Enhanced Modeling and Control of Organic Rankine Cycle Systems via AM-LSTM Networks Based Nonlinear MPC

Abstract

The organic Rankine cycle (ORC) serves as an effective means of converting low-grade heat sources into power, playing a pivotal role in environmentally friendly production and energy recovery. However, the inherent complexity, strong and unidentified nonlinearity, and control constraints pose significant challenges to designing an optimal controller for ORC systems. To address these issues, this research introduces a novel modeling and control framework for ORC systems. Leveraging an attention mechanism-based long short-term memory (AM-LSTM) network, the dynamic characteristics of ORC systems, which are subject to non-Gaussian disturbances, are accurately modeled. A performance metric based on survival information potential (SIP) is developed to optimize the network parameters. Furthermore, a multi-objective optimization approach that integrates nonlinear model predictive control (NMPC) with the multiverse optimizer (MVO) algorithm is implemented to ensure effective control under varying operating conditions and constraints. Through extensive simulations, the proposed framework demonstrates superior accuracy, robustness, and control performance for ORC systems.