Development of modelling and digitalization tools for alumina refinery

Abstract

Global metal industry is progressively relying on various digitalization tools i.e. information and communication technology (ICT) for improved process control and optimization. Hindalco, major metal producer, leverages high-fidelity ICT tools for smooth and optimized operation of refineries and smelters. In the present study, the application of ICT at Hindalco alumina refinery is detailed, wherein alumina is extracted from bauxite ore, which is further processed to get aluminium metal. Evaporation and calcination are key stages in Bayer process defining the quality of alumina as well as the carbon footprint. In the present work, a framework of modelling tools, which include predictive models based on the machine learning algorithm as well as physics-based models are developed for these key processes in alumina refinery. For evaporation circuit, first-principle based model using Aspen is developed to get better insights into the operation as well as to provide essential guidelines to develop ML model. Then, Random forest ML model is employed using historian data to predict steam economy. Validation using real-time DCS data on a minute-wise basis is performed. The developed model is capable of real time prediction of the steam economy within acceptable deviation of ± 5%. The model is now integrated with a control system at Hindalco alumina refinery for online monitoring as well as providing necessary predictive and corrective actions to plant personnel for stable and energy efficient operation. For calcination stage, physics-based model using CFD is developed for calciner and holding vessel to get necessary understandings into flow, temperature, and concentration profiles to predict alpha alumina generated. Additionally, extreme gradient boosting type ML model is developed for predicting alpha alumina and LOI using plant historian data. The validation showed that 77% of the predictions are falling in the acceptable range of 0–10% deviation. The predictive model as well as suggestion is now connected through graphical user interface/dashboard (GUI) in Hindalco refinery control panel for taking corrective action.