Atmospheric Distillation Column Research Articles

HAZOP ANALYSIS OF AN ATMOSPHERIC PETROLEUM DISTILLATION UNIT AND SIMILARITY IN SUSTAINABLE AVIATION FUELS (SAFs) PRODUCTION PROCESSES

The production of petroleum derivatives stands out worldwide among the non-renewable energy production processes, as the most well-known worldwide. In this study, an atmospheric distillation column that processed 100,000 barrels/day (662.5 m3/h) of crude oil was simulated using CHEMCAD® software. From this procedure, the Process Flow Diagram (PFD) and the Piping and Instrumentation Diagram (PI&D) for the unit, corresponding to its automation, were obtained. Subsequently, a HAZOP (Hazard and Operability Study) analysis was conducted in order to identify risks in the unit that, although not dangerous, could compromise its ability to achieve its productivity. The main risks of the process were related to fires, explosions and environmental contamination from leaks and ruptures in pipes, pumps, heat exchangers, the column itself, among other equipment. The HAZOP analysis was able, through the use of guide words, to identify possible operational risks arising from deviations in operating intentions, such as the possibility of fires, explosions, environmental contamination and their consequences. It was possible to identify the risks associated with the selection of materials, the mechanical design of the equipment and the specifications of the accessories. Therefore, the study carried out is an important survey for reducing possible failures in the oil industry. And, since this study is an in-depth study on the distillation processes for petroleum-derived products. It is understood that their in-depth study, as a result of the development of new renewable products, especially sustainable aviation fuels (SAFs), because they are similar purification processes, employ the same methodology to identify possible failures in production.

Thermodynamic Modeling and Process Simulation of Kumkol Crude Oil Refining

The Crude Distillation Unit (CDU) mechanism is commonly regarded as the first stage in petroleum refining. In this study, Aspen Plus® is used to simulate the basic process of a CDU, which consists of an Atmospheric Distillation Column (ATC) and a Vacuum Distillation Column (VC). These columns are fed with two types of crude oil: KUMKOL from Kazakhstan and Soviet Export Blend, in the proportions of 0.75:0.25, 0.50:0.50, and 0.25:0.75, respectively. The goal was to do a parametric analysis and analyze the resultant streams of naphtha, kerosene, Atmospheric Gas Oil (AGO), Light Vacuum Gas Oil (LVGO), and Heavy Vacuum Gas Oil (HVGO). The simulation used the CHAO-SEA thermodynamic model, which included the Chao-Seader correlation, the Scatchard-Hildebrand model, the Redlich-Kwong equation of state, the Lee-Kesler equation of state, and the API gravity technique. Temperature, pressure, mass flow, enthalpy, vapor percentage, and average molecular weights of the streams at various phases within the CDU system were estimated. For both the ATC and VC columns, curves indicating Temperature- Pressure vs the number of stages, as well as ASTM D86 (temperature) versus stream volume % distillation, were developed. The results show that when compared to feed streams containing 0.25 and 0.50 StdVol of Kumkol Kazakhstan Oil, the feed stream with 0.75 StdVol produces more Heavy, Medium, and Light Vacuum Gas Oil (H-VGO, M-VGO, and L-VGO), as well as more Vacuum Gas (VG). These findings indicate that Kumkol Kazakhstan Oil is of high quality and has fewer contaminants, such as sulfur when compared to other accessible mixes throughout the world.

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

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

Modeling and optimization of AVT-3 and AT-2 crude оil distillation units at Atyrau refinery

The article shows examples of the use of Aspen Hysys software for optimization of the AVT-3 and AT-2 crude oil distillation units at Atyrau Oil Refinery. The Aspen Hysys software was implemented at the Atyrau Refinery for the first time.Calculations were carried out to optimize AVT-3 and AT-2 crude distillation units of the Atyrau refinery with the construction of a model in Aspen Hysys. As a result of the calculations, the possibility of improving the efficiency of individual parts of units was revealed, pilot runs on the units were carried out to confirm. During the pilot run on the AVT-3 unit, a restriction was revealed on regulating the temperature of the cube of the steam column, which affects the stabilization of the kerosene fraction beginning boiling point. During the pilot run at the AT-2 unit, it was revealed that an increase in steam consumption in the main atmospheric oil distillation column contributes to a decrease in the content of light fractions in straight-run fuel, while the yields of gasoline and kerosene-gasoil fractions increase. Thus, the positive effect of Aspen Hysys application at the Atyrau refinery for optimization of crude oil distillation units and identification of technological limitations is shown.

A soft-sensing method for product quality monitoring based on particle swarm optimization deep belief networks

A novel soft-sensing method for quality parameters of aviation kerosene in atmospheric distillation column based on least absolute shrinkage and selection operator and particle swarm optimization deep belief network (LASSO-PSO-DBN) is proposed. First, to reduce the dimension of the input variables, the least absolute shrinkage and selection operator (LASSO) algorithm is used to select the input variables that are irrelevant to the soft sensor of aviation kerosene quality parameters. Then, to improve the generalization of soft sensor model, a deep learning algorithm, deep belief network (DBN), is proposed for soft sensing of aviation kerosene quality parameters. Considering that the structure characteristics and parameters of DBN algorithm have a great impact on the learning and prediction results, the parameters of DBN are optimized based on particle swarm optimization (PSO) algorithm. The benchmark data sets and the industrial atmospheric distillation column data are used for simulation analysis and evaluation of the soft-sensing performance. The simulation results show that the novel proposed algorithm can effectively reduce the dimension of the input variables and simplify the structure of the soft sensor model. It also has good generalization ability and the predicted value is in good agreement with the actual measured value.

Design of Atmospheric Petroleum Distillation Column

Design of Atmospheric Petroleum Distillation Column

Studies and Modeling for Upgrading Units for Heavy Oil Refineries

The primary objective of this study is to predict the optimum operating conditions required tooperate an existing atmospheric distillation column to distillate heavier crude oils in same unit whichdesigned mainly to fractionate moderate and lighter crude oils.Detailed simulation model of atmospheric distillation column was made using Aspen Hysys V.8.4 software. The model built depending on the data of real plant which located in Al-Daura midlandrefinery company at Baghdad/Iraq. The crude oils were described using the true boiling point (TBP)assays and Peng-Robinson package were used to predict thermodynamic properties. Three differenttypes of crude oils (Kirkuk light, Basrah light and Basrah medium) were selected to identify themodel validity. The simulation results agree very well with the industrial plant results. Finally theproposed model was used to predict optimum operating conditions required to distillate a blend oflight and heavy Basrah crude oil with different mixing ratios. The objective of the optimization is topredict the maximum profit of the products within the required specifications. At optimum operatingconditions the total yearly profit for distillation of a blend of 50% vol. light+50% vol. heavy basrahcrude oil was found to be 713.28 M$.

Pinch Analysis Applied to Atmospheric Distillation Column

To increase the efficiency of industrial processes, mainly in petroleum refineries, the strategies to energy optimization are being developed. Thus, there is a need to know the energy consumption analysis in term of loss level and to define the procedure to recovery that. The analyses involve the flow rate study, based on energy balances that define the best strategy to use the energy, with the installation of heat exchanger networks. With the use of this procedure, we can reduce the operational costs, associated with a reduction of the level energy consumption. The study developed here was related to refinery atmospheric distillation column, using pinch technology, based on energy conservation law. The pre-flash, atmospheric distillation column and heat exchangers networks were evaluated in this study. The dates used in this study were collected from Angolan refinery, during 30 days and the results were used to mass and energy balances. Previously, the flow sheet process and established the relations between the operational parameters in industrial equipment were evaluated. The results from energy balances were used to integration study, supported by the pinch analysis using spreadsheet software, and the dates showed as composite, great composite and cascade curves. For this, were used the heat exchanger battery based on pinch analysis, to make the new networks of energy integration. After the assessment of each network with 4 to 12 heat exchangers, it was optimized to know that one with better energy consumption. The new network proposed is evaluated in term of reduction of energy consumption, minor number of heat exchangers and networks optimization. As a result of this methodology, we can see the reduction of energy consumption of 785,4 KW (utility) and 1277,1 KW (cold), respectively. On other hand, the optimization strategy adopted here allowed increase the temperature to 301ºC.

Feasibility study on replacement of atmospheric distillation column with new sequences in a natural gas condensate refinery

The atmospheric distillation unit is the main separation unit in crude oil and natural gas condensate refineries. An atmospheric distillation column in a natural gas condensate unit usually consists of a single column with 3 pump-arounds and 2 side strippers. In this work a new column sequences have been synthesised for the first time using the modified separation matrix method and the results had been compared to other well-known configuration through economic, exergy and exergoeconomic analysis. The results illustrate that the conventional atmospheric distillation configuration has a medium performance compared to other sequences. As an example, comparing the results obtained from the direct sequence, to the conventional distillation sequence, leads in almost 30% less annual cost, 54% less exergy loss rate and 5% more exergoeconomic factor. However, the 84.87% exergoeconomic factor for the conventional configuration could justify the application of conventional sequence in the refinery.

Optimal estimation of physical properties of the products of an atmospheric distillation column using support vector regression

Atmospheric distillation column is one of the most important units in an oil refinery where crude oil is fractioned into its more valuable constituents. Almost all of the state-of-the art online equipment has a time lag to complete the physical property analysis in real time due to complexity of the analyses. Therefore, estimation of the physical properties from online plant data with a soft sensor has significant benefits. In this paper, we estimate the physical properties of the hydrocarbon products of an atmospheric distillation column by support vector regression using Linear, Polynomial and Gaussian Radial Basis Function kernels and SVR parameters are optimized by using a variety of algorithms including genetic algorithm, grid search and non-linear programming. The optimization-based data analytics approach is shown to produce superior results compared to linear regression, the mean testing error of estimation is improved by 5% with SVR 4.01 °C to 3.8 °C.

COMPUTER SCIENCE, COMPUTER ENGINEERING AND MANAGEMENT SYNTHESISING THE MODEL OF THE PREDICTIVE MAINTENANCE OF ATMOSPHERIC PIPE STILL

Objectives. The aim of the study is to develop a model of a predictive maintenance system allowing the output parameters of a high-capacity distillation unit to be stabilised.Method. The problem of controlling the collection of oil products of a given fractional composition can be reduced to the task of controlling the temperature regime at draw-off trays. In this case, temperature control tasks are considered as control actions. In order to implement the proposed control principle, a mathematical model was constructed allowing the oil fraction content to be converted to directly controlled temperatures.Results. Based on the analysis of the technological process and design features of the atmospheric distillation column, the vectors of maintaining and disturbing actions are determined along with the optimisation criterion for the maintenance problem. In order to improve the dynamic characteristics of the control object, oil product quality parameters are introduced into the predictive maintenance model along with the supplementation of existing control loops with intermediate parameters.Conclusion. An analysis of the structural features of the atmospheric pipe still and the existing approach to the distillation column maintenance reveals certain disadvantages associated with the lack of operational quality control of the collected oil products, as well as the need to expand the list of stabilised technological parameters in order to optimise the technological process. It is shown that in order to switch to the proposed maintenance approach, it is necessary to develop a static model reflecting the dependency of the potential yield of the required fractions on the raw material composition along with a dynamic model in the form of a multi-parameter optimisation problem.

Ceramics coating materials for corrosion control of crude oil distillation column: Experimental and theoretical studies

Corrosion of stainless steels in atmospheric crude oil distillation column was studied in presence and absence of the ceramic coatings at different temperatures and exposure times using the weight loss technique. Thermal spray coating method was employed to deposit the coating. Weight loss of the stainless steel increased with temperature and time, and significantly decreased in the presence of a coating. Maximum coating efficiency of 93.3% was approached in the presence of Al2O3 coating. Surface morphology investigations using atomic force microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy showed a lower corrosion effect in the presence of a coating. Thickness and porosity of coating were determined to be the most significant factors. Mathematical and statistical analyses were performed as a powerful tool for data representation. Second-order polynomial and exponential models were suggested to represent weight loss data as a function of time and temperature with high correlation coefficients. Corrosion rate data varied with temperature according to the Arrhenius equation. Activation energy without coating is lower, than that with the coating, which indicates that the reaction needs a lower energy in the absence of coating. Petroleum chemistry data and local investigations showed that the main factor influencing the corrosion of crude oil distillation columns is the sulphur content, which had a significant effect more than other contaminates of crude oil.

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

Abstract 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.

Thermal cracking of atmospheric residue versus vacuum residue

Current practice subjects atmospheric residue (AR) to vacuum distillation, while feeding the vacuum residue (VR) for upgrading. This study explores the direct upgrading of AR and gauges the potential for eliminating the vacuum distillation unit, while recycling the upgraded oil to the atmospheric distillation column. Thermal cracking of Arabian AR and VR was carried out in an autoclave for 1 h at 400 °C. AR provided much higher liquid yield and quality with limited coke yield (<1 wt%). Higher temperature (420 °C) and longer residence time (2 h) were tested to further improve the quality of the liquid product. In addition, drill cuttings, previously demonstrated as an effective thermal cracking catalyst, were added to selected runs. Drill cuttings promoted the formation of short chain molecules, suppressed coke formation and significantly increased the quality of the product oil compared to control AR runs. Catalytic thermal cracking of AR at 420 °C for 2 h resulted in gas and coke yields of ~9 wt% and ~6 wt%, respectively, with the remainder being a premium quality liquid having °API gravity of 34. Upgrading of VR at the same conditions produced lower °API gravity liquid of 28 with 1.5 times more gas, 3 times more coke, and ~15 wt% less liquid yield. Simulated distillation showed that the product oil from both AR and VR upgrading contained ~20 wt% residue. Nevertheless, a mass balance of the whole refining/upgrading process revealed two times more liquified petroleum gas/light gas oil cut, with 50% less vacuum gas oil and 33% less coke yield for AR upgrading.

Comparative study of the physical and chemical properties of the crude oil mixture of the El-Wafa field in practically and through the use of an advanced program [Aspen HYSYS

The accuracy in measuring the physical properties of any crude oil has become a very difficult task because the price of crude oil in the global market depends on it in addition to the process of producing lubricating oil , chemical products and refinery. Accordingly, this research effort focuses on comparative study of Libyan crude oil (El-Wafa field) by measuring the physical and some chemical properties experimentally and by Aspen HYSYS V8.4 programs. Also, test the possibility to know the volume percentage of the production of lubricants from this crude oil by the two methods mentioned above.Test carried out on these measurements practically and theoretically the physical properties are include: normal boiling point, pour point, Specific gravity (Sp. gr.), standard density (API), dynamic viscosity , kinematics viscosity , molecular weight, Acentric factor and Watson factor (K or Kw ). Some chemical properties have also been experimentally measured, for example the sulphur content ( wt.%), Asphaltenes content( wt.%) , water and sediment content (vol.%), Conradson carbon residue ( wt.%) , salt content (as NaCl, mg\L), ash content(wt. %) and metals content ( wt.%).The results from the tests showed that, there is a very small convergence between the results of calculations of physical properties by use this modern software with experimental measurements in the lab to the crude oil of El-Wafal field up to 99% and this blend is not suitable for production the lubricated oil at atmospheric distillation column. Also, the results observed that’s the useful mole percentage of lubricated oil cutoff has volume percentage range not significant from 1 to 1.8% for this type of Libyan crude oil. Through practical and theoretical results possible to classify this type of crude oil as Brent mix according to the global market for the sale of crude oil, which is characterized by its lack of sulfur element and metals in addition to its standard relative density API is very high more than 55.

Method and Device for Quantitative Measurement of Crude Oil Fouling Deposits of Several Crude Oils and Blends at a Higher Temperature and the Impact of Antifoulant Additives

Since the establishment of crude oil refining, refiners have encountered the problem of fouling in heat exchanger trains, heaters, and atmospheric and vacuum distillation columns. Crude oil fouling is one of the major problems that triggers efficiency reduction, environmental impact, unplanned shutdown, and loss of revenue and yield. In the topic of crude oil fouling, most published papers were focused on heat exchanger fouling and, to a lesser extent, the distillation columns and heaters. However, with the absence of a vacuum column, some small refineries operate and flash the crude oil at or above 700 °F for better recovery. In this study, a simple and affordable bench-type research laboratory rig was fabricated and used for the determination of the gravimetric fouling deposits for several crude oils and blends at an elevated temperature of 720 °F or more. To address the high-temperature impact on crude oil fouling, the ΔAPI and Δmicrocarbon were used to indicate the extent of the thermal cracking. Ultr...

Plantwide control for optimal operation of industrial-scale crude distillation unit processes

ABSTRACTIn this article, a combination of the wavelet neural network framework and the line-up competition algorithm is used to solve the economic optimization algorithm for an industrial-scale atmospheric distillation column (ADC) process. Compared to the relevant measuring data from Sinopec Wuhan Petroleum Group Company, China, the first optimal operating conditions show that the increments of the duties of furnace and pump-arounds of the ADC can effectively improve oil production. In our approach, the preflash column (PFC) coupled with ADC is denoted as an industrial-scale crude distillation unit (CDU) process. Since the PFC can produce light naphtha and reduce the furnace duty and steam consumption of ADC, it is verified that the CDU process provides the higher economic potential than ADC. Based on the second optimal operating conditions, the plantwide control strategy is employed to operate the system safely as well as regulate the outputs of the plant in the presence of inlet perturbations. Within the plantwide control framework, the inventory control aims to keep the controlled variables close to the desired operating condition and the quality control loops use a combination of inferential predictions and feedforward ratios to effectively suppress the temperature spikes of trays and furnaces. Finally, the simulations show that the product quality is guaranteed due to no offset ASTM D86 distillation temperature responses.

Kalman filter-based centralized controller design for non-square multi-input multi-output processes

In this paper, a simple and effective centralized controller is presented to control non-square multi-input multi-output industrial processes with heavy interactions and significant time-delays. The control objective is to achieve a good load disturbance rejection and set point tracking. The controller and process model equations are represented in a stochastic state-space. Also, the state and measurement equations respectively use general random walk model and finite impulse response model of the plant. To design the controller, a quadratic cost function including the control error vector as well as the input vector and its changes is introduced. A Kalman filter algorithm is used to solve the discrete algebraic Riccati equation and to estimate the state vector of the controller recursively. This novel use of Kalman filter combines the observer and controller in a block. No square down/up, pairing and pseudo-inversing, is required for the proposed control structure. This leads to the simplicity of controller design. To evaluate the performance of the proposed control strategy, it is applied to a simulation example and an industrial case study with heavy interactions and significant time delays, an atmospheric distillation column with 7 inputs and 4 outputs. Simulation results show that the proposed controller has a good performance in both load disturbance rejection and setpoint tracking.

Design and implementation of a control structure for quality products in a crude oil atmospheric distillation column

In recent years, interest for petrochemical processes has been increasing, especially in refinement area. However, the high variability in the dynamic characteristics present in the atmospheric distillation column poses a challenge to obtain quality products. To improve distillates quality in spite of the changes in the input crude oil composition, this paper details a new design of a control strategy in a conventional crude oil distillation plant defined using formal interaction analysis tools. The process dynamic and its control are simulated on Aspen HYSYS® dynamic environment under real operating conditions. The simulation results are compared against a typical control strategy commonly used in crude oil atmospheric distillation columns.

Thermodynamic optimization of atmospheric distillation unit

This paper presents a methodology for optimising the exergy efficiency of atmospheric distillation unit without trading off the products qualities and process throughput. The presented method incorporates the second law of thermodynamics in data driven models. Bootstrap aggregated neural networks (BANN) are used for enhanced model accuracy and reliability. The standard error of the individual neural network predictions is taken as the indication of model prediction reliability and is incorporated in the optimization objective function. The economic analysis of the recoverable energy (sum of internal and external exergy losses) reveals the energy saving potential of the proposed method, which will aid the design and operation of energy efficient atmospheric distillation columns.