Advanced analytics to improve energy efficiency of steel industry - A systematic review on ladle logistics - Akhil Raja Keshetti; Victor S.P. Ruela; Hao Chen; Marcos R. Machado (Cleaner Engineering and Technology)
The steel industry, a significant contributor to global energy consumption and CO2 emissions, must adopt innovative approaches to improve efficiency and sustainability. This systematic literature review focused on identifying advanced analytical methods that have the capability of enabling informed decision-making in optimising steel ladle logistics—a key process within steel-making that influences energy use and emissions. The scientific landscape has State-of-the-Art optimiser algorithms built using mathematical models to generate ladle logistics schedules. The evaluation of such decision support systems is generally carried out using various techniques. This review uniquely highlights how discrete event simulation (DES) can be integrated with optimization models for robust validation of scheduling decisions. This paper explores validation techniques incorporating historical operational data and simulation modelling to ensure that theoretical optimization translates to practical, real-world applications. Key sustainability indicators, such as CO2 emission intensity and energy consumption per tonne of steel, are identified and assessed for their role in aligning steel production with environmental goals such that they can be adapted to validate the levels reported by the optimization model against the simulation model. The findings reveal that integrating DES alongside the optimization model enhances the feasibility and robustness of scheduling models. This approach supports the industry's shift towards sustainable practices by providing decision-makers with reliable tools for optimising logistics, reducing energy consumption, and minimizing emissions.