本文選題：安全生產(chǎn) + 預(yù)測(cè)預(yù)警��； 參考：《北京郵電大學(xué)》2013年碩士論文

【摘要】：本文首先闡述了凈化廠的基本情況,包括生產(chǎn)設(shè)備、工藝流程、DCS系統(tǒng),然后根據(jù)生產(chǎn)流程中各個(gè)采集數(shù)據(jù)點(diǎn)的關(guān)系,將數(shù)據(jù)點(diǎn)分為幾個(gè)不同的模塊,每個(gè)模塊可以單獨(dú)表示生產(chǎn)工藝中的一個(gè)流程。之后,針對(duì)項(xiàng)目建設(shè)的需求提出了對(duì)預(yù)警系統(tǒng)的設(shè)計(jì)思路,同時(shí)介紹了作為系統(tǒng)構(gòu)架平臺(tái)的ECM,提出了模型構(gòu)建和數(shù)據(jù)處理的基本流程和方法。 本文的主要研究對(duì)象是對(duì)預(yù)測(cè)預(yù)警平臺(tái)系統(tǒng)的構(gòu)建,因此本文隨后研究數(shù)據(jù)分析的各種方法的理論知識(shí),確定了PCA為系統(tǒng)中需要應(yīng)用的主要的分析方法,并重點(diǎn)介紹了PCA算法的計(jì)算流程,另外,對(duì)于PLS、神經(jīng)網(wǎng)絡(luò)、小波、EWMA等分析方法也做了總體的概述。之后,提出了針對(duì)脫硫、脫水、硫磺回收、蒸氣四個(gè)生產(chǎn)單元的模型設(shè)計(jì)方法。 最后,在ECM中以主成分分析法PCA為主要模塊建模,建立了脫硫、脫水、硫磺回收、蒸汽等生產(chǎn)單元的故障預(yù)測(cè)預(yù)警的模型,并且對(duì)模型中的每個(gè)元件進(jìn)行了詳細(xì)的介紹；在壓縮機(jī)的模型中,由于開(kāi)關(guān)機(jī)狀態(tài)的分辨比較復(fù)雜,所以提出了以時(shí)間平移為核心的算法來(lái)確定機(jī)器的開(kāi)關(guān)機(jī)狀態(tài)。通過(guò)建模后組成的系統(tǒng)研究長(zhǎng)壽分廠設(shè)備運(yùn)行的狀態(tài),保證了監(jiān)測(cè)對(duì)象的安全生產(chǎn)。 本項(xiàng)目實(shí)現(xiàn)了對(duì)凈化廠生產(chǎn)過(guò)程的實(shí)時(shí)監(jiān)控,使得在生產(chǎn)中的可能發(fā)生危險(xiǎn)之前及時(shí)發(fā)現(xiàn)潛在的問(wèn)題并排除成為了可能,為凈化廠安全生產(chǎn)提供了保障。
[Abstract]:This paper first describes the basic situation of the purification plant, including the production equipment, process flow and DCS system, then according to the relationship between the data points collected in the production process, the data points are divided into several different modules. Each module can represent a single process in the production process. After that, the design idea of early warning system is put forward according to the requirement of project construction. At the same time, the ECM, as the platform of system architecture, is introduced, and the basic flow and method of model construction and data processing are put forward. The main research object of this paper is the construction of the prediction and early warning platform system, so this paper then studies the theoretical knowledge of various methods of data analysis, and determines PCA as the main analysis method that needs to be applied in the system. The calculation flow of PCA algorithm is introduced in detail. In addition, the analysis methods such as PLS, neural network and wavelet EWMA are also summarized. After that, the model design method for desulfurization, dehydration, sulfur recovery and steam production unit is put forward. Finally, the principal component analysis (PCA) is used as the main module in ECM to model the failure prediction and warning model of desulfurization, dehydration, sulfur recovery, steam and other production units, and each component of the model is introduced in detail. In the compressor model, due to the complexity of the state resolution of the switch machine, an algorithm based on time translation is proposed to determine the state of the machine switch machine. After modeling, the system is used to study the operation state of the plant equipment, which ensures the safety of the monitoring object. The project realizes the real-time monitoring of the production process of the purification plant, which makes it possible to find the potential problems and eliminate the potential problems in time before the possible danger in the production, thus providing a guarantee for the safe production of the purification plant.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP277;TE687

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