本文選題：尾礦庫(kù) + 安全評(píng)價(jià)��； 參考：《浙江工業(yè)大學(xué)》2015年碩士論文

【摘要】：當(dāng)下的經(jīng)濟(jì)和工業(yè)快速發(fā)展使得礦產(chǎn)資源的需求快速增加,同時(shí)尾礦庫(kù)的數(shù)量也隨之增長(zhǎng)。由于運(yùn)行期間壩高和庫(kù)容都會(huì)上升,尾礦庫(kù)逐漸變?yōu)闈撛诘母邉?shì)能危險(xiǎn)源,對(duì)礦山和選礦廠(chǎng)本身的安全運(yùn)營(yíng)、下游群眾的生命財(cái)產(chǎn)安全以及周邊自然環(huán)境都構(gòu)成了巨大威脅。因此設(shè)計(jì)一個(gè)高效、合理的尾礦庫(kù)安全評(píng)價(jià)方法,對(duì)尾礦安全的趨勢(shì)作出準(zhǔn)確預(yù)測(cè)具有重大現(xiàn)實(shí)意義。為了充分挖掘影響尾礦庫(kù)安全的因素中的隱藏信息和內(nèi)在聯(lián)系,獲得盡可能高的安全預(yù)測(cè)準(zhǔn)確率,本文設(shè)計(jì)了一種基于深度神經(jīng)網(wǎng)絡(luò)的尾礦庫(kù)安全趨勢(shì)預(yù)測(cè)方法。全文的主要工作和主要成果如下:1)總結(jié)尾礦庫(kù)安全評(píng)價(jià)方法的研究現(xiàn)狀,分析本領(lǐng)域目前為止所使用的理論和方法,對(duì)它們的優(yōu)點(diǎn)和缺點(diǎn)進(jìn)行了比較。2)介紹了深度神經(jīng)網(wǎng)絡(luò)原理,闡述了堆棧式自編碼器的設(shè)計(jì)思路和算法,包括深度神經(jīng)網(wǎng)絡(luò)的逐層優(yōu)化訓(xùn)練思想和用于參數(shù)訓(xùn)練LM-BP算法等。3)根據(jù)尾礦庫(kù)的事故原因統(tǒng)計(jì)分析和其工程結(jié)構(gòu)特點(diǎn),分析導(dǎo)致尾礦庫(kù)事故的主要原因,給出了尾礦庫(kù)安全關(guān)鍵因素。4)應(yīng)用深度神經(jīng)網(wǎng)絡(luò)進(jìn)行尾礦庫(kù)安全趨勢(shì)預(yù)測(cè)實(shí)驗(yàn),給出了詳細(xì)仿真結(jié)果分析。結(jié)果表明深度神經(jīng)網(wǎng)絡(luò)在特征抽象、表征學(xué)習(xí)、預(yù)測(cè)準(zhǔn)確率等方面都具有優(yōu)越性。5)給出了尾礦庫(kù)安全評(píng)價(jià)軟件的設(shè)計(jì)思路,并使用PYTHON及相關(guān)的軟件包實(shí)現(xiàn)了軟件原型,以便算法得到實(shí)際應(yīng)用。本文成果驗(yàn)證了深度神經(jīng)網(wǎng)絡(luò)是可行的安全狀態(tài)趨勢(shì)預(yù)測(cè)方法,為尾礦庫(kù)安全評(píng)價(jià)工作提供了新的思路和理論支持。
[Abstract]:With the rapid development of economy and industry, the demand for mineral resources is increasing rapidly, and the number of tailings is also increasing.Due to the rise of dam height and reservoir capacity during operation, the tailing reservoir gradually becomes a potential high potential energy hazard source, which poses a great threat to the safe operation of mine and concentrator itself, the safety of life and property of downstream people and the surrounding natural environment.Therefore, it is of great practical significance to design an efficient and reasonable method to evaluate the safety of tailings.In order to fully excavate the hidden information and internal relation of the factors affecting the safety of tailings reservoir and obtain the highest accuracy of safety prediction, a method of predicting the safety trend of tailing reservoir based on depth neural network is designed in this paper.The main work and main results of this paper are as follows: 1) summarizing the research status of tailing reservoir safety evaluation methods, analyzing the theories and methods used so far in this field.The principle of depth neural network is introduced, and the design idea and algorithm of stack self-encoder are described.Including the depth neural network layer by layer optimization training idea and LM-BP algorithm for parameter training, etc. 3) according to the statistical analysis of the accident cause of tailing reservoir and its engineering structure characteristics, the main reasons leading to the tailing reservoir accident are analyzed.The key factor of tailing reservoir safety. 4) the experiment of predicting the safety trend of tailing reservoir by using depth neural network is presented, and the detailed simulation results are given.The results show that the depth neural network has advantages in feature abstraction, representation learning and prediction accuracy. (5) the design idea of safety evaluation software for tailings reservoir is given, and the software prototype is realized by using PYTHON and related software packages.So that the algorithm can be applied in practice.The results of this paper verify that the depth neural network is a feasible method for predicting the trend of safety state, and provides a new way of thinking and theoretical support for the safety evaluation of tailings reservoir.
【學(xué)位授予單位】：浙江工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TD926.4;TP183

【共引文獻(xiàn)】

相關(guān)會(huì)議論文 前2條

1 田W，

本文編號(hào)：1749745