You will be working for the research group Biobased Resources & Energy within the center of expertise MNEXT – Material and Energy Transition. MNEXT is powered by the Avans and HZ Universities of Applied Sciences and helps businesses achieve their biobased goals by working with them to modernize higher professional education and to carry out applied research that makes a difference. The Biobased Resource & Energy research group is focused on valorizing residual streams from nature, agriculture or industrial sources, and the production and use of biobased energy.

Assignment description

PHA is a biodegradable bioplastic that can be produced by mixed microbial cultures. Over the past years, several (inter)national research projects have been undertaken on microbial PHA production from waste stream feedstocks. These research projects were vital to realized advancements in PHA production including successful pilot-scale testing, and the realization of a demo-plant in the Netherlands.

Despite these major advancements, there are still some remaining fundamental bottlenecks to be solved to maximize yield, quality, and properties of produced PHA. One of these bottlenecks is the control of the process. Although producing PHA in a controlled manner will benefit the predictability and stability of the product, current control techniques are limited to mainly physical models. The process of PHA fermentation is complicated with several dynamic steps. Numerous parameters, including pH, temperature, nutrition depletion, air and microbe behavior could largely influence the outcome of the production.

In this project, we aim to make a predictive model that could help to control (steps within) the production of PHA fermentation from residual streams.

Your tasks:

  1. Model integration: existing models including published and in-house algorithms will be collected. In particular, offline data from previous in-house production will be used to evaluate and optimize public machine learning predictive models based on fitness and accuracy. The selected models should be implementable on top of existing mechanistic model predictive control (MMPC) to form a hybrid system.
  2. In-silico validation: to ensure the robustness and reliability of the system, an evaluation process will be undertaken. This includes sensitivity analysis, Monte Carlo simulations and Stress Testing.
  3. Model documentation: to construct a github repo with detailed documentation of the modelling system and user manual how to use the model.
  4. (Optional) Implementation tests: you will work closely with the lab fermentation experts to test your models. the models will be applied as part of the online control process of PHA production. The performance of the models will be assessed by comparing against external benchmarking towards maximal (theoretical) yield.
  5. (Optional) Dissemination of the results by participating in manuscript drafting and presentations at congresses.

What you will learn:

  1. PHA fermentation principle
  2. Academic writing and scientific communication
  3. Work in an inter-disciplinary environment

Desirable skills/qualities of the student

Bachelor students with a suitable background are welcome to apply.

Working language: English, some researchers speak Dutch.

Work location: can be hybrid, we request at least 1 day a week on location at Lovensdijkstraat 63, Breda.

Starting date & compensation

The proposed starting date is the start of September 2024 until February 2025 (can be negotiated). The duration of the project is up to 20 weeks.

The student will get an internship fee of €350,- per month.


If you’re interested, send an application with CV and motivation to Dr. Miaomiao Zhou (

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