Doelgroep
This course is designed for professionals in the food processing industry, including production entities, technology providers, and integrators. It is particularly beneficial for engineers and technical experts involved in R&D, production, or quality assurance, who seek actionable insights into modern sensor technologies. A technical background is highly recommended.
Doelstelling
This course offers a comprehensive exploration of cutting-edge sensor technologies and smart data analysis and how to apply them in food processing. Participants will:
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- understand the latest sensor technologies and their role in enhancing operational efficiency and adaptability
- learn to select and implement sensor systems that align with specific production needs and cost considerations
- develop skills to integrate sensor data with AI-driven analytics for informed decision-making and process optimization
- recognize how improved efficiency and adaptability contribute to sustainable practices as a natural outcome
Inhoud
1. State-of-the-art overview of sensor technology in food processing
Ensuring product quality is a top priority in the food industry and advances in sensor technology are enabling faster, more reliable and non-invasive quality assessment methods. In this session you will gain a broad yet practical overview of sensing technologies relevant to food processing and quality control. We will begin with fundamental sensing principles and then explore more recent technologies that can rapidly assess key quality attributes such as composition, freshness, contamination, and texture. Through real-world examples and industry case studies, we will discuss the strengths and limitations of different sensor systems and their suitability for various food applications. To conclude, we will introduce a technology-application matrix, a practical decision-making tool to help food companies identify the most effective sensor solutions for their specific challenges.
Lecturers:
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- Bart De Ketelaere, Research Manager, Mechatronics, Biostatistics and Sensors (MeBioS), KU Leuven
- Tim Van Meer, Technical Director, Pomuni
2. System integration & data infrastructure
Collecting data in rural and harsh environments has quite some challenges. Mostly, data captured at sensors have to use non-traditional means of communication to be collected at the server, where analysis is done. In this session, you will learn how to define data, how to format it and how to send it over a very constrained environment and limited bandwidth to the server. We will discuss some common wireless communication methods and give insight into their advantages and disadvantages. Further, we will dive into the concept of edge computing, where data is analysed at the edge using embedded computing. We will conclude with the contemporary trend of machine learning at the edge and how complex analysis can be done at battery-powered constrained devices.
Lecturers:
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- Prof. Hans Hallez, Distributed and Secure Software (DistriNet), KU Leuven
- Jonathan Kesteloot, Co-founder, Captic
3. Data analysis techniques
In this session, we will delve into the complex task of transforming raw data into meaningful information, which must be delivered to the right user at the right moment. This phase of data analysis and processing presents its own set of challenges. Often, decisions need to be made swiftly in real-time during production processes, requiring the use of sophisticated, real-time AI solutions that can process data quickly and accurately. Despite these technological hurdles, when effectively implemented, sensors offer substantial advantages by improving quality, safety, and efficiency. Techniques for data analysis, including artificial intelligence (AI), machine learning, deep learning, and decision-making processes, are essential for extracting insights from sensor technology. During the session, concrete use-cases will be presented in which AI is applied to interpret acoustic or radar signals for food quality prediction and for estimating the remaining useful life of processing equipment.
Lecturers:
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- Matthias De Ryck, Innovation Manager, Declarative Languages and Artificial Intelligence (DTAI) ,KU Leuven - Brugge
- Prof. Peter Karsmakers, Declarative Languages and Artificial Intelligence (DTAI) ,KU Leuven - Geel
All sessions will be in English, including lecture materials.
Duur
3 halve dagen