The position will be embedded in the Department’s Chemical and Biochemical Reactor Engineering and Safety research group at KU Leuven's Faculty of Engineering Technology (Campus De Nayer, Sint-Katelijne-Waver, Belgium). The research of CREaS Campus De Nayer is situated in the domain of resource recovery from waste(water), biomass and side streams. It is the core competence of CREaS to design, optimize, monitor and control such processes to the benefit of the end-users. As such, research at CREaS is strongly technology oriented.
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Project
The paradigm of wastewater treatment plants (WWTPs) operation is undergoing a transformation. While WWTPs play a critical role in safeguarding the environment and the well-being of both humans and animals, traditional operation approaches struggle to address emerging sustainability concerns. Energy consumption, efficient chemicals usage, and the generation of process-related greenhouse gases like nitrous oxide (N2O) are pressing challenges. However, defining optimal operation strategies for WWTPs is challenging due to the intricate combination of physical, chemical, and biological processes involved.
To address these challenges, we propose harnessing the power of advanced production optimization methods, like economic model predictive controller. By integrating cutting-edge decision-making algorithms to the WWTPs operation, we aim to optimize the operation not only for profitability but also for achieving climate change neutrality by minimizing greenhouse gas emissions from these facilities. In this Ph.D. project, the focus will be on one of these two areas depending on the skills of the candidate:
- Production Optimization Methods: here, we will explore different advanced production optimization methods to achieve this task. We will determine the most efficient algorithm for practical applications in WWTPs in terms of robustness, adaptability, and real-time efficiency. Some examples are: economic model predictive control, real-time optimization with persistent parameter estimation, decentralized control using selectors for optimal steady-state operation.
- Soft-sensors: The performance of any production optimization method is no better than the quality of the information obtained from the plant. In this avenue, we will evaluate different algorithms for WWTPs feedback (such as Moving Horizon Estimation, Implicit Dynamic Feedback, etc.) and how they can lend themselves to the development of new soft-sensors for estimating process-related greenhouse gas emissions (especially N2O). The latter feature is of key importance for effective climate change mitigation strategies.
Expected Activities:
- Conduct innovative research on this topic, contributing to advancements in the field
- Disseminate your findings by sharing your research through publications in prestigious journals and presentations at international conferences
- Gain valuable mentorship experience by supervising and co-supervising undergraduate students
- Teach specific classes and/or serve as teaching assistant in courses in the Bachelor and Master of Science in Engineering Technology – Chemical Engineering curriculum
- Participate actively in the research group's and department's activities and meetings
Profile
We seek a highly motivated PhD candidate with a Master's degree in Engineering or Engineering Technology (preferably in Chemical, Process, Automation, or Electrical Engineering). Here is what is expected of a candidate:
- Documented experience applying numerical optimization methods in process control and/or estimation methods. Knowledge of numerical methods and system identification is a plus
- Strong grades in courses relevant to the project
- Excellent written and spoken English language skills for effective research communication
- Curious, self-motivated, and possess a strong interest in multidisciplinary research
- Be respectful, flexible, and have good organizational skills to thrive in a collaborative research environment
Offer
We offer a 4-year PhD position in a stimulating international work environment equipped with state-of-the-art facilities. As part of your doctoral program (Arenberg Doctoral School), you will engage in diverse training, education, and career development activities.
You will work as a doctoral student in a stimulant and challenging work environment with a high degree of flexibility and autonomy.
At KU Leuven, the main teaching language is Dutch, while the general communication for research is in English. The appointee must accept the necessary mobility required for fulfilling their teaching and research assignments.
Interested?
For more information please contact Prof. dr. ir. Jose Matias Assumpcao, tel.: +32 15 68 80 71, mail: jose.matias@kuleuven.be.
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