基于潜在特征选择性集成建模的二噁英排放浓度软测量

二噁英(Dioxin，DXN)是导致城市固废焚烧(Municipal solid waste incineration, MSWI)建厂存在“邻避现象”的主要原因之一. 工业现场多采用离线化验手段检测DXN浓度, 难以满足污染物减排控制的需求. 针对上述问题, 本文提出了基于潜在特征选择性集成(Selective ensemble, SEN)建模的DXN排放浓度软测量方法. 首先, 采用主元分析(Principal component analysis, PCA)分别提取依据工艺阶段子系统及全流程系统过程变量的潜在特征, 并依据预设贡献率阈值进行特征初选; 接着, 采用互信息(Mutual information, MI)度量初选特征与DXN间的相关性, 并自适应确定再选的上下限及阈值; 最后, 采用具有超参数自适应选择机制的最小二乘−支持向量机(Least squares — support vector machine, LS-SVM)算法建立多源特征的候选子模型, 基于分支定界(Branch and bound, BB)优化和预测误差信息熵加权算法进行集成子模型的优化选择和加权组合, 进而得到软测量模型. 基于某MSWI焚烧厂DXN检测数据仿真验证了所提方法的有效性.

Dioxin Emission Concentration Soft Measurement Based on Multi-source Latent Feature Selective Ensemble Modeling for Municipal Solid Waste Incineration Process

One of the main reasons leading to “not in my backyard (NIMBY)” of municipal solid waste incineration (MSWI) plant construction is dioxin (DXN) emission from such process, which is a highly toxic substance to the ecological environment. In practical industrial process, the DXN emission concentration is detected by off-line. It is difficult to meet the requirements of optimal control. Aim at the above problem, a new DXN emission concentration soft measurement approach based on multi-source latent feature selective ensemble (SEN) modeling is proposed. Firstly, MSWI process is divided into different subsystems according to industrial processes. Principal component analysis (PCA) was used to extract their latent features. Primary selection of these features is made based on empirical pre-set threshold of contribution rate. Then, mutual information (MI) is used to measure the correlation between these primary selected features and DXN. The upper and lower limits and thresholds for re-selected feature are adaptively determined. Finally, based on the re-selected feature, the least squares-support vector machine (LS-SVM) algorithm with hyper-parameter adaptive selection mechanism is used to construct sub-models. A strategy based on branch and bound (BB) and prediction error information entropy weighting algorithm is used to select sub-model and calculate the weight coefficient. Thus, an SEN soft sensing model is obtained. The proposed method is verified by using DXN detection data of MSWI process in Beijing.