基于改進PLS算法的延遲焦化裝置建模研究與應(yīng)用
發(fā)布時間:2018-02-09 01:00
本文關(guān)鍵詞: 延遲焦化 軟測量 偏最小二乘算法 混合PI-SIGMA模糊神經(jīng)網(wǎng)絡(luò) 核偏最小二乘算法 出處:《華東理工大學(xué)》2015年碩士論文 論文類型:學(xué)位論文
【摘要】:隨著輕質(zhì)原油資源的逐漸消耗,迫使石油化工企業(yè)向原油重質(zhì)化、產(chǎn)品輕質(zhì)化轉(zhuǎn)型。為了充分利用有限的石油資源,滿足輕質(zhì)油的大量需求,重油加工技術(shù)迅速發(fā)展。重油加工技術(shù)中以延遲焦化工藝發(fā)展最為快速,使用最為普遍。延遲焦化裝置本身投資低、費用少,而且能夠加工各種重質(zhì)渣油,提升汽油和柴油產(chǎn)量,因而如何使延遲焦化技術(shù)更加成熟顯得尤為重要。本文針對延遲焦化裝置進行了建模研究,具體內(nèi)容可分為以下幾個方面: (1)本文利用偏最小二乘法提取有效信息、去噪能力強等優(yōu)點,結(jié)合混合PI-SIGMA模糊神經(jīng)網(wǎng)絡(luò)算法極強的處理非線性問題能力,提高了偏最小二乘算法處理非線性問題的能力。將該算法運用于焦炭塔生焦速率建模,并根據(jù)生焦速率預(yù)測生焦周期焦層高度。經(jīng)檢驗生焦高度的預(yù)測精度具有較高的準確性,滿足生產(chǎn)要求。 (2)為了實現(xiàn)延遲焦化分餾塔柴油95%點的軟測量預(yù)測,減少人工分析柴油95%點的次數(shù),提高裝置調(diào)整的效率,通過研究延遲焦化裝置的工藝流程,分析了影響柴油95%點的因素,提出了一種基于改進核偏最小二乘法的柴油95%點的軟測量方法。結(jié)果表明該方法能夠有效地、快速地處理各變量之間的非線性關(guān)系,軟測量結(jié)果明顯優(yōu)于偏最小二乘方法等方法。 (3)針對延遲焦化生產(chǎn)裝置,設(shè)計開發(fā)了延遲焦化分析測算軟件。該軟件的功能包括實時數(shù)據(jù)通信、分餾塔柴油95%點在線計算、焦炭塔生焦高度預(yù)測、結(jié)果在線顯示和報警、電子生產(chǎn)報表生成等。該應(yīng)用軟件自實際投運以來取得了較好的效果。
[Abstract]:With the gradual consumption of light crude oil resources, petrochemical enterprises are forced to transform to heavy crude oil and light products. In order to make full use of the limited petroleum resources and to meet the large demand for light oil, Heavy oil processing technology is developing rapidly. In heavy oil processing technology, delayed coking process is the most rapid development and most widely used. The delayed coking plant itself has low investment, low cost, and can process all kinds of heavy residuals. It is very important to improve gasoline and diesel production, so it is very important to make delayed coking technology more mature. In this paper, the modeling of delayed coking plant is studied, which can be divided into the following aspects:. In this paper, we use the partial least square method to extract effective information and have strong denoising ability, and combine with the hybrid PI-SIGMA fuzzy neural network algorithm to deal with nonlinear problems. The ability of partial least square algorithm to deal with nonlinear problems is improved. The algorithm is applied to modeling coke generation rate of coke tower, and the height of coking period is predicted according to coke generation rate. The prediction accuracy of coking height is higher than that of other methods. Meet production requirements. 2) in order to realize the soft-sensing prediction of 95% points of diesel oil in delayed coking fractionator, reduce the frequency of 95% points of manual analysis of diesel oil, and improve the efficiency of equipment adjustment, the factors affecting 95% points of diesel oil are analyzed by studying the technological process of delayed coking unit. A soft sensing method for diesel oil 95% points based on improved kernel partial least square method is proposed. The results show that this method can deal with the nonlinear relationship between variables effectively and quickly. The results of soft sensing are obviously superior to those of partial least square method and so on. The software for analysis and calculation of delayed coking is designed and developed for delayed coking production unit. The functions of the software include real-time data communication, on-line calculation of 95% points of diesel oil in fractionator, prediction of coke height in coke tower, and on-line display and alarm of the results. The application software has achieved good results since it was put into operation.
【學(xué)位授予單位】:華東理工大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2015
【分類號】:TE96;TP18
【參考文獻】
相關(guān)期刊論文 前6條
1 曾三友,孫星明,夏利民,金可音;基于Chebyshev多項式的自適應(yīng)偏最小二乘回歸建模[J];長沙鐵道學(xué)院學(xué)報;2001年01期
2 胡浩志;;城鎮(zhèn)各階層收入分配公平性的影響因素——基于PLS的分析[J];當代財經(jīng);2008年08期
3 耿偉華;孫衢;張翠霞;陳曉燕;;基于改進的模糊神經(jīng)網(wǎng)絡(luò)的短期負荷預(yù)測[J];電力系統(tǒng)及其自動化學(xué)報;2007年05期
4 翁欣欣;陸峰;王傳現(xiàn);亓云鵬;;近紅外光譜-BP神經(jīng)網(wǎng)絡(luò)-PLS法用于橄欖油摻雜分析[J];光譜學(xué)與光譜分析;2009年12期
5 王雪松;袁志祥;尹魯江;王安杰;;延遲焦化工藝的技術(shù)進展[J];工業(yè)催化;2006年04期
6 靖永志;肖建;;基于T-S模糊神經(jīng)網(wǎng)絡(luò)的齒槽效應(yīng)補償方法研究[J];傳感技術(shù)學(xué)報;2013年08期
,本文編號:1496701
本文鏈接:http://sikaile.net/kejilunwen/shiyounenyuanlunwen/1496701.html
最近更新
教材專著