Process Research and Intelligent Systems Modeling Group · for a liquid reactor (changes with...

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PRISM Group APMonitor.com prism.groups.et.byu.net

Process Research and Intelligent Systems Modeling Group Process Systems Engineering

Department of Chemical Engineering, Brigham Young University, Provo, UT, 84602

Dr. John D. Hedengren

Department of Chemical Engineering

john.hedengren@byu.edu

350 Clyde Building

Brigham Young University

Provo, UT 84602

Boundary Management interfaces to live systems to

provide advanced diagnostics, meet safety and

environmental constraints, and drive the process to

economic optimum. With changing boundary

constraints, this enables instant and continual

realignment to real operating objectives.

Boundary Management may include fixed or dynamic

constraints. Fixed constraints may include pressure relief

valve burst limits, temperature limits for catalyst deactivation,

or environmental emissions levels. Dynamic constraints may

change constantly. A couple examples include bubble point

for a liquid reactor (changes with composition), operating

conditions for hydrate formation in upstream production, and

start-up or shut-down constraints. Dynamic boundary

management requires a modeling platform for rigorous or

empirical process models, even for large-scale or complex

systems.

Unmanned aerial systems may consist of multiple agents with

coordinated actions. Modeling and control of the system leads to

increasingly complex systems that may be posed as an optimization

problem. In collaboration with BYU’s MAGICC Lab, the groups are

solving the problems with APMonitor modeling language to demonstrate

that complex dynamic systems can perform coordinated and optimal

actions to achieve a shared objective.

One of the major drawbacks to solar

or wind energy is the intermittent

nature of the supply. Energy storage

allows an intermittent source of

energy to be harvested and re-

distributed in accordance with some

demand schedule. The aim of this

research is in creating models and

novel algorithms for Model Predictive

Control and optimization of energy

generating systems such as combined

heat and power plants (CHP) and

solar power plants. The optimization is

targeted at better integrating energy

storage, demand and weather

forecasts, and economic constraints

to increase profitability and energy

conservation.

“Developments in optimization and advanced

control of energy generation systems will

enable the launch of a “smart grid” to bring

the nation’s electricity delivery system into the

21st century” – US Department of Energy

Jose Mojica

MS student

A solid oxide fuel cell (SOFC)

produces electricity directly

from oxidizing a fuel at high

temperatures. The largest

challenge is preserving oxide

integrity and fuel cell lifetime.

SOFC modeling is used to

maintain performance and

operational integrity subject to

load-following, efficiency

maximization, and

disturbances.

Lee Jacobsen

BS graduate

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Biomedical research has generated

a vast amount of experimental data

that can reveal reaction pathways,

molecular transport, and population

dynamics. While simulations of

these biological systems have been

successfully applied for many years,

the alignment to available

measurements for complex systems

continues to be a challenge.

The aim of this project is to apply the

most advanced solution techniques from

the hydrocarbon processing industry to

computational biology by using

APMonitor, a cutting-edge software

package used by petrochemical

companies for large-scale models of

differential and algebraic equations

(DAEs).

Casey Abbott

BS student

David Grigsby

BS graduate

Reza Asgharzadeh

PhD student

James Memmott

BS student

Trevor Slade

BS graduate

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Petroleum exploration and production critically

depends on pipelines to bring oil from remote and

often inhospitable locations in a safe and

environmentally friendly manner. To ensure the flow

of these valuable resources, advanced process

monitoring promises a real-time holistic view of

critical data and provide smart notifications for early

leak detection, reductions of unplanned shutdowns,

and abnormal situation management .

The APMonitor Modeling Language is optimization software

for differential and algebraic equations. It is coupled with

large-scale nonlinear programming solvers for data

reconciliation, real-time optimization, dynamic simulation,

and nonlinear predictive control. It is available as a web

service through APM MATLAB, APM Python, or with a

browser interface at http://apmonitor.com.

A gravity-drained-tank model predictive control (MPC)

application is being developed to demonstrate an

improvement over Proportional-Integral-Derivative (PID)

controllers that are ubiquitous in industrial practice.

Process Research and Intelligent Systems Modeling (PRISM) is developing a

world class collaborative research group for the application of innovative

advanced process control and optimization techniques. This involves the

development of novel algorithms and techniques for large-scale and complex

systems. These applications improve safety, reduce environmental impact,

investigate new drug treatments, and maximize profitability.

BYU is the lead institution for the Center for

Friction Stir Processing, a multi-institutional

National Science Foundation Research

Center (I/UCRC). The PRISM group is

collaborating with the CFSP to provide

advanced modeling and control technology

for improved start-up, temperature, and

depth control of Friction Stir Welding. Gravity-drained Tank Model

Boundary management case study for thermal oxidizer

Deep water fiber optic sensor for flow assurance monitoring

Off-shore platform fiber optic sensor

installation

Friction stir welding tool in operation

Multi-scale Modeling of the SOFC system

Schematic of potential components in a “smart” grid operation

Schematics of a highly integrated CHP plant (top),

and solar-thermal power plant (bottom)

Friction stir welding tool

Abraham Martin

BS student

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