京都大学 · 工学
神野学教授の研究室は、化学プロセスの監視・制御を目的とした統計的プロセスモニタリング技術に注力しています。特に、独立成分分析（ICA）や分散の差異評価（DISSIM）を用いた故障検出手法の開発が特徴で、高次元で相関の高いプロセス変数から本質的な要因を抽出・監視することで、プロセスの安定性と生産性の向上を図っています。また、仮想センサ技術や局所線形回帰モデル（LW-PLS）の改良を通じて、実際の工業プロセスにおける推定精度の向上にも貢献しています。
Figures are computed from collected data and may differ slightly.
Abstract A chemical process has a large number of measured variables, but it is usually driven by fewer essential variables, which may or may not be measured. Extracting such essential variables and monitoring them will improve the process‐monitoring performance. Independent component analysis (ICA) is an emerging technique for finding several independent variables as linear combinations of measured variables. In this work, a new statistical process control method based on ICA is proposed. For i
The sustainability of petroleum-based fuel supply has gained broad attention from the global community due to the increase of usage in various sectors, depletion of petroleum resources, and uncertain around crude oil market prices. Additionally, environmental problems have also arisen from the increasing emissions of harmful pollutants and greenhouse gases. Therefore, the use of clean energy sources including biodiesel is crucial. Biodiesel is mainly produced from unlimited natural resources thr
Virtual sensing technology is crucial for high product quality and productivity in any industry. This review aims to clarify the trend of research and application of virtual sensing technology in process industries. After a brief survey, practical issues are clarified by introducing recent questionnaire survey results: 1) changes in process characteristics and operating conditions, 2) individual difference of equipment, and 3) reliability of soft-sensors. Since input variable selection is crucia
Abstract Multivariate statistical process control (MSPC) has been widely used for monitoring chemical processes with highly correlated variables. In this work, a novel statistical process monitoring method is proposed based on the idea that a change of operating condition can be detected by monitoring a distribution of process data, which reflects the corresponding operating conditions. To quantitatively evaluate the difference between two data sets, a dissimilarity index is introduced. The moni
Locally weighted partial least squares (LW-PLS) is one of Just-in-Time (JIT) modeling methods; PLS is used to build a local linear regression model every time when output variables need to be estimated. The prediction accuracy of local models strongly depends on the definition of similarity between a newly obtained sample and past samples stored in a database. To calculate the similarity, the Euclidean distance and the Mahalanobis distance have been widely used, but they do not take account of t
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