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M.Sc. Pourya Azadi

M.Sc. Pourya Azadi Photo of M.Sc. Pourya Azadi

(+49)231 755-6092

(+49)231 755-5129


Fakultät Bio- und Chemieingenieurwesen
Lehrstuhl für Systemdynamik und Prozessführung
Geschossbau 2

Room 617


Short CV

Pourya Azadi studied Chemical Engineering at the University of Tehran, Iran. He completed his B.Sc. with a thesis on “Kinetic Modeling of Environmental Pollutant Degradation under Visible-light using Photocatalytic Reactor of Modified TiO2”. Afterwards, he studied Process Systems Engineering (PSE) at the TU Dortmund University and received his M.Sc. in May 2017. He conducted his Master thesis in the Process Dynamics and Operations Group on “An Integrated RTO-NMPC Scheme under Uncertainties for the Optimal Operation of a Multiphase Homogeneously Catalyzed Process”. Additionally, he has industrial work experience in process systems engineering tool development, focusing on predictive data-driven modelling. Currently, he is working on modelling and energy-efficient operation of blast furnaces in the HyPro project, “Development of dynamic models for hybrid process control”.

Consultation Hour

By arrangement via E-mail.

Research Interests
  • Dynamic first principles and data-based modelling
  • System identification and parameter estimation
  • Process performance optimization
  • Real-time optimization
  • Model predictive control


HyPro: “Hybrid process control by the integration of model-based, statistical and empirical techniques for energetic optimization of complex processes”

Process control of many energy-intensive industrial processes like blast furnaces is still done manually by decisions of persons. The reason is that a lot of complex partial processes are running linked at the same time, and only simplified process models are available. The objective of the HyPro project is to solve this situation with a new hybrid conception and develop new strategies of process control with increased energetic efficiency.



  • Data-based Dynamic Modeling
  • Introduction to MATLAB
  • DYN11 Scheduling
  • DYN23 Learn to Control
  • DYN26 Learn to Control


  • Multivariable control
  • PSE Group project
  • DYN8 Flow Measurement


  • Data-based Dynamic Modeling
  • Introduction to MATLAB
  • DYN6a,b Control with PLC
  • DYN23 Learn to Control
  • DYN26 Learn to Control


  • PSE Group project
  • Process Performance Optimization
  • DYN3 Temperature Control
  • DYN10 Estimation of the heat of reaction and heat transfer coefficient


  • Data-based Dynamic Modeling
  • Single-loop and Multi-loop Controller Design
  • DYN21 Simulation of a tubular reactor
  • DYN10 Estimation of the heat of reaction and heat transfer coefficient


  • Control Theory and Application
  • DYN3 Temperature Control

Supervised Theses
  • Master thesis: Amitha Asokkumar - Model Predictive Control of Molten Iron Quality Indices in Blast Furnace Ironmaking using a Data-Driven Model



articleAzadi, Winz, Leo, Klock, Engell

A hybrid dynamic model for the prediction of molten iron and slag quality indices of a large-scale blast furnace

Computers and Chemical Engineering, 2021

proceedingsAzadi, Klock, Engell

Efficient Utilization of Active Carbon in a Blast Furnace through a Black-Box Model-Based Optimizing Control Scheme

16th IFAC Symposium on Advanced Control of Chemical Processes ADCHEM 2021, 54, 2021

proceedingsAzadi, Ahangari Minaabad, Bartusch, Klock, Engell

Nonlinear Prediction Model of Blast Furnace Operation Status

30th European Symposium on Computer Aided Process Engineering (ESCAPE30), 47, 2020

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