platform for advanced control and estimation (pace), plays
TRANSCRIPT
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Platform for Advanced Control and Estimation (PACE), plays a key role in Air Liquide's drive for Operational Excellence. A key component in Air Liquide’s Remote Operations Success is adopting PACE as their next
generation APC technology for Nonlinear State Space Model Predictive Control.
BRUCE ENG and ARNOLD (MARTY) MARTIN, Air Liquide.
FADY KANTARA and BRIAN BURGIO, KBC (A Yokogawa Company).
12-January-2021
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Table of Content:
00. ABSTRACT
01. INTRODUCTION
02. PROCESS OVERVIEW
03. ASU OPERATION & CONTROL CHARACTERISTICS
04. AL APC SOLUTION SELECTION & SUSTAINABILITY GOALS
05. PACE TECHNICAL BENEFITS
06. CASE STUDY
07. RESULTS
08. FUTURE OUTLOOK
09. CONCLUSION
10. Author(s) Bio(s)
11. REFERENCES / APPENDIX
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00. ABSTRACT (SUMMARY)
Multivariable Model Predictive Control (MPC) has helped Air Liquide (AL) for more than twenty years control Air Separation Units
(ASU’s). In the last few years AL realized that their legacy MPC software was becoming increasingly costly to maintain and was
not taking advantage of technological progress in the field. Platform for Advanced Control and Estimation (PACE) was selected to
be AL’s new standard MPC platform. A multi-year campaign is underway to deploy PACE at AL’s operational facilities
domestically and internationally. PACE’s unique and differentiating features are discussed with respect to how they solve many
of the challenges that were inherent in AL’s legacy MPC and how they are empowering AL’s control engineers to develop and
deploy better control of their ASUs. Improvements to project execution workflow, plant efficiency & economics, sustainability of
the benefits and higher operation acceptance through minimal need for operator intervention during steady-state and rapid plant
production ramping challenges and results are explored.
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01. INTRODUCTION
[1]Figure 1: Air Liquide’s Production and Supply Chain Business Overview
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Air Liquide (AL) is a world leader in delivering gas products, separation technologies and services to the chemical, energy, food,
metals, medical, health, and pharmaceutical industries. Oxygen (O2), Nitrogen (N2), and hydrogen (H2) are among the essential
molecules for life, matter and energy, which in turn are embedded in AL’s scientific core and area of expertise since it was
founded in 1902.
PACE is a modern platform for advanced process control (APC) and estimation that was co-developed by Yokogawa & Shell and
released for commercial use in 2015. PACE enjoys a prestigious lineage. It is the successor to SMOC (Shell Multivariable
Optimization Controller) which Shell has made extensive use of its own refineries, hydrocarbon crackers, polymers among other
chemical plants. It was re-built to overcome traditional APC challenges, harness advancements in computing power and to
leverage learnings gained over thirty years of MPC design and operational experience. Completing this has shown higher
operational acceptance because it gives flexibility to tailor and prioritize economics as well as giving operators better tools and
insights. The improved performance that is mainly due to the unique modeling powerful structure that combines over ten
advanced manufacturing software products into one platform with two flexible workspace environments: off-line Design Time for
design, testing and configuration of applications, and online Run Time HMI for the deployment in the field.
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• [2]Figure 2: PACE Technology Functional Overview, an “All in one integrated APC platform”.
AL has recently deployed PACE on several different production units including Air Separation Units (ASU’s), hydrogen + carbon
monoxide (CO) plants (aka HyCO), and power cogeneration units (Cogens) as part of a program to modernize their MPC
technology. This article will focus on the benefits of PACE when applied to ASU technology.
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02. ASU PROCESS OVERVIEW
An ASU separates air into its main components: O2, N2, and Ar - delivered in gas or liquid states. The liquid products are stored in
storage tanks then supplied to customers via trucks. Gas products are supplied to customers via gas pipelines in a circuit of
production plants.
[1]Figure 3: Simplified Diagram of an Air Separation Unit (ASU)
Electricity is the largest operating expense. The cryogenic ASU is energy intensive due to the use of large compressors for air, O2
and N2. A single large ASU can consume over fifty Megawatt/hour (MWh) to operate the Main Air Compressor (MAC), Booster Air
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Compressor (BAC) and product compressors. AL adapts to customer supply and real time energy pricing and uses several
optimization strategies to make production decisions to operate efficiently and consistently.
03. ASU OPERATION & CONTROL CHARACTERISTICS
A typical ASU is subject to several disturbances and production demands which must be controlled. These are:
● Changing production supply and demand
● Ambient weather conditions
● Front End Purification (FEP) cycle disturbances
Production Supply & Demands Variability
Overall production rate as well as product split is dictated by external factors such as customer demand and electricity price.
Failure to quickly adapt to production demands will either result in venting products and unproductive power consumption or
necessitating the vaporization of stored product - liquid oxygen (LOX) or liquid nitrogen (LIN) from tanks. This is illustrated in the
figure below:
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Figure 4: ASU Motivation for Production Rate Ramping up and down (i.e. min-max capacity).
Ambient Weather Conditions
Ambient air temperature and relative humidity are both typical disturbances to the ASU. Increasing air temperature will decrease
inlet air density. Since centrifugal and axial compressor flow laws operate on the actual volumetric flow through the compressor
stage inlets, this will result in a reduced molar flow of air. Similarly, when relative humidity increases, the dew point rises and the
cooling tower will produce hotter cooling water supply water which in turn will result in higher inter-stage cooling temperatures,
thereby reducing inlet density to later stages. MACs are equipped with inlet guide vanes in order to control these disturbances,
but at high rates or warmer air temperatures, these will be saturated open and the maximum air throughput into the plant will be
limited.
FEP Cycle Disturbances
The FEP cycle disturbances are a type of periodic disturbance in a typical ASU. The FEP unit contains two dryer vessels which
remove CO2 and H2O from the air to prevent it from freezing within the cold-box. These dryers operate in a cycle with one dryer
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online while the other dryer regenerates. Regeneration is done at high temperatures and low pressures which means that the
cycle affects the downstream process in a couple of ways. First there is the introduction of a heat bump caused by the enthalpy of
adsorption during re-pressurization when the regenerated dryer fist comes online. This heat bump changes the thermal balance
of the process, causing a disturbance. Secondly and more significantly there is a loss of air, which needs to be replaced by the
MAC and could cause a temporary decrease in product flows .
There is a wide range of dynamic response times within the ASU. Pressure effects are quick, thermal effects are slow, and the
accumulation of argon within the distillation section is extremely slow. These varying transient dynamic responses as well as
tightly integrated distillation columns and heat exchangers makes it challenging to efficiently control an ASU. Traditional
regulatory schemes for controlling product qualities, require excessive operator interventions especially during production rate
ramping of the plant.
Typical ASU MPC Objectives
1) Efficiently maintain product qualities within customer specifications.
2) Automatic & rapid plant load capacity changes.
3) Minimize operator interventions & reduce process variability for smooth autonomous plant operation.
4) Maximize argon by-product recovery yield and adapt for the argon down mode.
5) Optimize economic profit by changing production targets to adapt to meet customer demand at minimum operation cost.
6) Remote operational strategy – centralized command center for multi-unit pipeline dynamic optimization and unattended operation.
Table 1: Typical ASU MPC Objectives
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04. AL MPC SOLUTION SELECTION & SUSTAINABILITY DRIVERS
Model Predictive Control (MPC) also known as Multivariable Predictive Control (MVPC) technology is a type of APC technology
that is a solution to the control challenges of an ASU. AL began deploying MPC to ASUs in the 1990s, but not without some
technology limitations. Most of these limitations were overcome with custom scripting. Custom scripting provided a programmatic
way of customizing the MPC applications on each ASU site. Before the introduction of PACE, scripting was required to
supplement MPC systems to achieve the control custom functionality needed. Scripting had to reside outside the MPC
application, either in the DCS or another coding software added to the DCS logic. This introduced additional risk, was difficult to
do and time consuming to develop, also long term maintenance was challenging. AL’s legacy MPC applications employed
scripting to achieve many objectives including automated step testing, modification of models and tuning parameters based on
plant mode, custom control for specific scenarios like bed swapping, variable transformations and conditioning.
ASU’s Challenges to the legacy MPC ASU’s Solution from PACE
Highly integrated Energy & Mass Balance into cryogenic distillation columns in series.
User specified estimation and control model structure with intermediate models and calculated process output variables, to integrate and sequence global and component mass and energy balances.
Missing workflow with capabilities to better manage the “uncertainty” in model dynamics, nor the ability to explicitly structure the model with intermediates measurements based on process cause and effect knowledge.
Proactive model calibration approach which accounts for the user to specify the uncertainty factor for each SISO model dynamic. State estimation with intermediate variable modelling that handles intermittent quality measurements.
CVs with a wide range of different model dynamics and settling times.
Adaptive Dynamic Optimization based on an infinite horizon formulation, where the closed-loop response of MV and CV full non-linear trajectories are optimized until each of the CV models settles at its best operating value.
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Lack of tuning handles. Maximum moves over, used as a handle.
Simplified tuning handles with workable defaults. The tuning of the static is decoupled from the dynamic tuning handles. However, they both have their own X factor (tuning parameter), which are based on a dimensionless speed that is comparable to each other.
Many interacting software products needed for the development of an APC application adding complexity, and time of project execution.
All in one unified non-linear state space MPC platform with high-speed processors, data collector, online commissioning tools allowing very large applications up to 200 MVs, 200 DVs, 1000 CVs, 1000 POVs and 50 Economic Functions (EF) per controller, up to 15 processors per application, and 20 applications per APC server. [2] Note: The actual number of processors depends on the size of the processors, memory size and CPU performance of the APC Server.
The inability to formulate or control the speed of optimization in a simple manner, in a plant under constant change in operation mode and economic objectives based on customer demand and real time energy prices.
Formulation of multiple EF tailored to the business needs, with a priority rank against the CVs. Price updates are in real time and turn on or off from the APC HMI.
Table 2: ASU’s Traditional MPC challenges versus PACE Technical Solutions
A few years ago, AL undertook a project to modernize their legacy MPC system with one that better aligns with their sustainability
drivers:
● Avoid obsolescence of legacy software.
● Reduce required engineering support, while maximizing run time. i.e. eliminate need for excessive scripting.
● Provide Integrated workflow with better tools to reduce the cost and time of deployment at new sites or retrofits.
● State of the art algorithms to maximize process efficiency particularly at large production units and multi-unit sites.
● An industry standard software with global support so that AL can deploy a standard MPC solution in all geographies
thereby allowing different regions to share learnings, resources and reduce the overall cost of ownership.
● Versatile multi-functional integrated MPC platform that would complement and support AL’s remote operation strategy for
continuously optimizing production efficiency and supply chain reliability with minimal operator intervention.
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The result of AL’s evaluation and testing led them to select PACE as their MPC platform.
One differentiating characteristic of PACE is that the product is co-developed with Shell who uses it internally across their plants.
This provided AL a level of comfort that development was being guided by operational feedback and reduced cost of long term
ownership.
05. AL’s DEPLOYMENT EXPERIENCES
AL has been able to apply some of the new features of PACE to find new solutions to the traditional MPC challenges. These have
reduced ALs reliance on scripting, making the solution more sustainable. The features highlighted are the ones that AL has found
to be most valuable for the operation of an ASU but are only a small subset of the features available in PACE.
PACE MODEL STRUCTURE
One of the major differentiators between PACE and the legacy MPC software is the ability for the control designer to specify the
dynamic model structure to more accurately represent the actual system. AL’s legacy software only supported a single layer of
MV and DV inputs with dynamic FIR models identified as a First Order Plus Deadtime to CVs. However, PACE supports many
different structural components. MV to CV Models can be composed with a variable number of layered Intermediate Variables
(IVs). These IVs can be measured or calculated from a formula and can be controlled (have CV relationships and constraints
including high and low limits and setpoints) or uncontrolled. Additionally, models can either be dynamic SISO models built out of
fourteen different types of transfer functions or can be made of non-linear calculation blocks which can be used to represent like
product of flows and product composition ratios.
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[3]Figure 5: PACE seamless ability to integrate heat and energy balance in the model of an ASU
The model structures in PACE allow collaboration among plant operation, process and control engineers to be much more
expressive when modelling the behavior of the plant. In the past, it was common to find a single model between a flow SP and a
downstream product concentration purity. Representing the model of the plant in logical steps and sequences of unit operations
has several advantages. First, it improves maintainability: if the relationship between flow setpoint and the actual flow changes
due to re-tuning the PID loop, it is only necessary to re-identify this easy to find model. Second, it improves control performance.
If there is a disturbance to any of the measured intermediates (e.g. intermediate purity or measured flow), the controller will
correctly anticipate and make corrective control moves to reduce the impact on the final controlled variable. Base Layer Control
(BLC) is a special subset of the intermediate variable (IV) feature concept. It is designed to model the close loop behavior such
as the MV loop’s setpoint (SP) to process variable (PV) and SP to the output of a PID loop. These relationships are present
when MPC acts on top of PID loops in the DCS. PACE’s BLC handling adds additional benefits to a normal IV. BLC control
handles the saturation of a valve or final control element. For example, in a flow control loop, it realizes that if the valve is
saturated, increasing the flow SP will not result in additional flow (or any other process change for the matter). Instead PACE will
lower its SP to meet the measured flow even if other competing priorities call for additional flow. This has the advantage that the
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MV SP is not “wound up” with respect to what the control valve can physically provide. In the future, if it becomes necessary to
decrease flow, then PACE can do so immediately without having to counteract many moves of windup. This feature significantly
reduces the number of CV’s traditionally added for managing the MV’s output valve physical limits. Additionally, BLC control,
properly handles disturbances of the PV away from the SP. Unlike with a normal IV, PACE knows that even if it takes no action,
the PID controller (when not saturated) will move the loop output to bring the PV back to SP. Therefore, PACE adjust it’s move
plan accordingly and does not introduce a “double cascade correction” which is where both the outer (MPC) and inner (DCS) loop
of a cascade scheme react and over correct for a disturbance on a MV loop.
PREDICTION ONLY MVs
In AL’s legacy MPC implementation, it was often useful to specify that certain MVs would be used to control certain CVs. One
reason is that it is often the case that some models are better (more consistently represent the process) than others. It is
acceptable to use the imperfect MV-CV models as a feedforward disturbance, but better if the controlling MV has accurate
models. However, due to limitations in the previous generation of MPC technology, the way to make an MV act a disturbance to a
CV was to add a DV that was identical to the MV and add the relationship there (and delete it in original relationship), which
caused a major disadvantage: the future trajectory of the MV was then not used to predict the future of CV. Only past moves of
the MV were accounted for.
PACE provide the designer with the ability to select between “Full Control” or “Prediction Only” of the MV to CV relation for each
SISO model element. A MV set as “Prediction Only” for a CV, will be treated as a DV and will not be included in the controller’s
move plan for direct control of that CV, but it will be included in the estimation problem for the prediction of future trajectories.
This results in a major benefit during plant rate ramping (see figures in the result section). One typical production ramp strategy is
to use the air flow to control at the desired production rate and then use the gaseous oxygen (GOX) flow to maintain internal
component mole balances and product purities. In the legacy MPC, when a DV-CV relationship was created between the air flow
and the product purities, the GOX flow would not adjust quickly enough at the start of a ramp. This is because it had to wait until
the air moved to understand the future impact of the disturbance that the air would have on the product purity. By the time it
responded, the air had moved again. Therefore, it was necessary to create additional targets for the GOX flow and use scripting
to force it to move quickly enough. These scripts had to modify penalties and account for multiple corner cases and proved to be
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a maintenance and sustainability challenge. With the PACE “prediction only” feature, scripting is no longer needed. Figure 6
below shows the simulated result of choosing “Prediction Only” vs a traditional MV as a DV.
[3]Figure 6: Simulation of the Prediction Only control vs MV as a DV control
In figure 6, the steady state of GOX is immediately determined when the ramp starts and the controller moves the GOX a few
minutes sooner, rather than having to wait to see what the air is going to do in the MV as a DV case. With identical tuning, the
maximum potential impact of this disturbance was reduced by a factor of three. Since no scripting is forcing behavior, the MPC is
well informed during the ramp and free to manage other disturbances and constraints.
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Infinite Horizon Control Traditional MPC solvers usually are solving a Linear Programming (LP) problem for the best dynamic move plan of the
MVs based on a fixed total number (N) of maximum discretized number of elements or discrete points to represent the
near future. N is the maximum number of moves for each MV and with the legacy MPC it was limited to 10. Hence, the
legacy MPC was short sighted leading to a negative impact, especially for slow CVs. This limitation of the number of future
moves calculated by traditional solvers had many negative consequences, but two of the main ones are “jittery” control and
controllers that failed to control the faster CVs in the presence of slower CVs in the same controller. For this reason, using
the legacy MPC product required the use of two controllers on an ASU. These two sub-controllers had to interface with one
another and required additional steps to setup properly making it challenging and cumbersome.
PACE has a plant-wide estimation model that generates the open loop prediction for the control model layer to solve a sequence
of steady-state (where to go) followed by solving the dynamic optimization problem to determine the optimum move plan based a
full dynamic trajectory to steady state (how to get there). The output results in a set of optimum full trajectories of the MVs, CVs,
and EFs.
06. CASE STUDY
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[4]Table 3: Case Study Overview of Problem Statement, Solution & Results of PACE on an ASU.
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[3]Figure 7: Case Study Overview of PACE index-flow modeling with IVs that combined two controllers into one.
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To further illustrate usage of PACE within an ASU, the control of the argon sub-system for one particular ASU cycle is described.
For this ASU, a mixture of oxygen, argon, and trace N2 is removed from the low pressure (LP) column and fed into the Crude
Argon Distillation Column (CARC). The main measurement of this feed stream is the mole percent of O2 vapor stream (referred to
as “bubble” purity). Within the CARC, the argon is concentrated in the overheads, nevertheless this plant has an older column
design which is unable to eliminate the O2 in this crude argon stream. Therefore this stream is warmed close to ambient
temperatures (which is why this is known as a warm argon process) and then mixed with a nearly stoichiometric amount of H2
(targeting a small excess to ensure complete reaction) and then fed into a catalyst deoxidation bed reactor where the
hydrogenation exothermic reaction occurs: 2*H2 + 2*O2 ⇒ 4*H2 O + rx heat. O2 is the limiting reactant, hence the concentration of
O2 in the feed and maintaining a constant ratio of %O2 to excess %H2 is critical for controlling the kinetic rate of reaction and heat
generation for efficient performance. The produced water is then removed by dryer vessels and the product is re-cooled to
cryogenic temperatures. It is then sent to a final distillation step in the Pure Argon Distillation Column (PARC) where the bottom
stream is the pure argon product that gets stored into large product tanks.
[3]Figure 8: PACE Partial Model ASU’s Input and Output used in Case Study
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[3]Figure 9: PACE Graphical Model View (GMV) show improvements of the control model structure.
The graphical model view (GMV) in PACE enable users to seamlessly extract and share a graphical representation (block-
diagram) of a selected subset of the MPC model structure. GMV feature provide a graphical way to verify that the MPC scheme
and model structure are indeed an accurate representation of the cause-effect based on process knowledge. GMV is also used in
the project post-documentation deliverables (i.e. operation and engineering training manuals), which in turn reduce the overall
time to deliver a project and ensure the effective transfer of knowledge within AL.
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[3]Figure 10: Traditional (left) vs PACE (right) MPC Design Variables to enable explicit Plant Model Structural Design
On the left (before) is an illustrative example of the traditional MPC design variables (MV,DV,CV) available to structure the plant
model which generally do not provide the needed flexibility to accurately structure and represent the plant model
vs on the right (after) with PACE provides additional design variables called Process Output Variables (POV), Intermediate
Variables (IV), in addition to the traditional CV, MV and DV.
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[3]Figure 11: PACE solves for the Control Law internally and shows it in the convolution model layer.
PACE’s model structure accurately represents the system dynamics of distillation columns in series via the intermediate variable
modelling feature. In figure 7 to 11 above, the AIR and GOX flows are MVs to the Bubble_%O2 which is an intermediate quality
CV that is an input to the CAR column. The Bubble_%O2 CV is also set by the control designer to be an IV to the CAR_%O2 CV
(i.e. O2 concentration in the crude argon). PACE internally calculates the transfer functions between the IV’s inputs, to directly
relate them to the final output CV CAR_%O2. In this case study, by specifying the Bubble_%O2 to be an IV to the CAR_%O2 CV,
PACE internally identifies the dynamic relationship between the MVs (AIR_FC.SP and GOX_FC.SP) directly to the CAR_%O2
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CV. It infers the control law of the system, which was then displayed for the control designer to verify the intended process control
structure is correct.
The control designer can now structure the control model to ensure quality by design using the IV feature. PACE has the ability to
internally calculate the transfer function relating the inputs of IV to the output of its CV using the convolution integral theorem
where the transfer function is defined as the ratio of the Laplace transform of the output to the Laplace transform of the inputs.
This enables the control engineer to directly apply various engineering principles, thermodynamic laws and properties. For
example, a non-linear steady state temperature profile of the deoxo catalyst packed bed reactor can be directly calculated from
the heat of combustion of hydrogen, by incorporating the reaction kinetic rate laws when available, coupled to the global, and
component balance equations.
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07. RESULTS
[4]Figure 12: The before vs after steady state resulting profiles delivered with PACE on an ASU
The comparison of before and after project results shows argon yield improvement by 9% and a reduction of the standard
deviation of the outlet catalyst reactor bed outlet temperature by 70% from 45 to 14 deg.F, along with a 30% reduction in
standard deviation of the %O2 in the crude flow. The standard deviation on the argon yield CV has improved by 42%, and that is
on top of an average increase of 9% in GOX and argon recovery yields efficiency improvements.
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PACE’s Model Structure with Intermediate Variables have resulted in better model predictions leading to better control
performance and eliminating the need for operator interventions which has led to higher operational acceptance of MPC
operation. The improvement in the move plans smoothness and dynamic ability to adapt is clearly represented in the figure below
of the before vs after MV trajectory move plans.
Figure 13: [4] PACE’s ASU Result of the before & after Ramping Profile of the MVs & Waste CV
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[4]Figure 14: ASU Production Ramp Profile comparing the baseline with legacy MPC (left) vs (right) improvements achieved with PACE.
08. FUTURE OUTLOOK
AL’s MPC sustainability project originally envisioned a two-phase execution approach:
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● Phase I: would be a feature equivalent replacement of the legacy MPC at each
facility in order to deal with challenges around impending obsolescence.
● Phase II: would consist of going back to each facility that was migrated to PACE in
phase one and enhancing the control performance using the new technology’s
differentiating features.
Currently AL is still in phase I. However, in practice during each replacement project, AL have
learned new things and made improvements to the existing control schemes. PACE’s new
features are already showing tangible economic benefits. Therefore, phase two will consist of
cycles of continuous improvement to MPC performance for higher operation acceptance.
Additionally, although not expanded on here, PACE’s capabilities are proving applicable to
other AL technologies such as in hydrogen production and paying dividends there as well.
09. CONCLUSION
The promise of MPC is the ability to tell the MPC engine how the plant works and what the
goals and constraints are and let it worry about how to achieve these. AL’s legacy MPC
software was never able to get to this level, mostly due to limitations with the model structure,
solver, and tuning limitations that led the APC engineers to achieve their control objectives with
time consuming workarounds and extended maintenance support. With PACE, MPC is very
close to realizing this pure formulation for an ASU. Intermediate Variables, non-linear blocks
and BLC models make it possible to explicitly represent process knowledge, constraints and
non-linear economic objectives are easily expressed. With improved prediction only control and
an infinite horizon formulation, and a nonlinear state-space solver provides smooth, adaptive,
robust control even with frequent changes in operating modes and production rates. MPC is an
established technology that has proven to provide significant benefits to the Oil and Gas,
petrochemical and chemicals manufacturing industries. PACE improves capabilities and
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usability from an operation and a deployment perspective meaning the challenges of the APC
engineer are shifting from working around software limitations to focusing on providing value.
AL selected PACE as it overcomes the challenges posed in traditional MPC allowing them to
extend the boundaries of operation efficiency, reliability, and profitability.
09. Author(s) Bibliography(s)
1. Bruce Eng is currently an international expert in smart manufacturing and process control at Air
Liquide, with thirteen years of experience in MPC, procedural automation, and dynamic
simulation. He holds a B.S. in Chemical Engineering from Rice University, is a licensed
professional engineer and holds a patent in HyCO technology.
2. Fady Kantara is KBC’s APC Principal Consultant and leads the integration of the Platform for
Advanced Control and Estimation (PACE) technology across customer plant sites. Fady has over
a decade of experience in plant operation, control system design, commissioning & start-ups, and
specialized in delivering nonlinear advanced control and production optimization proven solutions
across many process technologies and automation platforms in the chemical manufacturing
industry. Fady holds a master’s in chemical engineering from the University of Massachusetts.
3. Brian Burgio is KBC’s APC Program Manager. He has over thirty years of international
operational improvement experience in the chemical, petrochemical and refining industries. He
has spent most of his career in the automation area helping clients drive maximum profitability
from their assets . Brian has a B.S. in Chemical Engineering from Georgia Tech, Master of
Engineering Management from University of Tennessee and an MBA from Vanderbilt University.
4. Arnold “Marty” Martin, PE, CAP is currently an international expert in smart manufacturing and
process control at Air Liquide and Director of Process Control Technology. He has over thirty
years’ experience in continuous and batch operations, MPC and operations technology. He was
honored by Smart Industry as one of the top 50 Digital Innovators for 2017 . Marty earned a B.S.
in Chemical Engineering from the University of Maryland.
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10. REFERENCES
1. Air Liquide Reference Public Document 2018, p. 20-23
2. PACE General Specifications (002) GS 36J06D01-01EN 2018 by Yokogawa Electric
Corporation.
3. PACE Design-Time V5.20.02 by Shell & Yokogawa Electric Corporation
PACE Run-Time V5.20.02 (APC HMI) by Shell & Yokogawa Electric Corporation
4. Project Results from PACE deployment on Air Liquide’s Air Separation Units
11. ACRONYMS
● APC: Advanced Process Control
● MPC: Model Predictive Control
● MVPC: Multivariate Predictive Control (same as MPC)
● MIMO: Multiple Input Multiple Output
● SISO: Single Input Single Output
● PACE: Platform for Advanced Control, & Estimation
● PACE-RT: PACE Run-Time Online Workspace for Configuring APC HMI
● PACE-DT: PACE Design-Time Offline Workspace
● SMOC: Shell Multivariable Optimization Controller
● DCS: Distributed Control System
● PID: Regulatory Control Loop Standard Object (Proportional, Integral, Derivative)
● HMI: Human Machine Interface (i.e. APC HMI or DCS HMI for operating the plant)
● VM: Virtual Machine
● MV: Manipulated Variable by MPC
● DV: Disturbance Variable in MPC
● FF: Feed-forward proactive control
● CV: Constraint Controlled Variable by MPC
● POV: Process Output Variable (new Type of MPC design variable in PACE)
● IV: Intermediate Variable (i.e. new Type of MPC Feature specific to PACE)
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● BLC: Base Layer Control (i.e. DCS Regulatory Control
● EF: Economic Functions for optimization within PACE
● PV: Process Variable (current measured value from a process instrument)
● OP: PID Control Loop Output Variable
● SP: Set Point (Target) for a PID loop or MV or CV; i.e. MVSP
-----------------------------THE END & THANK YOU-----------------------------