【作者】 刘林；

【导师】 马邕文；

【作者基本信息】 华南理工大学 ， 环境工程， 2017， 硕士

【摘要】 随着我国工业化水平不断的发展,水污染问题尤为突出,水污染控制依然任重而道远。厌氧消化工艺由于其高效性、污泥产量小、能生成沼气等优点,其在工业废水处理中越来越重要。然而,厌氧消化体系是一个对环境因素敏感的复杂的过程,特别是产甲烷菌对环境的改变非常敏感,因此为保持废水处理的高效稳定,对废水厌氧处理过程中厌氧反应器的状态进行监控就显得格外重要。近年来,人们逐渐将计算机技术、自动化技术以及人工智能技术应用于废水处理过程中,取得了较好的成果。而随着对这些技术的深入研究,软测量技术为解决上述问题提供了的新的思路,软测量模型的设计、参数优化等逐渐受到越来越多的关注,是当前废水处理监测与控制领域一向备受关注的研究课题。本论文在较为全面地分析了废水厌氧处理工艺、软测量模型在废水处理过程中的应用现状以及支持向量机、粒子群算法和微生物动力学基本原理的基础上,系统地研究了基于SVM的软测量模型的建模思路与方法,探索了在传统SVM模型引入对原始数据集进行有效分类以及引入微生物动力学量化环境因素对厌氧消化体系的影响的建模新思路,分别建立了基于PSO-SVM的废水厌氧处理过程中COD去除率和出水VFA浓度的软测量模型以及基于微生物动力学和PSO-SVM的厌氧废水处理产气量的混合软测量模型。本文对软测量技术在废水厌氧处理中的应用做了一些探索性的工作,为监控、优化和理解厌氧消化过程提供了指导。本文主要研究内容与结论如下:1.在实验室搭建了基于IC反应器以及带有相关指标的监控系统的厌氧处理系统,分别以消化污泥和颗粒污泥成功启动了两个IC厌氧反应器。以颗粒污泥为接种污泥,属于“二次启动”,反应器在整个启动过程中运行较为平稳;而以消化污泥为接种污泥,反应器在启动阶段前期运行状态波动较大,前期产气量较小均在0.50L/h以下,反应器内部pH值波动较大,最低可达5.5,随着消化污泥完成“颗粒化”,反应器在启动后期逐渐运行稳定;2.以颗粒污泥为接种污泥成功启动的反应器为基础,在不同工况下共运行60d,为建立模型共获得数据159组。反应器在不同工况下运行时,当厌氧体系碱度过低时,厌氧体系pH值较低,VFA浓度相应较高,整个厌氧消化体系容易出现“酸败”现象。此时应适当提高进水碱度,以此缓解厌氧消化系统的酸度下降;容积负荷的提升(尤其是突然提升至较高水平),反应器运行状态波动较大,处理效果和产气量均受到影响。3.在分析了粒子群算法和支持向量机原理的基础上,引入分类策略对原始数据集进行有效分类建立了基于PSO-SVM的废水厌氧处理过程中COD去除率和出水VFA浓度的软测量模型。原始数据集分类之前,pso-SVM模型对废水厌氧处理体系COD去除率及出水总VFA浓度的仿真表现较好,测试样本的整体预测数据与实际数据的相关系数分别为65.86%,85.25%;通过加入分类策略提升了模型的表现,模型预测系统COD去除率测试样本数据相关系数分别为92.34%,83.41%;模型预测系统出水总VFA浓度测试样本数据相关系数分别为99.14%,99.59%。4.在分析了微生物动力学模型的基本原理及优缺点的基础上,引入微生物动力学相关函数量化环境因素对厌氧消化体系的影响的建模新思路,建立了基于微生物动力学和PSO-SVM的厌氧废水处理产气量的混合软测量模型。原始数据集经过动力学模型修正后均匀分布,降低了混合模型的输入与输出量之间的关系的杂乱性与噪点。相对于传统模型,混合模型在预测废水厌氧处理产气量中提升较大,相关系数R由86.71%提升至95.73%。更多还原

【Abstract】 With the continuous improvement of the degree of industrialization in china,water pollution problem is particularly prominent,and treating is still a heavy task.Anaerobic digestion(AD)technology process is becoming more and more important in wastewater treatment,because it has many advantages,such as high efficiency,low sludge yield and biogas production.However,anaerobic digestion is a complex,nonlinear,biochemical process.In particular,methanogenic bacteria are very sensitive to changing surroundings.Therefore,AD process control and analysis is believed to be one of the main limitations for effective organic matter degradation.Recently,computer technology,automation technology and artificial intelligence(AI)technology are gradually used in wastewater treatment process,and people have achieved good results.And with the in-depth study of these technologies,soft-sensing technology to solve the above problems provided a new way of thinking.Soft-sensing model design,parameter optimization and so on are getting more and more attention,and is an important research topic in the current wastewater treatment monitoring and control field.Base on the all-around review and analysis of the progress of wastewater treatment study,the theories of the soft-sensing technology,including the theories of the support vector machine(SVM),the Particle Swarm Optimization(PSO)and the microbial dynamics,this study systematically study the modeling idea and method of soft sensor model based on SVM.And in this study,a soft-sensing model of COD removal rate and effluent VFA concentration in anaerobic treatment of waste water based on the PSO-SVM model was established,and a mixed soft-sensing model of gas production based on microbial kinetics and the PSO-SVM model was established.Some pioneering and exploratory work on the soft-sensing model used in wastewater treatment was studied in this study.Main contents and results are as follows:1.An anaerobic treatment system based on the IC reactor and the monitoring system with related indexes was built in the laboratory.Activated sludge and granular sludge were used as inoculated sludge to activate two IC anaerobic reactors,respectively.The reactor,which used the granular sludge as inoculum sludge,runs smoothly in the whole start-up process.While,in the early stage of the whole start-up,the other reactor fluctuated greatly.The gas production was less than 0.50L/h,and the pH fluctuated greatly,the minimum pH was up to 5.50.With the granulated process finished,the reactor was gradually stabilized.2.The reactor,which used the granular sludge as inoculum sludge,was operated for 60 d under different operating conditions.And we collected 159 sets of data.When the alkalinity of the anaerobic system is low,the pH is correspondingly low,and the VFA concentration rises accordingly.In this situation,the system is easy to rancidity phenomenon.At the same time,in order to reduce the acidity,the alkalinity of the wastewater should be increased appropriately.3.Based on the analysis of particle swarm optimization(PSO)and the principle of support vector machine,the classification strategy of original data set was introduced in the soft-sensing model.And a soft-sensing model of COD removal rate and effluent VFA concentration in anaerobic treatment of waste water based on the PSO-SVM model was established.Before the original data set being classified,the results of the PSO-SVM model shows well.The correlation coefficient(R)is 65.86% and 85.25 for the COD removal rate and total VFA concentration prediction,respectively.After the data-classification strategy implying,the metadata set divided into two sets,and the performance of the hybrid model was significantly improved.The R of the COD removal rate modeling testing set is 92.34% and 83.41%,respectively.And The R of the total VFA concentration modeling testing set is 99.14% and 99.59%,respectively.4.Based on the analysis of microbial kinetics model,some microbial kinetics equations were intruded in the hybrid model.Thus,a mixed soft-sensing model of gas production based on microbial kinetics and the PSO-SVM model was established.The original data set is roughly evenly distributed after the kinetic model modified,and the messiness and noise of the original data set were controlled.The R value of the traditional model is 86.71%.In particularly,the kinetic model quantified the effects of pH,temperature and VFAs on AD process.And compared with traditional model,the performance of the hybrid model promote a lot,the R value of the hybrid model is 95.73%.更多还原