本文關(guān)鍵詞： 煙氣污染源 過程監(jiān)控 運行工況核查模型 支持向量機 治理效率預測模型　出處：《浙江理工大學》2017年碩士論文　論文類型：學位論文

【摘要】：隨著我國社會經(jīng)濟的持續(xù)發(fā)展和工業(yè)技術(shù)的不斷提高,化石能源需求和環(huán)境污染之間的矛盾愈發(fā)突出,區(qū)域性大氣復合污染持續(xù)加劇。針對嚴峻的大氣污染形勢,國家對環(huán)境保護、總量減排、污染治理等工作越來越重視,并大力開發(fā)了國控污染源在線監(jiān)控系統(tǒng),實現(xiàn)對企業(yè)污染源排放的在線監(jiān)控。隨著環(huán)境監(jiān)測技術(shù)的發(fā)展,如何深化污染源在線監(jiān)控系統(tǒng),來取締傳統(tǒng)的現(xiàn)場檢查污染治理設(shè)施運行狀況的方式,防止污染排放過程中的設(shè)施無故停運、企業(yè)偷排、漏排等現(xiàn)象的發(fā)生,實現(xiàn)從“點末端監(jiān)控”到“全過程監(jiān)控”的轉(zhuǎn)變,是目前亟待解決的問題。針對當前煙氣污染源在線監(jiān)控系統(tǒng)只監(jiān)測污染排放結(jié)果,而不重視污染排放過程的監(jiān)控現(xiàn)狀,本文設(shè)計了一個煙氣污染源排放過程監(jiān)控系統(tǒng)。系統(tǒng)從煙氣污染源的產(chǎn)生、治理、排放等環(huán)節(jié)進行監(jiān)控,并通過對污染治理工藝過程參數(shù)和排污口監(jiān)測數(shù)據(jù)進行挖掘分析,設(shè)計了運行工況智能核查規(guī)則模型和治理預測模型,實時的對現(xiàn)場端采集的監(jiān)測數(shù)據(jù)進行決策分析,幫助環(huán)境管理部門實時、高效地監(jiān)管企業(yè)的污染排放。本文主要研究內(nèi)容包括以下幾個方面:(1)設(shè)計了智能化的設(shè)施運行工況核查模型。利用企業(yè)生產(chǎn)工藝過程參數(shù)、治理環(huán)節(jié)的關(guān)鍵參數(shù)、排污口監(jiān)測數(shù)據(jù),建立了全面、準確的運行工況核查規(guī)則模型,用于判定生產(chǎn)設(shè)施和治理設(shè)施的運行狀況和治理效果,及時發(fā)現(xiàn)污染排放過程中的問題,為排污收費、總量減排、移動執(zhí)法等提供依據(jù)。(2)設(shè)計了一個基于偏最小二乘與支持向量機(PLS-SVM)的治理效率預測模型。利用偏最小二乘法(Partial Least Squares Regression,PLS)對影響煙氣治理效率的過程因素進行分析,提取對治理效率影響較強的成分作為支持向量機的輸入(Support Vector Machine,SVM)建立治理效率預測模型,為煙氣排放監(jiān)測儀器的可靠性和監(jiān)測數(shù)據(jù)的真實性做判定依據(jù)。(3)設(shè)計了煙氣污染源排放過程監(jiān)控系統(tǒng)平臺。包括系統(tǒng)需求分析、系統(tǒng)網(wǎng)絡結(jié)構(gòu)設(shè)計、系統(tǒng)架構(gòu)設(shè)計、功能模塊詳細設(shè)計、數(shù)據(jù)庫設(shè)計等。(4)系統(tǒng)實現(xiàn)及功能測試。系統(tǒng)功能模塊進行代碼實現(xiàn),并部署相應的測試環(huán)境對系統(tǒng)進行嚴格的測試分析,確保系統(tǒng)的可靠性和完整性等。
[Abstract]:With the sustainable development of social economy and the continuous improvement of industrial technology, the contradiction between fossil energy demand and environmental pollution is becoming more and more prominent, and the regional atmospheric compound pollution continues to intensify. The state has paid more and more attention to the work of environmental protection, total emission reduction and pollution control, and has vigorously developed an on-line monitoring system for state-controlled pollution sources to realize on-line monitoring of emissions from enterprise pollution sources. With the development of environmental monitoring technology, How to deepen the pollution source online monitoring system in order to ban the traditional mode of on-site inspection of the operation status of pollution control facilities, and to prevent the facilities in the process of pollution discharge from stopping operation without any reason, the enterprises stealing the discharge, the leakage of the discharge, and so on. It is an urgent problem to realize the transition from "point end monitoring" to "whole process monitoring". This paper designs a monitoring system of flue gas pollution source discharge process. The system monitors the generation, treatment and discharge of flue gas pollution source, and analyzes the process parameters of pollution control process and the monitoring data of sewage outlet. The intelligent verification rule model and the governance prediction model are designed, which can help the environmental management department to make decision and analyze the monitoring data collected on the spot in real time. The main research contents of this paper include the following aspects: 1) designing an intelligent verification model of the operating conditions of the facilities. Using the production process parameters of the enterprises and the key parameters of the management links, the main contents of this paper are as follows: (1) the main contents of this paper are as follows: 1. The monitoring data of sewage discharge outlet have established a comprehensive and accurate operation condition verification rule model, which can be used to judge the operation status and control effect of production facilities and treatment facilities, to discover problems in the process of pollution discharge in time, and to charge for sewage discharge. Based on partial least squares and support vector machine (PLS-SVM), this paper designs a prediction model of governance efficiency based on partial least squares (PLS) and support vector machine (SVM). Partial Least Squares regulation (PLS) is used to analyze the process factors that affect the efficiency of flue gas treatment. The prediction model of governance efficiency is established by extracting components that have strong influence on governance efficiency as input of support vector machine (SVM). Based on the reliability of flue gas emission monitoring instrument and the authenticity of monitoring data, the platform of flue gas pollution source emission process monitoring system is designed, which includes system requirement analysis, system network structure design, system architecture design, etc. Function module design, database design, etc.) system implementation and function testing. The system function module is implemented by code, and the corresponding test environment is deployed to strictly test and analyze the system to ensure the reliability and integrity of the system.
【學位授予單位】：浙江理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：X84;TP277

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本文編號：1525065