Process Modeling and Optimization Engineer

KeyLogic; United States; Remote; USD 70,000 - 110,000; Full time

Job summary

An established industry player is seeking an entry to mid-level process modeling and optimization engineer to join their dynamic team. This exciting role involves developing advanced mathematical models and applying optimization techniques to enhance energy systems. You will collaborate with experts in scientific computing and process systems engineering, tackling complex challenges in simulation and modeling. Ideal candidates will have a strong background in chemical, mechanical, or computer science engineering, with experience in programming and commercial modeling tools. Join a forward-thinking organization dedicated to innovation in energy technology and make a significant impact in the field.

Qualifications

• Entry to mid-level position requiring MS/PhD in relevant engineering fields.
• Experience in process modeling and optimization of energy systems is essential.

Responsibilities

• Develop rigorous mathematical models and apply optimization techniques.
• Conduct analyses in simulation, modeling, and optimization of energy systems.

Skills

Mathematical Modeling

Optimization Techniques

Statistical Analysis

Process Design

Simulation

Programming in Pyomo

Programming in Julia

Process Modeling

Education

MS in Chemical Engineering

PhD in Mechanical Engineering

PhD in Computer Science

Tools

gPROMS

Aspen Plus

Aspen Dynamics

Pyomo

GAMS

Job description

Process Modeling and Optimization Engineer

KeyLogic Systems Inc. has an exciting opportunity for an entry to mid-level process modeling and optimization engineer. KeyLogic is the prime contractor supporting the National Energy Technology Laboratory under the Strategic Analysis (SA) site-support contract. This position is intended to support the Process Systems Engineering Research (PSER) team under the Systems Strategic Engineering Analysis (SSEA) Directorate of NETL. The employee will work on projects within the PSER program, which is building and applying a next generation modeling and optimization platform to accelerate development of advanced energy systems. Primary responsibilities will include developing rigorous mathematical models of energy systems, applying mathematical optimization techniques, and/or statistical analysis of lab-scale, pilot-scale, or commercial-scale process data while fulfilling the following general functions:

• Conduct exploratory and applied analyses in the areas of simulation, modeling and optimization, scientific computing, and the science of flowing materials, including multi-phase and chemically reactive flows.
• Investigate theoretical and fundamental phenomena as necessary to support PSER needs and develop new concepts.
• Provide technical expertise in the areas of virtual demonstration, process systems engineering and optimization.

Position Requirements:

• Ability to pass any required background checks and obtain any necessary security clearances required by our clients.
• Entry– to Mid-level MS/PhD in Chemical/Mechanical/Computer Science Engineering with 0 – 10 years of experience in the process modeling of advanced energy systems.

The ideal candidate will have experience in one or more of the following areas:

• Development and utilization of rigorous steady-state and dynamic process models, including physical properties, thermodynamics and kinetics, utilizing at least one of the following classes of modeling platforms:

• Programming in open-source algebraic modeling languages such as Pyomo, Julia, CasADi, GAMS, AIMMS, AMPL, and/or MODELICA.
• Commercial process modeling tools such as gPROMS, Aspen Plus, Aspen Economic Analyzer, Aspen Dynamics, and Aspen Custom Modeler.

• Expertise in modeling complex energy system processes including:

• Process design (including cost estimation), synthesis, and intensification (including heat/mass integration).
• Process modeling and simulation (steady-state and dynamic).
• Uncertainty quantification (e.g., Bayesian approaches).
• Initializing and solving large scale mathematical programming problems.

• Familiarity with the development of reduced order models (ROM) to facilitate multi-scale simulations.
• Training neural networks, surrogate models, black box models, and use of trust regions methodologies for optimization.

• Modeling of advanced and integrated energy systems.
• Collaborating with experimentalists or process/equipment designers to develop and validate models.
• Experience using scientific and high-performance computing.