Full
Outline
A layered optimization model of hydrometallurgy process based on deep learning is proposed. The model adopts hierarchical design, including process level optimization and process level optimization. At the process level, Transformer model is innovatively introduced to establish a nonlinear mapping model of material consumption and quality index, and optimize each process parameter with minimum material consumption as the goal. In the process layer, the innovative DDPG algorithm is introduced to adjust the operating variables and maximize the economic benefits of the whole process. In addition, generative adversarial network (GAN) is introduced to generate optimization samples to further improve the robustness of the optimization model. After the sodium cyanide and zinc powder additions are adjusted in real time by the Transformer optimization model, the gold production can be significantly increased and the material consumption cost can be significantly reduced. Compared with the benchmark test, the economic benefit increased by 15%-20%, the leaching rate and the replacement rate increased by 2%-4% respectively. The model is superior to the traditional deep learning method in terms of economic benefit, process stability and uncertainty adaptability, and can provide a new way for intelligent production in hydrometallurgical industry.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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