Crude Oil Distillation Research Articles

Acute, Subchronic, and Developmental Toxicological Properties of Lubricating Oil Base Stocks

Lubricating oil base stocks (LOBs) are substances used in the manufacture of finished lubricants and greases. They are produced from residue remaining after atmospheric distillation of crude oil that is subsequently fractionated by vacuum distillation and additional refining steps. Initial LOB streams that have been produced by vacuum distillation but not further refined may contain polycyclic aromatic compounds (PACs) and may present carcinogenic hazards. In modern refineries, LOBs are further refined by multistep processes including solvent extraction and/or hydrogen treatment to reduce the levels of PACs and other undesirable constituents. Thus, mildly (insufficiently) refined LOBs are potentially more hazardous than more severely (sufficiently) refined LOBs. This article discusses the evaluation of LOBs using statistical models based on content of PACs; these models indicate that insufficiently refined LOBs (potentially carcinogenic LOBs) can also produce systemic and developmental effects with repeated dermal exposure. Experimental data were also obtained in ten 13-week dermal studies in rats, eight 4-week dermal studies in rabbits, and seven dermal developmental toxicity studies with sufficiently refined LOBs (noncarcinogenic and commonly marketed) in which no observed adverse effect levels for systemic toxicity and developmental toxicity were 1000 to 2000 mg/kg/d with dermal exposures, typically the highest dose tested. Results in both oral and inhalation developmental toxicity studies were similar. This absence of toxicologically relevant findings was consistent with lower PAC content of sufficiently refined LOBs. Based on data on reproductive organs with repeated dosing and parameters in developmental toxicity studies, sufficiently refined LOBs are likely to have little, if any, effect on reproductive parameters.

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

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

Determination of Basic Nitrogen in Residues of Crude Oil Distillation Using ATR-FTIR and Chemometric Methods

A method for basic nitrogen determination in residues of crude oil distillation using infrared spectroscopy and chemometrics algorithms was developed. Interval partial least squares, synergy interval partial least squares, and backward interval partial least squares were evaluated for calibration model construction. The samples were divided into a calibration and prediction set containing 40 and 15 samples, respectively. The first derivative with a Savitzky-Golay filter and the mean centered data showed the best results and were used in all calibration models. The backward interval partial least squares algorithm with spectra divided in 60 intervals and combinations of 4 intervals (1407 to 1372; 1117 to 1082; 971 to 936; 914 to 879 cm−1) showed the best root mean square error of prediction of 0.016 wt%. This calibration model displayed a suitable correlation coefficient between reference and predicted values.

A Report on the Calibration of an Atmospheric and Vacuum Distillation Unit

For a crude oil distillation unit of 5 million tons/year (595.2 t/h), its calibration of heavy load was done in the paper. The yields of atmospheric gas oil and total distillate oil can reach 24.69% and 69.53%, respectively, close to their design values, and comprehensive energy consumption of the unit was 341.38 MJ/t, less than its design value, which can show the operation of the unit met design requirements. The bottleneck restricting safe and smooth operation of unit was found and some measurements of modification were proposed by calibration of the unit.

Ti-incorporated SBA-15 mesoporous silica as an efficient and robust Lewis solid acid catalyst for the production of high-quality biodiesel fuels

One-pot synthesized Ti-SBA-15 mesoporous materials with various Ti loadings of 0.808–6.78mol% were applied as heterogeneous solid acid catalysts for simultaneous esterification and transesterification of vegetable oils with methanol into high-quality biodiesel fuel (BDF) at 200°C under autogeneous pressure. According to the diffuse-reflectance (DR) UV–vis spectra, diffuse-reflectance infrared Fourier transform (DRIFT) spectra and pulsed ammonia (NH3) chemisorption studies combined with other conventional characterizations, the catalytically active site for high-quality BDF synthesis was mostly related to the tetrahedral Ti4+ species with weak Lewis acid character, which differential heat of NH3 adsorption was lower than 90kJmol−1. Due to that the tetrahedral Ti4+ species were accessible on largely mesoporous framework, the Ti-SBA-15 catalyst gave much higher activity in transesterification of crude Jatropha oil (CJO) with methanol than microporous titanosilicate of TS-1 and commercial TiO2 nanocrystallites. Among them, the 3Ti-SBA-15 catalyst with a Ti loading of 2.46mol% showed a highest fatty acid methyl ester (FAME) content of 90mass% at 200°C for 3h using a methanol-to-oil molar ratio of 27. When the reaction period and methanol-to-oil molar ratio were increased to 3–6h and 108, respectively, a great variety of edible and non-edible vegetable oils with various acid values (0.06–190mgKOHg−l), including refined soybean oil (RSO), refined rapeseed oil (RRO), waste cooking oil (WCO), crude palm oil (CPO), CJO and palm fatty acid distillates (PFAD), was directly transformed into high-quality BDFs, which met with a European standard (EN 14214:2009), over 3Ti-SBA-15 catalyst at 200°C. The used 3Ti-SBA-15 catalyst was easily regenerated by calcination and its high activity was maintained. Most importantly, the 3Ti-SBA-15 catalysts could resist 5wt% of water or 30wt% of free fatty acid (FFA), which tolerance levels were several ten times better than those of homogeneous and heterogeneous catalysts in the current BDF production technology.

Optimization Model of Crude Oil Distillation Units for Optimal Crude Oil Blending and Operating Conditions

This work presents the mathematical formulation of a nonlinear programming (NLP) model which optimizes simultaneously crude oil blending and operating conditions for a system of several crude oil distillation units (CDUs) at a Colombian refinery. The CDU system consists of three industrial units processing a blending of five extra-heavy crude oils and producing two commercial fuels, Jet-1A and Diesel. The NLP model involves typical restrictions (e.g., flow rate according to capacity of pumps, distillation columns, etc.) and the heat integration of streams from atmospheric distillation towers (ADTs) and vacuum distillation towers (VDTs) with the heat exchanger networks for crude oil preheating. A metamodeling approach is used so as to represent the ADTs. Preheating networks are modeled with mass, energy balances, and design equations of each heat exchanger. The NLP model has been implemented in GAMS using CONOPT as solver. Different cases are solved by the NLP model such that the optimal case with less profit increment had an economical benefit of 13% with respect to its case without optimization. In each optimal case the extra-heavy crude oils in the feed blending of each CDU required more severe operating conditions such as higher temperature of the crude oil at the entrance to the towers, greater flow rate of stripping steam at the bottom, and minor pressure of the tower tops.

Comparison of API 510 pressure vessels inspection planning with API 581 risk-based inspection planning approaches

To ensure mechanical integrity, all pressure vessels shall be inspected at the intervals provided in inspection codes or based on a risk-based inspection (RBI) assessment. The RBI assessment may allow previously established inspection intervals to be extended.This paper describes the methodology, analysis and results of two RBI studies conducted on 293 pressure vessel components in two crude oil distillation units. Based on API RBI methodology in API 581 (2008), risk target concept was used for determining inspection dates. It was shown that when thinning is the major active damage, the RBI recommended intervals are as long as twice the API 510 intervals. This paper summarizes that, as a fundamental step in the risk calculation, RBI has a more defined methodology for evaluating equipment for multiple damage mechanisms and a more defined approach to specify the use of other inspection technologies beyond the traditional visual, ultrasonic, and radiography tests.

Operational optimization of crude oil distillation systems using artificial neural networks

A new methodology for optimizing heat-integrated crude oil distillation systems is proposed in this work. The new procedure considers an artificial neural networks (ANN) model for representing the distillation column. Models of the distillation column and the associated existing heat exchanger network are incorporated in an optimization framework to systematically determine the operating conditions that improve the overall process economics. Of particular interest is the problem of optimizing the net value of the products obtained from the column by increasing the yield of higher-value products at the expense of less valuable products, while taking into account feasibility of the distillation specifications, heat recovery, energy and equipment constraints. A two-stage procedure is applied to first optimize the column operating conditions based on minimum utility requirements. In the second stage the heat exchanger network is designed.

Risk analysis of corrosion failures of equipment in refining and petrochemical plants based on fuzzy set theory

Corrosion failures of process equipment have been one of the main sources of risk to refining and petrochemical plants. For reducing failure risk levels and optimizing inspection plans, risk analysis of equipment failures resulting from corrosion need to be implemented. However, due to the complexity and uncertainty of the refining and petrochemical units and risks, effective analyses are hard to accomplish by using conventional risk techniques. With respect to this, a new model for risk analysis of corrosion failures of equipment is developed base on fuzzy set theory. In this model, two essential parts of failure risk (i.e., failure likelihood and severity of failure consequence) are first estimated by using fuzzy synthetic evaluation, and then integrated into a risk index by fuzzy risk graph which is established based on fuzzy logic system. In order to demonstrate the feasibility of this model, an application example in an overhead system of a crude-oil distillation unit was presented. The results show that this model is effective and feasible.

Removal of naphthenic acids from crude oil using amino acid ionic liquids

A number of tetraalkylammonium and tetraalkylphosphonium amino acid based ionic liquids (AAILs) have been successfully used and recycled for the reactive extraction of naphthenic acids from crude oil and crude oil distillates. Spectral studies show that the mechanism by which this occurs is through the formation of a zwitterionic complex. Therein, the amino acid anion plays a key role in the formation of this complex.

Study on Plate Washer Sewage Treatment

To study the plate washer sewage treatment, the mature theories and structures in other sectors were introduced the printing industry. Plate washer is a kind of printing auxiliary equipment, mainly used for the printing plate cleaning and washing wastewater treating and recycling, the sewage treatment system including filtration part and distillation part, filtering part which is based on design of automatic self-cleaning filter have two stage filtration system, distillation part use crude oil distillation tower as the distillation system model.

Autocatalytic Photo-Oxidation Process of C<sub>3</sub>-C<sub>4 </sub>Fraction to Methanol

The direct conversion of propane and butane fraction (existing as mixtures rather than separate products) to methanol as well as the reaction mechanism under visible light source (λ=420 nm) and the catalytic action of •NO2 have been evaluated. The purpose of this work is to explore novel pathways for the production of methanol by suggesting a simplified model and result-oriented scheme to avoid the drawbacks associated with methane activation (incredibly high activation energy of about 440 kJ/mol even under mild processing conditions) in the course of its transformation to methanol which is characterized by high yield and selectivity. It is important to consider propane and butane fraction (recovered from primary crude oil distillation and by cracking of heavy molecules) as a possible feedstock for methanol production exhibiting a sufficiently high degree of conversion per pass. Thereby, a relatively simple and economical method for methanol production from refined crude oil composed of propane and butane (a one step co-processing) have been tendered.

A New Optimisation based Retrofit Approach for Revamping an Egyptian Crude Oil Distillation Unit

Crude oil atmospheric distillation systems are an energy intensive processes; it was evaluated that the energy requirement for such plants is equivalent to a 2% of the total crude oil processed [1]. An existing crude oil atmospheric unit is very expensive to modify due its complex configuration and interactions, and existing constraints of structure, limited space area, matches, bottlenecked equipments, etc. Thus, a few new crude distillation units are built and projects for revamping existing equipments are rather common. Revamping an existing plant is a difficult task, more complex than a new process design; many parameters must be considered and sometimes it is not possible to quantify all of them.This paper presents a new methodology based on rigorous simulation and optimisation framework that addresses both the distillation column and the heat exchanger network simultaneously to maximise the use of existing equipments. The methodology considers process changes and structural modifications together with the interactions between the existing distillation process and heat recovery system. The new method includes multiple objective functions such as energy savings, emissions reduction, capacity enhancement, and profit improvement.An actual atmospheric plant for MIDOR, an Egyptian refinery, has been considered for applications of the new optimisation methodology. Several retrofit solutions have been obtained, ranging from zero-modifications and simple additional exchanger areas to additional units or equipments.

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

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

Distillation fraction-specific ecotoxicological evaluation of a paraffin-rich crude oil

Crude oil is a complex mixture of petroleum hydrocarbons (PHC) with distinct chemical, physical and toxicological properties relevant for contaminated site risk assessment. Ecotoxicological effects of crude oil distillation fractions on luminescent bacteria (Vibrio fischeri), earthworms (Dendrobaena hortensis) and invertebrates (Heterocypris incongruens) were tested using two spiked soils and their elutriates. Fraction 2 (F2) had an equivalent carbon number (ECN) range of >10 to 16, and F3 from >16 to 39. F2 showed a substantially higher ecotoxicological effect than F3 for Vibrio and Dendrobaena. In contrast, severe inhibition of Heterocypris by the poorly soluble F3 is attributed to mechanical organ blockage. Immediate sequestration of PHC to the organic matter-rich soil effected reduced toxicity for all organisms. This study indicates that a more differentiated consideration (i) of PHC mixtures based on ECN range and (ii) of model soil properties employed for ecotoxicity testing should be included into PHC-contaminated site risk assessment.

Synergy effect of naphthenic acid corrosion and sulfur corrosion in crude oil distillation unit

The synergy effect of naphthenic acid corrosion and sulfur corrosion at high temperature in crude oil distillation unit was studied using Q235 carbon-manganese steel and 316 stainless steel. The corrosion of Q235 and 316 in corrosion media containing sulfur and/or naphthenic acid at 280°C was investigated by weight loss, scanning electron microscope (SEM), EDS and X-ray diffractometer (XRD) analysis. The results showed that in corrosion media containing only sulfur, the corrosion rate of Q235 and 316 first increased and then decreased with the increase of sulfur content. In corrosion media containing naphthenic acid and sulfur, with the variations of acid value or sulfur content, the synergy effect of naphthenic acid corrosion and sulfur corrosion has a great influence on the corrosion rate of Q235 and 316. It was indicated that the sulfur accelerated naphthenic acid corrosion below a certain sulfur content but prevented naphthenic acid corrosion above that. The corrosion products on two steels after exposure to corrosion media were investigated. The stable Cr5S8 phases detected in the corrosion products film of 316 were considered as the reason why 316 has greater corrosion resistance to that of Q235.

Operational optimization of a simulated atmospheric distillation column using support vector regression models and information analysis

Like any other production processes, atmospheric distillation of crude oil is too complex to be accurately described with first principle models, and on-site experiments guided by some statistical optimization method are often necessary to achieve the optimum operating conditions. In this study, the design of experiment (DOE) optimization procedure proposed originally by Chen et al. (1998) and extended later by Chu et al. (2003) has been revised by using support vector regression (SVR) to build models for target processes. The location of future experiments is suggested through information analysis which is based on SVR models for the performance index and observed variables and reduces significantly the number of experiments needed. A simulated atmospheric distillation column (ADC) is built with Aspen Plus (version 11.1) for a real operating ADC. Kernel functions and parameters are investigated for SVR models to represent suitably the behavior of the simulated ADC. To verify the effectiveness of the revised DOE optimization procedure, three case studies are carried out: (1) The modified Himmelblau function is minimized under a circle constraint; (2) the net profit of the simulated ADC is maximized with all the 15 controlled variables free for adjusting in their operational ranges; (3) the net profit of the simulated ADC is maximized with fixed production rates for the three side-draws.

Gasoline, Kerosene, and Diesel Fingerprinting via Polar Markers

Venturi easy ambient sonic-spray ionization mass spectrometry working in the liquid mode (VL-EASI-MS) is shown to provide rapid and reliable characterization of crude oil distillates. With no extraction or pretreatments, samples of gasoline, kerosene, diesel, and admixtures of gasoline/diesel and gasoline/kerosene were directly analyzed by VL-EASI(+)-MS. Homologous series of natural N-heteroaromatics were detected in their protonated forms providing typical profiles of polar markers. For gasoline, VL-EASI(+)-MS detects a homologous series of mainly C1–C5 alkyl pyridines. For kerosene, a typical series of alkylated tetrahydroquinolines is detected. For diesel, the V-EASI(+)-MS profile is much richer because of the detection of several classes of many N-heteroaromatics. VL-EASI(+)-MS is also shown to provide typical spectra for petrochemical gasolines detecting specific antioxidants. Admixtures of gasoline/kerosene and gasoline/diesel are also clearly characterized with limits of detection of 10 and 1% (v/v), respectively. Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR MS) has been used to confirm assignments of the polar markers detected by VL-EASI(+)-MS.

Robust demand service achievement for the co-production newsvendor

The co-production newsvendor problem is motivated by two-stage production processes that simultaneously yield a set of output products of different grades from the same input stocks. Co-production is a characteristic feature of processes such as semiconductor manufacturing and crude oil distillation. In the first stage, the newsvendor executes the order quantities for the input stocks prior to learning the actual demands and grading fractions of the products. In the second stage, the available production is allocated to satisfy the realized demands. Downward substitution is allowed in the allocation; i.e., demands for lower grades can always be filled by higher grades but not vice versa. The co-production newsvendor seeks to achieve maximum demand service level, subject to resource or budget constraints. This article proposes the use of the aspiration level approach to model the decision problem. Furthermore, it is assumed that only the means and supports of the uncertain demands and grading fractions are available, and the model is extended using robust optimization techniques. The resulting model is a linear program and can be solved very efficiently. Computational tests show that the proposed model performs favorably compared to other stochastic optimization approaches for the same problem.

Inferential estimation of kerosene dry point in refineries with varying crudes

A bootstrap aggregated model approach to the estimation of product quality in refineries with varying crudes is proposed in this paper. The varying crudes cause the relationship between process variables and product quality variables to change, which makes product quality estimation by soft-sensors a difficult problem. The essential idea in this paper is to build an inferential estimation model for each type of feed oil and use an on-line feed oil classifier to determine the feed oil type. Bootstrap aggregated neural networks are used in developing the on-line feed oil classifier and a bootstrap aggregated partial least square regression model is developed for each data group corresponding to each type of feed crude oil. The amount of training data in crude oil distillation is usually small and this brings difficulties for classification and estimation modelling. In order to enhance model reliability and robustness, bootstrap aggregated models are developed. The inferential estimation results of kerosene dry point on both simulated data and industrial data show that the proposed method can significantly improve the overall inferential estimation performance.