Enrik Ramos Lima
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Enrik Ramos Lima has a total of 13 co-authors. Here are the top co-authors by number of shared publications:
- Miguel Angel Orellana Postigo
Postgraduate Program in Electrical Engineering (PPGEEL), State University of Amazonas (UEA), Manaus 69050-020, Brazil
4 shared publications - Israel Gondres Torné
Postgraduate Program in Electrical Engineering (PPGEEL), State University of Amazonas (UEA), Manaus 69050-020, Brazil
3 shared publications - Afonso Aguiar de Carvalho
Amazonas State University
2 shared publications - Angilberto Muniz Ferreira Sobrinho
Postgraduate Program in Electrical Engineering, State University of Amazonas UEA, Manaus 69050-020, Brazil
2 shared publications - Fábio de Sousa Cardoso
State University of Amazonas
2 shared publications - Gabriel Augusto Tavares Dibo2 shared publications
Latest Publications
- 1 June 2026
- Published by Springer Nature
- p. 232-241
Intelligent Control of Aquaponics Systems Using LabVIEW
- 1 June 2026
- Published by Springer Nature
- p. 232-241
Intelligent Control of Aquaponics Systems Using LabVIEW
- 1 June 2026
- Published by Springer Nature
- p. 71-79
Development of a Microgrid Control Device Based on the IEC-61850 Reference Architecture
- 1 June 2026
- Published by Springer Nature
- p. 71-79
Development of a Microgrid Control Device Based on the IEC-61850 Reference Architecture
- Open Access
- 14 July 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 13, 122530-122539
Use of Asset Administration Shell for Monitoring and Control of Industrial Processes in Legacy Systems
Industry 4.0 is revolutionizing industrial processes by promoting automation and digitalization across various sectors. However, it is some times necessary to replace legacy systems with those more suited to this new reality, which demands resources that, in the case of small and medium-sized enterprises, pose a challenge for this transition. This study investigates the application of the Asset Administration Shell (AAS) for monitoring and controlling industrial processes, using an Level, Flow, Pressure, and Temperature (NVPT) test bench as an example of a legacy system. The Reference Architectural Model for Industry 4.0 (RAMI 4.0) was adopted to structure the system architecture. Virtualization was carried out using three AASs: two passive ones, responsible for data acquisition, and one reactive one, in charge of temperature and level control. The AASs were modeled using the AASX Package Explorer and implemented in Python using the Opua asyncio library. Execution was performed on a Raspberry Pi to explore the benefits of edge computing. The solution enabled digitalization of the test bench, allowing remote monitoring and automated control of the system variables. The tests demonstrated the feasibility of the method, highlighting its applicability in integrating legacy systems into Industry 4.0, in an economical and efficient manner.
- 14 July 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 13, 122530-122539
Open AccessUse of Asset Administration Shell for Monitoring and Control of Industrial Processes in Legacy Systems
Industry 4.0 is revolutionizing industrial processes by promoting automation and digitalization across various sectors. However, it is some times necessary to replace legacy systems with those more suited to this new reality, which demands resources that, in the case of small and medium-sized enterprises, pose a challenge for this transition. This study investigates the application of the Asset Administration Shell (AAS) for monitoring and controlling industrial processes, using an Level, Flow, Pressure, and Temperature (NVPT) test bench as an example of a legacy system. The Reference Architectural Model for Industry 4.0 (RAMI 4.0) was adopted to structure the system architecture. Virtualization was carried out using three AASs: two passive ones, responsible for data acquisition, and one reactive one, in charge of temperature and level control. The AASs were modeled using the AASX Package Explorer and implemented in Python using the Opua asyncio library. Execution was performed on a Raspberry Pi to explore the benefits of edge computing. The solution enabled digitalization of the test bench, allowing remote monitoring and automated control of the system variables. The tests demonstrated the feasibility of the method, highlighting its applicability in integrating legacy systems into Industry 4.0, in an economical and efficient manner.