Advanced analytics to improve energy efficiency of steel industry - A systematic review on ladle logistics
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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Cleaner Engineering and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666790825000308 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850053716842905600 |
|---|---|
| author | Akhil Raja Keshetti Victor S.P. Ruela Hao Chen Marcos R. Machado |
| author_facet | Akhil Raja Keshetti Victor S.P. Ruela Hao Chen Marcos R. Machado |
| author_sort | Akhil Raja Keshetti |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e86e87082b984b63adbd942c187b3f33 |
| institution | DOAJ |
| issn | 2666-7908 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cleaner Engineering and Technology |
| spelling | doaj-art-e86e87082b984b63adbd942c187b3f332025-08-20T02:52:27ZengElsevierCleaner Engineering and Technology2666-79082025-03-012510090710.1016/j.clet.2025.100907Advanced analytics to improve energy efficiency of steel industry - A systematic review on ladle logisticsAkhil Raja Keshetti0Victor S.P. Ruela1Hao Chen2Marcos R. Machado3University of Twente, Faculty of Electrical Engineering, Mathematics, and Computer Science, AE Enschede, 7500, Netherlands; Tata Steel Nederland, Ceramics Research Centre, Velsen-Noord, 1951 JZ, NetherlandsTU Wien, Institute of Energy Systems and Thermodynamics, Vienna, 1040, Austria; Tata Steel Nederland, Ceramics Research Centre, Velsen-Noord, 1951 JZ, NetherlandsUniversity of Twente, Faculty of Behavioural, Management and Social Sciences, Department of High-Tech Business and Entrepreneurship, AE Enschede, 7500, Netherlands; Corresponding author.University of Twente, Faculty of Behavioural, Management and Social Sciences, Department of High-Tech Business and Entrepreneurship, AE Enschede, 7500, NetherlandsThe 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.http://www.sciencedirect.com/science/article/pii/S2666790825000308Discrete event simulationEnergy efficiencySchedulingSteel logisticsValidation framework |
| spellingShingle | Akhil Raja Keshetti Victor S.P. Ruela Hao Chen Marcos R. Machado Advanced analytics to improve energy efficiency of steel industry - A systematic review on ladle logistics Cleaner Engineering and Technology Discrete event simulation Energy efficiency Scheduling Steel logistics Validation framework |
| title | Advanced analytics to improve energy efficiency of steel industry - A systematic review on ladle logistics |
| title_full | Advanced analytics to improve energy efficiency of steel industry - A systematic review on ladle logistics |
| title_fullStr | Advanced analytics to improve energy efficiency of steel industry - A systematic review on ladle logistics |
| title_full_unstemmed | Advanced analytics to improve energy efficiency of steel industry - A systematic review on ladle logistics |
| title_short | Advanced analytics to improve energy efficiency of steel industry - A systematic review on ladle logistics |
| title_sort | advanced analytics to improve energy efficiency of steel industry a systematic review on ladle logistics |
| topic | Discrete event simulation Energy efficiency Scheduling Steel logistics Validation framework |
| url | http://www.sciencedirect.com/science/article/pii/S2666790825000308 |
| work_keys_str_mv | AT akhilrajakeshetti advancedanalyticstoimproveenergyefficiencyofsteelindustryasystematicreviewonladlelogistics AT victorspruela advancedanalyticstoimproveenergyefficiencyofsteelindustryasystematicreviewonladlelogistics AT haochen advancedanalyticstoimproveenergyefficiencyofsteelindustryasystematicreviewonladlelogistics AT marcosrmachado advancedanalyticstoimproveenergyefficiencyofsteelindustryasystematicreviewonladlelogistics |