Department of Chemical and Metallurgical Engineering

Process control and automation

The group led by Prof. Francesco Corona and Prof. Iiro Harjunkoski is a trans-disciplinary research team that builds up on chemical engineering, applied mathematics and computer science. Our research is at the interface of automatic control, machine learning, and optimisation, we emphasise the computational and inferential thinking of process systems.
CMET_PCA group

We develop fundamental methodologies and concrete tools of process systems engineering that are capable of sensing, learning, reasoning, and acting on chemical and physical systems based on observational data and domain knowledge.

The research develops on four foundational pillars:

  1. Data
  2. Phenomenological and probabilistic modelling
  3. Statistical inference/learning
  4. Optimal decision/control

The formal framework for modelling and control is given as a model of the system in which the uncertainties associated with our knowledge and the measuring process are clearly stated.

An important objective in our research is the design and learning of flexible models that can capture complex dynamics.

Open positions

Doctoral students - Two positions in probabilistic modelling and stochastic control of chemical and biochemical systems

Master's students - Two positions in dynamical simulation and identification of dynamical systems

  • Email Francesco Corona for details

    Latest publications:

    Large-scale selective maintenance optimization using bathtub-shaped failure rates

    Teemu Ikonen, Hossein Mostafaei, Yixin Ye, David Bernal, Ignacio Grossmann, Iiro Harjunkoski 2020 Computers and Chemical Engineering

    Reinforcement learning of adaptive online rescheduling timing and computing time allocation

    Teemu J. Ikonen, Keijo Heljanko, Iiro Harjunkoski 2020 Computers and Chemical Engineering

    Industry 4.0 based process data analytics platform

    James Clovis Kabugo, Sirkka Liisa Jämsä-Jounela, Robert Schiemann, Christian Binder 2020 International Journal of Electrical Power and Energy Systems

    Optimization of multi-plant capacitated lot-sizing problems in an integrated supply chain network using calibrated metaheuristic algorithms

    Maryam Mohammadi, Siti Nurmaya Musa, Mohd Bin Omar 2020 International Journal of Operational Research

    Performance analysis of waste-to-energy technologies for sustainable energy generation in integrated supply chains

    Maryam Mohammadi, Iiro Harjunkoski 2020 Computers and Chemical Engineering

    Sustainable Bio-based Waste to Energy Supply Chains with Centralized and Decentralized Processing Networks

    Maryam Mohammadi, Iiro Harjunkoski 2020 4th SEE Conference on Sustainable Development of Energy, Water and Environment Systems

    Data-Driven Approach to Grade Change Scheduling Optimization in a Paper Machine

    Hossein Mostafaei, Teemu Ikonen, Jason Kramb, Tewodros Deneke, Keijo Heljanko, Iiro Harjunkoski 2020 Industrial and Engineering Chemistry Research

    Control Strategy of a Multiple Hearth Furnace Enhanced by Machine Learning Algorithms

    Jose Gomez Fuentes, Sirkka-Liisa Jämsä-Jounela, David Moseley, Tom Skuse 2019 4th Conference on Control and Fault Tolerant Systems (SysTol)

    Decision-making of online rescheduling procedures using neuroevolution of augmenting topologies

    Teemu Ikonen, Iiro Harjunkoski 2019 Proceedings of the 29th European Symposium on Computer Aided Chemical Engineering
    More information on our research in the Research database.
    Research database
    • Published:
    • Updated:
    Share
    URL copied!