Neural network-based surrogate modeling and optimization of a multigeneration system

Multi-Objective Optimization (MOO) poses a computational challenge, particularly when applied to physics-based models. As a result, only up to three objectives are typically involved in simulation-based optimization. To go beyond this number, Surrogate Models (SMs) need to replace such high-fidelity models. In this exploratory study, the objectives are to perform comprehensive regression surrogate modeling and to conduct MOO for a Multi-Generation System (MGS). The most suitable SM was chosen among four neural-network models: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and an ensemble model developed through brute-force search using the three aforementioned models. The final model was found to be superior to others, achieving R2 values ranging from 0.9830 to 0.9999. Next, an optimization problem with six conflicting objectives was defined and performed at four distinct values of Direct Normal Irradiation (DNI), a time-dependent feature. This aimed to provide multi-criteria decision-making information based on atmospheric transparency. As a result, new understandings were gained: (I) exergy efficiency, production cost, and freshwater production rate were found to be highly influenced by DNI, and (II) the critical range of operation was observed within the DNI interval of 100 to 400 W/m2. Furthermore, we compared the result of the six-objective optimization with that of the bi-objective optimization obtained in our simulation-based study and found that all objectives showed improvements ranging from 1.9% to 12.7%. Finally, based on the findings obtained in the present study, some practical recommendations were put forward for applying the proposed methodology to similar MGSs.