MULTI-SCENARIO DEMAND FORECAST OF RAW
NICKEL IN CHINA BASED ON GRAY GM-BP NEURAL NETWORK COMPOUND MODEL

doi:10.13776/j.cnki.resourcesindustries.20231123.001

Abstract

Abstract: National Mineral Resources Planning (2016-2020), issued in 2016, lists nickel as strategic mineral. China is the largest nickel consumer, but insufficient in nickel resources with high dependence on external supply. Scientific forecast of raw nickel demand is of significance to secure nickel production and supply chains. This paper uses gray correlation to select China’s stainless steel production, GDP per capita, electroplating market scale, urbanization rate, industrial structure, and new energy vehicle production as driving variables to forecast nickel demand under different scenarios from demand side, and combines gray GM(1, 1) model with BP neural network to construct a GM-BP model based on residual minimization, which is used to forecast China’s nickel demand from 2015 to 2035. This compound model is effective in forecasting non-linear sequence data, with the fitting error smaller than GM(1, 1) model. China’s demand for raw nickel is forecasted at 1 822.2 kt in 2025, 2 720.8 kt in 2030 and 3 951.7 kt in 2035 at an annual rising rate of 4.26% in the 14^th Five-Year Plan, 10.54% in the 15^th Five-Year Plan and 9.78% in the 16^th Five-Year Plan. The rising demand trend for raw nickel will lead to a conflict between its supply and demand. China has to raise its supply capacity and decrease dependence on imports. This paper presents suggestions on upgrading stainless steel industry, optimizing processing and producing new alloy materials, and on boosting nickel exploration and development, and on diversifying import sources, and on supporting Chinese investors’ overseas nickel projects.

Key words: GM-BP model, BM neural network, raw nickel demand, scenario forecast

摘要：

2016年我国颁布《全国矿产资源规划(2016—2020年)》，首次将镍列为战略性矿产资源。我国是全球最大的镍消费国，但镍资源储量少，对外依存度高，科学预测镍原矿需求量对保障镍矿产业链与供应链安全具有重要的现实意义。从需求侧出发，利用灰色关联度法选取中国不锈钢产量、人均GDP、电镀行业市场规模、城镇化率、产业结构、新能源汽车产量作为镍原矿需求情景预测的驱动变量，再在灰色GM(1,1)模型预测基础上，与BP神经网络算法相结合，构建基于残差优化的GM-BP组合模型，对2025—2035年中国镍原矿需求展开多情景预测。研究结果表明：组合模型实现了对小样本非线性时间序列数据的有效预测，且比GM(1,1)模型拟合误差更小，预测精度更高；根据组合模型，2025年、2030年、2035年我国镍原矿多情景需求均值分别为182.22万t、272.08万t、395.17万t，“十四五”“十五五”“十六五”期间需求年均增长4.26%、10.54%、9.78%。镍原矿需求呈稳定上升态势，镍矿供需矛盾将进一步加剧，我国必须提高镍供应能力，降低对进口镍的依赖程度。对此，提出如下政策建议：1)推进国内不锈钢产业的转型升级，优化生产工艺和产品结构，推广新型合金材料的应用；2)加大对镍矿勘探和开发的支持力度，如鼓励矿业企业技术创新，提高勘探效率和精度，同时积极推动国际合作，吸引国外先进技术、设备进入国内市场；3)促进进口多元化，与多个供应国建立合作关系，鼓励国内企业参与海外镍矿项目。

关键词: GM-BP模型, BP神经网络, 镍原矿需求, 情景预测

CLC Number:

F205

ZHOU Wenxiao, ZHAN Cheng, ZHANG Zhouyi, et al.

MULTI-SCENARIO DEMAND FORECAST OF RAW NICKEL IN CHINA BASED ON GRAY GM-BP NEURAL NETWORK COMPOUND MODEL [J]. Resources & Industries, 2024, 26(2): 53-66.

周文潇, 詹成, 张周益, 等.

基于灰色GM-BP神经网络组合模型的中国镍原矿多情景需求预测 [J]. 资源与产业, 2024, 26(2): 53-66.

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MULTI-SCENARIO DEMAND FORECAST OF RAW NICKEL IN CHINA BASED ON GRAY GM-BP NEURAL NETWORK COMPOUND MODEL

基于灰色GM-BP神经网络组合模型的中国镍原矿多情景需求预测

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