BOUSSAADA, Nesrine; BOUSSAADA, Zina; LLARIA, Alvaro; TERRASSON, Guillaume; CUREA, Octavian

doi:10.1109/CISTEM55808.2022.10044049

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BOUSSAADA, Nesrine
ESTIA - Institute of technology [ESTIA]

BOUSSAADA, Zina
ESTIA - Institute of technology [ESTIA]

LLARIA, Alvaro

ESTIA - Institute of technology [ESTIA]

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2022 IEEE International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), 2022 IEEE International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), 2022-10-26, Tunis. 2022-10p. 1-6

IEEE

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The evolution towards the Industry 4.0 concept implies a huge development of the connectivity and the smart automation in all the industrial areas. For the chemical processes, the supervision systems are generally fairly basic, and a number of operations are still conducted by the operators. In order to simplify their daily tasks, expert systems able to discern each stage of the process and any possible drifts can be considered. To be implemented, the expert system needs a knowledge base which, in our case, will contain a set of images representative of the main stages of the process. The embedded system which will make the image capture must be autonomous from an energy point of view. Thus, the energy consumption optimization is necessary to ensure the maximal lifetime for the system. In this frame, this paper presents the energy management solution applied to an image acquisition system, based on a Raspberry Pi, for the chemical industry. The energy saving is achieved by the choice of the physical architecture, together with the application of a duty-cycle strategy.< Réduire

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Energy management

Raspberry Pi

Duty-cycle

Smart industry

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Energy Consumption Optimization of a Raspberry Pi-based Image Acquisition Embedded System

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