Dynamic Modelling of Renewable-driven CO2 Methanation using Recurrent Neural Networks

A recurrent neural network (RNN) model for a CO2 methanation reactor was developed based on synthetic data generated from a validated mechanistic model of the same unit. The model was used to predict the main properties of the reactor – methane productivity and hotspot temperature – during a dynamic operation of the unit. The dynamic profile of feedstock availability was simulated taking into account the H2 flow that can be produced from PV-powered water electrolysis using solar irradiation profiles over a year in Milan, Italy. The dataset therefore consists of 366 data instances (one for each day), each composed of one datapoint per minute of sunlight. The best agreement between the predictions from the RNN and the target output values from the mechanistic model was found using a shallow RNN of 20 hidden-layer neurons, trained with a batch size of 10 and an 80/20 training-testing split. This showed that RNNs can constitute a reliable tool for dynamic surrogate modelling of energy conversion reactors. The surrogate model was embedded in an energy system model to provide dynamic predictions of the energy conversion efficiency in the reactor, providing a more realistic performance assessment compared to the average efficiency models commonly used in the literature.