發(fā)布時(shí)間：2018-02-01 21:25

  本文關(guān)鍵詞： 煤氣化 配煤預(yù)測(cè) 配煤優(yōu)化 動(dòng)態(tài)配煤　出處：《西安科技大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：煤氣化技術(shù)是目前大型煤化工企業(yè)廣泛應(yīng)用的一項(xiàng)重要生產(chǎn)技術(shù),該項(xiàng)技術(shù)的長(zhǎng)期運(yùn)行實(shí)踐表明,煤質(zhì)的不穩(wěn)定會(huì)影響氣化爐裝置長(zhǎng)期穩(wěn)定的運(yùn)行,采用配煤來(lái)解決該問(wèn)題是簡(jiǎn)便、經(jīng)濟(jì)、可行的方法之一,現(xiàn)有的配煤技術(shù)因?qū)Ψ蔷�性變化的配煤煤質(zhì)預(yù)測(cè)不準(zhǔn)確,配比計(jì)算時(shí)對(duì)氣化爐的煤質(zhì)制約因素考慮不夠周全,導(dǎo)致配煤煤質(zhì)仍有波動(dòng),無(wú)法保證氣化爐裝置長(zhǎng)期穩(wěn)定的運(yùn)行,且人工配煤計(jì)算已經(jīng)無(wú)法滿足新時(shí)期煤炭企業(yè)的發(fā)展需求。論文首先根據(jù)對(duì)配煤后煤質(zhì)的變化規(guī)律的認(rèn)識(shí),分別利用多元線性回歸、BP神經(jīng)網(wǎng)絡(luò)以及遺傳算法(GA)優(yōu)化BP神經(jīng)網(wǎng)絡(luò)三種方法建立了基于灰流動(dòng)溫度的配煤預(yù)測(cè)模型,通過(guò)三種模型的擬合效果以及誤差分析和預(yù)測(cè)結(jié)果對(duì)比,得出:采用GA-BP神經(jīng)網(wǎng)絡(luò)構(gòu)建的灰流動(dòng)溫度預(yù)測(cè)模型具有一定的可行性和優(yōu)越性;并通過(guò)對(duì)目前較為經(jīng)典的灰粘度預(yù)測(cè)模型對(duì)本論文所研究煤炭煤質(zhì)的適應(yīng)性研究,得出易于實(shí)現(xiàn),且能有效預(yù)測(cè)氣化用煤配煤灰粘度特性的基于灰粘度的配煤預(yù)測(cè)模型,從而達(dá)到準(zhǔn)確預(yù)測(cè)配煤煤質(zhì)的目的。其次根據(jù)寧東煤化工基地氣化爐的實(shí)際入爐煤質(zhì)關(guān)鍵制約因素,建立以水分、灰分、揮發(fā)分、硫份、發(fā)熱量、灰流動(dòng)溫度、灰粘度為約束條件,以配煤價(jià)格,硫份、流動(dòng)溫度為目標(biāo)函數(shù)的多目標(biāo)配煤優(yōu)化模型,通過(guò)MATLAB優(yōu)化函數(shù)與遺傳算法分別對(duì)模型求解,并從理論上和實(shí)際求解結(jié)果上分析對(duì)比,得出:遺傳算法用于求解多目標(biāo)配煤優(yōu)化模型效果更佳,建立基于遺傳算法的配煤優(yōu)化模型,達(dá)到計(jì)算最優(yōu)配比的目的。最后,通過(guò)對(duì)煤化工基地氣化配煤生產(chǎn)過(guò)程的分析,采用C/S系統(tǒng)架構(gòu),Microsoft Visual Studio 2008開(kāi)發(fā)平臺(tái),MFC等技術(shù)設(shè)計(jì)并實(shí)現(xiàn)了一套適用于氣化用煤配煤的動(dòng)態(tài)配煤系統(tǒng),并將基于GA-BP神經(jīng)網(wǎng)絡(luò)的配煤預(yù)測(cè)模型及基于遺傳算法的配煤優(yōu)化模型應(yīng)用到系統(tǒng)中。論文對(duì)氣化用煤配煤模型及動(dòng)態(tài)配煤系統(tǒng)的研究,能夠充分考慮氣化用煤配煤需要滿足的約束條件,及要達(dá)到的經(jīng)濟(jì)、環(huán)保和氣化爐穩(wěn)定運(yùn)行的目標(biāo),根據(jù)倉(cāng)庫(kù)中現(xiàn)有原料煤,計(jì)算出滿足不同氣化爐需求的最合理配煤比例,并能準(zhǔn)確的預(yù)測(cè)配煤后煤質(zhì),降低氣化爐因入爐煤質(zhì)波動(dòng)造成的故障率,且可以及時(shí)更新庫(kù)存原料煤、氣化爐等信息,從而解放勞動(dòng)力,提高企業(yè)的經(jīng)濟(jì)效益,降低因煤質(zhì)硫份過(guò)高造成的空氣污染率。
[Abstract]:Coal gasification technology is an important production technology widely used in large scale coal chemical enterprises at present. The long-term operation practice of this technology shows that the instability of coal quality will affect the long-term stable operation of gasifier plant. It is one of the simple, economical and feasible methods to solve this problem by coal blending. The existing coal blending technology is not accurate for the nonlinear change of coal blending quality. When calculating the proportion of gasifier coal quality constraints are not fully considered, resulting in coal blending quality still fluctuate, which can not guarantee the long-term stable operation of gasifier equipment. The artificial coal blending calculation has been unable to meet the development needs of coal enterprises in the new period. Firstly, according to the understanding of the coal quality change law after coal blending, the multivariate linear regression is used respectively. BP neural network and genetic algorithm (GA) optimization BP neural network three methods based on ash flow temperature prediction model of coal blending. Through the fitting effect of the three models and the comparison of error analysis and prediction results, it is concluded that the grey flow temperature prediction model based on GA-BP neural network has certain feasibility and superiority; And through the research on the adaptability of the classical grey viscosity prediction model to the coal quality studied in this paper, it is easy to realize. And the coal blending prediction model based on ash viscosity can effectively predict the viscosity characteristics of coal ash for gasification. In order to accurately predict the coal quality of coal blending. Secondly according to the key factors of coal quality in coal gasifier in Ningdong coal chemical base the moisture ash volatile sulfur calorific value and ash flow temperature are established. The multi-objective coal blending optimization model with coal blending price sulfur content and flow temperature as objective function is solved by MATLAB optimization function and genetic algorithm respectively. From the theoretical and practical analysis and comparison of the results, it is concluded that the genetic algorithm for solving multi-objective coal blending optimization model is more effective, and establish a coal blending optimization model based on genetic algorithm. Finally, through the coal chemical base gasification coal blending production process analysis, using the C / S system architecture. A dynamic coal blending system suitable for gasification coal blending is designed and implemented by Microsoft Visual Studio 2008 development platform. The coal blending prediction model based on GA-BP neural network and the coal blending optimization model based on genetic algorithm are applied to the system. The coal blending model and dynamic coal blending system for gasification are studied in this paper. Can fully consider the gasification coal blending needs to meet the constraints, and to achieve the goals of economic, environmental protection and gasifier stable operation, according to the existing raw coal in the warehouse. Calculate the most reasonable proportion of coal to meet the different gasifier demand, and can accurately predict the coal quality after blending, reduce the failure rate caused by coal quality fluctuation in gasifier, and can update the stock of raw coal in time. The information of gasifier can liberate the labor force, improve the economic benefit of the enterprise, and reduce the air pollution rate caused by the excessive sulfur content of coal.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP18;TP311.52;TQ546

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