中国科技核心期刊
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日本科学技术振兴机构数据库(JST)
基于BP神经网络的炭浆提金工艺指标优化
- 作者：
朱德宇|沈岩柏|张丽颖
- 作者单位：
- 1
基金项目：
- 贵州省科技计划项目（ZX20230117）
详细信息：
- 作者简介：
- 朱德宇（1988—），男，高级工程师，硕士，从事选矿工作；E‑mail：307521045@qq. com
- PDF下载
Optimization of carbon‑in‑pulp gold extraction process metrics based on the BP neural network
- English Author：
Zhu Deyu¹ ², Shen Yanbai¹, Zhang Liying³
- Unit：
- （1. School of Resources and Civil Engineering, Northeastern University; 2. Hebei Dongliang Gold Mining Co., Ltd., China National Gold Group Corporation; 3. Shenyang Honest Safety Technology Service Group Co., Ltd.）
- 摘要
- 在线预览
- 参考文献
摘要:
炭浆提金工艺在黄金选矿领域应用广泛，然而，受其工艺特性影响，指标控制存在滞后性，当前多采用事后问题分析的方式进行指标调控，显著增加了工艺控制的不确定性。为有效应对这一问题，构建了三层 BP神经网络模型，其输入层、隐含层及输出层的神经元个数分别设置为5，8和1，用于预测炭浆提金工艺的尾矿品位。基于东梁金矿的实际生产数据对该网络进行训练，结果显示网络拟合效果良好。运用训练好的网络对现场30组生产数据进行仿真验证，仿真准确率高达 85.52 %，有效解决了炭浆提金工艺中尾矿品位滞后的难题。此外，通过 BP神经网络对现场工艺进行优化，确定了最佳工艺参数，优化后尾矿品位降低至0.08 g/t，显著提升了金资源利用率。
关键词:
BP神经网络；炭浆提金工艺；指标预测；工艺优化；单因素分析；资源利用
Abstract:
The carbon-in-pulp (CIP) gold extraction process is widely used in the gold beneficiation field. However, due to the inherent characteristies of the process, there is a lag in metrics control. At present, metries adjustment is typically carried out through post-analysis of issues, which significantly increases the uncertainty in process control. To effectively address this issue, a three-layer BP neural network model was constructed, with the number of neurons in the input, hidden, and output layers set to 5, 8, and 1, respectively, for predicting the tailings grade in the CIP process. The network was trained using actual production data from the Dongliang Gold Mine, and the results showed good network fitting performance. The trained network was then used to simulate and verify 30 sets of on-site production data, achieving a simulation accuracy of 85.52 %, effectively resolving the issue of lagging tailings grade in the CIP process. In addition, pro-cess optimization was performed using the BP neural network, and the optimal process parameters were identified. After optimization, the tailings grade was reduced to 0.08 g/t, significantly improving the utilization rate of gold resources.
Keywords:
BP neural network; carbon‑in‑pulp gold extraction process; metrics prediction; process optimization; single‑factor analysis; resource utilization
