Environment 4.0: How digitalization and machine learning can improve the environmental footprint of the steel production processes

Abstract

The concepts of Circular Economy and Industrial Symbiosis are nowadays considered by policy makers a key for the sustainability of the whole European Industry. However, in the era of Industry4.0, this results into an extremely complex scenario requiring new business models and involve the whole value chain, and representing an opportunity as well. Moreover, in order to properly consider the environmental pillar of sustainability, the quality of available information represents a challenge in taking appropriate decisions, considering inhomogeneity of data sources, asynchronous nature of data sampling in terms of clock time and frequency, and different available volumes. In this sense, Big Data techniques and tools are fundamental in order to handle, analyze and process such heterogeneity, to provide a timely and meaningful data and information interpretation for making exploitation of Machine Learning and Artificial Intelligence possible. Handling and fully exploiting the complexity of the current monitoring and automation systems calls for deep exploitation of advanced modelling and simulation techniques to define and develop proper Environmental Decision Support Systems. Such systems are expected to extensively support plant managers and operators in taking better, faster and more focused decisions for improving the environmental footprint of production processes, while preserving optimal product quality and smooth process operation. The paper describes a vision from the steel industry on the way in which the above concepts can be implemented in the steel sector through some application examples aimed at improving socio-economic and environmental sustainability of production cycles.

Interrelationships between circular economy and Industry 4.0: A research agenda for sustainable supply chains

“…However, from the systematic review, positive CE-I4.0 interrelationships were also identified in terms of both approach and useful Repurposing with some I4.0 technologies such as IoT (Ingemarsdotter et al, 2019;Nobre & Tavares, 2020b), BDA (Bag & Pretorius, 2022;Jabbour et al, 2020;Kazancoglu et al, 2021;Nobre & Tavares, 2020a, 2020b and AI (Bag, Pretorius, et al, 2021;Colla et al, 2020 F I G U R E 6 CE-I4.0 guidelines for a research agenda for sustainable supply chains.…”

Big Data Handling in Process Surveillance and Quality Control of Secondary Metallurgical Processes

“…Image classification or categorization is widely used in many contexts, [ 25 ] most famously for the evaluation of social media content, but has slowly also become more accepted in industrial applications. [ 4 ] Visual surveillance of production plants has been used for many years, but the use of computer vision and automated evaluation of images to assess the process and detect problems or anomalies is a relatively new approach. The methodology for image classification with deep‐learning methods such as CNNs [ 13–15 ] is extensive, and range from object classification and identification to the detection of specific features in an image.…”

“…While many of the methods and algorithms to deal with large quantities of data have been developed already years ago, only recently there has been an increase of big data and machinelearning (ML) approaches to industrial processes. [4,5] Specific solutions ranging from the prediction of process variables or behavior in steelmaking [6][7][8] or quality monitoring and prediction [9,10] have been developed; however, the application of ML and big data tools is in general not widespread in the steel industry. In the following, we will discuss the possibilities of data analyses with big data methods and ML, considering the different data available from the RH plant.…”

Transformacja cyfrowa samorządu gminnego w Polsce

“…Coraz powszechniejsze są więc dyskusje np. o publicznym transporcie 4.0 (Tolkiehn i in., 2018), środowisku 4.0 (Colla i in., 2020), zdrowiu 4.0 (Ćwiklicki i in., 2021), samorządzie terytorialnym 4.0.…”