ANN-Based Real-Time Prediction of Heat and Mass Transfer in the Paper-Based Storage Enclosure for Sustainable Preventive Conservation

The storage enclosures are vital for stabilising the micro-environment within, facilitating preventive conservation efforts and enabling energy savings by reducing the need for extensive macro-environmental control within the room. However, real-time conformity monitoring of the micro-environment to ensure compliance with preventive conservation specifications poses a practical challenge due to a limitation in implementing physical sensors for each enclosure. This study aims to address this challenge by using an ANN-based prediction for temperature and RH changes in response to macro-environmental fluctuations. A numerical model was developed to simulate transient heat and mass transfer between macro and micro environments, and then employed to determine an acceptable macro-environmental range for sustainable preventive conservation and to generate a dataset to train a sequence-to-sequence ANN model. This model was specially designed for real-time prediction of heat and mass transfer and to simulate the micro condition under varying levels of control accuracy over the macro environment The effectiveness of the prediction model was tested through a real trial application in the laboratory revealed a robust prediction of micro-environments inside different enclosures under various macro-environmental conditions. This modelling approach offers a promising solution for monitoring the micro-environmental conformity and further implementing the relaxing control strategy in the macro-environment without compromising the integrity of the collections stored inside the enclosures.