Journal ArticleDOI
Machine learning to predict refractory corrosion during nuclear waste vitrification
TLDR
In this paper, the effects of model nuclear waste glass composition on the corrosion of Monofrax® K-3 refractory, using machine learning (ML) methods for data investigation and modeling of published borosilicate glass composition data and refining performance.Abstract:
The goal of this study was to determine the effects of model nuclear waste glass composition on the corrosion of Monofrax® K-3 refractory, using machine learning (ML) methods for data investigation and modeling of published borosilicate glass composition data and refractory corrosion performance. First, statistical methods were used for exploration of the data, and the list of features (model terms) was determined. Several model types were explored, and the Bayesian Ridge type was the most promising due to low mean average error and mean standard error as well as high R2 value. Parameters and model results using previously identified model features and those from this study are compared. ML methods appear to give results at least as good as previously available models for describing the effects of glass composition on refractory corrosion.read more
Citations
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Proceedings ArticleDOI
Evaluation of Smart Environmental Protection Systems and Novel UV-Oriented Solution for Integration, Resilience, Inclusiveness and Sustainability
Hao Yuan,Zetong Yin,Chenjun Zhao,Zhiyuan Yang,Shengyue Gao,Sinuo Zhao,Lixin Xu,Tian Tan,Yajun Fang +8 more
TL;DR: In this paper, the authors evaluate the challenges of the environment system, including land pollution, water pollution and air pollution, and the current status of smart environmental protection systems based on the framework of closed feedback control loop: data acquisition, communication, decision-making and action.
Journal ArticleDOI
Glass-contact refractory of the nuclear waste vitrification melters in the United States: a review of corrosion data and melter life
Tong-shun Jin,M. A. Hall,John D. Vienna,William C. Eaton,Jake W. Amoroso,Bruce J. Wiersma,Alexander W. Abboud,Donna Post Guillen,Albert A. Kruger +8 more
TL;DR: In this paper , the performance of the refractory lining in glass melters used for nuclear waste vitrification is critical to the melter reliability for long-term continuous operation.
References
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Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature
TL;DR: In this article, the root mean square error (RMSE) and the mean absolute error (MAE) are used to evaluate model performance and it is shown that the RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian.
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TL;DR: In this paper, the basic principles of glass formation and composition are discussed, including linear elasticity, phase separation and liquid immiscibility, and dielectric properties of glass.