Feng Yan

Zhejiang University | ZJU · Department of Control Science and Engineering

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 sensing techniques have been successfully used in both the sintering process and blast furnace, they have not been comprehensively reviewed. In this work, we provide an overv...

Predicting burn-through point (BTP) in advance is a quite critical task for the sintering process. However, sintering is a complex physicochemical reaction process, and the strong spatial-temporal correlations of data make the multi-step prediction task very challenging. The previous BTP multi-step prediction model only extracts spatial features in...

Sintering process, as a primary modus of the blast furnace ironmaking industry, has enormous economic value and environmental protection significance for the iron and steel enterprises. Recently, with the emergence of artificial intelligence and big data, data‐driven modeling methods in the sintering process have increasingly received the researche...

Sinter ore is the main raw material of the blast furnace, and burn-through point (BTP) has a direct influence on the yield, quality, and energy consumption of the ironmaking process. Since iron ore sintering is a very complex industrial process with strong nonlinearity, multivariable coupling, random noises, and time variation, traditional soft-sen...

Accurate and real-time estimation of iron ore sintering quality index is essential for the stability of the production process. However, the sintering process data is generally characterized by high dimensionality, collinearity, nonlinearity, and dynamic features, which seriously hinder the modeling performance. To cope with the complex properties...

Recently, explorations of the relationships between material properties and their compositions and the process parameters based on machine learning have increasingly become new research paradigms for material science. However, the previous research regarding the mechanical properties of steel had largely focused only on single alloy datasets. The a...

Nowadays, data-driven soft sensors have become a mainstream for the key performance indicators prediction, which guarantees the safety and stability of the industrial process. The typical autoencoder (AE) has been widely used to extract potential features through unsupervised pretraining and supervised fine-tuning. However, most existing studies fa...

It is not completely understood fatigue strength at this time due to its complex formation mechanism. Therefore, in order to address this issue, machine learning has been used to examine the important factors involved in predicting fatigue strength. In this study, a hybrid model was proposed based on the modified bagging method by combining XGBoost...

Active collision avoidance system has received more and more attraction, which has the capability to avoid potential accidents and reduce driver burden. This paper proposes an active collision avoidance system which consists of a path planner and a coordinated lateral controller. In the path planner, cubic B-spline is developed to obtain collision-...

Exploring the relationships between the properties of steels and their compositions and manufacturing parameters is extremely crucial and indispensable to understanding the science of materials, and subsequently developing new materials. Tensile strength and plasticity, as two important properties of steels, are key to the improvement and optimizat...