The organic Rankine cycle (ORC)-based waste heat recovery (WHR) system operating under a supercritical condition has a higher potential of thermal efficiency and work output than a traditional subcritical cycle. However, the operation of supercritical cycles is more challenging due to the high pressure in the system and transient behavior of waste heat sources from industrial and automotive engines that affect the performance of the system and the evaporator, which is the most crucial component of the ORC. To take the transient behavior into account, the dynamic model of the evaporator using renowned finite volume (FV) technique is developed in this paper. Although the FV model can capture the transient effects accurately, the model has a limitation for real-time control applications due to its time-intensive computation. To capture the transient effects and reduce the simulation time, a novel fuzzy-based nonlinear dynamic evaporator model is also developed and presented in this paper. The results show that the fuzzy-based model was able to capture the transient effects at a data fitness of over 90%, while it has potential to complete the simulation 700 times faster than the FV model. By integrating with other subcomponent models of the system, such as pump, expander, and condenser, the predicted system output and pressure have a mean average percentage error of 3.11% and 0.001%, respectively. These results suggest that the developed fuzzy-based evaporator and the overall ORC-WHR system can be used for transient simulations and to develop control strategies for real-time applications.

The aim of this paper is to develop a real time conditional monitoring of the inverter based on IGBT. IGBT failure model has been developed by using fuzzy logic that adapts prognostic model with the fuzzy nature of failure mechanism. In this way it is possible to efficiently estimate the remaining useful life (RUL) of Insulated Gate Bipolar Transistor (IGBT).

The pharmaceutical industry is undergoing a significant transition from batch to continuous manufacturing, driven by increasing regulatory requirements and sustainability pressures. Digital twins (DTs) play a pivotal role in facilitating this transition by enabling real-time data visualisation, process optimisation, and predictive analytics. While substantial progress has been made in the development and application of DTs, particularly in industries such as energy and automotive, there remains a critical need for further research and development focused on creating sustainability-oriented digital twins tailored to pharmaceutical processes. In the pharmaceutical sector, DTs are being progressively utilised not only for real-time monitoring and analysis but also as dynamic training platforms for engineers and operators, enhancing both operational efficiency and workforce competency. This paper examines the University of Sheffield’s Diamond Pilot Plant (DiPP), a facility showcasing the future of pharmaceutical manufacturing through the integration of Industry 4.0 technologies and advanced sensors. This paper focuses on developing a data-driven model to predict energy consumption in a twin-screw granulator (TSG) within the DiPP. The model, based on second-degree polynomial regression, demonstrates strong predictive accuracy with R-squared values exceeding 0.8. By optimising energy performance indicators, this work aims to improve the sustainability of pharmaceutical manufacturing processes. This research contributes to the field of pharmaceutical manufacturing by providing a foundation for creating energy models and advancing the development of comprehensive DT.