Estimation of in-situ fluid properties from the combined interpretation of nuclear, dielectric, optical, and magnetic resonance measurements

During the last few decades, the quantification of hydrocarbon pore volume from borehole measurements has been widely studied for reservoir descriptions. Relatively less effort has been devoted to estimating in-situ fluid properties because (1) acquiring fluid samples is expensive, (2) reservoir fluids are a complex mixture of various miscible and non-miscible phases, and (3) they depend on environmental factors such as temperature and pressure. This dissertation investigates the properties of fluid mixtures based on various manifestations of their electromagnetic properties from the MHz to the THz frequency ranges. A variety of fluids, including water, alcohol, alkane, aromatics, cyclics, ether, and their mixtures, are analyzed with both laboratory experiments and numerical simulations. A new method is introduced to quantify in-situ hydrocarbon properties from borehole nuclear measurements. The inversion-based estimation method allows depth-continuous assessment of compositional gradients at in-situ conditions and provides thermodynamically consistent interpretations of reservoir fluids that depend greatly on phase behavior. Applications of this interpretation method to measurements acquired in two field examples, including one in a gas-oil transition zone, yielded reliable and verifiable hydrocarbon compositions. Dielectric properties of polar liquid mixtures were analyzed in the frequency range from 20 MHz to 20 GHz at ambient conditions. The Havriliak-Negami (HN) model was adapted for the estimation of dielectric permittivity and relaxation time. These experimental dielectric properties were compared to Molecular Dynamics (MD) simulations. Additionally, thermodynamic properties, including excess enthalpy, density, number of hydrogen bonds, and effective self-diffusion coefficient, were computed to cross-validate experimental results. Properties predicted from MD simulations are in excellent agreement with experimental measurements. The three most common optical spectroscopy techniques, i.e. Near Infrared (NIR), Infrared, and Raman, were applied for the estimation of compositions and physical properties of liquid mixtures. Several analytical techniques, including Principal Component Analysis (PCA), Radial Basis Functions (RBF), Partial Least-Squares Regression (PLSR), and Artificial Neural Networks (ANN), were separately implemented for each spectrum to build correlations between spectral data and properties of liquid mixtures. Results show that the proposed methods yield prediction errors from 1.5% to 22.2% smaller than those obtained with standard multivariate methods. Furthermore, the errors can be decreased by combining NIR, Infrared, and Raman spectroscopy measurements. Lastly, the ¹H NMR longitudinal relaxation properties of various liquid mixtures were examined with the objective of detecting individual components. Relaxation times and diffusion coefficients obtained via MD simulations for these mixtures are in agreement with experimental data. Also, the ¹H-¹H dipole-dipole relaxations for fluid mixtures were decomposed into the relaxations emanate from the intramolecular and intermolecular interactions. The quantification of intermolecular interactions between the same molecules and different molecules reveals how much each component contributes to the total NMR longitudinal relaxation of the mixture as well as the level of interactions between different fluids. Both experimental and numerical simulation results documented in this dissertation indicate that selecting measurement techniques that can capture the physical property of interest and maximize the physical contrasts between different components is important for reliable and accurate in-situ fluid identificationPetroleum and Geosystems Engineerin

Prediction of Vapour-Liquid Equilibrium Data Using Neural Network for Hydrocarbon Ternary System (ETHANE-PROPANE-N-BUTANE)

The prediction of vapour- liquid equilibrium is useful in process simulation and control as well making process engineering design decisions. Prediction of vapour-liquid equilibrium data was carried out using MATLAB software. Pre-existing data of hydrocarbon ternary system (ethane-propane-n-butane) in terms of phase composition, temperature and pressure was trained by iteratively adjusting networks, initializing weights and biases to minimize the network performance function net. MATLAB a software package containing artificial neural network was employed to predict the point where there is no change in composition of both liquid and vapour formed when liquid mixtures of ethane-propane-n-butane vapourises. Predicted values show reasonable and good correlation results when compared to the experimental data thus indicating that the network is an efficient and a good prediction tool for vapour-liquid equilibrium ternary systems

Artificial intelligence and chemical kinetics enabled property-oriented fuel design for internal combustion engine

Fuel Genome Project aims at addressing the forward problem of fuel property prediction and the inverse problems of molecule design, retrosynthesis and reaction condition prediction. This work primarily addresses the forward problem by integrating feature engineering theory, artificial intelligence (AI) technologies, gas-phase chemical kinetics. Group contribution method (GCM) is utilized to establish the GCM-UOB (University of Birmingham) 1.0 system with 22 molecular descriptors and the surrogate formulation is to minimize the difference of functional group fragments between target fuel and surrogate. The improved QSPR (quantitative structure–activity relationship)-UOB 2.0 system with 32 molecular features couples with machine learning (ML) algorithms to establish the regression models for fuel ignition quality prediction. QSPR-UOB 3.0 scheme expands to 42 molecular descriptors to improve the molecular resolution of aromatics and specific fuel types. The obtained structural features combining with ML algorithms enable to predict 15 physicochemical properties with high fidelity and efficiency. In addition to the technical route of ML-QSPR models, another route of deep learning-convolution neural network (DL-CNN) is proposed for property prediction and yield sooting index (YSI) is taken as a case study. The predicted accuracy of DL-CNN is inferior to the ML-QSPR model at its current status, but its benefit of automated feature extraction and rapid advance in classification problem make it a promising solution for regression problem. A high-throughput fuel screening is performed to identify the molecules with desired properties for both spark ignition (SI) and compression ignition (CI) engines which contains the Tier 1 physicochemical properties screening (based on the ML-QSPR models) and Tier 2 chemical kinetic screening (based on the detailed chemical mechanisms). Polyoxymethylene dimethyl ether 3 (PODE3) and diethoxymethane (DEM) are promising carbon-neutral fuels for CI engines and they are recommended by the virtual screening results. Their ignition delay time, laminar flame speed and dominant reactions of PODE3 and DEM are examined by chemical kinetics and a new DEM mechanism including both low and high-temperature reactions is constructed. Concluding remarks and research prospects are summarized in the final section

Modelling, simulation and multi-objective optimization of industrial hydrocrackers

Ph.DDOCTOR OF PHILOSOPH

Aqueous hydrocarbon systems: Experimental measurements and quantitative structure-property relationship modeling

Scope and Method of Study: The experimental objectives of this work were to (a) evaluate existing mutual hydrocarbon-water liquid-liquid equilibrium (LLE) data, and (b) develop an experimental apparatus capable of measuring accurately the hydrocarbon-water (LLE) mutual solubilities. The hydrocarbon-water systems studied included benzene-water, toluene-water, and 3-methylpentane water. The modeling efforts in this study focused on developing quantitative structure-property relationship (QSPR) models for the prediction of infinite-dilution activity coefficient values (gamma infinity i) of hydrocarbon-water systems. Specifically, case studies were constructed to investigate the efficacy of (a) QSPR models using multiple linear regression analyses and non-linear neural networks; and (b) theory-based QSPR model, where the Bader-Gasem activity coefficient model derived from a modified Peng-Robinson equation of state (EOS) is used to model the phase behavior, and QSPR neural networks are used to generalize the EOS binary interaction parameters. The database used in the modeling efforts consisted of 1400 infinite-dilution activity coefficients at temperatures ranging from 283 K to 373 K.Findings and Conclusions: A continuous flow apparatus was utilized to measure the LLE mutual solubilities at temperatures ranging from ambient to 500 K, which is near the three-phase critical end point of the benzene-water and toluene-water systems. The well-documented benzene-water system was used to validate the reliability of the sampling and analytical techniques employed. Generally, adequate agreement was observed for the benzene-water, toluene-water, and 3-methylpentane-water systems with literature data. An error propagation analysis for the three systems indicated maximum expected uncertainties of 4% and 8% in the water phase and organic phase solubility measurements, respectively. In general, the use of non-linear QSPR models developed in this work were satisfactory and compared favorably to the majority of predictive models found in literature; however, these model did not account for temperature dependence. The Bader-Gasem activity coefficient model fitted with QSPR generalized binary interactions was capable of providing accurate predictions for the infinite-dilution activity coefficients of hydrocarbons in water. Careful validation of the model predictions over the full temperature range of the data considered yielded absolute average deviations of 3.4% in ln gamma infinity i and 15% in gamma infinity i, which is about twice the estimated experimental uncertainty. This study provides valuable LLE mutual solubility data and further demonstrates the effectiveness of theory-framed QSPR modeling of thermophysical properties

Prediction of the physical properties of pure chemical compounds through different computational methods.

Ph. D. University of KwaZulu-Natal, Durban 2014.Liquid thermal conductivities, viscosities, thermal decomposition temperatures, electrical conductivities, normal boiling point temperatures, sublimation and vaporization enthalpies, saturated liquid speeds of sound, standard molar chemical exergies, refractive indices, and freezing point temperatures of pure organic compounds and ionic liquids are important thermophysical properties needed for the design and optimization of products and chemical processes. Since sufficiently purification of pure compounds as well as experimentally measuring their thermophysical properties are costly and time consuming, predictive models are of great importance in engineering. The liquid thermal conductivity of pure organic compounds was the first investigated property, in this study, for which, a general model, a quantitative structure property relationship, and a group contribution method were developed. The novel gene expression programming mathematical strategy [1, 2], firstly introduced by our group, for development of non-linear models for thermophysical properties, was successfully implemented to develop an explicit model for determination of the thermal conductivity of approximately 1600 liquids at different temperatures but atmospheric pressure. The statistical parameters of the obtained correlation show about 9% absolute average relative deviation of the results from the corresponding DIPPR 801 data [3]. It should be mentioned that the gene expression programing technique is a complicated mathematical algorithm and needs a significant computer power and this is the largest databases of thermophysical property that has been successfully managed by this strategy. The quantitative structure property relationship was developed using the sequential search algorithm and the same database used in previous step. The model shows the average absolute relative deviation (AARD %), standard deviation error, and root mean square error of 7.4%, 0.01, and 0.01 over the training, validation and test sets, respectively. The database used in previous sections was used to develop a group contribution model for liquid thermal conductivity. The statistical analysis of the performance of the obtained model shows approximately a 7.1% absolute average relative deviation of the results from the corresponding DIPPR 801 [4] data. In the next stage, an extensive database of viscosities of 443 ionic liquids was initially compiled from literature (more than 200 articles). Then, it was employed to develop a group contribution model. Using this model, a training set composed of 1336 experimental data was correlated with a low AARD% of about 6.3. A test set consists of 336 data point was used to validate this model. It shows an AARD% of 6.8 for the test set. In the next part of this study, an extensive database of thermal decomposition temperature of 586 ionic liquids was compiled from literature. Then, it was used to develop a quantitative structure property relationship. The proposed quantitative structure property relationship produces an acceptable average absolute relative deviation (AARD) of less than 5.2 % taking into consideration all 586 experimental data values. The updated database of thermal decomposition temperature including 613 ionic liquids was subsequently used to develop a group contribution model. Using this model, a training set comprised of 489 data points was correlated with a low AARD of 4.5 %. A test set consisting of 124 data points was employed to test its capability. The model shows an AARD of 4.3 % for the test set. Electrical conductivity of ionic liquids was the next property investigated in this study. Initially, a database of electrical conductivities of 54 ionic liquids was collected from literature. Then, it was used to develop two models; a quantitative structure property relationship and a group contribution model. Since the electrical conductivities of ionic liquids has a complicated temperature- and chemical structure- dependency, the least square support vector machines strategy was used as a non-linear regression tool to correlate the electrical conductivity of ionic liquids. The deviation of the quantitative structure property relationship from the 783 experimental data used in its development (training set) is 1.8%. The validity of the model was then evaluated using another experimental data set comprising 97 experimental data (deviation: 2.5%). Finally, the reproducibility and reliability of the model was successfully assessed using the last experimental dataset of 97 experimental data (deviation: 2.7%). Using the group contribution model, a training set composed of 863 experimental data was correlated with a low AARD of about 3.1% from the corresponding experimental data. Then, the model was validated using a data set composed of 107 experimental data points with a low AARD of 3.6%. Finally, a test set consists of 107 data points was used for its validation. It shows an AARD of 4.9% for the test set. In the next stage, the most comprehensive database of normal boiling point temperatures of approximately 18000 pure organic compounds was provided and used to develop a quantitative structure property relationship. In order to develop the model, the sequential search algorithm was initially used to select the best subset of molecular descriptors. In the next step, a three-layer feed forward artificial neural network was used as a regression tool to develop the final model. It seems that this is the first time that the quantitative structure property relationship technique has successfully been used to handle a large database as large as the one used for normal boiling point temperatures of pure organic compounds. Generally, handling large databases of compounds has always been a challenge in quantitative structure property relationship world due to the handling large number of chemical structures (particularly, the optimization of the chemical structures), the high demand of computational power and very high percentage of failures of the software packages. As a result, this study is regarded as a long step forward in quantitative structure property relationship world. A comprehensive database of sublimation enthalpies of 1269 pure organic compounds at 298.15 K was successfully compiled from literature and used to develop an accurate group contribution. The model is capable of predicting the sublimation enthalpies of organic compounds at 298.15 K with an acceptable average absolute relative deviation between predicted and experimental values of 6.4%. Vaporization enthalpies of organic compounds at 298.15 K were also studied in this study. An extensive database of 2530 pure organic compounds was used to develop a comprehensive group contribution model. It demonstrates an acceptable %AARD of 3.7% from experimental data. Speeds of sound in saturated liquid phase was the next property investigated in this study. Initially, A collection of 1667 experimental data for 74 pure chemical compounds were extracted from the ThermoData Engine of National Institute of Standards and Technology [5]. Then, a least square support vector machines-group contribution model was developed. The model shows a low AARD% of 0.5% from the corresponding experimental data. In the next part of this study, a simple group contribution model was presented for the prediction of the standard molar chemical exergy of pure organic compounds. It is capable of predicting the standard chemical exergy of pure organic compounds with an acceptable average absolute relative deviation of 1.6% from the literature data of 133 organic compounds. The largest ever reported databank for refractive indices of approximately 12 000 pure organic compounds was initially provided. A novel computational scheme based on coupling the sequential search strategy with the genetic function approximation (GFA) strategy was used to develop a model for refractive indices of pure organic compounds. It was determined that the strategy can have both the capabilities of handling large databases (the advantage of sequential search algorithm over other subset variable selection methods) and choosing most accurate subset of variables (the advantages of genetic algorithm-based subset variable selection methods such as GFA). The model shows a promising average absolute relative deviation of 0.9 % from the corresponding literature values. Subsequently, a group contribution model was developed based on the same database. The model shows an average absolute relative deviation of 0.83% from corresponding literature values. Freezing Point temperature of organic compounds was the last property investigated. Initially, the largest ever reported databank in open literature for freezing points of more than 16 500 pure organic compounds was provided. Then, the sequential search algorithm was successfully applied to derive a model. The model shows an average absolute relative deviations of 12.6% from the corresponding literature values. The same database was used to develop a group contribution model. The model demonstrated an average absolute relative deviation of 10.76%, which is of adequate accuracy for many practical applications

Anaerobic wastewater treatment to yield biogas

Anaerobic treatment of industrial wastewater has became a viable technology in recent years due to the rapid development of high-rate reactors, such as anaerobic filter, upflow anaerobic sludge blanket (UASB) (Fang et al., 1996; Dinsdale et al., 1997) both upflow and downflow stationary packed beds (Nebot et al., 1995), and fluidized or expanded beds (Chen et al., 1988; Breintenbucher et al., 1990; Hickey et al., 1991; Iza, 1991; Perez et al., 1997a; Seckler et al., 1996). This development is due to the fact that the method combines a number of significant advantages, including low energy consumption, low excess sludge production and enclosure of odours

Multi-objective optimization of the molecular structure of refrigerants

The aim of this work was to develop a new methodology, which can be used to design new refrigerants that are better than the currently used refrigerants. The methodology draws some parallels with the general approach of computer aided molecular design. However, the mathematical way of representing the molecular structure of an organic compound and the use of meta models during the optimization process make it different. In essence, this approach aimed to generate molecules that conform to various property requirements that are known and specified a priori. A modified way of mathematically representing the molecular structure of an organic compound having up to four carbon atoms, along with atoms of other elements such as hydrogen, oxygen, fluorine, chlorine and bromine, was developed. The normal boiling temperature, enthalpy of vaporization, vapor pressure, tropospheric lifetime and biodegradability of 295 different organic compounds, were collected from open literature and data bases or estimated. Surrogate models linking the previously mentioned quantities with the molecular structure were developed. Constraints ensuring the generation of structurally feasible molecules were formulated and used in commercially available optimization algorithms to generate molecular structures of promising new refrigerants. This study was intended to serve as a proof-of-concept of designing refrigerants using the newly developed methodology

Thermofluid optimisation of turboexpanders for mobile organic Rankine cycle systems

The Organic Rankine Cycle (ORC) is a closed-loop thermodynamic cycle used for low-temperature heat recovery, from sources as diverse as solar, geothermal and industrial processes. Within the ORC, power is produced with the use of a dense-gas vapour expansion in a suitable device and the efficiency of this process is a key influence on cycle efficiency. This thesis therefore investigates the design, optimisation and validation of nozzled radial-inflow turboexpanders, applied to the recovery of vehicle exhaust waste heat. A 1-dimensional meanline performance prediction methodology is developed, capable of modelling the real-gas effects prevalent in dense, organic working fluids. In addition, choking effects within flow passages are explicitly accounted for in both nozzle and rotor, allowing accurate prediction at the high Pressure Ratios typically observed in ORC expanders. A validation case is subsequently presented, tuning loss correlation coefficients to match 6 high Pressure Ratio experimental test cases, reducing average mass flow rate and efficiency errors to 1.33% and 2.04%, respectively. This 1D model is used to create a methodology in which expander optimisation is performed across an entire vehicle duty cycle. A discretised heat exchanger model is developed, capable of modelling multiple fluid phases and real gas effects. A number of simplifications including surrogate models generated by neural network fits, lead to an O(10^3) reduction in computing effort. A case study is performed to quantify benefits to a 11.7 L diesel engine running a 1200-point Non-Road Transient Cycle, resulting in a 21.9% improvement in total cycle energy recovery against a single-point baseline case. The methodologies contained within this thesis are used to design a turboexpander for an industrial research project, focusing on a 15 L diesel Tier 4 engine. Across two generations of design, a combination of computational and experimental methods are applied to produce an expander containing non-radial rotor inlet blading, producing a simulated power output of 12.4 kW at the design point. Finally, in order to facilitate validation of real-gas Computational Fluid Dynamics (CFD) simulations and better understand the underlying flow physics, a blowdown facility is constructed for working fluid r1233zd(E), centred around a converging-diverging test section producing a Mach 2 expansion at the exit plane. Comparison of CFD and centreline pressure measurements for both nitrogen and refrigerant produced maximum errors in Pressure Ratio of 5.7% and 12.1% respectively, suggesting an overestimation by computational methods in predicting expander output power. The methodologies, designs and experimental results contained within this thesis provide improvements to the modelling, optimisation and validation of dense-gas turboexpanders, with the aim of improving design processes and aiding the implementation of ORC for vehicle waste heat recovery.Open Acces