The challenge of digitalization in the steel sector
TA Branca, B Fornai, V Colla, MM Murri, E Streppa… - Metals, 2020 - mdpi.com
Digitalization represents a paramount process started some decades ago, but which
received a strong acceleration by Industry 4.0 and now directly impacts all the process and …
received a strong acceleration by Industry 4.0 and now directly impacts all the process and …
Deep learning for time series forecasting: Advances and open problems
A time series is a sequence of time-ordered data, and it is generally used to describe how a
phenomenon evolves over time. Time series forecasting, estimating future values of time …
phenomenon evolves over time. Time series forecasting, estimating future values of time …
Deep echo state network (deepesn): A brief survey
C Gallicchio, A Micheli - arXiv preprint arXiv:1712.04323, 2017 - arxiv.org
The study of deep recurrent neural networks (RNNs) and, in particular, of deep Reservoir
Computing (RC) is gaining an increasing research attention in the neural networks …
Computing (RC) is gaining an increasing research attention in the neural networks …
A review of designs and applications of echo state networks
Recurrent Neural Networks (RNNs) have demonstrated their outstanding ability in sequence
tasks and have achieved state-of-the-art in wide range of applications, such as industrial …
tasks and have achieved state-of-the-art in wide range of applications, such as industrial …
Industrial symbiosis and energy efficiency in European process Industries: A review
TA Branca, B Fornai, V Colla, MI Pistelli, EL Faraci… - Sustainability, 2021 - mdpi.com
Over the last few decades, process industries have invested increasing efforts in developing
technical and operating solutions related to industrial symbiosis and energy efficiency in …
technical and operating solutions related to industrial symbiosis and energy efficiency in …
Artificial intelligence and mathematical models of power grids driven by renewable energy sources: A survey
To face the impact of climate change in all dimensions of our society in the near future, the
European Union (EU) has established an ambitious target. Until 2050, the share of …
European Union (EU) has established an ambitious target. Until 2050, the share of …
[HTML][HTML] Environment 4.0: How digitalization and machine learning can improve the environmental footprint of the steel production processes
V Colla, C Pietrosanti, E Malfa… - Matériaux & …, 2020 - mattech-journal.org
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 …
policy makers a key for the sustainability of the whole European Industry. However, in the …
Integration of renewable hydrogen production in steelworks off-gases for the synthesis of methanol and methane
The steel industry is among the highest carbon-emitting industrial sectors. Since the steel
production process is already exhaustively optimized, alternative routes are sought in order …
production process is already exhaustively optimized, alternative routes are sought in order …
A Deep Learning-based approach for forecasting off-gas production and consumption in the blast furnace
This article presents the application of a recent neural network topology known as the deep
echo state network to the prediction and modeling of strongly nonlinear systems typical of …
echo state network to the prediction and modeling of strongly nonlinear systems typical of …
Refinery 4.0, a Review of the Main Challenges of the Industry 4.0 Paradigm in Oil & Gas Downstream
IG Olaizola, M Quartulli, E Unzueta, JI Goicolea… - Sensors, 2022 - mdpi.com
Industry 4.0 concept has become a worldwide revolution that has been mainly led by the
manufacturing sector. Continuous Process Industry is part of this global trend where there …
manufacturing sector. Continuous Process Industry is part of this global trend where there …