Piping and instrumentation diagrams (p&id) for technical process documentation in an aquaculture plant

Authors

DOI:

https://doi.org/10.71112/aqghh102

Keywords:

aquaculture plant, industrial instrumentation, ISA S5.1, P&ID, technical documentation.

Abstract

Technical documentation for an aquaculture plant must clearly represent the ambient-temperature water, hot-water, aeration, thermal-energy, and auxiliary-service paths that support its operation. This study organized information from field walkdowns, available records, and consultations conducted during data collection. A team of three Electronics and Automation students, with instructor guidance and review, identified equipment, piping, valves, instruments, and connections and transferred the information into piping and instrumentation diagrams prepared using ISA-5.1 symbology. The resulting package contains 51 P&ID sheets and represents the relationships among water intake and pumping, gravel filters, reservoirs, boilers, heat distribution, recirculation, production rooms, aeration, and the LPG station. The findings are descriptive: they document the observed functional configuration and provide a potential basis for locating assets, interpreting continuity between plant areas, supporting maintenance, and preserving technical knowledge. The authors claim no performance improvements or quantitative indicators unless supported by facility measurements.

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References

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

Published

2026-10-02

Issue

Section

Ciencias Sociales

How to Cite

Gómez-Morales, Óscar W. ., Mendoza Lombana, S. P. ., Rovira Jurado, R. H. ., & Bayas Sanpedro, M. M. . (2026). Piping and instrumentation diagrams (p&id) for technical process documentation in an aquaculture plant. Multidisciplinary Journal Epistemology of the Sciences, 3(4), 126-148. https://doi.org/10.71112/aqghh102