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 stabilizing 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 (Artificial Neural Network)-based prediction for temperature and RH (Relative Humidity) 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 24 h real-time prediction of heat and mass transfer and to simulate the micro-environmental conditions 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, which revealed a robust prediction of micro-environments inside different enclosures under various macro-environmental conditions. This modeling 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.