In this review, the industry’s current issues regarding intelligent manufacture are presented. This work presents the status and the potential for the I4.0 and I5.0’s revolutionary technologies. AI and, in particular, the DRL algorithms, which are a perfect response to the unpredictability and volatility of modern demand, are studied in detail. Through the introduction of RL concepts and the development of those with ANNs towards DRL, the potential and variety of these kinds of algorithms are highlighted. Moreover, because these algorithms are data based, their modification to meet the requirements of industry operations is also included. In addition, this review covers the inclusion of new concepts, such as digital twins, in response to an absent environment model and how it can improve the performance and application of DRL algorithms even more. This work highlights that DRL applicability is demonstrated across all manufacturing industry operations, outperforming conventional methodologies and, most notably, enhancing the manufacturing process’s resilience and adaptability. It is stated that there is still considerable work to be carried out in both academia and industry to fully leverage the promise of these disruptive tools, begin their deployment in industry, and take a step closer to the I5.0 industrial revolution.

Microwave heating, which is caused by the interaction of electromagnetic radiation and materials, has become an important component in industrial operations across numerous industries. Despite their importance, conventional numerical simulations of microwave heating are computationally intensive. Concurrently, advances in artificial intelligence (AI), particularly machine learning algorithms, have transformed data processing by increasing accuracy while decreasing computational time. This study tackles the difficulty of efficient and accurate modelling in microwave heating by combining convolutional neural networks (CNNs) with traditional simulation techniques. The major goal of this research is to use CNNs to forecast temperature profiles in a variety of industrial materials, including susceptors, semi-transparent, and microwave-transparent materials, under varying power settings and heating periods. This unique strategy greatly reduces prediction times, with up to 60-fold speed increases over standard methods. Our research is based on examining the electromagnetic and thermal responses of these materials under microwave heating. This study’s findings emphasise the need for extensive datasets and show the transformational potential of CNNs in optimising material processing. It uses artificial intelligence to pave the way for more effective and exact simulations, supporting breakthroughs in industrial microwave heating applications.

The presented design integrates concentrated solar power (CSP) into a compact ultra-high temperature latent heat thermal energy storage (UHT-LHTES) system using Phase Change Materials (PCM) and Thermophotovoltaic (TPV) cells, creating a Solar-to-Heat-to-Power (S2H2P) storage system ten times more compact than traditional CSP storage. This innovative solution aligns with the European strategy's focus on renewable energy source (RES) flexibility and dispatchability. To optimise this complex system, COMSOL Multiphysics® 6.1 facilitates multiphysics coupling, enabling key parameter optimisation and shape improvements. The results support the selection of the best materials and design configurations. For instance, just with very simple adjustments in the angle of a wall, the system yielded an improvement of 6% by reducing the PCM melting time. This is the first time that an innovative design of this kind has been simulated with a complete CFD model for all the elements compounding the innovative S2H2P system.