Wind power prediction is essential for ensuring the stability and efficient operation of modern power systems, particularly as renewable energy integration continues to expand. This paper presents a comprehensive review of machine learning techniques applied to wind power prediction, emphasizing their advantages over traditional physical and statistical models. Machine learning methods, especially deep learning approaches such as Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ensemble learning techniques like XGBoost, excel in addressing the nonlinearity and complexity of wind power data. The review also explores critical aspects such as data preprocessing, feature selection strategies, and model optimization techniques, which significantly enhance prediction accuracy and robustness. Challenges such as data acquisition difficulties, complex terrain influences, and sensor quality issues are examined in depth, with proposed solutions discussed. Additionally, the paper highlights future research directions, including the potential of multi-model fusion, emerging deep learning technologies like Transformers, and the integration of smart sensors and IoT technologies to develop intelligent, automated, and reliable prediction systems. By addressing existing challenges and leveraging advanced machine learning techniques, this work provides valuable insights into the current state of wind power prediction research and offers strategic guidance for enhancing the applicability and reliability of prediction models in practical scenarios.

As renewable resource of reducer, biochar prepared by pine saw dust is proposed for copper slag direct reduction in this paper. Combined with thermodynamic analysis, effects of reduction time, temperature and CaO addition ratio on solid copper slag reduction characteristics are discussed. The oxide of iron in copper slag are Fe₃O₄ and 2FeO·SiO₂. The reduction processes are carried out step by step respectively: Fe₃O₄→FeO→Fe and 2FeO·SiO₂→Fe. The porous structure of biochar exhibits higher reduction reactivity and reaction rate than coal. CaO reduced the Gibbs free energy of reduction reactions and facilitated the reduction of 2FeO·SiO₂ with C and CO. When CaO was added, separation reaction of FeO and SiO₂ take place and α-SiO₂ and β-SiO₂ are produced. When addition ratio of CaO is above 0.3, CaO·SiO₂ and 2CaO·SiO₂ are produced. Reduction process of copper slag was established as follow: (a) dehydration and fast pyrolysis; (b) reduction of iron oxides by C and CO; (c) sweating metallic iron outflows from cracks in pellet. Besides, direct reduction reaction mechanism and transport process of Cu are established based on reduction experiments, XRD and SEM-EDS analysis.

Against the backdrop of global energy crises and climate change, the iron and steel industry, as a typical high energy consumption and high-emission sector, faces rigid constraints for energy conservation and emission reduction. This paper systematically reviews the research progress and application effects of energy-saving technologies across the entire steel production chain, including coking, sintering, ironmaking, steelmaking, continuous casting, and rolling processes. Studies reveal that technologies such as coal moisture control (CMC) and coke dry quenching (CDQ) significantly improve energy utilization efficiency in the coking process. In sintering, thick-layer sintering and flue gas recirculation (FGR) technologies reduce fuel consumption while enhancing sintered ore performance. In ironmaking, high-efficiency pulverized coal injection (PCI) and hydrogen-based fuel injection effectively lower coke ratios and carbon emissions. Integrated and intelligent innovations in continuous casting and rolling processes (e.g., endless strip production, ESP) substantially reduce energy consumption. Furthermore, the system energy conservation theory, through energy cascade utilization and full-process optimization, drives dual reductions in comprehensive energy consumption and carbon emission intensity. The study emphasizes that future advancements must integrate hydrogen metallurgy, digitalization, and multi-energy synergy to steer the industry toward green, high-efficiency, and low-carbon transformation, providing technical support for China’s “Dual Carbon” goals.