[HTML][HTML] LSTENet: Cement productivity prediction using a self-attention spatio-temporal variational autoencoder
In the advent of the Industry 4.0 paradigm, intelligent manufacturing has gained prominence
with the integration of advanced Artificial Intelligence (AI) technologies aimed at augmenting …
with the integration of advanced Artificial Intelligence (AI) technologies aimed at augmenting …
STGNets: A spatial–temporal graph neural network for energy consumption prediction in cement industrial manufacturing processes
Energy consumption is an essential indicator for conserving energy and reducing emissions
in industrial manufacturing processes. However, the industrial production chain is a multi …
in industrial manufacturing processes. However, the industrial production chain is a multi …
Multiscale convolutional and recurrent neural network for quality prediction of continuous casting slabs
Quality prediction in the continuous casting process is of great significance to the quality
improvement of casting slabs. Due to the uncertainty and nonlinear relationship between the …
improvement of casting slabs. Due to the uncertainty and nonlinear relationship between the …
A spatio-temporal data decoupling convolution network model for specific surface area prediction in cement grind process
X Hao, G Huang, Z Li, L Zheng, Y Zhao - ISA transactions, 2023 - Elsevier
The specific surface area is one of the important indicators for measuring the quality of
cement products. Realizing accurate prediction for specific surface area is very important for …
cement products. Realizing accurate prediction for specific surface area is very important for …
Temperature control optimization in a steel‐making continuous casting process using a multimodal deep learning approach
GW Song, BA Tama, J Park, JY Hwang… - steel research …, 2019 - Wiley Online Library
Continuous casting is the process of concretion of hot molten liquid in a continuous
groundwork. As the process of secondary cooling has a critical impact on strand surface …
groundwork. As the process of secondary cooling has a critical impact on strand surface …
Quality Control of Cement Clinker through Operating Condition Classification and Free Calcium Oxide Content Prediction
X Lyu, D Chu, X Lu, J Mu, Z Zhang, D Yun - Applied Sciences, 2024 - mdpi.com
Recent advances in artificial intelligence (AI) technologies such as deep learning open up
new opportunities for various industries, such as cement manufacturing, to transition from …
new opportunities for various industries, such as cement manufacturing, to transition from …
Spatial and sequential deep learning approach for predicting temperature distribution in a steel-making continuous casting process
Continuous casting is the procedure of the successive casting for solidification of the steel,
which contains several cooling processes along the caster to coagulate the molten steel. It is …
which contains several cooling processes along the caster to coagulate the molten steel. It is …
Data-Driven AI Models within a User-Defined Optimization Objective Function in Cement Production
O Manis, M Skoumperdis, C Kioroglou, D Tzilopoulos… - Sensors, 2024 - mdpi.com
This paper explores the energy-intensive cement industry, focusing on a plant in Greece and
its mill and kiln unit. The data utilized include manipulated, non-manipulated, and …
its mill and kiln unit. The data utilized include manipulated, non-manipulated, and …
[HTML][HTML] A multiscale adaptive framework based on convolutional neural network: Application to fluid catalytic cracking product yield prediction
N Liu, CM Zhu, MX Zhang, XY Lan - Petroleum Science, 2024 - Elsevier
Since chemical processes are highly non-linear and multiscale, it is vital to deeply mine the
multiscale coupling relationships embedded in the massive process data for the prediction …
multiscale coupling relationships embedded in the massive process data for the prediction …
Quality variable prediction for nonlinear dynamic industrial processes based on temporal convolutional networks
Soft sensors have been extensively developed to estimate the difficult-to-measure quality
variables for real-time process monitoring and control. Process nonlinearities and dynamics …
variables for real-time process monitoring and control. Process nonlinearities and dynamics …