Crude Distillation Unit Research Articles

Decreasing the Tendency of Water to Form Scale and Corrosion in Cooling Towers – South Refineries Company (SRC) – Iraq

Scale formation and corrosion phenomena are major technical problems at Basra Refinery/ South Refineries Company (SRC). These technical problems are concentrated in cooling towers which are used to supply cooling water to the processing units. The Scales and corrosion products precipitate inside cooling units, heat exchangers and pipeline networks affecting negatively efficiency of refinery’s equipment. In this work, a real plant data was collected from four cooling towers which is supplied the coolant to the crude distillation units of the refinery and also from the raw water supplier. The collected data was fitted by Langelier model to predict the tendency of cooling water for scale forming and activation of corrosion. The obtained result shows that the cooling water has a tendency to form scale (CaCO3 Precipitation) at cooling tower units. Also the feed water has the same tendency for scale forming.After analyzing the LSI (Langelier Saturation Index) results, the research team recommends the particular company to overcome this problem by installing a Reverse Osmosis (RO) plant for treating raw water to decrease the concentrations of total dissolved solids (TDS) which result scale and corrosion in the parts of cooling tower as an option to solve the problem.

Decreasing the Tendency of Water to Form Scale and Corrosion in Cooling Towers – South Refineries Company (SRC) – Iraq

Scale formation and corrosion phenomena are major technical problems at Basra Refinery/ South Refineries Company (SRC). These technical problems are concentrated in cooling towers which are used to supply cooling water to the processing units. The Scales and corrosion products precipitate inside cooling units, heat exchangers and pipeline networks affecting negatively efficiency of refinery’s equipment. In this work, a real plant data was collected from four cooling towers which is supplied the coolant to the crude distillation units of the refinery and also from the raw water supplier. The collected data was fitted by Langelier model to predict the tendency of cooling water for scale forming and activation of corrosion. The obtained result shows that the cooling water has a tendency to form scale (CaCO3 Precipitation) at cooling tower units. Also the feed water has the same tendency for scale forming.After analyzing the LSI (Langelier Saturation Index) results, the research team recommends the particular company to overcome this problem by installing a Reverse Osmosis (RO) plant for treating raw water to decrease the concentrations of total dissolved solids (TDS) which result scale and corrosion in the parts of cooling tower as an option to solve the problem.

Leakage of gasoil from side cut piping in crude distillation unit of a petroleum refinery

This paper reports failure of elbow from side cut piping after three years of refinery gasoil service and piping metallurgy was SS316L austenitic stainless steel. Systematic investigations including visual inspection coupled with laboratory studies were carried out to determine the cause of failure. The samples from failure location were examined for chemical analysis, hardness, fractography, elemental analysis and metallographic evaluation. Further, crude feedstock was evaluated as well. Various characterization studies concluded that presence of material defect encapsulated in elbow prior to start of initial distillation column (side cut piping) operation was the root cause of failure. Localized stresses induced from internal pressure act on remaining wall thickness have lead to brittle fracture and resulted in gasoil leakage. The existence of material defect in failed elbow prior to initial operation had revealed poor pre-commissioning inspection practice. The origination of detected material defect was discussed and similar leakages shall be avoided by defect free piping components provided with proper support system at critical piping locations. The results in this article intended for petroleum refinery piping/energy pipeline operators for use of qualified piping components at critical locations especially during plant/piping commissioning stage.

Enriched Oxygen for Crude Oil Preheating in Petroleum Refining

The crude distillation unit is one of the energy-intensive processes in the refinery. This is because of the crude preheater that suffers from excessive energy loss due to the use of air in the combustion furnace. Alternatively, fuel combustion by enriched oxygen can improve heat efficiency, minimize fuel consumption and reduce emissions. In this paper, enriched oxygen has been simulated by UniSim for preheating Kuwaiti crude in one of the distillation columns in the Clean Fuels Project. Results show that the use of 30 mol% of concentrated oxygen reduced fuel consumption by 5%. Carbon dioxide emissions were also minimized by 22,240 tons per year. A membrane system made from perfluoropolymer was simulated for the production of 5,298 tons of enriched oxygen (per day) and it required an area of 39,000 m2 with a capital investment of 6.9 million $.

Improving Rectification Technology in Daura Refinery Crude Distillation Units by HYSYS

It is well known that the capacity of crude distillation units in any refinery reflect the capacity of refinery itself, and that is why because we consider that these units the first stage and all other units such as vacuum distillation, hydrotreating, reforming, catalytic cracking, and others depend on them. So we devoted our effort to keep these units work with high performance and efficiency.
 The purpose of this work is to develop crude distillation units in Daura oil refinery, rise their performance level and detect were the weak points are concentrated to overcome.
 Our work here conducted by simulation program (HYSYS) and consists of two stages, the stage before changing operating conditions and the stage after changing operating conditions and we will discuss each one separately later.
 From this work we found that these old units contain many mistakes due to long time of operation since the year 1954 and these mistakes are concentrated in miss operation due to absence of daily close monitoring to the operation conditions and product specifications as to gap and overlap conditions and it was really effect badly on rectification efficiency

Industrial, large-scale model predictive control with structured neural networks

The design of neural networks (NNs) is presented for treating large, linear model predictive control (MPC) applications that are out of reach with available quadratic programming (QP) solvers. First, we introduce a new feedforward network architecture that enables practitioners to obtain offset-free closed-loop performance with NNs. Second, we discuss the data generation procedure to sample the state space relevant to training the NNs based on anticipated online setpoint changes and plant disturbances. Third, we use the input-to-state stability results available in the MPC literature and establish robustness properties of NN controllers. Finally, we present illustrative simulation studies on process control examples. We apply the NN design approach and compare the performance with online QP based MPC on an industrial crude distillation unit model with 252 states, 32 control inputs, and a control-sample horizon length of 140. Parallel computing is used for data generation and graphical processing units are used for network training. Anticipated plant operational scenarios with setpoints and disturbances that may change during operation must be sampled for NN training. After the offline design phase, NNs execute MPC three to five orders of magnitude faster than an available QP solver with less than 1% loss in the closed-loop performance.

Unisim Based Simulation and Analysis of Crude Oil Distillation

Crude distillation is the process of separating the hydrocarbons in crude oil based on their boiling point. Though refining process consists of a wide variety of units the major part is done in Crude Distillation Unit (CDU). In this study an attempt is made to make a train of heat integration network for the crude distillation process. Steady state simulations of cases with different arrangement of heat exchangers were studied in this work. Since the cost of energy has increased dramatically in last year’s, the ability to optimize the use of resources and in particular of energy is becoming an extremely important skill for chemical engineers. Steady state simulation and dynamic simulation of crude plant with optimized heat exchanger design were then performed using Unisim software.

Corrosion in Crude Oil Distillation Unit Overhead: A recent Case Study

Despite all the technological advances and the control mechanisms that exist today to achieve the control of corrosion in overhead systems in atmospheric crude oil distillation units, there is still a high level of difficulty in predicting the phenomenon due to the complexity of the chemical compounds involved in the entire process. The study of this practical case aims to determine what control mechanisms should be optimized in a refinery located in the Autonomous Region of Iraqi Kurdistan to prevent corrosion phenomena’s in this specific system and how it should be done to achieve these improvements. It has been suggested that the corrosion mechanisms by hydrochloric acid and by ammonium chloride have been those that have acted in the operational context of the mentioned facility. To test this hypothesis, a study of the unit’s operational conditions and analytical tests were carried out on the construction material of various components and the products (sediments) found inside the tower. The results show that a lack of control in the salts and sediments content of the feed crude have caused an increase in the hydrolysis process of certain components upstream of the tower and thus the generation of acids and salts was promoted. On this basis, it is necessary to optimize the primary treatment of crude oil and update the chemical treatments and washing water systems to adapt to the new conditions of the feed of the refinery.

Active learning in distance education of crude distillation unit by virtual immersive laboratory: The Eye4edu project

The use of virtual immersive laboratories is an appealing and synergistic possibility for education in science, technology and engineering, complementary to experimental activities performed in laboratory or exercises delivered in traditional rooms. A new educational project, called Eye4edu, proposed different exercises on a virtual immersive Crude Distillation Unit (VCDU), with the aim to apply an active scenario for the learning of the structure of the single unit operations, the whole plant and the control and intervention operating procedures. The project was proposed to the bachelor and master students for the degree in Industrial Chemistry in the Milan University in the years 2019 and 2020. VCDU is based on the combination of the action of “Aveva XR for Training”, formerly Eyesim, and Dynsim software, from Aveva Company, for the physical representation and the dynamic chemical behaviour of the plant. Several exercises on the plant were proposed to the students both for practical and management operations in the plant. The students were required to work on the basis of the information available in the technical documents, as PFD and P&ID. The project was proposed both in presence and as distance learning, respectively before and during the health emergency due to SARS-CoV-2 pandemic. The description of the virtual laboratory and an analysis of its use and educational impact in face-to-face and remote delivery is reported in this paper. Copyright © 2021, AIDIC Servizi S.r.l.

Product tri‐section based crude distillation unit model for refinery production planning and refinery optimization

AbstractAccuracy of a crude distillation unit (CDU) model has a significant impact on refinery production planning. High accuracy is typically accomplished via nonlinear models which causes convergence difficulties when the entire refinery model is optimized. CDU model presented in this work is a mixed‐integer linear model with a modest number of binary variables; its accuracy is on par with rigorous tray to tray CDU models. The model relies on the observation12 that a line through the middle of the product true boiling point (TBP) curve depends on the crude feed properties and the yields of the adjacent products. Novelty of the product tri‐section CDU model is that it does not require models of individual distillation towers comprising the CDU, thereby leading to a much simpler model structure. Significant reduction in the computational effort required for the optimization of nonlinear refinery models is illustrated by comparison with previous work.

Biofuel from Pyrolysis Waste Lube Oil of Refinery Unit III Using Fly Ash of Coal Combustion as a Catalyst

Lube oil waste obtained from Utilities Unit, Crude Distillation Unit, and Crude Distillation & Light End Unit at Refinery Unit III Plaju Palembang South Sumatra. The process of pyrolysis of lube oil waste using fly ash catalyst is expected to produce fuel oil. Lube oil waste pyrolysis uses Fly Ash catalysts with a total catalyst of 500 g with a temperature range variation of 0-85, 85-16, 165-250, 250-300, and 300-350oC, while pyrolysis of waste lube oil without catalysts with a variation in the temperature range of 0-85, 85-165, 165-250, 250-300, and 300-350 oC. Temperature range variations are referenced based on boiling route solvent fraction (0-85 C), premium fraction (85-165 oC), kerosene fraction (165-250 C) and diesel (250-350 C). Solvent fraction cannot be analyzed because there is no product result whether it is pyrolysis process using catalyst or without catalyst, Premium Fraction is only produced using catalyst, analysis result Octane Number 76.6. Sulfur content, Density and flash point analysis of pyrolysis products using catalysts and without catalysts in accordance with kerosene products in the market. Solar fraction of pyrolysis process using catalyst and without catalyst, Analysis results cetane numbers 43.2 and 45.6 have not met the specifications of solar products

Indicators for maintenance planning based on energy efficiency in heat exchanger networks

Thanks to the asset management regulations, modern maintenance practices are rapidly getting a more managerial role, and is used to achieve savings and optimize energy use. Therefore, knowing at all times the status of the equipment allows for timely and necessary interventions that generate value to the company, and recover the initial conditions of them. Heat exchanger networks, rather than a productive asset, constitute an energy-saving strategy, to have lower fuel costs, emission control, rational use of energy, etc. in the atmospheric furnaces of the crude distillation units, and other units that perform similar processes. Thus, keeping them in their best conditions, most of the time is necessary. In this paper, we propose a methodology for the diagnosis of the equipment of the network, and the maintenance planning justified by the energy efficiency of them and the economic impact of the intervention. In addition, an indicator is presented to provide an economic justification for maintenance interventions, as well as for briefly showing the results of the application of maintenance mainly on efficiency and the use of the KPI J proposed for programming the maintenance schedule of some of the equipment (pilot test) of the heat exchanger network under study. The methodology developed uses real operation values, and its results provided savings up to USD 150,000.

Estimation of thermal effects of fouling growth for application in the scheduling of heat exchangers cleaning

Energy-saving heat recovery in an industrial heat exchanger network may by impaired by fouling build-up on heat transfer surfaces of the exchangers. During longer periods of network operation, monitoring of fouling growth based on measurement data makes it possible to schedule and perform exchanger cleaning if needed for the mitigation of adverse effects of fouling. Three different methods of measurement-aided fouling monitoring are outlined: two simple methods known from literature and a least-squares based method introduced by the authors. A case study is performed on their hypothetical application in a real-life network of shell-and-tube heat exchangers used for heat recovery in crude distillation unit. Using historical measurement data recorded during three-year continuous operation of the network, the different methods are first employed to estimate the thermal effects of fouling and then used in the simulation of attainable energy and economic savings resulting from two alternative options of cleaning interventions in the network. The least-squares based method of measurement-aided fouling monitoring is identified as best suited for the evaluation of effectiveness of cleaning interventions in heat exchanger networks.

Optimal design of flexible heat-integrated crude oil distillation units using surrogate models

The design of distillation columns often considers a given fixed feedstock and nominal operating conditions. Here we present an optimization-based approach for the optimal design of these units considering a flexible operation under a range of potential feedstocks. Our method combines an artificial neural network with a support vector machine to model the crude oil distillation unit. The artificial neural network model predicts the performance of the distillation unit for a given crude oil feedstock whilst the support vector machine classifier filters out infeasible design alternatives from the solution space (i.e., designs that are unlikely to converge when simulated using a rigorous model). The inputs to the artificial neural network include the column structural variables and operating conditions, whilst the outputs are process variables linked to the column performance. The artificial neural network models and support vector machines constructed for different crude oil feedstocks are integrated into a two-stage optimization framework in order to optimize the column structural variables and operating conditions, where the minimum utility demand is estimated using the pinch analysis. An effective solution strategy that combines stochastic and deterministic optimization algorithms is applied to search for economically viable and flexible design alternatives that can operate over a given range of crude oil feedstocks while satisfying the product quality specifications. The capabilities of the proposed approach are illustrated using an industrially-relevant case study, where we clearly show that the proposed approach can identify design alternatives capable of handling various feedstocks effectively.

Application of Artificial Neural Network (ANN) in the Optimization of Crude Oil Refinery Process: New Port-Harcourt Refinery

Background: Optimizing the process conditions of the crude distillation unit is a main challenge for each refinery. Optimization increases profit by producing the required range of distillates at maximum yield and at minimum cost. To achieve an acceptable control of product quality an artificial neural network (ANN) can be used. ANNs are used for engineering purposes, such as pattern recognition, forecasting, and data compression. In the petroleum refinery industry, ANN has been used as controller in for the crude distillation unit. The aim of the current study was to use ANN to optimize and achieve control of product quality of crude distillation unit of an oil refinery.
 Materials: The research was carried out using the following materials; The design flowchart and the operating data of the crude distillation unit of the New Port Harcourt refinery, Simulation software (HYSYS 2006.5) and Matlab for the ANN.
 Results: The ANN predicted the optimum operating conditions at which the atmospheric distillation unit (ADU) can operate with the least irreversibility and without changing the design and compromising the products quality. The corresponding exergy efficiency after optimization with ANN for the input variable combinations was 70.6% which was a great improvement because the exergy efficiency increased as compared to the base case of 51.9%.
 Conclusion: Optimization using ANN, improved the efficiency of the ADU with the least irreversibility and without changing the design and compromising the products quality.

Comparison of Optimization of Exergy Efficiency of a Crude Distillation Unit Using Artificial Neural Network (ANN) and Response Surface Methods (RSM)

The study carried out simulation of the Crude Distillation Unit (CDU) of the New Port Harcourt Refinery (NPHR) and performed exergy analysis of the Refinery. The Crude Distillation Unit (CDU) of the New Port Harcourt refinery was simulated using HYSYS (2006.5).
 The Atmospheric Distillation Unit (ADU) which is the most inefficient unit and where major separation of the crude occurs was focused on. The simulation result was exported to Microsoft Excel Spreadsheet for exergy analysis. The ADU was optimized using statistical method and Artificial Neural Network. Box-Behnken model was applied to the sensitive operating variables that were identified. The statistical analysis of the RSM was carried out using Design Expert (6.0). Matlab software was used for the Artificial Neural Network. All the operating variables were combined to give the best optimum operating conditions.
 Exergy efficiency of the ADU was 51.9% and 52.4% when chemical exergy was included and excluded respectively. The optimum operating conditions from statistical optimization (RSM) are 586.1 K for liquid inlet temperature, 595.5 kPa for liquid inlet pressure and condenser pressure of 124 kPa with exergy efficiency of 69.6% which is 33.0% increment as compared to the base case.
 For the ANN optimization, the exergy efficiency of the ADU was estimated to be 70.6%. This gave an increase of 34.9% as compared to the base case.
 This study concluded that enormous improvement can be achieved both in design feasibility and improved efficiency if the feed operating parameters and other sensitive parameters are carefully chosen. Furthermore, ANN optimization gave better exergy efficiency of 70.6% than RSM optimization of 69.6%.

Immersive virtual crude distillation unit learning experience: The EYE4EDU project

The new educational EYE4EDU project, based on proposing to bachelor degree students different exercises on a Virtual immersive Crude Distillation Unit (VCDU), is described. The VCDU combines the action of EYESIM and DYNSIM software from AVEVA Company, for the physical representation and the dynamic chemical behavior of the plant.The educational impact of the project was quantified. In fact, before using the immersive software, a test with 22 questions was proposed to all participants about the theory of the Crude Distillation Unit plants. After the virtual exercises, the students repeated the test, without further study time, doubling their average rating. The average marks were 10.5/30 and 20.8/30 before and after the virtual exercise, respectively. The opinion of the students about this project and the software was strongly positive, with an average evaluation of the 14 questions proposed in a final survey equal to 4.58 / 5.

Hinging Hyperplanes Crude Oil Mixing Model for Production Planning Optimization

Refinery feed, usually a mixture of several crude oils, is separated via crude distillation unit (CDU). Crude true boiling point (TBP) distillation curve determines the amount of products which can...

Improving energy saving of crude oil distillation units with optimal operations

As the most energy intensive sector in refinery industry, crude oil distillation unit can affect the overall energy consumption of a plant by up to 20%. Improving energy saving is an important factor of cleaner production and environmental impacts for crude oil distillation units. Many technologies regarding retrofit of heat exchanger network and optimal operations have been proposed to increase the energy and economic efficiencies of crude oil distillation units in the last decades. However, it is still a challenge of implementing traditional methods in practice, for instance, retrofitting are commonly restricted by process topology, safety and maintenance constraints and may lead to high capital costs because of considerable civil works; while operation models usually suffers from the computational difficulty caused by complex unit performances. To deal with the above problems, this paper presents an optimal operation strategy for improving energy efficiency of crude oil distillation units without any structural modifications. First, detailed process operations are formulated and expressed as a complex nonlinear programming model, which is then solved with a double-loop algorithm. The objective of the proposed method is to minimize process energy utilization while retain its economic benefit. This work provides an efficient tool to help engineers handily adjust process key parameters to achieve higher energy saving than the traditional approaches, especially up to 1.06 MW of energy reduction without additional retrofit investment in an industrial case. This is also equivalent to 2800 tons of CO2 emission reduction per year, and thus intensifying the overall sustainability profile of the crude oil distillation processes.

Integration of crude-oil scheduling and refinery planning by Lagrangean Decomposition

In this work, a Mixed-Integer Nonlinear Programming (MINLP) modeling framework for integrating short-term Crude-oil Scheduling (CS) and mid-term Refinery Planning (RP) has been developed and effectively solved by a proposed Lagrangean Decomposition (LD) algorithm. The principles of this integration are based on the fact that both Crude-oil Scheduling and Refinery Planning have their economic net values as their objectives, and that they are physically linked by Crude Distillation Units (CDUs). A multi-scale approach is proposed in the framework to aggregate continuous- and discrete-time formulations in CS and RP, respectively. Compared to hierarchically solving the non-integrated CS and RP, computational results show significant improvement regarding the economic objective values. Moreover, the proposed LD approach requires less CPU time converging to a small (1%-5%) optimality gap when compared to the monolithic approach using state-of-the-art MINLP solvers.