Deep learning prediction of photocatalytic water splitting for hydrogen production under natural light based on experiments

Abstract

Harnessing hydrogen through photocatalytic water splitting represents a pivotal stride towards carbon neutrality. Evaluating ongoing experiments under natural light conditions is critical for scaling this technology. Given the inherent instability of natural light, devising precise hydrogen production forecasts has emerged as a core focus to streamline project timelines and financial governance. This research introduces a robust system for continuous photocatalytic water splitting, leveraging natural light for hydrogen production. Analysis and prognostication of hydrogen generation are conducted using experimental data from 21 distinct groups. Critical factors, including radiation intensity, reaction temperature, reactant concentration, solar concentrator specifics, and hydrogen production rate (HPR), undergo thorough correlation scrutiny. Employing a deep-learning framework, gated recurrent units (GRU) neural network is utilized for nuanced feature apprehension, enhancing hydrogen output forecasting precision. The results illustrate that the mean daily solar-to-hydrogen (STH) energy conversion efficiency across the 21 groups is approximately 0.25 %. Radiation intensity prevails as a significant influencer, with reaction temperature also playing a substantial role. Solar concentrator utilization proves beneficial for augmenting HPR. Nevertheless, the unevenness of solar concentrator presents adverse effects, necessitating consideration during system conceptualization. The optimized prediction model's outcomes align closely with experimental observations, and with a test loss of 2.61 × 10−3 and 682 epochs, the model's predictive superiority is affirmed. The continuous hydrogen production experiments leveraging natural light provide the feasible reference for the large-scale application of this technology. The correlation analysis of critical factors provides the optimization direction for practical engineering design. The hydrogen production prediction model aims to provide scientific basis for the decision-making of this technology.