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Lab-scale Automation of Anaerobic Digestion with Model Predictive Control

Stellenbeschreibung

BACKGROUND:
To safeguard electricity production from biogas plants via anaerobic digestion (AD), novel monitoring and control approaches for AD plants need to be investigated. Our working group has developed an automated feeder that allows for dynamic feeding schedules and varying substrates for labscale AD reactors. Moreover, rich software has been developed on model predictive control (MPC), multirate extended Kalman filtering (MR-EKF) and parameter identification (PI), which delivered promising results in simulative studies and initial experimental trials.
This internship aims to integrate all software and hardware components and demonstrate model-based monitoring and control of a lab-scale AD reactor on the automated feeder. As an intern in our working group, you will be thoroughly guided in understanding the underlying AD process model as well as the methods used in the software on your own. You will thereby gain hands-on experience for your coding skills, solidify your expertise in practical lab work and deepen your knowledge of the AD process.

YOUR TASKS:

  • Get familiar with existing hardware (automated feeder, experimental setup, data) and software (MPC, MR-EKF, PI, AD model)
  • Fine-tune and debug MPC setup for a virtual gas storage and multiple substrates
  • Fine-tune data base for online and offline data storage, run PI to obtain optimal model parameters, and fine-tune MR-EKF for state estimation
  • Coordinate experiments for proof-of-principle of the automated feeder
  • Document the implementation and results in a report/thesis

OUR EXPECTATION:

  • Perseverance and intrinsic motivation
  • Solid knowledge in math and control theory
  • Elementary knowledge in Matlab and python programming
  • If possible: experience in mechatronics and data handling

WE OFFER:

  • A good introduction to the topic as well as competent and motivated support in the processing of the tasks
  • A family-friendly, modern working environment in a collegial working atmosphere
  • Good public transport connections