## 1 Introduction
Mantle hot water tanks are essential components in various systems like solar domestic hot water (SDHW) systems, where efficient heat storage and transfer are paramount. By leveraging artificial neural networks (ANNs), researchers aim to enhance the accuracy and efficiency of predicting thermal behaviors in these tanks. Studies such as [PERSON] et al. have delved into analyzing thermal stratification in mantled hot water storage tanks under different water inlet velocities, highlighting the significance of understanding how flow dynamics impact temperature distribution within the tank [21]. Additionally, research by [PERSON] et al. emphasizes the importance of thermal performance assessment and improvement in solar domestic hot water tanks with phase change materials (PCMs) in the mantle, showcasing the practical implications of optimizing tank designs for enhanced thermal efficiency [22]. Furthermore, the application of artificial neural networks extends beyond tank design to encompass broader aspects of thermal systems. [PERSON] et al. proposed a methodology that integrates multi-resolution analysis and artificial neural networks to forecast outdoor temperature and thermal power consumption in a multi-energy district boiler, showcasing the versatility of ANNs in optimizing energy systems [23]. Similarly, [PERSON] and [PERSON] explored the use of ANNs in predicting hot water consumption in dwellings, demonstrating the adaptability of these networks in forecasting energy demand profiles [24]. These studies collectively underscore the diverse applications of artificial neural networks in predicting thermal behaviors across various domains, emphasizing their efficacy in enhancing predictive capabilities and optimizing system performance. Moreover, the integration of artificial neural networks with experimental data has been a focal point in research endeavors. [PERSON] et al. investigated the effects of nanoparticles and temperature on the thermal conductivity of water using ANNs and experimental data, showcasing the synergy between computational modeling and empirical observations [25]. Additionally, [PERSON] et al. utilized ANNs to predict the thermo-physical properties of nanofluids, highlighting the role of artificial intelligence in advancing predictive modeling in complex thermal systems [26]. [PERSON] et al. studied an artificial neural network model and mathematical correlations were created using experimental data to forecast thermal conductivity as a function of temperature and concentration [27]. These studies underscore the significance of combining theoretical frameworks with empirical data to enhance the accuracy and reliability of thermal predictions, showcasing the interdisciplinary nature of research in this field. Furthermore, the development of artificial neural network models for predicting various parameters in thermal systems has been a prevalent theme in recent research. [PERSON] and [PERSON] focused on predicting water saturation around wells using ANNs, demonstrating the applicability of these networks in geotechnical engineering contexts [28]. Similarly, [PERSON] et al. utilized artificial neural network models to predict thermal performances of thermoelectric generators for waste heat recovery, showcasing the versatility of ANNs in diverse thermal applications [29]. These studies highlight the broad spectrum of predictive modeling enabled by artificial neural networks, emphasizing their utility in forecasting complex thermal behaviors and optimizing system efficiencies. In conclusion, the utilization of artificial neural networks for predicting the thermal behavior of mantle hot water tanks represents a cutting-edge approach that amalgamates computational intelligence with thermal dynamics. By leveraging the predictive capabilities of ANNs, researchers can enhance the efficiency, accuracy, and optimization of thermal systems, ranging from solar hot water tanks to energy distribution networks.