著者

Yuki Sugawara Masaaki Kotera Kenichi Tanaka Kimito Funatsu

出版者

Division of Chemical Information and Computer Sciences The Chemical Society of Japan

雑誌

Journal of Computer Aided Chemistry (ISSN:13458647)

巻号頁・発行日

vol.20, pp.7-17, 2019 (Released:2019-06-11)

参考文献数

27

被引用文献数

4

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Fluorescent substances are used in a wide range of applications, and the method that effectively design molecules having desirable absorption and emission wavelength is required. In this study, we used boron-dipyrromethene (BODIPY) compounds as a case study, and constructed high precision wavelength prediction model using ensemble learning. Prediction accuracy improved in stacking model using RDKit descriptors and Morgan fingerprint. The variables related to the molecular skeleton and the conjugation length were shown to be important. We also proposed an applicability domain (AD) estimation model that directly use the descriptors based on Tanimoto distance. The performance of the AD models was shown better than the OCSVM-based model. Using our proposed stacking model and AD model, newly generated compounds were screened and we obtained 602 compounds which were estimated inside the AD in both absorption wavelength and emission wavelength.

著者

Hiromasa Kaneko Kimito Funatsu

出版者

The Society of Chemical Engineers, Japan

雑誌

JOURNAL OF CHEMICAL ENGINEERING OF JAPAN (ISSN:00219592)

巻号頁・発行日

vol.50, no.6, pp.422-429, 2017-06-20 (Released:2017-06-20)

参考文献数

18

被引用文献数

1 2

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Multivariate statistical process control (MSPC) is important for monitoring multiple process variables and their relationships while controlling chemical and industrial plants efficiently and stably. Although many MSPC methods have been developed to improve the accuracy of fault detection, noise in the operating data, such as measurement noise and sensor noise, conceals important variations in process variables. This noise makes it difficult to recognize process states, but has not been fully considered in traditional MSPC methods. In this study, to improve the process state recognition performance, we apply several smoothing methods to each process variable. The best smoothing method and its hyperparameters are selected based on the normal distribution and variation of the reduced noise. Through case studies using numerical data and dynamic simulation data from a virtual plant, it is confirmed that the fault detection and identification accuracy are improved using the proposed method, which leads to enhanced state recognition performance.