PhD position in "Control of heating, ventilation, and air conditioning systems of a building: towards ambient air conditioning, at the lowest energy and environmental cost" - MSCA Cofund SEED programme

2 Feb 2024
Job Information

Organisation/Company

IMT Atlantique

Department

Doctoral division

Research Field

Engineering
Computer science

Researcher Profile

First Stage Researcher (R1)

Country

France

Application Deadline

14 Feb 2024 - 12:00 (Europe/Paris)

Type of Contract

Temporary

Job Status

Full-time

Hours Per Week

37

Offer Starting Date

1 Sep 2024

Is the job funded through the EU Research Framework Programme?

HE / MSCA COFUND

Marie Curie Grant Agreement Number

101126644

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description
The PhD position is offered under an industrial track (2 years at IMT Atlantique + 9 months at Veolia Recherche et Innovation, Maisons-Laffitte et Aubervilliers (Paris region), France + 3 months at an international partner
1.1. Domain and scientific/technical context

Heating and cooling of buildings account for one-third of global final energy consumption (2018). Unlike heating demand, which remains relatively stable, the global demand for cooling is increasing at an accelerated rate (300% since 1990, 33% since 2010, and 5% between 2017 and 2018), and it is projected to surpass heating demand by 2060. Maintaining a comfortable thermal and hygrometric environment in a building is achieved through heating, ventilation, and air conditioning (HVAC) systems, and optimizing these systems plays a crucial role in improving the energy efficiency and environmental performance of buildings. Due to rising energy costs and environmental concerns related to CO2 emissions, it has become imperative to enhance the energy efficiency of these systems and resort to decarbonized or alternative energy sources. Developing optimal control strategies for these systems is an important lever to achieve this goal.

1.2. Scientific/technical challenges

Several control methods for HVAC systems are available in the literature, ranging from simple ON/OFF control to predictive optimal control. While these methods allow for controlling the system based on predefined setpoints (e.g. ambient temperature or humidity), most of them struggle to simultaneously ensure thermal comfort and energy/environmental efficiency. Moreover, methods focusing on the HVAC system, decoupled from the building, do not modulate setpoints, which is necessary for improving the overall energy performance of the building.

Predictive optimal control techniques are currently the most studied approach for efficiently managing the trade- offs related to HVAC system operation (energy consumption, comfort, CO2 emissions). These methods rely on a model of the system to be controlled and the formulation of an optimization problem, the resolution of which leads to the desired trade-offs. Challenges associated with implementing these techniques include: 1) difficulty in developing accurate and computationally efficient dynamic models, 2) online resolution of the optimization problem within a timeframe compatible with real-time decision-making.

1.3. Considered methods, targeted results and impacts

The research avenues identified in the literature as the most promising will be explored. We will particularly consider among these:

• predictive control based on a model with a receding prediction horizon, called MPC (for Model Predictive Control)
• reinforcement learning (AR)

While extensively studied, the first point requires dedicated methodological developments for solving the targeted problem. Its success depends on the enlightened combination of methodological processes involving recent developments in modeling (knowledge and data-based) and control (MPC control implementation). Reinforcement learning constitutes an attractive alternative to air conditioning in that it seeks a solution based on data alone, without the intermediate development of a model. The algorithms allowing its implementation are still faced with obstacles which hinder their adoption, which need to be overcome, for example by developing a hybrid AR algorithm (Physics Informed Reinforcement Learning), which will be compared to conventional solutions and the MPC approach.

2. Partners and study periods
2.1. Supervisors and study periods

• IMT Atlantique: Prof. Philippe Chevrel  and Mohamed Tahar Mabrouk , IMT Atlantique, Nantes, France

  The PhD student will stay 2 years at this lab.

• International partner: Dr. Yuqi Wang , Veolia Recherche et Innovation, Maisons-Laffitte et Aubervilliers (Paris region), France

  The PhD student will stay 9 months at Dr. Wang's lab.

• International academic partner(s): not yet determined but several universities in Europe and elsewhere are under considerations

2.2. Hosting organizations
2.2.1. IMT Atlantique

IMT Atlantique , internationally recognized for the quality of its research, is a leading French technological university under the supervision of the Ministry of Industry and Digital Technology. IMT Atlantique maintains privileged relationships with major national and international industrial partners, as well as with a dense network of SMEs, start-ups, and innovation networks. With 290 permanent staff, 2,200 students, including 300 doctoral students, IMT Atlantique produces 1,000 publications each year and raises 18€ million in research funds.

2.2.2. Veolia

Veolia  designs solutions for water, waste, and energy management. In 2022, Veolia provided drinking water to 111 million people and sanitation services to 97 million, produced nearly 44 million MWh of energy, and valorized 61 million tons of waste, generating a consolidated turnover of 43 billion euros. The student will be welcomed at one of the Veolia Research and Innovation sites and will be supervised by the Digital Solutions team

Requirements

Research Field

Engineering

Education Level

Master Degree or equivalent

Skills/Qualifications

The topic is highly interdisciplinary and requires expertise in HVAC engineering, physical modeling of complex energy systems, model reduction, artificial intelligence, and control theory. It also demands operational expertise and knowledge of economic, human, and regulatory constraints to be considered during the development of this control strategy.

Languages

ENGLISH

Level

Excellent

Research Field

Engineering

Additional Information
Benefits
A PhD programme of high quality training : 4 reasons to apply

• SEED is a programme of excellence that is aware of its responsibilities: to provide a programme of high quality training to develop conscientious researchers, including training in responsible research and ethics. 
• SEED’s unique approach of providing interdisciplinary, international and cross-sector experience is tailored to work in a career-focused manner to enhance employability and market integration.
• SEED offers a competitive funding scheme, aiming for an average monthly salary of EUR 2,000 net per ESR, topped by additional mobility allowances as well as optional family allowances.
• SEED is a forward-looking programme that actively engages with current issues and challenges, providing research opportunities addressing industrial and academic relevant themes.

Eligibility criteria

Eligibility criteria. In accordance with MSCA rules, SEED will open to applicants without any conditions of nationality nor age criteria. SEED applies the MSCA mobility standards and necessary background. Eligible candidates must fulfil the following criteria

• Mobility rule: Candidates must show transnational mobility by having not resided or carried out their main activity (work, studies, etc.) in France for more than 12 months in the three years immediately before the deadline of the co-funded program's call (Jan 31, 2024 for Call#1). Compulsory national service, short stays such as holidays and time spent as part of a procedure for obtaining refugee status under the Geneva Convention are not taken into account.
• Early-stage researchers (ESR): Candidates must have a master’s degree or an equivalent diploma at the time of their enrolment and must be in the first four years (full-time equivalent research experience) of their research career. Moreover, they must not have been awarded a doctoral degree.
  Extensions may be granted (under certain conditions) for maternity leave, paternity leave, as well as long-term illness or national service.

Selection process

The selection process is described on the guide for applicants available here: https://www.imt-atlantique.fr/en/research-innovation/phd/seed/documents

Additional comments

Applications can only be provided through the application system available under the SEED website: http://www.imt-atlantique.fr/seed

Website for additional job details

https://www.imt-atlantique.fr/en/research-innovation/phd/seed

Work Location(s)

Number of offers available

1

Company/Institute

IMT Atlantique

Country

France

City

Nantes

Street

4, rue Alfred Kastler - La Chantrerie

Geofield

Where to apply

Website

https://www.imt-atlantique.fr/en/research-innovation/phd/seed

Contact

City

Nantes

Website

https://www.imt-atlantique.fr/en/research-innovation/phd/seed

Street

4, rue Alfred Kastler - La Chantrerie

Postal Code

44307

E-Mail

[email protected]

STATUS: EXPIRED