HEИндивидуална стипендия2023–2025

RELIC · Robust and data-Efficient Learning for Industrial Control

„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“

Период: 2023-06-05 → 2025-06-04
Финансиране от ЕС: 210 911 €
Участници: 2
Схема: HORIZON-TMA-MSCA-PF-EF

Линиите свързват координатора с партньорите.

Накратко на български

Системите за управление на разпределителни мрежи за електричество, газ или топлина се оптимизират чрез алгоритми, които съчетават физични закони и реални данни. Това помага за повишаване на енергийната ефективност и намаляване на вредните емисии в индустрията.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Robust and data-Efficient Learning for Industrial Control

Increasing energy and resource efficiency in industrial systems is key to decrease harmful emissions by 90% by 2050. Reaching the environmental targets requires a holistic approach to how resources and energy are delivered to the industry by means of distribution networks, such as heat networks, electricity networks, or gas transport networks. Existing control frameworks are usually application specific and have limited use in large-scale systems. In the project, I wanted to advance theory in data analytics and optimisation and build on my industrial experience to develop operating strategies for distribution networks that will enable safe implementation and reaching the environmental targets. The goal of the project was to address the gaps by designing a framework to solve control problems using minimal information about the system and minimal computational power. The objectives of the project were: A. Development of a learning control system using knowledge of physics and new information available in real time; B. Development of an efficient learning control algorithm to satisfy long-term environmental targets and safety requirements; C. Development of a numerically robust control algorithm considering limited computational power available. The first step of the project was to develop an efficient control framework for large-scale systems combining the knowledge about the physics with measured data. To enable efficient use of data in control, the next step was to analyse how the quality of data from monitoring systems affects learning control frameworks. Implementation in distribution networks requires considering limitations in how industrial equipment, such as pumps or compressors, can be safely operated. To achieve this goal, I worked on improving numerical implementation of safe-learning algorithms.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Increasing energy and resource efficiency in industrial systems is key to decrease harmful emissions by 90% by 2050. Reaching the environmental targets requires a holistic approach to how resources and energy are delivered to the industry by means of distribution networks, such as heat networks, electricity networks, or gas transport networks. I will devise new control strategies that ensure robust operation of distribution networks while ensuring safety and satisfaction of environmental objectives.The environmental performance of the whole system hinges on the performance of distribution networks. Optimal control of such networks is complex due to timescales, from milliseconds to ensure safe operation of pumps or generators, to days or months to include environmental goals, spatial complexity, uncertainty related to varying operating conditions, incomplete information available, and limited computational power. Existing control frameworks are usually application specific and have limited use in large-scale systems. In the project, I will advance theory in data analytics and optimisation, and build on my industrial experience to develop operating strategies for distribution networks that will enable safe implementation and reaching the environmental targets.There is a potential in integrating machine learning in control design to overcome the complexity while satisfying safety constraints, as shown in robotics and automotive industry. However, IPCC indicated that ""The key challenge for making an assessment of the industry sector is the diversity in practices, which results in uncertainty, lack of comparability, incompleteness, and quality of data available in the public domain on process and technology specific energy use and costs"". The research question I will address in this project is if and how incorporating data-driven learning in design of control algorithms leads to improved environmental performance and safe operation of large-scale industrial networks.""

Оригинален текст от CORDIS (на английски).

Участници

NORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET NTNU · TrondheimКоординаторНорвегия
AKADEMIA GORNICZO-HUTNICZA IM. STANISLAWA STASZICA W KRAKOWIE · KrakowПолша