• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Symposium on Application of Materials Science and Energy Materials >Dynamic soft sensing of organic pollutants in effluent from UMIC anaerobic reactor for industrial papermaking wastewater
【24h】

Dynamic soft sensing of organic pollutants in effluent from UMIC anaerobic reactor for industrial papermaking wastewater

机译：工业造纸废水Umic Anaerobic反应器中有机污染物的动态软感

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

With the rapid development of paper industry, the pressure of environmental pollution is going more and more serious. Recently, resource utilization of wastewater by anaerobic digestion has become a feasible way to solve this problem. In order to maintain the safe and efficient production of the process, a novel adaptive soft sensor model was developed to infer the chemical oxygen demand (COD) of paper mill effluent in this paper. First, the principal component analysis technique was performed in this model so as to eliminate the col-linearity between the process variables and accordingly obtain the low-dimensional feature principal component. Then, the least square support vector machine method was used to construct a quantitative regression model between principal component and the effluent COD. Along with it, particle swarm optimization was implemented to search for the best value of the LSSVM model parameters, namely the kernel parameters and the regularization factor. Finally, an online calibration strategy was designed to adapt to the process dynamic changes in an adaptive iterative manner. When the constructed model tested for performances in a full-scale factory, the average relative deviation and maximum deviation are 1.80% and 6.26%, respectively. The experimental results show that this proposed soft sensor model is featured with high accuracy and strong dynamic stability, and it can provide good guidance for COD prediction and optimal control of paper mill wastewate treatment.
机译：随着造纸业的快速发展，环境污染的压力越来越严重。最近，厌氧消化废水的资源利用已成为解决这个问题的可行方法。为了保持该过程的安全有效的生产，开发了一种新颖的自适应软传感器模型，以推断出本文造纸厂的化学需氧量（COD）。首先，在该模型中执行主成分分析技术，以便消除过程变量之间的Col-LineSity，并因此获得低维特征主组件。然后，使用最小二乘支持向量机方法来构建主成分和流出鳕鱼之间的定量回归模型。除此之外，实现了粒子群优化，以搜索LSSVM模型参数的最佳值，即内核参数和正则化因子。最后，旨在适应自适应迭代方式的过程动态变化的在线校准策略。当构建模型在全尺寸工厂进行性能时，平均相对偏差和最大偏差分别为1.80％和6.26％。实验结果表明，这一提出的软传感器型号具有高精度和强大的动态稳定性，可为COD预测和造纸厂的最佳控制提供良好的指导。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号