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Affiliation |
Graduate School of International Resource Sciences Department of Earth Resource Engineering and Environmental Science |
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Date of Birth |
1969 |
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Laboratory Address |
1-1 Tegata Gakuen-cyou, Akita, 010-8502, Japan, Faculty of International Resource Sceinces, Akita University |
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Laboratory Phone number |
+81-18-889-2468 |
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Laboratory Fax number |
+81-18-889-2468 |
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Mail Address |
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ADACHI Tsuyoshi
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Graduating School 【 display / non-display 】
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-1992.03
Kyoto University Graduated
Graduate School 【 display / non-display 】
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-1994.03
Kyoto University Graduate School,Division of Engineering Master's Course Completed
Studying abroad experiences 【 display / non-display 】
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2012.10-2013.03
Curtin University Academic Visiting
Campus Career 【 display / non-display 】
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2016.04-Now
Akita University Graduate School of International Resource Sciences Department of Earth Resource Engineering and Environmental Science Professor
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2014.04-2016.03
Akita University Faculty of International Resource Sciences Department of International Resource Sciences Dept. of Resource Policy and Management Professor
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2010.09-2015.03
Akita University International Center for Research and Education on Mineral and Energy Resources Professor
External Career 【 display / non-display 】
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2016.04
Akita University Graduate School of International Resource Sciences Department of Earth Resource Engineering and Environmental Science Professor
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2014.04
Akita University Faculty of International Resource Sciences Department of International Resource Sciences Dept. of Resource Policy and Management Professor
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2006.06-2010.08
The University of Tokyo Environmental Science Center Associate Professor
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2006.03-2010.08
The University of Tokyo Institute of Industrial Science Associate Professor
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1995.04-2006.03
The University of Tokyo Graduate School of Engineering Assistant Professor
Research Areas 【 display / non-display 】
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Energy Engineering / Earth resource engineering, Energy sciences
Thesis for a degree 【 display / non-display 】
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Evaluation of Sustainability for Mineral Resources Development
Tsuyoshi Adachi
2001.07
Single author
Research Achievements 【 display / non-display 】
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Long-Term Sustainability of Copper and Iron Based on a System Dynamics Model
Larona Teseletso, Tsuyoshi Adachi
Resources ( Resources ) 11 ( 4 ) 2022.04 [Refereed]
Research paper (journal) Domestic Co-author
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One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
Kursat Kilic, Hisatoshi Toriya, Yoshino Kosugi, Tsuyoshi Adachi, Youhei Kawamura
Applied Sciences (Switzerland) ( Applied Sciences (Switzerland) ) 12 ( 5 ) 2022.03 [Refereed]
Research paper (journal)
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Brian Bino Sinaice, Narihiro Owada, Hajime Ikeda, Hisatoshi Toriya, Zibisani Bagai, Elisha Shemang, Tsuyoshi Adachi, Youhei Kawamura
Minerals ( Minerals ) 12 ( 2 ) 2022.02 [Refereed]
Research paper (journal)
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Exploring the optimal reverse supply chain for e-waste treatment under Chinese government subsidy
Juntao Wang, Wenhua Li, Nozomu Mishima, Tsuyoshi Adachi
Waste Management ( Waste Management ) 137 128 - 138 2022.01 [Refereed]
Research paper (journal)
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Mohammad Rahman Ardhiansyah, Tsuyoshi Adachi, Junichiro Oda
Mineral Economics ( Mineral Economics ) 36 ( 3 ) 371 - 381 2022 [Refereed]
Research paper (journal)
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Electricity generating resources portfolio optimization; Kenya’s case.
Ojiambo N. Malala, Tsuyoshi Adachi
14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ( 14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ) 2017
Research paper (international conference proceedings)
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Evaluation of rare earth element in Mongolia using real option analysis
Sambuudorj Erdenebat, Tsuyoshi Adachi
14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ( 14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ) 2017
Research paper (international conference proceedings)
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How macroeconomic factors influence volatility of metal prices?
Wenhua Li, Tsuyoshi Adachi
14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ( 14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ) 2017
Research paper (international conference proceedings)
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Investment of long-term supply of electricity through coal production in Botswana
Larona Teseletso, Tsuyoshi Adachi
14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ( 14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ) 2017
Research paper (international conference proceedings)
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What causes copper price changes: Stock markets or Speculation?
Kegomoditswe Koitsiwe, Tsuyoshi Adachi
14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ( 14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ) 2017
Research paper (international conference proceedings)
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MANJATE Elsa Pansilvania Andre, OHTOMO Yoko, ARIMA Takahiko, ADACHI Tsuyoshi, Miguel BENE Bernardo, KAWAMURA Youhei
Ｉｎｔｅｒｎａｔｉｏｎａｌ Ｊｏｕｒｎａｌ ｏｆ ｔｈｅ Ｓｏｃｉｅｔｙ ｏｆ Ｍａｔｅｒｉａｌｓ Ｅｎｇｉｎｅｅｒｉｎｇ ｆｏｒ Ｒｅｓｏｕｒｃｅｓ ( 日本素材物性学会 ) advpub ( 0 ) 2024.03
<p>Mining methods selection (MMS) is one of the most critical and complex decision-making tasks in mine planning. The selection of underground mining methods is considered to be the most problematic due to the complexity associated with the orebody geometry, geology, and geotechnical properties. This study integrated artificial intelligence and machine learning in the MMS process by introducing the recommendation systems (RS) approach in MMS through the nonnegative matrix factorization (NMF) algorithm. As such, the weighted nonnegative matrix factorization (WNMF) algorithm is applied to build a model for underground MMS. The study's input dataset is based on thirty mining projects' historical data. In the experiments, we evaluate the capability of the WNMF to predict underground mining methods using five input variables: ore strength, host-rock strength, orebody thickness, shape, and dip. The results show that the WNMF model achieved an average prediction accuracy of 67.5%, considered reasonable and realistic. Further findings reveal that the WNMF model is sensitive to the imbalanced class dataset used in the experiments, thus, suggesting the need to improve the dataset's quality. These results reveal the model's effectiveness in predicting underground mining methods; therefore, with continuous improvement, the WNMF model can be effectively applied in underground MMS.</p>