基于新陈代谢算法优化的GM(1,N)动态网络灰分拟合

Research of GM(1,N) dynamic network based on the metabolic algorithm optimization for ash fitting

煤炭是我国能源资源安全的压舱石,“双碳”背景下实现煤炭清洁加工与高效利用意义重大,而灰分检测对煤炭清洁化和智能化发展尤为重要。针对现有灰分检测存在的检测精度有待提高的突出问题,以两淮矿区典型煤样为研究对象,通过慢灰和X射线荧光(X ray fluorescence,XRF)测试系统地探究了煤样的灰分和元素组成分布规律,并结合机器学习理论构建了灰分-元素特征数据集;结合灰色系统理论和新陈代谢算法,构建了自适应的GM(1,N)动态网络灰分拟合优化模型,并详细设计了动态网络算法流程;提出了GM(1,N)动态模型的关键超参数,并通过与常规拟合方法对比,全面地评价了模型拟合性能。结果表明:两淮矿区煤可视为由可燃元素和成灰元素共同构成,且成灰元素中质量分数占比最高为Si和Al,次之为S、Fe和Ca等,最少为P和Cl等,并且煤中成灰元素总含量与灰分呈正相关,而可燃元素与之相反;以灰分为标签值、以组成元素为特征值,形成了煤的灰分-元素特征数据集;以样本数据划分→动态网络灰分拟合→模型评价机制→动态拟合模型自适应优化→鲁棒性提升→多轮迭代优化为主线设计了GM(1,N)动态网络灰分拟合模型及其算法流程,有效提升了数据集稳定性和新鲜度,并且迭代收敛速度快,灰分误差阈值5%时其准确率达100%;对比经典GM(1,N)模型和常规多元线性回归模型,证明了新模型的灰分拟合性能得到显著提升,其相对误差为0.16%~4.96%、误差均值仅2.29%。

Coal is the ballast stone of China's energy and resource security.It is of great significance to realize the clean processing and efficient utilization of coal in the context of “dual carbon”.In particular,ash detection is important for the clean and intelligent utilization of coals.To address the outstanding problems of the existing ash detection that need to be improved in terms of detection accuracy,the distribution of ash and elemental composition of coal samples were systematically studied using slow-ashing and X-ray fluorescence(XRF) detection methods around typical coal in the Huainan and Huaibei mining areas.On this basis,the ash-elements feature dataset was constructed by adopting machine learning theory.Combined with Gray System Theory and Metabolic Algorithm,an adaptive GM(1,N) dynamic network gray fitting optimization model was constructed and the dynamic network algorithm flow was designed.For the GM(1,N) dynamic model,the key hyper-parameters were proposed and the model fitting performance was comprehensively evaluated by comparing with conventional fitting methods.The results show that the coal in the Huainan and Huaibei mining areas can be regarded as the composition of combustible elements and ash-forming elements.In the ash-forming elements,the highest contents are Si and Al,followed by S,Fe,and Ca,etc.,and the lowest contents are P and Cl,etc.Moreover,the total content of ash-forming elements in coal is positively correlated with ash content,while negatively correlated with combustible elements.The GM(1,N) dynamic network ash fitting model and its algorithm flow were designed in the main line of sample data division → dynamic network ash fitting → model evaluation mechanism → dynamic fitting model adaptive optimization → robustness enhancement → multi-round iterative optimization,which effectively improve the stability and freshness of the data set with fast iterative convergence.The accuracy of the GM(1,N) dynamic network model is up to 100% when the ash fitting error threshold is 5%.Comparing with the classical GM(1,N) model and the conventional multiple linear regression model,it is demonstrated that the ash fitting performance of the new model is significantly improved,with the relative errors between the fitted and true values ranging from 0.16% to 4.96% and the mean error value of only 2.29%.