Academic literature on the topic 'Steam distillation model'

Steam distillation as one of the important mechanisms has a great role in oil recovery in thermal methods and so it is important to simulate this process experimentally and theoretically. In this work, the simulation of steam distillation is performed on sixteen sets of crude oil data found in the literature. Artificial intelligence (AI) tools such as artificial neural network (ANN) and also adaptive neurofuzzy interference system (ANFIS) are used in this study as effective methods to simulate the distillate recoveries of these sets of data. Thirteen sets of data were used to train the models and three sets were used to test the models. The developed models are highly compatible with respect to input oil properties and can predict the distillate yield with minimum entry. For showing the performance of the proposed models, simulation of steam distillation is also done using modified Peng-Robinson equation of state. Comparison between the calculated distillates by ANFIS and neural network models and also equation of state-based method indicates that the errors of the ANFIS model for training data and test data sets are lower than those of other methods.

The present work deals with modeling the kinetics of essential oils extraction from plant materials by water and steam distillation. The experimental data were obtained by studying the hydrodistillation kinetics of essential oil from juniper berries. The literature data on the kinetics of essential oils hydrodistillation from different plant materials were also included into the modeling. A physical model based on simultaneous washing and diffusion of essential oil from plant materials were developed to describe the kinetics of essential oils hydrodistillation, and two other simpler models were derived from this physical model assuming either instantaneous washing followed by diffusion or diffusion with no washing (i.e. the first-order kinetics). The main goal was to compare these models and suggest the optimum ones for water and steam distillation and for different plant materials. All three models described well the experimental kinetic data on water distillation irrespective of the type of distillation equipment and its scale, the type of plant materials and the operational conditions. The most applicable one is the model involving simultaneous washing and diffusion of the essential oil. However, this model was generally inapplicable for steam distillation of essential oils, except for juniper berries. For this hydrodistillation technique, the pseudo first-order model was shown to be the best one. In a few cases, a variation of the essential oil yield with time was observed to be sigmoidal and was modeled by the Boltzmann sigmoid function.

Summary The interactions of heavy oil and injected steam in the mature steamflood at the Kern River Field have been extensively studied to gain insight into the effect of steam on compositional changes of oil during the recovery process and to provide input for compositional thermal simulation. Steam distillation behavior of this 13°API California oil between 300 and 467°F under a variety of process conditions, along with extensive analysis of distilled hydrocarbons were incorporated to give a more in-depth description of what is happening to the oil and what changes are occurring in the distillates or produced oil. This information was further integrated with analysis of the field distillate, "casing blow," to infer what is happening in the field. The results show that steam distillation is temperature dependent and more important than originally thought. The data developed in this study are a basis for improvement of numerical thermal models with potential for better designed steamfloods and reservoir management. The results may also impact certain logging techniques used in steamfloods and possible heavy oil upgrading techniques. Kern River oil is more than 10% distillable at 300°F and 15% distillable at 400°F in dynamic laboratory steam distillation tests at steam throughputs of four times the initial volume of oil. Distillate physical properties of density, viscosity, molecular weight, and hydrocarbon composition of the distillates changed significantly. Distillate properties increased in value with increasing steam throughput, and at higher temperatures. This information is important in the tuning of equations of state, including hydrocarbon-water interaction parameters for compositional thermal simulation. Analysis of the field distillate, "casing blow," showed properties similar to laboratory distillates at low steam throughputs. The observation of a light field distillate production in a mature steamflood compared to laboratory measurements implies that the casing system temperature is a major controlling factor in "casing blow" composition and quantity. Background The phase equilibrium behavior of reservoir fluids is an important phenomenon in petroleum production, particularly in enhanced oil recovery processes. However, phase behavior for heavy oils (<15°API) under steamflood has generally been felt to be unimportant or a minimal effect to be neglected.1 A major question exists about whether the phases and fluids in a steamflood are in equilibrium or not. Proper modeling of a reservoir production process would be expected to include knowledge of the phases and their equilibrium compositions. In heavy oil, devoid of significant C1 to C6 composition, it has been sufficient to treat the oil as a dead oil or a nonvolatile phase for steamflood modeling purposes. A history match numerical study2 of steamflood performance in the Kern River Field treated the oil as nonvolatile, and was conducted without the inclusion of hydrocarbon compositional effects. Through the classic works of Willman et al.,3 Volek and Pryor,4 and Closmann and Seba,5 steam distillation has been shown to be an important component mechanism in the overall steamflooding process.6–10 The practical limit of how much of a reservoir fluid can be distilled, is obtained in dynamic steam distillation experiments developed by Brown and Wu,11,12 extended by Hseuh, Hong, and Duerksen,13,14 and refined by Wu and co-workers.15,16 This body of work demonstrates that steam distillation is an operative mechanism in laboratory models, but it has been difficult to translate this to a quantitative contribution to the field recovery process of steamflooding. Laboratory steam distillation experiments have generally been conducted as dynamic tests, that may or may not be near equilibrium. Experiments near equilibrium with extensive analysis of the phases will yield values for the vapor-liquid equilibrium (VLE) ratios (K values), another way of assessing the importance of compositional changes in steamflooding. A major recent steam distillation study by Northrup and Venkatesan17 has been completed on the South Belridge oil. Compositional data from simple distillation and laboratory steamfloods of oils in the range 13 to 33°API, including Kern River oil, has recently been reported.18 The current report is an extension of that work to include analyses of produced field samples for the Kern River steamflood. Compositional reservoir simulators demand greater emphasis on obtaining more crude oil compositional data, which would be used as input into an equation of state (EOS) or to calculate equilibrium ratios, K values. An appreciable amount of incremental oil19,20 could be recovered by steamflooding due to steam distillation depending on the composition of the crude oil. The present work establishes laboratory data to facilitate such efforts. The EOS approach and table look-up for two-phase K values are applied in thermal numerical simulation models, even though they do not fully represent three-phase separation (steam distillation). A three-component system approximation was used by Coats and Smart21 to incorporate steam distillation effects by adding water as a component in the vapor phase. The compositional variations due to steam distillation cannot be fully described by Coats' model. A difficulty in this model is the lack of three-phase laboratory steam distillation data for high-temperature and high-pressure conditions. A future goal of this research is to obtain three-phase laboratory steam distillation data to better understand the effects of water and its vapor on the hydrocarbon separation processes at high-temperature and high-pressure conditions. This includes the investigation on both the pure hydrocarbon component/water systems and crude oil/water systems. The three-phase equilibrium ratios or K values determined from these laboratory investigations are necessary to accurately describe the effects of steam distillation in mathematical reservoir simulation. Experiment Steam Distillation Cell and Procedures. In order to describe the existing laboratory procedures, Fig. 1 is presented. This experimental setup is used to perform three different types of tests:Static system pressure test (SPT).Dynamic distillation test (DDT).Stagewise isochoric distillation test (SWID). The experimental apparatus is composed of the injection assembly (Ruska pumps and the gas bottles), the distillation cell assembly, the withdrawal assembly (condenser, separator) and the automation/data acquisition assembly. The steam distillation apparatus has been extensively described elsewhere.22

A comparative analysis of models describing the change in yield of essential oil over time is presented in the article. Nonlinear models, third-order polynomial and second exponential model describe with sufficient precision the change of experimental data over time. These models can be used to predict the extraction time of essential oils. The results can be useful in planning and managing the production of essential oils. For this purpose, further research is needed to determine the diffusion coefficient and to analyze the impact of the individual elements of the process on the production of essential oils.

Essential oils from parts of plants such as stump, flower, kernel, and seed are usually produced by extraction, distillation and mechanical press. In practice, steam distillation is commonly used for the extraction of crude essential oils since it is not only a simple process but also applicable at various scales. Furthermore, the method makes it possible for a keeping of precious components of the oils unchanged. Therefore, studies on kinetics and modeling of the essential oil steam distillation are needed for the optimization of the operating conditions, energy requirement, and the process scale-up.In this work, experiments of lemongrass (Cymbopogon Citratus) steam distillation were carried out and a kinetics model was developed for the extraction of lemongrass essential oil. Raw materials were pretreated by natural drying, primarily crushing and chopping prior to the distillation. The oil yield obtained is in the range of 2.1 – 2.9 ml/kg of raw materials. Composition of the crude oil extracted was measured by GC-MS. Measurements showing that the oil product contains 70.5 % of precious component–Citral in which Neral is 29.8 % and Geranial 40.7 %. The kinetics parameters were estimated from experimental data conducted at various operating conditions for different preparation of the raw materials. The process rate constant (extraction rate constant) describing the extraction efficiency obtained from this study is varied from 0.02 to 0.027 min-1 using first-order kinetic model.

Steam distillation is the conventional means by which oils are extracted in the flavour and fragrance industry. A mathematical model for the steam distillation extraction (SDE) of air-dried Ocimum basilicum (basil) leaves has been developed and tested using a small-scale pilot plant. The model predicts the removal of oil components from the plant matrix and subsequent transfer to the steam. It also accounts for the diffusional transfer of components within the leaf and the simultaneous convective transfer into the vapour phase while also respecting the individual components’ volatilities. It has been applied vertically on an element-by-element basis through the bed for a mixture of major and minor components. The proposed SDE model appears to be a good match between predicted values and the experimental data. The model predicts a faster initial extraction rate for components such as α-pinene and α-terpinene, possibly due to preferential extraction of light, volatile components present in larger quantities.

To improve the efficiency of bioethanol production, an advanced process was required to extract ethanol from solid-state fermented feedstock. With regard to the characteristics of no fluidity of solid biomass, a continuous solid-state distillation (CSSD) column was designed with a proprietary rotary baffle structure and discharging system. To optimize the operation condition, fermented sweet sorghum bagasse was prepared as feedstock for a batch distillation experiment. The whole distillation time was divided into heating and extracting period which was influenced by loading height and steam flow rate simultaneously. A total of 16 experiments at four loading height and four steam flow rate levels were conducted, respectively. Referring to packing, rectifying column, mass, and heat transfer models of the solid-state distillation heating process were established on the basis of analyzing the size distribution of sweet sorghum bagasse. The specific heat capacity and thermal conductivity value of fermented sweet sorghum bagasse were tested and served to calculate the ethanol yielding point and concentration distribution in the packing. The extracting process is described as the ethanol desorption from porous media absorbent and the pseudo-first-order desorption dynamic model was verified by an experiment. Benefit (profit/time) was applied as objective function and solved by successive quadratic programming. The optimal solution of 398 mm loading height and 8.47 m3/h steam flow rate were obtained to guide a 4 m in diameter column design. One heating and two extracting trays with 400 mm effective height were stacked up in an industrial CSSD column. The steam mass flow rate of 0.5 t/h was determined in each tray and further optimized to half the amount on the third tray based on desorption equation.

Natural precious products such as aroma compounds, essential oils, and bio-activated materials are usually extracted from about 30,000 botanical species. These extracts are often high competitive market due to their small content (less than 1 %) in plants and high purification cost. Thus, development of a modeling for the optimization of the crude oil extraction is highly paid attention. In this work, a modeling of Vietnam lemongrass oil extraction using steam distillation is developed and the optimization of the process parameters is performed using response surface methodology (RSM). The operating parameters considered for the modeling and optimization are specific area of raw materials, moisture content of feedstock, and steam rate. Experimental data show that the oil yield from steam distillation of Vietnam lemongrass is significantly affected by the three mentioned factors. Box-Behnken design (BBD) and analysis of variance (ANOVA) are used to examine the effects of operating parameters on the extraction efficiency. On the basis of the measurements and RSM, a quadric regression model as a function of steam rate, specific area and moisture content of materials is estimated. The optimized operating conditions of the lemongrass hydrodistillation are also obtained by applying the proposed modeling.

Due to growing concerns about carbon emissions, Carbon Capture and Storage (CCS) techniques have become an interesting alternative to overcome this problem. CO2-Argon-Steam-Oxy (CARSOXY)-fuel gas turbines are an innovative example that integrates CCS with gas turbine powergen improvement. Replacing air-fuel combustion by CARSOXY combustion has been theoretically proven to increase gas turbine efficiency. Therefore, this paper provides a novel approach to continuously supply a gas turbine with a CARSOXY blend within required molar fractions. The approach involves H2 and N2 production, therefore having the potential of also producing ammonia. Thus, the concept allows CARSOXY cycles to be used to support production of ammonia whilst increasing power efficiency. An ASPEN PLUS model has been developed to demonstrate the approach. The model involves the integrations of an air separation unit (ASU), a steam methane reformer (SMR), water gas shift (WGS) reactors, pressure swing adsorption (PSA) units and heat exchanged gas turbines (HXGT) with a CCS unit. Sensitivity analyses were conducted on the ASU-SMR-WGS-PSA-CCS-HXGT model. The results provide a baseline to calibrate the model in order to produce the required CARSOXY molar fraction. A MATLAB code has also been developed to study CO2 compression effects on the CARSOXY gas turbine compressor. Thus, this paper provides a detailed flowsheet of the WGS-PSA-CCS-HXGT model. The paper provides the conditions in which the sensitivity analyses have been conducted to determine the best operable regime for CARSOXY production with other high valuable gases (i.e., hydrogen). Under these specifications, the sensitivity analyses on the (SMR) sub-model spots the H2O mass flow rates, which provides the maximum hydrogen level, the threshold which produces significant CO2 levels. Moreover, splitting the main CH4 supply to sub-supply a SMR reactor and a furnace reactor correlates to best practices for CARSOXY. The sensitivity analysis has also been performed on the (ASU) sub-model to characterise its response with respect to the variation of air flow rate, distillation/boiling rates, product/feed stage locations and the number of stages of the distillation columns. The sensitivity analyses have featured the response of the ASU-SMR-WGS-PSA-CCS-HXGT model. In return, the model has been qualified to be calibrated to produce CARSOXY within two operability modes, with hydrogen and nitrogen or with ammonia as by-products.

This thesis deals with the batch controlling of a distillation column model. The thesis is divided into several parts. The first part, which is more theoretical, summarizes the description of the batch controlling, the distillation process and the equipment used, including the model of the distillation column itself. The second part is focused on the practical implementation. The beginning of this part displays different types of the models produced by the Standard 88. This section is followed by a description of a device database in the environment of FactoryTalk Batch Equipment Editor, creation of a logical phase codes using functions of the PhaseManager in the environment of RSLogix5000 and a creation of a recipe in the environment of FactoryTalk Batch Recipe Editor. The last part of this thesis deals with the visualization created in the FactoryTalk View Studio SE and final testing of the entire process.