Modeling and Study Influence of the Temperature Parameter on Corrosion Factors in the Atmospheric Distillation Column of Crude Oil

Atmospheric distillation is the first step in separating crude oil into by-products. It uses the different boiling temperatures of the components of crude oil to separate them. But crude oil contains a large quantity of acids and corrosive gases, including sulfur compounds, naphthenic acids, carbon dioxide, oxygen, etc. However, the temperature has an important influence on the aggressiveness of the corrosion factors in the atmospheric distillation column. This paper aims to investigate the role of temperature on corrosive products in the atmospheric distillation column. The results of the developed model show that the temperature increases the corrosion rate in the atmospheric distillation column but above a certain temperature value (about 600 K), it decreases. This illustrates the dual role played by temperature in the study of corrosion within the atmospheric distillation column.

Simultaneous optimization of heat-integrated crude oil distillation systems

Crude oil distillation is important in refining industry. Operating variables of distillation process have a critical effect on product output value and energy consumption. However, the objectives of minimum energy consumption and maximum product output value do not coordinate with each other and do not lead to the maximum economic benefit of a refinery. In this paper, a systematic optimization approach is proposed for the maximum annual economic benefit of an existing crude oil distillation system, considering product output value and energy consumption simultaneously. A shortcut model in Aspen Plus is used to describe the crude oil distillation and the pinch analysis is adopted to identify the target of energy recovery. The optimization is a nonlinear programming problem and solved by stochastic algorithm of particle warm optimization.

A Review of an Expert System Design for Crude Oil Distillation Column Using the Neural Networks Model and Process Optimization and Control Using Genetic Algorithm Framework

This paper presents a comprehensive review of various traditional systems of crude oil distillation column design, modeling, simulation, optimization and control methods. Artificial neural network (ANN), fuzzy logic (FL) and genetic algorithm (GA) framework were chosen as the best methodologies for design, optimization and control of crude oil distillation column. It was discovered that many past researchers used rigorous simulations which led to convergence problems that were time consuming. The use of dynamic mathematical models was also challenging as these models were also time dependent. The proposed methodologies use back-propagation algorithm to replace the convergence problem using error minimal method.

Investigations into Optimization Models of Crude Oil Distillation Column in the Context of Feed Stock and Market Value

This paper proposes optimization models of crude oil distillation column for both limited and unlimited feed stock and market value of known products prices. The feed to the crude distillation column was assumed to be crude oil containing naphtha gas, kerosene, petrol and diesel as the light-light key, light key, heavy key and heavy-heavy key respectively. The models determined maximum concentrations of heavy key in the distillate and light key in the bottom for limited feed stock and market condition. Both were impurities in their respective positions of the column. The limiting constraints were sales specification concentration of light key in the distillate [ ], heavy key in the bottom [ ] and an operating loading constraint of flooding above the feed tray. For unlimited feed stock and market condition, the optimization models determined the optimum separation [ and ] and feed flow rate that would give maximum profit with minimum purity sales specification constraints of light key in the distillate and heavy key in the bottom as stated above. The feed loading was limited by the reboiler capacity. However, there is need to simulate the optimization models for an existing crude oil distillation column of a refinery in order to validate the models.

Application of Artificial Neural Networks Based Monte Carlo Simulation in the Expert System Design and Control of Crude Oil Distillation Column of a Nigerian Refinery

This research work investigated comparative studies of expert system design and control of crude oil distillation column (CODC) using artificial neural networks based Monte Carlo (ANNBMC) simulation of random processes and artificial neural networks (ANN) model which were validated using experimental data obtained from functioning crude oil distillation column of Port-Harcourt Refinery, Nigeria by MATLAB computer program. Ninety percent (90%) of the experimental data sets were used for training while ten percent (10%) were used for testing the networks. The maximum relative errors between the experimental and calculated data obtained from the output variables of the neural network for CODC design were 1.98 error % and 0.57 error % when ANN only and ANNBMC were used respectively while their respective values for the maximum relative error were 0.346 error % and 0.124 error % when they were used for the controller prediction. Larger number of iteration steps of below 2500 and 5000 were required to achieve convergence of less than 10-7?for the training error using ANNBMC for both the design of the CODC and controller respectively while less than 400 and 700 iteration steps were needed to achieve convergence of 10-4?using ANN only. The linear regression analysis performed revealed the minimum and maximum prediction accuracies to be 80.65% and 98.79%;and 98.38% and 99.98% when ANN and ANNBMC were used for the CODC design respectively. Also, the minimum and maximum prediction accuracies were 92.83% and 99.34%;and 98.89% and 99.71% when ANN and ANNBMC were used for the CODC controller respectively as both methodologies have excellent predictions. Hence, artificial neural networks based Monte Carlo simulation is an effective and better tool for the design and control of crude oil distillation column.

Optimization of a crude distillation unit using a combination of wavelet neural network and line-up competition algorithm

The modeling and optimization of an industrial-scale crude distillation unit （CDU） are addressed. The main spec- ifications and base conditions of CDU are taken from a crude oil refinery in Wuhan, China. For modeling of a com- plicated CDU, an improved wavelet neural network （WNN） is presented to model the complicated CDU, in which novel parametric updating laws are developed to precisely capture the characteristics of CDU. To address CDU in an economically optimal manner, an economic optimization algorithm under prescribed constraints is presented. By using a combination of WNN-based optimization model and line-up competition algorithm （LCA）, the supe- rior performance of the proposed approach is verified. Compared with the base operating condition, it is validat- ed that the increments of products including kerosene and diesel are up to 20% at least by increasing less than 5% duties of intermediate coolers such as second pump-around （PA2） and third Dump-around （PA3）.

Soft-sensing modeling and intelligent optimal control strategy for distillation yield rate of atmospheric distillation oil refining process

It is a challenge to conserve energy for the large-scale petrochemical enterprises due to complex production process and energy diversification. As critical energy consumption equipment of atmospheric distillation oil refining process, the atmospheric distillation column is paid more attention to save energy. In this paper, the optimal problem of energy utilization efficiency of the atmospheric distillation column is solved by defining a new energy efficiency indicator - the distillation yield rate of unit energy consumption from the perspective of material flow and energy flow, and a soft-sensing model for this new energy efficiency indicator with respect to the multiple working conditions and intelligent optimizing control strategy are suggested for both increasing distillation yield and decreasing energy consumption in oil refining process. It is found that the energy utilization efficiency level of the atmospheric distillation column depends closely on the typical working conditions of the oil refining process, which result by changing the outlet temperature, the overhead temperature, and the bottom liquid level of the atmospheric pressure tower. The fuzzy C-means algorithm is used to classify the typical operation conditions of atmospheric distillation in oil refining process. Furthermore, the LSSVM method optimized with the improved particle swarm optimization is used to model the distillation rate of unit energy consumption. Then online optimization of oil refining process is realized by optimizing the outlet temperature, the overhead temperature with IPSO again. Simulation comparative analyses are made by empirical data to verify the effectiveness of the proposed solution.

Novel design of lubricant-type vacuum distillation process for lube base oils production from hydrocracking tail oil

Dividing-wall columns(DWCs)are widely used in the separation of ternary mixtures,but rarely seen in the separation of petroleum fractions.This work develops two novel and energy-efficient designs of lubricant-type vacuum distillation process(LVDP)for the separation of hydroisomerization fractions(HIF)of a hydrocracking tail oil(HTO).First,the HTO hydroisomerization reaction is investigated in an experimental fixed-bed reactor to achieve the optimum liquid HIF by analyzing the impact of the operating conditions.A LVDP used for HIF separation is proposed and optimized.Subsequently,two thermal coupling intensified technologies,including side-stream(SC)and dividing-wall column(DWC),are combined with the LVDP to develop side-stream vacuum distillation process(SC-LVDP)and dividing-wall column vacuum distillation process(DWC-LVDP).The performance of LVDP,SC-LVDP,and DWC-LVDP are evaluated in terms of energy consumption,capital cost,total annual cost,product yields,and stripping steam consumption.The results demonstrates that the intensified processes,SC-LVDP and DWC-LVDP significantly decreases the energy consumption and capital cost compared with LVDP.DWC-LVDP further decreases in capital cost due to the removal of the side stripper and narrows the overlap between the third lube oils and fourth lube oils.This study attempts to combine DWC structure into the separation of petroleum fractions,and the proposed approach and the results presented provide an incentive for the industrial implementation of high-quality utilization of HTO through intensified LVDP.

粗妥尔油的理化特性、脂肪酸组成和功能活性成分分析

粗妥尔油是造纸制浆工业副产物,本文对其理化性质、脂肪酸组成和功能活性成分进行了测定,并与主要植物油加工副产物脱臭馏出物的基本特性进行了比较。结果表明二者虽理化性质相差较大,但脂肪酸组成相似,且不饱和脂肪酸含量高。粗妥尔油的甾醇含量高于油脂脱臭馏出物,且β-谷甾醇含量最高,是脂肪酸和甾醇的优质天然来源。

炼厂原油掺炼清罐油存在的问题及思考

针对炼厂回炼清罐油时常见电脱盐罐电流波动的问题,对清罐油预处理、一级电脱盐罐运行状况实地考察并进行了优化调整。结果表明,阻止原油形成稳定的乳状液是核心问题,在清罐油净化过程中不加聚合氯化铝和聚丙烯酰胺这类絮凝剂,避免与原油形成稳定乳状液;控制脱前原油铁含量在0~5 mg/kg,抑制铁离子增加原油乳状液的稳定性和电导率;提前将破乳剂的注入量提至10~15 g/t;日检电脱盐罐乳化层情况并及时排尽。通过以上措施,常减压装置持续回炼清灌油139 d,一级电脱盐罐电流未出现波动,含盐污水环保排放指标合格。

原油常减压蒸馏装置热集成方案分析评价

根据原油常减压蒸馏装置用能特点,本文利用夹点分析方法对原油常减压蒸馏装置热集成方案进行分析评价,借助提出的热集成有效值评价指数,探讨基于夹点分析的常减压装置热集成优化策略,并研究该指标在夹点之下热集成、跨夹点热集成及夹点之上热集成等不同的热集成方案中的应用,所提出方法可用于判断常减压装置热集成方案是否切实有效,能够有效指导炼油企业常减压装置间的热集成。案例研究表明,跨夹点高温热水热集成方案中,热集成有效值仅为7%;夹点之上相同温度,不同热量催化油浆A与催化油浆B热集成方案中,热集成有效值分别为100%与60%,分别降低了装置热公用工程负荷约25%与30%。

常减压蒸馏装置原油切换智能化感知技术及其应用

针对原油种类变化导致常减压蒸馏装置操作负荷增大、产品质量波动加剧、装置能耗高等问题,基于工艺机理(包括分馏原理、物料平衡、能量平衡)和大数据开发了蒸馏塔在线工艺孪生模型与原油进料性质实时感知技术。该技术的特点是依据原油蒸馏塔的温度、压力、流量数据推算原料性质,无需安装原料实时分析仪,也无需原油数据库与储罐分析数据的输入。采用科学的验证方法与流程,利用常减压蒸馏装置的生产数据,验证了原料性质推算模型与孪生工艺模型的准确性,并开发了常压蒸馏塔智能优化控制系统。利用生产数据,验证了该系统的可靠性。通过系统优化减少原油切换对产品性质的影响,使常压蒸馏塔塔顶石脑油收率提高0.9%,常二线油收率提高0.8%,装置能耗降低1.43%。

常减压直馏煤油转产石脑油的工业实践

Z炼油厂航煤生产能力受内外贸市场需求变弱的影响,出现产品产量过剩,需要将过剩航煤转化为其他高价值产品,但由于航煤调入柴油过多会导致柴油闪点及十六烷值不合格,需要寻找解决过剩航煤新思路。利用工艺操作调整将常减压常一线航煤终馏点由243℃降至210℃,投用常二线柴油汽提蒸汽,提高混合柴油闪点,确保柴油闪点符合质量指标要求,同时掺炼中间-石蜡基原油提高常一线煤油的烷烃含量,利用技改技措新增管线将航煤改至石脑油罐区与加氢石脑油等馏分调和作为乙烯裂解原料。该工业实践表明:通过常减压常一线航煤终馏点及原油加工比例调整,可以将航煤转产符合乙烯蒸汽裂解质量要求的石脑油。本次航煤转产石脑油,共计实现将11.14×10^(4)t煤油转产石脑油,较直接调入柴油合计增效4319.53万元。常一线航煤转产石脑油不但可以有效化解航煤产量过剩问题,而且可以避免大量调入柴油带来的质量问题,同时提升企业效益。

进口俄罗斯原油综合性质评价

使用实沸点蒸馏仪对进口俄罗斯原油进行切割,分析所得馏分,并与已知性质的陕西榆林地区原油作对比,得出进口俄罗斯原油综合评价数据。结果表明:与榆林原油相比,进口俄罗斯原油的轻油馏分收率较高,按关键馏分分类属于中间基原油;15~175℃馏分精制后是高品质的乙烯裂解原料,65~175℃馏分可作催化重整原料,140~240℃馏分可生产3号喷气燃料;175~370℃馏分可生产-20号车用柴油,240~360℃馏分可生产-10号车用柴油,360~500℃馏分经脱蜡、精制后,可生产对黏度指数要求不高的润滑油基础油原料,360~540℃馏分是一般的催化裂化原料;>360℃重油馏分属于二类渣油,可部分掺入蜡油中用作催化裂化原料,>540℃渣油馏分属于三类渣油,可用作延迟焦化原料。

重油MIP-CGP装置加工俄罗斯原油常三线馏分

在中国石油大庆石化公司2.0 Mt/a重油多产异构烷烃并增产丙烯的催化裂化(MIP-CGP)装置上,进行了加工俄罗斯原油常三线馏分(以下简称常三线)催化裂化转化生产液化气和汽油的工业应用,旨在降低柴汽比。结果表明:当常三线在重油MIP-CGP装置混合加工原料中的质量占比为15.45%时,产物中干气、油浆、焦炭收率较加工常三线前依次下降了0.03,0.02,0.20个百分点;而液化气、汽油、柴油收率依次上升了0.19,0.05,0.01个百分点;常三线的裂化反应表观转化率高达81.13%,液化气、汽油的表观收率分别为25.86%,37.63%;液化气、稳定汽油、轻柴油的性质均较加工前变化不大。

波斯坎原油评价及加工方案研究

采用国家或行业标准、实沸点蒸馏评价等方法对波斯坎原油及汽油、柴油、蜡油和渣油等馏分油性质进行了分析与讨论,结果表明,该原油密度大、总酸低、硫含量高,属于低酸高硫中质原油,其汽油馏分收率13.69%、铜片腐蚀、硫醇不合格,但90%蒸发温度符合车用汽油(V)95号。柴油馏分收率24.23%、十六烷值指数高、凝点低、硫含量高,脱硫后可作为-10号柴油调合组分。蜡油馏分收率12.59%、黏度低、硫含量高,不宜做润滑油原料,渣油馏分收率45.72%、针入度、软化点均达到沥青质量指标AH-70的要求,是生产沥青的理想原料。结合收率和各馏分油性质,提出该原油用于生产重交通道路沥青的加工方案。

常减压装置工艺流程创新优化及新技术全面应用

针对大榭石化炼化一体化项目原油特征,结合全厂总流程及下游装置原料特定需求,在设计阶段提出对新建3#常减压装置工艺流程创新优化,提升资源价值。为践行中国海油低碳化行动方案,在新建3#常减压装置设计过程全面应用缠绕管换热器、95+高效余热回收系统、膜脱盐+电脱盐组合工艺、全机械抽真空等节能技术,装置设计能耗降至8 kg标油/t。

粗苯富油加热方式的对比

管式炉运行过程中以煤气作为原料,有明火产生,极易发生煤气中毒、火灾爆炸等事故。文章结合D厂多年以来粗苯蒸馏工段富油加热方式由管式炉煤气加热改造为蒸汽加热器加热的经验,从工艺指标控制、经济效益、环保排放、运行安全等四个方面将两种富油加热工艺进行对比。改用蒸汽加热后,大大提高了过程控制的稳定性,降低了能源消耗,粗苯收率显著提高,从而得出富油加热器蒸汽加热工艺更有优势。

焦化厂蒸汽加热富油代替管式炉加热富油实践

目前回收车间粗苯工序采用管式炉加热富油常压脱苯工艺,不仅存在安全隐患,还会带来新的环保问题,因此采用焦炉荒煤气显热产生蒸汽,进行蒸汽加热富油配套减压脱苯工艺替代原管式炉加热富油工艺,可有效降低工序能耗,具有良好的经济效益和环保效益。

原油化验室设备优化实现节能降耗

原油化验室是油田开发过程中的一个重要单元,是检验原油质量指标原油含水率和有机氯两大检测指标的来源地。通过对原油有机氯的来源与危害的分析,针对原油化验室原油蒸馏过程中存在的能耗高、检测时间长、检测效率低、温控效果差、员工劳动强度大等实际问题,提出实施支管优化措施、温控部分优化、接收器部分优化和工艺流程优化等原油化验室蒸馏设备优化措施,化验室现场应用及效果,并对综合效益进行了分析等。实践证明,实施优化措施后,该项目节油率达到51.6%、节电率达到33.4%,具有良好的节能降耗效果,改善了原油化验室蒸馏设备的性能,提高原油有机氯检测效率,特别是对高含水、高黏度原油的有机氯排查检测能力,缩短了检测时间,降低了员工的劳动强度,具有良好的推广应用前景。