In 2020, Emirates Global Aluminium (EGA) leveraged Industry 4.0 technologies to develop a predictive model using big data and machine learning for detecting anode spikes, a critical aspect of aluminum production. Despite its innovative approach, the implementation faced significant challenges, including variability in adherence to model predictions by potroom employees, and a noticeable degradation in model accuracy and model drift over time due to changing operating conditions and operational parameters, such as amperage fluctuations. Model drift, a common issue in machine learning, occurs when the predictive performance degrades as the underlying data patterns change, necessitating continuous model evaluation and updating.

Addressing these challenges, this paper underscores the importance of adaptive change management strategies in the era of digital transformation, particularly within the context of Industry 4.0 technology integration. Through a comprehensive case study of EGA's technological adoption of anode spike models in Al Taweelah DX and Jabel Ali D20 technologies, we explore the impact of model drift and the essential role of iterative model recalibration and employee engagement in maintaining the efficacy of such digital solutions.

Key to our findings is the application of a dynamic adaptation strategy to manage model drift, ensuring that predictive models remain accurate and sustainable amidst evolving operational conditions. This approach facilitated smoother transitions and significantly mitigated resistance among potroom employees, enhancing overall acceptance and effectiveness of the deployed Industry 4.0 solutions.

Nowadays, larger aluminium reduction cells are constructed with reduced bath-to-anode volume ratios to operate at higher currents and lower anode cathode distance. With aims of increasing production efficiency while lowering energy consumption, the significance of spatial variability in process variables within the cell has intensified. While conventional measurements such as cell voltage and line current fail to depict localised cell conditions, real-time individual anode distribution measurements offer insights into monitoring the spatial dynamics of process variables within the cell. This paper introduces the continuous measurement of anode current distribution through a smart Individual Anode Monitoring (IAM) system. To validate the IAM signals, a comprehensive campaign was conducted involving direct measurements of voltage drop from individual anode rods utilising C-clamps at a designated aluminium smelting cell. This validation process spans one complete anode change cycle, enabling the comparison of anode currents across various operational conditions, including idle shifts, routine manual practices, and other non-routine operations.

The durability and effectiveness of aluminum electrolysis cells heavily depend on the quality of the refractory materials lining. These materials are crucial for maintaining thermal equilibrium and shielding insulating bricks from extreme temperatures and chemical assaults. This article presents an investigation of the composition changes to evaluate the mechanical and thermal properties of Ordinary Refractory Bricks (ORB) in relation with electrolyte bath contamination at a laboratory scale. Through comprehensive chemical characterization techniques such as Thermogravimetry and differential thermal analysis (TG/DTA) and X-ray Powder Diffraction (XRD), this study quantified the concentration range of contamination experienced by ORB. Subsequently, ORB samples were exposed to electrolytic bath contamination at different temperatures (between 700 °C and 960 °C) for one hour. Both uncontaminated and contaminated samples underwent evaluations of the thermophysical properties. This approach helps to elucidate how thermal properties are affected by contamination, providing valuable insights into enhancing cell performance and longevity. By focusing on the thermal aspects alongside chemical characterization, this study seeks to improve understanding of ORB behavior during its degradation and contribute to advancements in aluminum reduction cell technology.

Due to their versatility pneumatic conveying systems are in use in several bulk handling plants worldwide. With small mechanical changes existing plants can be upgraded or extended. The efficiency of the new version is then not in any case optimized. A simple model to scale up pneumatic conveying systems is described to verify the efficiency of newly modified conveying systems.

Based on given dimension and existing field data the energy consumption of a conveying plant is calculated first. Then the ideal version for the task is calculated and the two version are compared. The engineering model allows for an easy monitoring of existing and new systems.