Crude Distillation Unit Research Articles

A Modeling Framework to Investigate the Influence of Fouling on the Dynamic Characteristics of PID-Controlled Heat Exchangers and Their Networks

The present work is an extension of the authors’ previous research, where changes in the dynamic behavior of heat exchangers induced by fouling build-up were studied. In the present work, the authors used the previously elaborated and validated mathematical model of transient heat exchange with the influence of thermal resistance of fouling taken into account. The behavior of specific Heat Exchanger Networks (HENs) coupled with a Crude Distillation Unit together with their control loops is simulated using Simulink/MATLAB and the influence of fouling build-up on specific indices of quality of operation is investigated. According to the presented results, the higher the number of heat exchangers in the PID control loop and the greater the number of heat exchangers interacting in the network, the smaller the influence of fouling on the control quality indices, and in the extreme case, this influence may be negligible. This might be caused by the compensation of the negative effects of fouling build-up when the heat exchangers are interacting in the HEN. Nevertheless, potential adverse effects of fouling on HEN operation can be prevented by periodic adjustments of the optimal values of PID gains.

Dealing with Multiple Tank Outflows and In-Line Blending in Continuous-Time Crude Oil Scheduling Problems

The crude oil scheduling problem has been focus of many studies in the past, which is justified by its importance in the oil industry. Optimizing crude oil blending is of paramount importance given that it can substantially impact the economic performance of a refinery. In this work, operational features of a real-world existing refinery are addressed. Only operations limited to the refinery battery are in scope such as splitting of parcels unloaded from a supplying pipeline segment, tank heels and capacities, brine settling time, multiquality tracking, crude distillation unit (CDU) straight-run products profile, multiple tank outputs, and multiple CDU inputs. Moreover, different policies as to the handling of the refinery tank farm are evaluated. The presented formulation is taking from the multioperation sequencing (MOS) proposed by Mouret, Grossmann, and Pestiaux ( Comput. Chem. Eng. 2011, 35, 1038−1063. DOI: 10.1016/j.compchemeng.2010.07.007). The novelty of the present work relies on how to accuratel...

Risk level Assessment of the Desalter and Preflash Column of a Nigerian Crude Distillation Unit

A study of operability and assessment of the safety level of a crude distillation unit in a Nigerian refinery has been carried out. In a crude distillation unit of a refinery, the desalter and preflash column, as well as their associated pipe works and pumps, are considered to have high-risk factors that can present operability problems and possibly hazardous conditions. Applying the HAZOP methodology, a total of 25 guide words were suitably applied on 4 nodes to study the possible deviations that may occur. The 89 causes of these deviations identified in the study could be classified under human errors and equipment malfunctioning. From the nature of the 46 consequences identified, the plant is likely to experience more of operability problems and less of deadly hazardous scenes. Suitable recommendations that will improve the operability and safety levels of the plant were however presented in the study and presented in tables. If the 61 recommendations given in this study can be incorporated into the design with the few safeguards already present in the original design of the plant, the operability problems of the plant will be greatly minimized and safety level will improve drastically.

Progressive crude oil distillation: An energy-efficient alternative to conventional distillation process

Distillation, the major process in crude oil refineries as of now. In this work we focused the attention to energy saving with respect to an industrial crude oil distillation unit. An alternative to the conventional crude oil distillation model present in the Bharat Petroleum Corporation, Kochi Refinery is proposed and simulated. The theoretical predictions as well as the simulated results indicate that the Progressive crude oil distillation reduces the utility burden as well as increase the extraction of more valuable light components. The simulation was carried out using Aspen HYSYS V8.8.2. Different crudes are taken into account and their properties and amount of distillate are analyzed. The optimization is done in an easy manner rather than the conventional mathematical method, together with the advanced process control tools; make it profitable in the operation in real time.

Industrial application of surrogate models to optimize crude oil distillation units

This work presents a new approach to optimize existing crude oil distillation systems. The main features of this approach are its ability to provide key insights of the main factors affecting product yields and energy consumption; and the consideration of system limitations, such as crude oil changes, column flooding, heat transfer bottlenecks and product quality specifications. The new approach showcases sophisticated and systematic modelling and optimization methodologies, namely artificial neural networks and simulated annealing. The approach has been applied successfully in a debottlenecking study for yield optimization of a medium-scale Spanish refinery. The implementation of the optimization results was carried out successfully in the refinery; initial projections confirm predicted benefits of $7.2 million per year.

Preventive crude oil scheduling under demand uncertainty using structure adapted genetic algorithm

Crude oil supply about 33% of the total energy consumption worldwide and is predominantly (>50%) processed in marine access refineries. Therefore, crude oil scheduling which enables the efficient processing of the crude to increase the profitability is an important problem. It often becomes challenging due to the presence of combinatorial constraints, discrete variables and uncertainties. This study addresses the crude oil scheduling under commonly present demand uncertainty. A proactive two-stage approach has been developed to solve such optimization problems. A discrete-time model is used with structure adapted genetic algorithm (SAGA) for solving single- and multi-objective scheduling optimizations. In the first stage, an initial schedule is generated with the nominal parameters provided a priory. The same schedule is then checked and accepted in the second stage only if it is robust with respect to demand uncertainty. This method is applied to four different industrial size problems with scheduling horizon of 3, 7, 14 and 20 days. The objective function in the single objective formulation is the maximization of profit. However, in multi-objective optimization, an additional objective of minimization of fluctuation in crude oil supply to crude distillation units is used as it provides a better control and operability of the plant. The proposed method is successfully implemented for the above four different crude oil scheduling problems to generate the robust schedule.

An Artificial Intelligence Method for Energy Efficient Operation of Crude Distillation Units under Uncertain Feed Composition

The crude distillation unit (CDU) is one of the most energy-intensive processes of a petroleum refinery. The composition of crude is subject to change on regular basis. The uncertainty in crude oil composition causes wastage of a substantial amount of energy in the CDU operation. In this study, a novel approach based on a multi-output artificial neural networks (ANN) model was devised to cope with variations (uncertainty) in crude composition. The proposed method is an extended version of another method of cut-point optimization based on hybridization of Taguchi and genetic algorithm. A data comprised of several hundred variations of crude compositions and their optimized cut point temperatures, derived from the hybrid approach, was used to train the ANN model. The proposed method was validated on a simulated CDU flowsheet for a Pakistani crude, i.e., Zamzama. The proposed method is faster and computationally less expensive than the hybrid method. In addition, it can efficiently predict optimum cut point temperatures for any variant of the crude composition.

Entropy generation minimisation of shell-and-tube heat exchanger in crude oil preheat train using firefly algorithm

This paper presents the entropy generation analysis and optimisation of typical shell-and-tube heat exchanger in the preheat train of crude oil distillation unit. The implication of entropy minimisation on energy consumption associated with design of heat exchanger was studied. The developed optimisation model was solved by employing the firefly algorithm. A number of constraints were applied with thirteen decision variables. The ε-NTU method and Delaware method were used for the heat exchanger design. Four cases were considered for each of two selected samples and were categorised under two studies. Total entropy generation rates for all the four cases considered were almost the same, and the dominant irreversibility distribution is by heat transfer. However, the sharp decrease in entropy generation due to fluid friction caused a great reduction in pumping power in the range of 51.4–82.1% and 54.8–92.2% for the two studies, respectively. The results of sensitivity study on the decision variables showed sharp reduction in entropy generation rate and increased pumping power as the mass flow rate increases for all the variables. Also, the choices of the tube diameter and tube number had greater impact on the changes in entropy generation rate and pumping power.

Analysis of Energy Loss through Conventional Refractory Lining in Refinery Heater and its Conservation Measures

The work of this project is to analyze theinsulation materials currently used in the furnace of CPCL refinery which is a crude distillation unit heater to minimize the heat loss through the radiant wall section by substituting them with advanced insulating materials.There are some advanced materials whose properties are well suited for the insulation purpose in the furnace wall which could apparently reduce the heat loss through the walls. The general properties which include weight, thickness, thermal conductivity, are calculated to prove that these materials can be used as a replacement to those of the regular insulation materials.The materials which have been taken for analysis can reduce heat loss and improve thermal efficiency it will not require much maintenance activities compared to the old insulation and installing the new insulation is much easier than old as it can be handled easily.This paper will prove that replaced materials will act as an effective insulation than old type.

Sparse Bayesian learning for data driven polynomial chaos expansion with application to chemical processes

Uncertainties are ubiquitous in process system engineering. Hence, to develop a safe and profitable process, uncertainty quantification (UQ) is necessary in a reliability, availability, and maintainability (RAM) analysis. Generalized polynomial chaos expansions can be used as an efficient approach to UQ and work efficiently under the assumption of perfect knowledge with regard to the probability density distribution function of uncertainties. However, this assumption can hardly be satisfied in a real process scenario, mainly because of the limited knowledge regarding the probability density distribution function of uncertainties. To solve these issues, this study investigates the performance of orthogonal polynomial chaos in the UQ of chemical processes, including synthesis gas production and natural gas dehydration. Simultaneously, the limitations of orthogonal polynomial chaos were also investigated by an overwhelming sparse Bayesian learning approach considering a complicated nonlinear crude oil distillation unit with moderate uncertainty numbers. We found that the application of orthogonal polynomial chaos was limited to a small number of uncertainties, mainly because of using the polynomial’s tensor product. Finally, the orthogonal polynomial chaos and sparse Bayesian learning approach were rendered computationally effective in comparison with the conventional Monte Carlo method (approximately 96.5% improvement).

Energy saving potential and the efficacy of using different control strategies for the heat exchanger network operation

Heat exchangers – typically arranged in heat recovery networks – belong to the devices most widely used in industrial production systems. The control performance of heat exchanger network (HEN) operation can be impaired by fouling which builds up on heat-exchange surface. Fouling leads to burning extra fuel to compensate for reduced heat recovery and induces increased costs of cleaning interventions, etc. In this work, a real-life benchmark system is considered. The controlled system represents a HEN coupled with a crude distillation unit (CDU). The mathematical model of HEN with stream splits and interactions between the branches, was built and validated based on real-life data recorded during CDU operation. The use of various control strategies (employing linear control systems with PID controllers) for HEN operation was studied with the aim to maximize heat recovery in the HEN involving splits. Using historical data on HEN operation, simulations of closed-loop control were performed in MATLAB/Simulink environment. The simulation results demonstrated the efficacy of the studied strategies and their potential to achieve energy savings in CDU operation.

On-line control of the heat exchanger network under industrial constraints

It is commonly known that the aim of the heat exchanger network (HEN) is maximization of heat recovery. Unfortunately, heat recovery is deteriorated by deposits mounting up on the heat transfer surface of the heat exchangers. Moreover, the process parameters (temperature, mass flow, etc.) are changing with time that also are influencing on heat recovery. Hence, to maximize heat recovery in real HEN, the authors proposed a mathematical model of on-line control of HEN. Unfortunately, the existing mathematical models of the heat exchanger are inaccurate because heat transfer coefficients are calculated with errors at values ranging from 10 to 40 %. To overcome this issue, the authors introduced a very accurate mathematical model of the heat exchanger. The model is based on industrial measurements of the process stream parameters. These parameters are used for on-line validation of the heat exchanger model. For the validated model, the results of calculation are very accurate, for the errors of calculations are less than 1 %. In this article, the proposed model is applied to HEN in Crude Distillation Unit (CDU), processing 800 t/h of crude oil. In proposed on-line model, the manipulated variables are mass flows of crude oil passing via several branches of the HEN. The results of the HEN simulation show that on-line control of the mass flow through several branches of the HEN gives an opportunity to 1.5 % increase of the total heat recovery.

Monitoring of Important Variables Affecting the Formation of Fouling in Crude Oil Heat Exchangers

Conventional methods for determination and monitoring of the total thermal resistance of fouling are based on computing the overall heat transfer coefficient U over time. Heat exchanger cleaning schedules are developed using historical process data on the periods when the thermal resistance of fouling has become too high. There are many different variables of the preheating process that determine the actual rate of fouling build-up in crude oil heat exchangers, namely: flow velocities of the fluids in both shell- and tube-side of exchanger; wall temperatures of the exchanger tubes and the shear rates at the tube surfaces. In this paper, using data records acquired during 5 y of operation of a heat exchanger network coupled with a Crude Distillation Unit, the monitoring of those variables is discussed and their influence on fouling growth is estimated.

Process Integration Accounting Fouling in Heat Exchanger Network: A Case Study of Crude Oil Distillation Retrofit

This paper presents the approach to predict the future performance of a heat exchanger network based on Pinch retrofit design and detailed historical performance of the unit to improve the operation efficiency, save an energy and increase a throughput. The main input is a finding of a profitable solution of heat exchanger network based on Pinch Analysis, fouling predictive management and a start-up and shut-down time. The approach demonstrates the importance of a dynamic assessment of the retrofit design and operation modes. The case study was developed for crude oil distillation unit with annual capacity of 6 Mt. Dynamic fouling models were built to assist with designing and revamping of heat exchanger network, to reduce the fouling and increase the throughput. Rigorous modelling of refinery heat exchangers network and Pinch Analysis were executed to achieve increased throughput, reduce operation cost and harmful emissions. A new heat exchangers network with improved operation conditions predicting efficient exchanger cleaning schedules under various economic and operational constraints was obtained. The calculations use an HTRI shell-and-tube heat exchanger models, operation data, properties of crude oil and cleaning schedule. Process Integration steps were done by HILECT software and new operation schedule of efficient heat exchangers network was created by HTRI software. The strategy and recommendations for the optimal operation of the retrofitted network were proposed taking into account the economic and process constraints such as exchanger by-passes, operation budget, furnace duty and pumping limit.

Diagnosis of the Fouling Effects in a Shell and Tube Heat Exchanger using Artificial Neural Network

In this paper, there presents an identification method of the fouling influence on the heat recovery in a heat exchanger. To evaluate the heat losses due to fouling there is proposed the method based on neural network approach. Because the correctness of anticipation of the heat exchanger behaviour depends on measurement data, it is very important to prepare an appropriate data. Unfortunately, the measured data contains errors caused by inaccurate instruments, disturbances in data transmission, transient state of the operated heat exchanger. To overcome this problem the authors proposed a new approach of filtering the row data using objective function based on standard deviation of measured parameters (temperature, flow rate, etc.) in the heat exchanger. Minimising the objective function, it makes it possible to eliminate gross errors and properly select the time intervals of steady state operation of the heat exchanger. The method was validated using measurement data for the heat exchanger belonging to heat exchanger network connected with a crude distillation unit processing 800 t/h of crude oil. Using artificial neural network the time dependencies between heat losses versus time were obtained.

Energy optimization of integrated atmospheric and vacuum crude distillation units in oil refinery with light crude

The heat integration and retrofit analysis of Arabian light crude distillation units was carried out to identify opportunity for energy savings using different design options, to achieve optimum heat exchanger network. Data used were extracted from an existing and operating crude oil refining plant. The pinch analysis of existing plant using process integration software (Heat-Int) Process Integration Limited, Cheshire, United Kingdom revealed that the hot and cold utilities consumptions at the prevailing ΔTmin of 77 °F in the plant were 680.23 and 521 MMBtu/h, respectively. Economic evaluation of existing plant revealed total operating cost of $4 829 625/year. Retrofit of existing plant using different design options generated an optimal network comprised four additional heat exchangers with reduction in ΔTmin from 77 °F to 57 °F. The hot and cold utilities consumptions also reduced to 623.23 and 464.44 MMBtu/h, respectively. Economic evaluation of the new design showed energy savings of $259 860/year. The trade-off between energy and capital costs in terms of the costs procurement and installation new heat exchangers gives overall cost savings of $32 800/year with payback period of 11 months. The study concluded that retrofitting of existing heat exchanger network to correct those exchangers that are transferring heat across the pinch could result in enormous energy savings. © 2013 Curtin University of Technology and John Wiley & Sons, Ltd.

Optimization Approaches for Efficient Crude Blending in Large Oil Refineries

To increase profit margin, refiners usually upgrade low cost crude oils by mixing them with light crudes to obtain blends of higher value. In recent years, this trend is favored by a shifting in the market demand from gasoline toward diesel fuels that makes it more attractive to process crude blends with higher diesel yields. Using in-line blending stations, feedstocks for crude distillation units (CDUs) with the desired properties are obtained by mixing flows of different types of crude oils using the right blending recipe. In large oil refineries, several CDUs are available to process a wide variety of crude oils stored in many dedicated tanks. The scheduler must not only select the cluster of tanks allocated to each CDU but also determine the scheduling of the blending operations providing the best qualified feedstocks for every distillation unit. Trace element compositions and the temperature boiling point (TBP) curve are the properties normally controlled to set the feedstock quality. In this work, two alternative approaches are proposed to solve this challenging scheduling problem: (a) an exact mixed-integer nonlinear (MINLP) formulation that simultaneously considers tank allocation and operations scheduling decisions; (b) an efficient sequential approach based on a pair of MINLP subproblems making the tank allocation at the upper level and the scheduling decisions at the lower one. After validation, the sequential approach is successfully applied to new nine case studies involving up to four CDUs, 60 charging tanks, and 14 types of crude oil.

Corrosion effect on inspection and replacement planning for a refinery plant

This paper presents an optimization model of inspection and replacement planning for a refinery plant under the consideration of corrosion in terms of cost. The management of corrosion is an essential task for processes that operate over several years without a shutdown. This is because corrosion can cause severe failures by thinning the wall thickness and eventually cause pipes or equipment to burst. However, required safety measures, such as the corrosion management, involve costly inspection and replacement. Therefore, a cost-effective safety-action strategy is proposed in this paper. The developed model presents an optimal combination of steel grade, design wall thickness, inspection number, and inspection timing under a given corrosion rate to minimize the cost of design, inspection, replacement, and failure. Three case studies using sensitivity analyses are applied to three major processes in a refinery plant: a crude distillation unit, visbreaker, and hydrocracker.

A novel MINLP model of front-end crude scheduling for refinery with consideration of inherent upset minimization

In this paper, a new methodology has been developed to deal with the front-end crude scheduling (FECS) problems with the consideration of inherent upset minimization (IUM). Specifically, the primary and secondary inherent upsets (IUs) have been defined and modeled to, respectively, address flowrate fluctuations of feeding crude distillation units and the long-distance pipeline (LDPL). Based on such IU characterizations, a new MINLP model with the unit-based continuous time representation is developed to determine the optimal FECS solution by minimizing the overall operating cost and instability along the entire scheduling time horizon. In addition to the IUM, another two merits are also included in this study: (i) the trans-mixing (TM) issue along with crude transportation inside the LDPL has been modeled; (ii) multiple types of crudes with multiple key properties have been simultaneously considered. The developed FECS MINLP model is solved by the ANTIGONE solver to obtain the global optima. The efficacy of the proposed methodology and the FECS model, and the effect of IUM have been investigated through various case studies.

High-temperature corrosion of a UNS K03006 steel pipe in a crude oil vacuum residue distillation unit

Abstract A carbon steel pipe of ASTM A106 grade B [UNS K03006] specification was in service of vacuum residue in a crude oil vacuum unit at temperatures ranging from 293 to 350 °C. After 20 years of service it failed due to a combination of sulfidic and naphthenic acid corrosion. This failure motivated this is work, where the mechanism of corrosion failure mechanism is studied, based on the microstructural characterization of the corroded surfaces of coupons tested in a laboratory simulated vacuum residue environment, with variations in temperature and exposure times. Experimental results showed that at 310 °C, the preferential formation of a homogeneous passive iron sulfide film occurred, which promoted general sulfide corrosion. However, the greatest corrosion rates occurred at 350 °C, due to the generation of sour primary distillates and the fragmentation of the vacuum residue molecule, which triggered a naphthenic acid corrosion mechanism, which caused localized attack corrosion. It is concluded that the most important factor to be monitored in vacuum residue service in crude oil vacuum residue distillation units, in order to predict and prevent corrosion, is the temperature. Specifically, operation temperatures above 310 °C should be avoided, because high corrosion rates and the combination with localized attack in the forms of pitting and grain boundary dissolution. In order to prevent failures in vacuum residue pipes, closed interval thickness surveys and close temperature monitoring are recommended.