Christophe Salzmann

Experimental data-driven model predictive control of a hospital HVAC system during regular use

Colin Neil Jones, Emilio Maddalena, Christophe Salzmann

Herein we report a multi-zone, heating, ventilation and air-conditioning (HVAC) control case study of an industrial plant responsible for cooling a hospital surgery center. The adopted approach to guaranteeing thermal comfort and reducing electrical energy consumption is based on a statistical non-parametric, non-linear regression technique named Gaussian processes. Our study aimed at assessing the suitability of the aforementioned technique to learning the building dynamics and yielding models for our model predictive control (MPC) scheme. Experimental results gathered while the building was under regular use showcase the final controller performance while subject to a number of measured and unmeasured disturbances. Finally, we provide readers with practical details and recommendations on how to manage the computational complexity of the on-line optimization problem and obtain high-quality solutions from solvers. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

ELSEVIER SCIENCE SA2022

Self-Organized Laboratories for Smart Campus

Denis Gillet, Christophe Salzmann

A smart campus provides students who are geographically scattered with online tools to get access to learning resources and laboratories. Although these remote laboratories have the potential and capabilities to implement different learning experiments, most of them are configured in a static fashion, being able to serve only one experiment for a given period of time. This lack of adaptability and flexibility causes long waiting queues of students in certain overloaded remote laboratories, while others are underused. To overcome this limitation, a smart campus can incorporate new techniques and paradigms such as Network function virtualization (NFV) and Software-defined networking (SDN), which are rapidly modifying current cloud services and applications to provide autonomous and adaptive solutions. In this context, the main contribution of this paper is an SDN/NFV-based architecture with autonomic capabilities to adapt to the remote laboratories configuration according to the end-user demand. The proposed architecture will be able to optimize computing resources to ensure the users' quality of service in a smart campus with remote laboratories. A use case with a remote laboratory, based on the control of a servo motor, shows how our solution can dynamically change the lab service under specific circumstances. Finally, experiments using diverse configurations and service descriptions show the performance and suitability of the proposal.

IEEE COMPUTER SOC2020

Enforcing optimal operation in solid-oxide fuel-cell systems

Dominique Bonvin, Timm Faulwasser, Christophe Salzmann, Jan Van Herle, Zacharie Wuillemin

This paper describes an optimization strategy for operating solid-oxide fuel-cell systems at optimal efficiency. Specifically, we present the experimental validation of a real-time optimization (RTO) strategy applied to a commercial solid-oxide fuel-cell system. The proposed RTO scheme effectively pushes the system to higher levels of efficiency and maintains the system there despite perturbations by tracking active constraints. The optimization approach uses either steady-state measurements, or transient measurements in combination with a dynamic model, and can deal effectively with plant-model mismatch. In the reported experiments, the approach drives the system to the desired power demand at optimal efficiency. The experimental fuel-cell system reached 65% DC electrical efficiency. As such, the proposed RTO scheme is a promising candidate for enforcing optimal micro-CHP operation. In addition, the approach can deal with slow drifts such as degradation without compromising on efficiency. Finally, and important from a practical point of view, we suggest guidelines for safe and optimal operation. (C) 2019 Elsevier Ltd. All rights reserved.

2019

Massive Open Online Labs (MOOLs): An Innovative Solution to Achieving SDGs in the Global South

Denis Gillet, Rayana Jaafar, Yves Henri Philippe Piguet, Christophe Salzmann

In engineering education, laboratories represent an important academic resource as they provide practical training in addition to the fundamental theories. However, the acquisition of new machinery and the maintenance of the equipment imply a large investment that only a limited number of universities can afford. This paper represents innovative online education activities through a collaborative widespread network with the global south countries, deploying remote laboratories in electrical, mechanical and control engineering at a large scale within MOOC infrastructures.

IEEE2019

New Tools for MOOC/MOOL to Sustain Continuity of Experimentation in Control

Denis Gillet, Yves Henri Philippe Piguet, Christophe Salzmann

The use of interactive tools combined with remote laboratories has shown to be very useful for students when offered in a MOOC. The EPFL hands-on Control MOOC enables students to switch back and forth between control and observation on a remote laboratory, as well as analysis and design, allowing continuity of experimentation. Currently the data acquired and generated in these activities remain within the MOOC infrastructure. An important aspect for the students is the ability to store and exchange different kinds of data and learning outputs with other platforms, thus giving complete freedom for further usage and ownership. LTI is an established standard to encapsulate remote laboratories into MOOCs and turn them into MOOLs (Massive Open Online Labs), but the experimentation data and learning outputs are locked in ad-hoc storage under the control of the LTI tools. This paper first presents requirements to ensure transparent data usage and ownership for guaranteeing the continuity of experimentation. Then, it details the limits of existing LTI solutions to share data between the MOOC tools and proposes extensions as well as new schemes to fulfil the elicited requirements. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Elsevier2019

Real-time optimization of an experimental solid-oxide fuel-cell system

Dominique Bonvin, Alejandro Gabriel Marchetti, Christophe Salzmann, Jan Van Herle, Zacharie Wuillemin

There still exists a large gap between simulation work and industrial applications in the context of control and optimization of solid-oxide fuel-cell (SOFC) systems. In an effort to bridge this gap, this study describes the experimental implementation of steady-state real-time optimization (RTO) to an SOFC system that consists of both hardware and software components. The proposed adaptive optimization scheme uses an approximate steady-state model of the fuel-cell system and corrects it "appropriately" so that it becomes "excellent" for optimization. This way, the plant can be steered efficiently toward optimality, while meeting the varying electric power demand. In these experiments, the plant efficiency was increased from 55% to 62% through application of RTO. Furthermore, although the SOFC system is characterized by slow thermal dynamics that may take a few hours to settle to steady state, it has been possible to reduce the time necessary to reach the power setpoint from 1 h to about 5 min thanks to the use of transient measurements and a dynamic model. This experimental work has shown that it is possible, not only to control the SOFC system at a desired operating point, but also to operate it near optimality despite changes in power demand.

ELSEVIER SCIENCE BV2019

Collecting Experience Data from Remotely Hosted Learning Applications

Denis Gillet, Christophe Salzmann

The ability to integrate multiple learning applications from different organizations allows sharing resources and reducing costs in the deployment of learning systems. In this sense, Learning Tools Interoperability (LTI) is the main current leading technology for integrating learning applications with platforms like Learning Management Systems (LMS). On the other hand, the integration of learning applications also benefits from data collection, which allows learning systems to implement Learning Analytics (LA) processes. Tin Can API is a specification for learning technology that makes this possible. Both learning technologies, LTI and Tin Can API, are supported by nowadays LMS, either natively or through plugins. However, there is no seamless integration between these two technologies in order to provide learning systems with experience data from remotely hosted learning applications. Our proposal defines a learning system architecture ready to apply advanced LA techniques on experience data collected from remotely hosted learning applications through a seamless integration between LTI and Tin Can API. In order to validate our proposal, we have implemented a LRS proxy plug-in in Moodle that stores learning records in a SCORM Cloud LRS service, and a basic online lab based on Easy JavaScript Simulation (EjsS). Moreover, we have tested our implementation using resources located in three European universities.

SPRINGER2018

Multi-time scale coordination of complementary resources for the provision of ancillary services

Luca Fabietti, Tomasz Tadeusz Gorecki, Colin Neil Jones, Faran Ahmed Qureshi, Christophe Salzmann

This paper presents a predictive control scheme for coordinating a set of heterogeneous and complementary resources at different time scales for the provision of ancillary services. In particular, we combine building thermodynamics (slow), and energy storage systems (fast resources) to augment the flexibility that can be provided to the grid compared to the flexibility that any of these resources can provide individually. A multi-level control scheme based on data-based robust optimization methods is developed that enables heterogeneous resources at different time scales (slow and fast) to provide fast regulation services, especially a secondary frequency control (SFC) service. A data-based predictor is developed to forecast the future regulation signal, and is used to improve the performance of the controller in real-time operation. The proposed control method is used to conduct experiments, for nine consecutive days, demonstrating the provision of SFC service fully complying to the Swiss regulations, using a controllable building HVAC system on the EPFL campus and a grid connected energy storage system. The experimental results show that optimally combining such slow and fast resources can significantly augment the flexibility that can be provided to the grid. Moreover, by providing SFC service, the building can reduce its operational costs by up to 46% on average while maintaining a high level of occupant comfort. To the best of author’s knowledge this work is the first experimental demonstration of coordinating heterogeneous demand-response to provide SFC service.

2018

Standardization Layers for Remote Laboratories as Services and Open Educational Resources

Denis Gillet, Wissam Halimi, Christophe Salzmann

Deliveringeducationandeducationalresourceshasevolvedfromclass- centered settings towards distributed, cloud-based models. This is mainly the con- sequence of publicly available educational resources such as documents, videos, and web applications. At the same time, emerging technologies in information and communication are enabling the development and deployment of remote lab- oratories on the Web. Today, these freely and openly available educational inter- active media are known as Open Education Resources (OERs). Learning manage- ment systems, MOOC platforms, and educational social media platforms provide a medium for teachers to create their teaching activities around OERs in a struc- tured way. To enjoy an effective and productive learning experience, it is neces- sary for the educational resources to be fully integrated in the hosting platform. While most platforms have a ready-to-embed infrastructure for certain types of OERs, they are not ready to host remote laboratories in an integrated fashion. In this paper, we define the necessary integration layers for remote labs in on- line learning environments. The work is validated by two implementations with different target platforms.

Springer2018

Christophe Salzmann

Experimental data-driven model predictive control of a hospital HVAC system during regular use

Self-Organized Laboratories for Smart Campus

Enforcing optimal operation in solid-oxide fuel-cell systems

Massive Open Online Labs (MOOLs): An Innovative Solution to Achieving SDGs in the Global South

New Tools for MOOC/MOOL to Sustain Continuity of Experimentation in Control

Real-time optimization of an experimental solid-oxide fuel-cell system

Collecting Experience Data from Remotely Hosted Learning Applications

Multi-time scale coordination of complementary resources for the provision of ancillary services

Next Steps in Supporting More Students in MOOL for Control Education

Standardization Layers for Remote Laboratories as Services and Open Educational Resources

Enforcing optimal operation in solid-oxide fuel-cell systems

Massive Open Online Labs (MOOLs): An Innovative Solution to Achieving SDGs in the Global South

Self-Organized Laboratories for Smart Campus

New Tools for MOOC/MOOL to Sustain Continuity of Experimentation in Control

Real-time optimization of an experimental solid-oxide fuel-cell system

Experimental data-driven model predictive control of a hospital HVAC system during regular use

Multi-time scale coordination of complementary resources for the provision of ancillary services

Next Steps in Supporting More Students in MOOL for Control Education

Collecting Experience Data from Remotely Hosted Learning Applications

Standardization Layers for Remote Laboratories as Services and Open Educational Resources