Process control and automation
We develop fundamental methodologies and concrete tools of process control and process systems engineering that are capable of sensing, learning, reasoning, and actuating on chemical and physical systems based on observational data and domain knowledge.
The research develops on four foundational pillars:
- Data
- Phenomenological and probabilistic modelling
- Statistical inference/learning
- Optimal control/decision
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 control of models that capture complex dynamics.
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Research team members:
Related content:
Professorship strengthens the cooperation of ABB and Aalto in Industrial Internet solutions
Dr. Iiro Harjunkoski has been appointed Adjunt Professor at the School of Chemical Engineering.
Latest publications:
Block particle filters for state estimation of stochastic reaction-diffusion systems
A Bayesian inferential sensor for predicting the reactant concentration in an exothermic chemical process
End-effect mitigation in multi-period stochastic programming of energy storage operations
Likelihood Maximization of Lifetime Distributions With Bathtub-Shaped Failure Rate
Stochastic programming of energy system operations considering terminal energy storage levels
Evidence of waste management impacting severe diarrhea prevalence more than WASH : An exhaustive analysis with Brazilian municipal-level data
Combining Machine Learning with Mixed Integer Linear Programming in Solving Complex Scheduling Problems
Design of an Event-Driven Rescheduling Algorithm via Surrogate-based Optimization
Surrogate-based optimization of a periodic rescheduling algorithm
Cross-domain fault diagnosis through optimal transport for a CSTR process
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