Matino et al., 2019
| Publication | Publication Date | Title |
|---|---|---|
| Matino et al. | Forecasting blast furnace gas production and demand through echo state neural network-based models: Pave the way to off-gas optimized management | |
| Matino et al. | Two innovative modelling approaches in order to forecast consumption of blast furnace gas by hot blast stoves | |
| Lu et al. | Data-driven real-time price-based demand response for industrial facilities energy management | |
| Porzio et al. | Reducing the energy consumption and CO2 emissions of energy intensive industries through decision support systems–An example of application to the steel industry | |
| Hu et al. | A multilevel prediction model of carbon efficiency based on the differential evolution algorithm for the iron ore sintering process | |
| Zhao et al. | A two-stage online prediction method for a blast furnace gas system and its application | |
| Porzio et al. | Process integration in energy and carbon intensive industries: An example of exploitation of optimization techniques and decision support | |
| CN104181900B (en) | Layered dynamic regulation method for multiple energy media | |
| Charitopoulos et al. | A unified framework for model-based multi-objective linear process and energy optimisation under uncertainty | |
| Maddaloni et al. | Multi-objective optimization applied to retrofit analysis: A case study for the iron and steel industry | |
| Zhang et al. | Carbon reduction from sustainable consumption of waste resources: An optimal model for collaboration in an industrial symbiotic network | |
| Matino et al. | Application of echo state neural networks to forecast blast furnace gas production: pave the way to off-gas optimized management | |
| Dettori et al. | Neural network-based modeling methodologies for energy transformation equipment in integrated steelworks processes | |
| Colla et al. | Assessing the efficiency of the off-gas network management in integrated steelworks | |
| Gomez et al. | A hybrid approach based machine learning models in electricity markets | |
| Dettori et al. | Optimizing methane and methanol production from integrated steelworks process off-gases through economic hybrid model predictive control | |
| Matino et al. | Machine learning-based models for supporting optimal exploitation of process off-gases in integrated steelworks | |
| Liu et al. | Event-triggered online scheduling for industrial-integrated energy system | |
| Maddaloni et al. | A quadratic programming model for the optimization of off-gas networks in integrated steelworks | |
| Kuramochi | Assessment of CO2 emissions pathways for the Japanese iron and steel industry towards 2030 with consideration of process capacities and operational constraints to flexibly adapt to a range of production levels | |
| Sisbot | Execution and evaluation of complex industrial automation and control projects using the systems engineering approach | |
| Cauz et al. | Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems | |
| Barbasova et al. | Blast-furnace melting blast control | |
| Ma et al. | Modeling and simulation method of enterprise energy consumption process based on fuzzy timed Petri nets | |
| Conejo | Predictive Models on Energy Consumption |