The Applied Modeling and Control research group (AMOC) performs research on sensor technology, industrial IT, model development with main emphasis on dynamic models, and model-based analysis and synthesis.

Analysis and synthesis include computational tools and simulation, optimization, design of control systems, monitoring and fault detection. One of our main focus areas is cross-utilizations of methods across different applications.

These research areas are important for digitalization, optimization and/or sustainability of industrial processes. Digitalization is also important for many other areas in today’s society.

AMOC co-operates with other research groups at USN regarding applications. It is possible to be an active participant in AMOC, and simultaneously be active in other research groups at USN. AMOC will carry out separate application studies that do not have a natural place within other research groups at USN. AMOC will co-operate with external partners on applications.

Important activities for AMOC include an AMOC forum, an annual AMOC workshop, increasing publication activities, and the establishment of research projects.

Research disciplines

Core disciplines for AMOC are within:

  • Sensor technology and industrial IT
  • Development of (dynamic) models:
    • experiment design
    • empirical models/regression models/machine learning models
    • mechanistic models
    • model fitting, parameter estimation

Models will both be for off-line use (design) and for on-line use:

  • Tools for simulation and analysis
  • Optimization methods
  • Design of control systems
  • Design of estimation and monitoring systems
  • Fault analysis
  • Model predictive control (MPC)
  • Predictive maintenance
  • Digitalization

Teaching activities within these research disciplines.

Some of the application areas:

  • Monitoring, control and optimization for the process industry
  • Energy management
  • Welfare technology

Research partners and network

Important partners/network include:

  • NTNU, Trondheim: control engineering.
  • UiO, Oslo: machine learning/artificial intelligence.
  • Imperial College London: petroleum technology.
  • MIT: scientific machine learning tools.
  • Linköping University: simulation tools.
  • NTUU «KPI», Kiev: energy in buildings, smart grid.
  • SINTEF Digital, SINTEF Industri: petroleum production simulation.
  • Equinor Research Center, Porsgrunn: offshore applications of modeling and control.
  • Kelda Drilling Control AS, Porsgrunn: offshore applications of modeling for control.
  • Kelda Automation Technology AS, Porsgrunn, industrial IT.
  • Kelda Kraft AS, Porsgrunn, smart grid, predictive maintenance.
  • Kongsberg Digital: petroleum production simulation.
  • Skagerak Kraft AS, Porsgrunn: applications of modeling, control, and optimization within hydro power production.
  • Statkraft, Oslo: applications of modeling, control, and optimization within hydro power production.
  • HydroCen FME, Trondheim: applications of modeling, control, and optimization within hydro power production.
  • Norwegian Institute of Public Health, Oslo: systems biology, PBPK.
  • Bamble, Porsgrunn and Skien municipalities: welfare departments.

Research projects

Some examples of on-going RnD projects:

  • Semi-kidd (2016-2020): oil/gas drilling models and sensors for kick/loss detection
  • HEFTY (2017-2021): population balance models and control for fertilizer production
  • SAM (2019-2023): “Self-Adapting Model-based system for Process Autonomy”
  • DigiWell (2020-2024): oil/gas production modeling, control, and optimization
  • A Modelica based simulation library for hydropower systems: OpenHPL (2016-)
  • Use of health data and AI to warn of negative health trends and prevent lifestyle diseases (2023-)
  • Energy use in buildings (2013-)
  • Flood management (2013-)

Group leader