INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET)

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1 INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 ISSN (Print) ISSN (Online) Volume 5, Issue 4, April (2014), pp IAEME: Journal Impact Factor (2014): (Calculated by GISI) IJARET I A E M E OPTIMIZATION AND PERFORMANCE EVALUATION OF AN EXISTING CRUDE OIL DISTILLATION SYSTEM Ali KhudhairKnehir, Dr. Ajeet Kumar Rai*, Dr. Abdulwahid A. Al-Hajjaj** Deptt of Fuel and energy Engg. Technical college of Basrah, Republic of Iraq *Deptt of Mech. Engg. SSET, SHIATS-DU Allahabad (U.P.) INDIA **Deptt of chemical Engg., University of Basrah,Republic of Iraq ABSTRACT Crude Distillation Units are key process plants in a petroleum refinery as they produce intermediate streams that are used in downstream process units. In fact, the crude oil separation is a complex atmospheric multi-components distillation process. The process complexity is arise mostly from the large number of components in the crude oil. An existing crude distillation unit is costly to modify due its complex configuration and existing limitations of structure, space area, matches, bottlenecked equipments, etc. Thus, a few new crude distillation units are built and most projects are directed to revamping existing equipments. Modifying an existing plant have a great impact on product yield and quality through operating these units at optimal conditions from technical and economical aspects; that means operating conditions such as temperatures, pressures and flows of the units that maximize their economic performance (increasing product yield), subject to their real physical restrictions and their design capabilities. Mathematical modeling has become very common to develop these optimization studies. In the present work, Genetic algorithm (GA) method will be adapted to solve the optimization problems. GA is a powerful optimization technique based on the principles of natural evolution and selection. In the specific case of selecting the optimum set of inputs from a larger set, GA can be used to search through a large number of input combinations with interdependent variables to be designed for the crude oil distillation column. For mathematical description of a distillation process in refining columns, the theoretical stage method is usually used. The number of theoretical stages of an existing column is estimated by multiplication of the real number of stages and column efficiency. The present methodology will apply to a local atmospheric plant for Basrah refinery, as an Iraqi refinery case study. The optimization approach will focus on the efficient reuse of existing equipment without major modifications and will be achieved by changing operating condition without adding any new equipment. The mathematical modeling will be carried out in the following procedure. Target variables: Total flow rate, reflux ratio, the concentration of key components, the draw amount of the key components and the liquid flow rate of the column 94

2 inter-stage. Case study data are industrial actual data obtained. The simulation frame work is studied and analysed using HYSYS software. INTRODUCTION The crude oil separation process becomes increasingly important because of the high energy costs and ecological requirements for quality oil products. Computer simulation is one of the most important steps of process optimization. However, due to complex composition of oil and complex design of oil fractionators, simulation of oil distillation requires a specific approach( Asok Kumar 2012) Crude oil (or petroleum) is a multicomponent mixture consisting of naturally occurring hydrocarbons, together with organic compounds of sulphur, nitrogen and oxygen, as well as trace amounts of metallic constituents, such as vanadium, nickel and iron. The origin of crude oil can have a significant effect on its composition. As a result, crude oils widely vary in volatility, density, viscosity and color. Crude oil may also contain dissolved inorganic gases, such as nitrogen, carbon dioxide, and hydrogen sulphide, at high pressure and temperature conditions. Water is another important constituent of produced crude oil. As water has limited miscibility with hydrocarbons, most of the water is usually found in the form of emulsified droplets or in a free water phase. The free water is usually separated at the well-head facilities, while the emulsified water is removed in the pre-refining operations (TomášPavlík 2009).In petroleum refining, the boiling point ranges are used instead of mass or mole frictions. Three types of boiling point analysis are known: ASTM D86, ASTM D158 and TBP (true boiling point). Properties of a petroleum stream are not specified in terms of composition. Instead, properties such as 5 % point, 95% point, final boiling point, flashpoint and octane number are used. The method for quantitative calculations of the petroleum frictions is to break them into pseudo components each pseudo component has its average boiling point, specific gravity, and molecular weight. Separation Units These units take an incoming stream and separate it into different components no chemical reactions occur in these units to the desalted Ions in the crude oil will corrode the pipes in the refinery and may deactivate the catalysts. It is important to remove these salts from the crude before any other processes are started. The process involves forcing water into the crude oil feed stream. This pulls out the salts and prevents corrosion. Figure 1: Crude distillation unit parts "Desalter 95

3 Atmospheric Distillation: The distillation is performed at atmospheric pressures. The outputs of the distillation unit include light ends, kerosene, diesel, heavy gas oil, atmospheric residue. Figure 2: Distillation tower bubble cup tray The Vacuum Distillation: This unit distills the atmospheric residue and produces light vacuum gas oil, heavy vacuum gas oil, and vacuum residue. The distillation occurs because the pressure inside of the unit is decreased to nearly zero, allowing the components of the atmospheric residue to boil at a lower temperature. Figure 3: vacuum distillation tower 96

4 PROCEDURE The crude oil first enters the desalter to remove any salts that may corrode the processing units. From there, the desalted crude enters the atmospheric distillation unit. The separated components are as follows: light distillates, kerosene, light gas oil (diesel), heavy gas oil (fuel oils), and atmospheric residue. The light distillates enter the light ends unit, where they are further separated into several more distinct components. The methane and ethane is captured and burned to provide heat for other processes. Propane and butane are first hydro treated before being sold as LPG. The light straight run naphtha is sent to the gasoline blending pool and the heavy straight run naphtha is hydro treated and then sent to the catalytic reform to produce high octane gasoline. The straight run kerosene is hydro treated before being sold as a final product, which is mostly jet fuel. The straight run diesel is also hydro treated before being sold to the public. Hydro treating the diesel remains extremely important due to the increasing governmental restriction on its sulfur content. The heavy gas oil is either sold as low grade fuel oil or more often it is upgraded using fluid catalytic cracking to produce more desirable products. The atmospheric residue is sent to vacuum distillation. The light vacuum gas oil is further refined using a visbreaker and then sold as a fuel oil. The heavy vacuum gas oil is hydrocracked to produce gasoline and other products. The vacuum residue is sent to a deasphalter, producing asphalt and a deasphalted oil, which is further treated to produce fuel oils, or to a coker. Finally, various processes, such as the fluid catalytic cracker and coker provide the feed stocks for the alkylater. RESULTS AND DISCUSSION 600 LIGHT STRIGHT RUN LIGHT GAS OIL REDUCED CRUDE BENZENE NAPTHA HEAVY KEROSENE TEMP :00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 24:00:00 1:00 2:00 3:00 4:00 5:00 6:00 time per day Figure (4): Variation of temperature of the products with time This figure shows the different types of the products in the outlet of the distillation unit as we can see here the light products such as "Benzene, Light gas oil, light straight run" with low TBP will be drawn off from the top of the distillation column. The remain products with high TBP will be drawn off from the bottom of the distillation tower. 97

5 . Figure (5): Variation of Temperature and Mass percent of the feed Figure (6): Variation of mass fraction of total oil and boiling point 98

6 60 58 R² = optimum valu ( m3/hr) Data sets Linear (Data sets) Operational value (m3/hr) Figure 7: Regression analysis between the operational and optimum flow rate of HGO R² = optimum valu ( m3/hr) Data sets Linear (Data sets) Operational value (m3/hr) Figure 8: Regression analysis between the operational and optimum flow rate of Naphtha 99

7 250 Optimal flow rate (m3/hr) R² = Predicted data Linear (Predicted data ) Simulated flow rate (m3/hr) Figure 9: The correlation between the optimal and regroius simulation results of products' flow rates In Figure(9) the correlation between the optimal and the simulated flow rate of the distillation column products is illustrated with best linear fit and the correlation coefficient. The value of the correlation coefficient (R 2 ) for the test data is 0.997, which indicates a very good correlation between optimum and operational data. Table 1: Comparison of product flow rate with rigorous simulation Products Benzene Naphtha Heavy kerosene Light distillate Simulated result m 3 /hr Optimal result m 3 /hr Light gas oil Heavy gas oil Reduced crude

8 CONCLUSION Simulation and optimization of the crude oil separation process becomes increasingly important because of the high energy costs and ecological requirements for quality oil products. Computer simulation is one of the most important steps of process optimization. However, due to complex composition of oil and complex design of oil fractionators, simulation of oil distillation requires a specific approach. The crude oil distillation unit is a unit of major importance in every refinery system. The present work concerned the application of HYSYS for the analysis of the operation of an atmospheric distillation unit of a crude oil refinery. Steady-state simulation of an existing atmospheric crude oil distillation column is performed using real operating data of Basrah refinery, IRQA. The simulation results were in good agreement with operational data.a hybrid optimization algorithm which combined the HYSYS simulation method and genetic algorithm (GA) is proposed. The proposed algorithm constitutes a reasonable framework, capturing both the operating condition and the production performance of the studied atmospheric distillation unit.the operational and the calculated values from the proposed network of flow rates of the major components of the distillation unit products is compared. The correlation coefficients (R 2 ) obtained for benzene, naphtha, Heavy kerosene (HK), Light distillate (LD), Light gas oil (LHO), Heavy gas oil (HGO) and Reduced crude (RC) flow rates are , , , , , and respectively, which indicates that the proposed methodology can be used to predict design variables (output variables) of the crude oil distillation column. REFERENCES 1. Riazi, M.R. and Daubert, T.E. 1980, Simplify Property Predictions. Hydrocarb. Process.59 (3): Petroleum & Coal, International Journal for Petroleum Processing, Petrochemistry and Coal Processing. ISSN , P&C is an Open Access Journal published. 2. TomášPavlík, JumaHaydary, 2009, STEADY-STATE AND DYNAMIC SIMULATION OF CRUDE OIL DISTILLATION USING ASPEN PLUS AND ASPEN DYNAMICS, Institute of Chemical and Environmental Engineering, Faculty of Chemicaland Food Technology, Slovak University of Technology, Radlinského 9, Bratislava, e. mail: juma.haydary@stuba.sk. 3. Mohammad Hossein Ordouei, 2009, requirement for the degree of Master of Applied Science, University of Waterloo, Waterloo, Ontario, Canada, D.Y. Peng and D.B. Robinson, 1976, Industrial and Engineering Chemistry Fundamentals, 15, p 59 (1976). 5. Gadalla, S., A. Aboul-Fotouh, et al. (2003), "Prevalence of smoking among rural secondary school students in Qualyobia governorate." Journal of the Egyptian Society of Parasitology 33(3 Suppl): A.Yu. Torgashov (2001), Nonlinear process model-based self-optimizing control of complex crude distillation column, Published by Elsevier JOURNAL,Volume 9, 2001, Pages Byoungmu Chang a, SeunghoonLee a, Hyeoktae Kwon, (1998), Rigorous industrial dynamic simulation of a crude distillation unit considered valve tray rating parameters, Published by Elsevier JOURNAL, Volume 22, Supplement 1, 15 March 1998, Pages S863 S Dhaval J. Dave, Murtuza Z. Dabhiya, S.V.K. Satyadev, Saibal Ganguly 1, Deoki N. Saraf(2003), Online tuning of a steady state crude distillation unit model for real time applications, Published by Elsevier B.V, Volume 13, Issue 3, April 2003, Pages Vineet Kumar, Anuj Sharma, Indranil Roy Chowdhury, Saibal Ganguly 2, Deoki N. Saraf (2001), A crude distillation unit model suitable for online applications, Published by Elsevier JOURNAL, Volume 73, Issue 1, 5 October 2001, Pages H. Al-Muslim a, I. Dincer, S.M. Zubair (2005), Effect of reference state on exergy efficiencies of one- and two-stage crude oil distillation plants, Published by International Journal of Thermal Sciences, Volume 44, Issue 1, January 2005, Pages

9 11. Haihua Yao, Jizheng Chu (2012), Operational optimization of a simulated atmospheric distillation column using support vector regression models and information analysis, Published by Elsevier B.V, Volume 90, Issue 12, December 2012, Pages Hasan Y. Alhammadi(2008), A systematic procedure for optimizing crude oil distillation systems, Published by Elsevier B.V, Volume 25, 2008, Pages J. Carlos Cárdenas-Guerra a, Teresa López-Arenas, RicardoLobo-Oehmichen a, Eduardo S. Pérez-Cisneros, (2010), A reactive distillation process for deep hydro desulfurization of diesel: Multiplicity and operation aspects, Volume 34, Issue 2, 8 February 2010, Pages J. González a, R. Aguilar a, J. Alvarez-Ramı rez b, G. Fernández c, M. Barrón a (1999), Linearizing control of a binary distillation column based on a neuro-estimator, Published by Elsevier JOURNAL, Volume 13, Issue 4, October 1999, Pages Lluvia M(2013). Ochoa-Estopier, Megan Jobson, Robin Smith, Operational optimization of crude oil distillation systems using artificial neural networks, Published by Elsevier B.V, Volume 59, 5 December 2013, Pages Lisa Starkey Ott, Beverly L. Smith, Thomas J. Bruno(2008), Advanced distillation curve measurements for corrosive fluids: Application to two crude oils, Published by ELSEVIER, Volume 87, Issues 13 14, October 2008, Pages Massimiliano Errico, Giuseppe Tola, Michele Mascia(2009), Energy saving in a crude distillation unit by a preflash implementation, Published by Elsevier B.V, Volume 29, Issues 8 9, June 2009, Pages Ajeet Kumar Rai and Ashish Kumar (2012), A Review on Phase Change Materials & Their Applications, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 3, Issue 2, pp , ISSN Print: , ISSN Online: AUTHOR S DETAIL Ali KhudhairKnehir was born in Basrah at September 1988, he received his B.Sc. from Basrah Technical college, Fuel and energy Eng. Tech department at 2010-Ministry of higher education and scientific research, Republic of Iraq, he completed his M.Tech. in Mechanical Engineering (Thermal Engineering) form Sam higginbottom Institute of Agriculture Technology &sciences, at 2014, Allahabad, U.P, India. Dr A. K. Rai is born in 1977, Distt. Ballia (Uttar Pradesh) India. He received his M.Tech Degree from MNNIT Allahabad in Design of Process Machines and Ph.D. from SHIATS- DU Allahabad in2011.he has been in GBPUAT Pant Nagar from 2003 to He is Joined SHIATS-DU Allahabad as assistant Professor in2005. He has published more than 25 papers in international journals. He has delivered expert lectures in many national and International conferences. Dr. Abdulwahid Al-Hajjaj is born in 1965, Basrah-Iraq. He graduated in 1987 with a BSc in Chemical Engineering from Basra University, Iraq followed by an MSc in Energy and Water Technologies also from Basra University. Dr. Al-Hajjaj gained his PhD from Faculty of Engineering and the Environment, Southampton University, UK. His work was investigating both experimental and theoretical novel nanoporousframework structures using various supports to improve their hydrogen uptake properties. Currently, He is an Associate Professor at Chemical Engineering Department, Basrah University. Dr. Al-Hajjaj has over 50 publications in E&W (Energy & Water) technologies and nanomaterials, including some in leading international, peer-reviewed journals. He has numerous invited talks and presentations in many national and International conferences. 102