Stable soft sensor modeling based on causality analysis
Data-driven soft sensors, aiming to estimate and predict hard-to-measure quality variables
using easy-to-measure process variables, have now become the key foundation for …
using easy-to-measure process variables, have now become the key foundation for …
Real-time tracking of renewable carbon content with AI-aided approaches during co-processing of biofeedstocks
Decarbonization of the oil refining industry is essential for reducing carbon emissions and
mitigating climate change. Co-processing bio feed at existing oil refineries is a promising …
mitigating climate change. Co-processing bio feed at existing oil refineries is a promising …
Bayesian-Based Causal Structure Inference With a Domain Knowledge Prior for Stable and Interpretable Soft Sensing
Due to the high-stakes nature of industrial processes, there is an immediate and pressing
need on soft sensors for stability and interpretability. In this regard, causality-inspired …
need on soft sensors for stability and interpretability. In this regard, causality-inspired …
Interpretable Industrial Soft Sensor Design Based on Informer and SHAP
Deep learning models have been widely employed in various domains, yet they have
certain limitations when it comes to industrial process applications. The two main challenges …
certain limitations when it comes to industrial process applications. The two main challenges …
化學工廠軟體儀表之研究-以Tennessee Eastman Process 為例
丁嘉源 - 清華大學智慧製造跨院高階主管碩士在職學位學程學位 …, 2021 - airitilibrary.com
化學工廠平時運作, 最重要除了操作安全之外, 產品品質及成本將會影響客戶採購的意願.
以往確認品質需要取樣至實驗室, 使用價格昂貴的分析儀器分析, 或安裝線上分析儀器 …
以往確認品質需要取樣至實驗室, 使用價格昂貴的分析儀器分析, 或安裝線上分析儀器 …
[引用][C] Contribuciones de inteligencia artificial aplicada en sistemas industriales
I Mendia Tellería - 2022