global refining and petrochemical operators.
Prior to Imubit, Mr. Cohen founded and led Cigol, a leading
provider of data center system-on-chip network security
solutions. He has published several academic papers on
ML and has more than 20 yr of experience in developing
new algorithms and applying them on previously unsolved problems in
various industries. Mr. Cohen earned BSc and MSc degrees in electrical
engineering, as well as a BSc degree in mathematics, all cum laude, from
Ben-Gurion University of the Negev in Israel.

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Hydrocarbon Processing - February 2021

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