Dioxin soft measuring method in municipal solid waste incineration based on virtual sample generation
Dioxin soft measuring method in municipal solid waste incineration based on virtual sample generation
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DOI：
10.1109/cac.2017.8244101
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
Jian Tang;J. Qiao;Ke Gu;Aijun Yan
中科院分区：
文献类型：
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作者：
Jian Tang;J. Qiao;Ke Gu;Aijun Yan
城市生活垃圾焚烧处理是目前最受欢迎的环保技术。这个过程产生了世界上最有毒的化学物质之一，即，多氯二苯并对二恶英和多氯二苯并呋喃。在现有工业装置的基础上，通过对垃圾焚烧工艺的操作优化和控制，严格限制二恶英（DXN）的产生。然而，由于DXN形成机理的复杂性和高成本、长时间的离线检测方法，很难实现DXN的在线实时连续测量。本文首次提出了一种基于虚拟样本生成（VSG）的软测量方法。利用选择性集成核偏最小二乘（SENKPLS）和先验知识，利用少量真实训练样本生成基于可行性规划（FBP）模型的虚拟训练样本。基于文献[31]数据集的HL MSWI过程仿真结果表明了该方法的有效性。
Municipal solid waste incineration (MSWI) becomes the most popular technique to enhance environment protection. This process produces one of the most toxic chemicals in the world, i.e., polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs). The dioxin (DXN) production should be restricted rigidly by using operation optimization and control of MSWI process based on present industrial devices. However, it is difficult to realize the on-line real-time continuous measuring of DXN duo to the complexity formation mechanism and high-cost long-time off-line detection approach. In this paper, a soft measuring method based on virtual sample generation (VSG) is used to address this problem at the first time. A few numbers of true training samples are used to produce virtual training samples based on feasibility-based programming (FBP) model using selective ensemble kernel partial least squares (SENKPLS) and prior knowledge. Simulation result based on dataset in reference [31] for a HL MSWI process shows effectiveness of the proposed method.