Hybrid Prediction Model-based Set point Alteration Control Scheme using AI for OC-OTEC Plant to Improve its Reliability

DOI

10.2991/978-94-6239-616-6_37How to use a DOI?

Keywords

OC-OTEC; SST; LSTM; ANN; MPC; PI Control; Setpoint alteration

Abstract

The Ocean Thermal Energy Conversion (OTEC) system is a sustainable technology that harnesses power and freshwater from seawater by utilizing the temperature gradient between surface seawater and deep seawater, making it suitable for remote islands. However, it is highly influenced by variations in Sea Surface Temperature (SST) due to changing climatic conditions. SST is considered a major disturbance variable that can lead to reduced system performance and potential component failures. To address this issue, disturbance rejection-based automatic control scheme is very essential. This paper focuses on a hybrid power prediction-based setpoint alteration control strategy for the Open-Cycle OTEC (OC-OTEC) process to improve its performance and reliability against seasonal variations. The data has been collected from 1kW capacity Laboratory scale OC-OTEC plant at National Institute of Ocean Technology (NIOT) Chennai. The hybrid power prediction model is developed by integrating an LSTM-based SST prediction model and an Artificial Neural Network (ANN) based power prediction model to track dynamic setpoint variations. Two controllers:-Proportional–Integral (PI) and Model Predictive Control (MPC) are designed to adjust the warm-water flow rate based on setpoint variations to compensate for the impact of temperature disturbances, and their performances are compared. Simulation results show that the MPC-based setpoint alteration controller outperforms the PI controller both qualitatively and quantitatively. The proposed setpoint-tracking-based predictive control scheme offers a feasible solution for real-time, large-scale OTEC plants operating under dynamic environmental conditions in remote islands.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - S. Sutha
AU  - Biren Pattnaik
AU  - G. Mohanapriya
AU  - N. Pappa
PY  - 2026
DA  - 2026/03/31
TI  - Hybrid Prediction Model-based Set point Alteration Control Scheme using AI for OC-OTEC Plant to Improve its Reliability
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 491
EP  - 506
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_37
DO  - 10.2991/978-94-6239-616-6_37
ID  - Sutha2026
ER  -