Diagramas de tuberías e instrumentación para la documentación técnica de procesos en una planta acuícola
DOI:
https://doi.org/10.71112/aqghh102Palavras-chave:
documentación técnica, instrumentación industrial, ISA S5.1, planta acuícola, P&ID.Resumo
La documentación técnica de una planta de maduración acuícola debe representar con claridad las rutas de agua a temperatura ambiente, de agua caliente, de aireación, de energía térmica y de servicios auxiliares que sustentan la operación. Este trabajo organizó la información obtenida mediante recorridos de campo, la revisión de antecedentes disponibles y las consultas realizadas durante el levantamiento. Un equipo de tres estudiantes de Electrónica y Automatización, con orientación y revisión docente, identificó equipos, tuberías, válvulas, instrumentos y conexiones, y trasladó la información a diagramas de tuberías e instrumentación elaborados con la simbología ISA-5.1. El paquete resultante contiene 51 láminas P&ID y representa la relación entre la captación y el bombeo, los filtros de grava, los reservorios, las calderas, la distribución térmica, la recirculación, las salas de producción, la aireación y la estación de GLP. Los resultados son descriptivos: documentan la configuración funcional observada y proporcionan una base potencial para localizar activos, interpretar continuidades entre áreas, apoyar el mantenimiento y conservar el conocimiento técnico. No se atribuyen mejoras de desempeño ni indicadores cuantitativos que no hayan sido medidos en la planta.
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Referências
Al-Mutairi, A. W., & Al-Aubidy, K. M. (2023). IoT-based smart monitoring and management system for fish farming. Bulletin of Electrical Engineering and Informatics, 12(3), 1435-1446. https://doi.org/10.11591/eei.v12i3.3365
Amaliah, F. I., Gunawan, A. I., Taufiqurrahman, Dewantara, B. S. B., & Saputra, F. A. (2023). Water quality level for shrimp pond at Probolinggo area based on fuzzy classification system. Jurnal Rekayasa Elektrika, 19(1). https://doi.org/10.17529/jre.v19i1.28631
Ayuso-Virgili, G., Jafari, L., Lande-Sudall, D., & Lummen, N. (2023). Linear modelling of the mass balance and energy demand for a recirculating aquaculture system. Aquacultural Engineering, 101, 102330. https://doi.org/10.1016/j.aquaeng.2023.102330
Chen, C.-H., Wu, Y.-C., Zhang, J.-X., & Chen, Y.-H. (2022). IoT-based fish farm water quality monitoring system. Sensors, 22(17), 6700. https://doi.org/10.3390/s22176700
Dhal, S. B., Bagavathiannan, M., Braga-Neto, U., & Kalafatis, S. (2022a). Can machine learning classifiers be used to regulate nutrients using small training datasets for aquaponic irrigation? A comparative analysis. PLOS ONE, 17(8), e0269401. https://doi.org/10.1371/journal.pone.0269401
Dhal, S. B., Bagavathiannan, M., Braga-Neto, U., & Kalafatis, S. (2022b). Nutrient optimization for plant growth in aquaponic irrigation using machine learning for small training datasets. Artificial Intelligence in Agriculture, 6, 68–76. https://doi.org/10.1016/j.aiia.2022.05.001
dos Santos, A. M., Bernardino, L. F., Attramadal, K. J. K., & Skogestad, S. (2023). Steady-state and dynamic model for recirculating aquaculture systems with pH included. Aquacultural Engineering, 102, 102346. https://doi.org/10.1016/j.aquaeng.2023.102346
Flores-Iwasaki, M., Guadalupe, G. A., Pachas-Caycho, M., & Chapa-Gonza, S. (2025). Internet of Things sensors for water quality monitoring in aquaculture systems: A systematic review and bibliometric analysis. AgriEngineering, 7(3), 78. https://doi.org/10.3390/agriengineering7030078
Food and Agriculture Organization of the United Nations. (2024). The state of world fisheries and aquaculture 2024: Blue transformation in action. FAO. https://doi.org/10.4060/cd0683en
Hegde, S., Kumar, G., Engle, C., Hanson, T., Roy, L. A., Cheatham, M., Avery, J., Aarattuthodiyil, S., Van Senten, J., Johnson, J., Wise, D., Dahl, S., Dorman, L., & Peterman, M. (2022). Technological progress in the US catfish industry. Journal of the World Aquaculture Society, 53(2), 367-383. https://doi.org/10.1111/jwas.12877
Hemal, M. M., Rahman, A., Nurjahan, Islam, F., Ahmed, S., Kaiser, M. S., & Ahmed, M. R. (2024). An integrated smart pond water quality monitoring and fish farming recommendation AquaBot system. Sensors, 24(11), 3682. https://doi.org/10.3390/s24113682
Hu, W.-C., Chen, L.-B., Wang, B.-H., Li, G.-W., & Huang, X.-R. (2022). An AIoT-based water quality inspection system for intelligent aquaculture. 2022 IEEE 11th Global Conference on Consumer Electronics. https://doi.org/10.1109/GCCE56475.2022.10014181
Inderaja, B. M., Tarigan, N. B., Verdegem, M. C. J., & Keesman, K. J. (2022). Observability-based sensor selection in fish ponds: Application to pond aquaculture in Indonesia. Aquacultural Engineering, 98, 102258. https://doi.org/10.1016/j.aquaeng.2022.102258
International Society of Automation. (2024). ANSI/ISA-5.1-2024: Instrumentation and control - Symbols and identification. ISA.
Jayadi, A., Samsugi, S., Ardilles, E. K., & Adhinata, F. D. (2022). Monitoring water quality for catfish ponds using fuzzy Mamdani method with Internet of Things. 2022 International Conference on Information Technology Research and Innovation. https://doi.org/10.1109/ICITRI56423.2022.9970242
Kumar, B. V., Bharat, A., Venkat, E. G., Harsha, Y. S., & Sree, A. U. (2022). Analysis of an IoT based water quality monitoring system. 2022 6th International Conference on I-SMAC. https://doi.org/10.1109/I-SMAC55078.2022.9987360
Kumar, S. R., Mohammed, T. K., Rao, S. S., Bakhare, R., Kumar, A., & Sarkar, S. (2023). IoT-based fish pond monitoring system to enhance its productivity. 2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics. https://doi.org/10.1109/ICIPTM57143.2023.10117894
Liu, L., Cheng, W., & Kuo, H.-W. (2025). A narrative review on smart sensors and IoT solutions for sustainable agriculture and aquaculture practices. Sustainability, 17(12), 5256. https://doi.org/10.3390/su17125256
Müller, J., Houben, N., & Pauly, D. (2023). On being the wrong size, or the role of body mass in fish kills and hypoxia exposure. Environmental Biology of Fishes, 106(7), 1651-1667. https://doi.org/10.1007/s10641-023-01442-w
Nagothu, S. K., Bindu Sri, P., Anitha, G., Vincent, S., & Kumar, O. P. (2025). Advancing aquaculture: Fuzzy logic-based water quality monitoring and maintenance system for precision aquaculture. Aquaculture International, 33, 32. https://doi.org/10.1007/s10499-024-01701-2
Olanubi, O. O., Akano, T. T., & Asaolu, O. S. (2024). Design and development of an IoT-based intelligent water quality management system for aquaculture. Journal of Electrical Systems and Information Technology, 11, 15. https://doi.org/10.1186/s43067-024-00139-z
Patrón, G. D., & Ricardez-Sandoval, L. (2023). Economic model predictive control of a recirculating aquaculture system. IFAC-PapersOnLine, 56(2), 6156-6161. https://doi.org/10.1016/j.ifacol.2023.10.723
Premkkumar, V. P., Gayathri, C., Priyadharshini, P., & Praveenkumar, G. (2023). AI & IoT based control and traceable aquaculture with secured data using blockchain technology. 2023 Second International Conference on Electronics and Renewable Systems. https://doi.org/10.1109/ICEARS56392.2023.10085006
Ranjan, R., Sharrer, K., Tsukuda, S., & Good, C. (2023). MortCam: An artificial intelligence-aided fish mortality detection and alert system for recirculating aquaculture. Aquacultural Engineering, 102, 102341. https://doi.org/10.1016/j.aquaeng.2023.102341
Setiawan, B., & Surantha, N. (2023). Penerapan quality function deployment pada desain smart aquaculture untuk sektor tambak udang vaname berbasis IoT. Journal of Information System Research, 4(2). https://doi.org/10.47065/josh.v4i2.2806
Silalahi, A. O., Sinambela, A., Pardosi, J. T., & Panggabean, H. M. (2022). Automated water quality monitoring system for aquaponic pond using LoRa TTGO SX1276 and Cayenne platform. 2022 International Conference of Computer, Science and Information Technology. https://doi.org/10.1109/ICOSNIKOM56551.2022.10034916
Singh, Y., & Walingo, T. (2024). Smart water quality monitoring with IoT wireless sensor networks. Sensors, 24(9), 2871. https://doi.org/10.3390/s24092871
Susanti, N. D., Sagita, D., Apriyanto, I. F., Anggara, C. E., Darmajana, D. A., & Rahayuningtyas, A. (2022). Design and implementation of water quality monitoring system in aquaculture using IoT at low cost. Advances in Biological Sciences Research. https://doi.org/10.2991/absr.k.220101.002
Viglia, S., Brown, M. T., Love, D. C., Fry, J. P., Scroggins, R., & Neff, R. A. (2022). Analysis of energy and water use in USA farmed catfish: Toward a more resilient and sustainable production system. Journal of Cleaner Production, 379, 134796. https://doi.org/10.1016/j.jclepro.2022.134796
Wei, T. Y., Tindik, E. S., Fui, C. F., Haviluddin, & Hijazi, M. H. (2023). Automated water quality monitoring and regression-based forecasting system for aquaculture. Bulletin of Electrical Engineering and Informatics, 12(1). https://doi.org/10.11591/eei.v12i1.4464
Xue, W., Zhang, C., & Zhou, D. (2023). Positive and negative effects of recirculating aquaculture water advanced oxidation. Water Research, 235, 119835. https://doi.org/10.1016/j.watres.2023.119835
Yang, J., Jia, L., Guo, Z., Shen, Y., Li, X., Mou, Z., Yu, K., & Lin, J. C.-W. (2023a). Prediction and control of water quality in recirculating aquaculture system based on hybrid neural network. Engineering Applications of Artificial Intelligence, 121, 106002. https://doi.org/10.1016/j.engappai.2023.106002
Yang, P.-Y., Liao, Y.-C., & Chou, F.-I. (2023b). Artificial intelligence in Internet of Things system for predicting water quality in aquaculture fishponds. Computer Systems Science and Engineering. https://doi.org/10.32604/csse.2023.036810
Zamzari, N. Z., Kassim, M., & Yusoff, M. (2022). Analysis and development of IoT-based aqua fish monitoring system. International Journal of Emerging Technology and Advanced Engineering. https://doi.org/10.46338/ijetae1022_20
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Copyright (c) 2026 Óscar Wladimir Gómez-Morales, Sonnya Patrica Mendoza Lombana, Ronal Humberto Rovira Jurado, Marcia Marisol Bayas Sanpedro (Autor/a)

Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.






