Knowledge-Inference-Based Intelligent Decision Making for Nonferrous Metal Mineral-Processing Flowsheet Design
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| Udgivet i: | Minerals vol. 15, no. 4 (2025), p. 374 |
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| Hovedforfatter: | |
| Andre forfattere: | , , , , |
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MDPI AG
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| Online adgang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3194635029 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2075-163X | ||
| 024 | 7 | |a 10.3390/min15040374 |2 doi | |
| 035 | |a 3194635029 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231539 |2 nlm | ||
| 100 | 1 | |a Yang, Jiawei |u School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China; sunchy@cae.cn (C.S.); zhou_jw@bgrimm.com (J.Z.) | |
| 245 | 1 | |a Knowledge-Inference-Based Intelligent Decision Making for Nonferrous Metal Mineral-Processing Flowsheet Design | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a With the increasing diversification of ore types and the complexity of processing techniques in the mining industry, traditional decision-making methods for mineral processing flowsheets can no longer meet the high efficiency and intelligence requirements. This paper proposes a knowledge graph-based framework for constructing a mineral-processing design knowledge base and knowledge reasoning, aiming at providing intelligent and efficient decision support for mining engineers. This framework integrates Chinese NLP models for text vectorization, optimizes prompt generation through Retrieval Augmented Generation (RAG) technology, realizes knowledge graph construction, and implements knowledge reasoning for nonferrous metal mineral-processing design using large reasoning models. By analyzing the genetic characteristics of ores and the requirements of processing techniques, the framework outputs reasonable flowsheet designs, which could help engineers save research time and labor in optimizing processes, selecting suitable reagents, and adjusting process parameters. Through decision analysis of the mineral-processing flowsheets for three typical copper mines, the framework demonstrates its advantages in improving process flowsheet design, and shows good potential for further application in complex mineral-processing environments. | |
| 653 | |a Reagents | ||
| 653 | |a Minerals | ||
| 653 | |a Deep learning | ||
| 653 | |a Knowledge bases (artificial intelligence) | ||
| 653 | |a Heavy metals | ||
| 653 | |a Optimization | ||
| 653 | |a Anniversaries | ||
| 653 | |a Mineralogy | ||
| 653 | |a Decision support systems | ||
| 653 | |a Semantic web | ||
| 653 | |a Design | ||
| 653 | |a Decision making | ||
| 653 | |a Nonferrous metals | ||
| 653 | |a Knowledge representation | ||
| 653 | |a Mineral processing | ||
| 653 | |a Decision analysis | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Graphs | ||
| 653 | |a Mining industry | ||
| 653 | |a Reasoning | ||
| 653 | |a Engineers | ||
| 653 | |a Ores | ||
| 653 | |a Knowledge based engineering | ||
| 653 | |a Natural language processing | ||
| 653 | |a Complexity | ||
| 653 | |a Process parameters | ||
| 700 | 1 | |a Sun Chuanyao |u School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China; sunchy@cae.cn (C.S.); zhou_jw@bgrimm.com (J.Z.) | |
| 700 | 1 | |a Zhou Junwu |u School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China; sunchy@cae.cn (C.S.); zhou_jw@bgrimm.com (J.Z.) | |
| 700 | 1 | |a Wang, Qingkai |u State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process, Beijing 102628, China; wang_qk@bgrimm.com (Q.W.); zhangkanghui@bgrimm.com (K.Z.); songtao@bgrimm.com (T.S.) | |
| 700 | 1 | |a Zhang Kanghui |u State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process, Beijing 102628, China; wang_qk@bgrimm.com (Q.W.); zhangkanghui@bgrimm.com (K.Z.); songtao@bgrimm.com (T.S.) | |
| 700 | 1 | |a Song, Tao |u State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process, Beijing 102628, China; wang_qk@bgrimm.com (Q.W.); zhangkanghui@bgrimm.com (K.Z.); songtao@bgrimm.com (T.S.) | |
| 773 | 0 | |t Minerals |g vol. 15, no. 4 (2025), p. 374 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3194635029/abstract/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3194635029/fulltextwithgraphics/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3194635029/fulltextPDF/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |