Data-driven soft sensing modeling is becoming a powerful tool in the ironmaking process
due to the rapid development of machine learning and data mining. Although various soft …

The introduction of domain knowledge opens new horizons to fuzzy clustering. Then
knowledge-driven and data-driven fuzzy clustering methods come into being. To address …

Applying image-based processing methods to original videos on a framewise level breaks
the temporal consistency between consecutive frames. Traditional video temporal …

The accuracy of short-term wind power forecasting (WPF) can be improved by effective
mining of numerical weather prediction data. In this article, a novel short-term WPF approach …

The process industry is an industrial field of interdisciplinary nature involving electrical
engineering, energy, petroleum, chemical, and metallurgy, which play a key role in the …

Burn-through point (BTP) is a very key factor in maintaining the normal operation of the
sintering process, which guarantees the yield and quality of sinter ore. Due to the …

Burn-through point (BTP) is an essential thermal state parameter in a sintering process,
which is a direct reflection of the stability of this process. However, it cannot be measured …

Accurate prediction of carbon efficiency is a prerequisite for achieving energy saving and
consumption reduction in an iron ore sintering process, and is the key to guaranteeing the …

The model bias caused by input outliers is a dramatic obstacle to the application of models
in industrial processes. To cope with this problem, this article proposes a robust modeling …

The highly dimensional characteristic of time series brings many challenges on direct mining
time series, such as high cost in time and space. Granular computing provides a potential …