Mechanism of the reversibility of asphaltene precipitation in crude oil

Asphaltene precipitation and deposition occur in petroleum reservoirs as a change in pressure, temperature and liquid phase composition and reduce the oil recovery considerably. In addition to these, asphaltene precipitates may deposit in the pore spaces of reservoir rock and form plugging, which is referred to as a type of formation damage, i.e. permeability reduction. In all cases above, it is of great importance to know under which conditions the asphaltenes precipitate and to what extent precipitated asphaltenes can be re-dissolved. In other words, to what extent the process of asphaltene precipitation is reversible with respect to change in thermodynamic conditions. In present work, a series of experiments was designed and carried out to quantitatively distinguish the reversibility of asphaltene precipitation upon the change in pressure, temperature and liquid composition. Experiments were conducted in non-porous media. Generally it was observed that the asphaltene precipitation is a partial reversible process for oil under study upon temperature change with hysteresis. However, the precipitation of asphaltene as a function of mixture composition and pressure is nearly reversible with a little hysteresis.     

Modeling the Effects of Conservation,Demographics, Price,and Climate on Urban Water Demand in Los Angeles,California

With a service area population exceeding four million people and with close to 90 % of the water supply being imported from sources outside the city, the Los Angeles water system is subject to multiple stressors, including climate change and population growth. The influence of various factors on water demand in Los Angeles was evaluated through development and application of multiple linear regression models for residential, commercial, industrial, and governmental water demand categories from 1970 to 2014 in the service area of the Los Angeles Department of Water and Power. Performance of the models in describing historical water demand was compared using the coefficient of determination, mean average percent error, and normalized root mean square error. Overall, the results of the linear regression models demonstrated that each water demand category is affected by different parameters. However, price and population were found to have the most significant impact on all categories. The seasonality of residential water demand was well described with the model based on monthly data, with precipitation and temperature being highly significant factors. Fitting of the residential data furthermore revealed that price and conservation have significantly counteracted the impact of population growth on water demand.     

Techno-economic study of bioethanol production from olive wastes cake (OWC) in Iran

Clean Technologies and Environmental Policy - In this study, a techno-economic analysis of bioethanol production from the olive waste cake in Iran has been performed. The biofuel plant with a...     

A nonlinear thick plate formulation based on the modified strain gradient theory

A geometric nonlinear first-order shear deformation theory-based formulation is presented to analyze microplates. The formulations derived herein are based on a modified strain gradient theory and the von Karman nonlinear strains. The modified strain gradient theory includes five material length scale parameters capable to capture the size effects in small scales. The governing equations of motion and the most general form of boundary conditions of an arbitrary-shaped plate are derived using the principle of virtual displacements. The analysis is general and can be reduced to the modified couple stress plate model or the classical plate model.     

Enhanced-Oxidation and Highly Sensitive Detection of Tartrazine in Foodstuffs via New Platform Based on Poly(5-Sulfosalicylic Acid)/Cu(OH)<Subscript>2</Subscript> Nanoparticles

Poly(5-sulfosalicylic acid) (PSSA)/Cu(OH)₂ nanoparticle–graphite (Gr) nanocomposite-modified glassy carbon electrode (PSSA/Cu(OH)₂–Gr/GCE) was utilized for sensitive determination of tartrazine using squarewave voltammetry (SWV). The structure of the nanocomposite was investigated by transmission electron microscopy (TEM) and field emission scanning electron microscopy (FESEM(. PSSA/Cu(OH)₂–Gr/GCE exhibited an enhancement in anodic peak current, electron transfer kinetics, effective surface area, and reactive sites and indicated good electrocatalytic activity toward the oxidation of tartrazine. The as-proposed modified electrode achieved a satisfactory dynamic range between the anodic peak current and the concentration of tartrazine at two concentration ranges of 0.01–0.6 and 0.6–10 μmol/L, and the detection limit was obtained to be 8 nmol/L (S/N = 3). The resulting sensor was successfully used to determine tartrazine in real samples such as candy, softdrink, orange juice powder, banana-flavored jelly powder, and candy-coated chocolate.     

Intelligent Modeling of Asphaltene Precipitation in Live and Tank Crude Oil Systems

Abstract

The study of asphaltene precipitation properties has been motivated by their propensity to aggregate, flocculate, precipitate, and adsorb onto interfaces. The tendency of asphaltenes to precipitation has posed great challenges for the petroleum industry. The most important parameters in asphaltene precipitation modeling and prediction are the asphaltene and oil solvent solubility parameters, which are very sensitive to reservoir and operational conditions. The driving force of asphaltene flocculation is the difference between asphaltene and the oil solvent solubility parameter. Since the nature of asphaltene solubility is yet unknown and several unmodeled dynamics are hidden in the original systems, the existing prediction models may fail in prediction the asphaltene precipitation in crude oil systems. One of ways in modeling such systems is using intelligent techniques that need some information about the systems; so, based on some intelligent learning methods it can provide a suitable model. The authors introduce a new implementation of the artificial intelligent computing technology in petroleum engineering. They have proposed a new approach to prediction of the asphaltene precipitation in crude oil systems using fuzzy logic, neural networks, and genetic algorithms. Results of this research indicate that the proposed prediction model with recognizing the possible patterns between input and output variables can successfully predict and model asphaltene precipitation in tank and live crude oils with a good accuracy.     

Determining the Particle Size of Asphaltenes

Abstract

Study of heavy organics and their behavior is of great importance while occurrence of their deposition in production and processing of hydrocarbon fluids. The physical properties of heavy organics, especially asphaltenes, have been a subject of controversy for several years. The aim of the present work is to determine and measure particle size of asphaltenes. Several mixtures of crude oil and n-heptane were prepared with various dilution ratios (R_v ). Two different techniques were employed to determine and measure the particle size of asphaltenes. The first was utilizing an OLYMPUS BX60, a polarizing microscope with an appropriate magnification, and the second was using high-resolution scanning electron microscopy (SEM) and X-ray diffraction techniques. The results revealed that the size of asphaltene particles is in the range of 1–4 μm for each mixture, regardless of its n-heptane content. Although the quantity of deposited asphaltene was increased by increasing the concentration of n-heptane in mixture, the particle size of asphaltene was independent of the n-heptane concentration.     

The Application of an Artificial Neural Network (ANN) and a Genetic Programming Neural Network (GPNN) for the Modeling of Experimental Data of Slim Tube Permeability Reduction by Asphaltene Precipitation in Iranian Crude Oil Reservoirs

Abstract

The authors introduce a new implementation of the neural network (ANN), genetic programming neural network (GPNN), and neuro-fuzzy (NF) technology in petroleum engineering. An intelligent framework is developed for calculating the amount of permeability reduction by asphaltene precipitation in Iranian crude oil reservoirs over a wide pressure, temperature, and solvent mole fraction range. Theoretical results and practical experience indicate that a feed-forward network can approximate a wide class of function relationships very well. In this work, a conventional feed-forward multilayer ANN, GPNN, and NF approach have been proposed to predict the amount of permeability reduction. The accuracy of the method is evaluated by predicting the amount of permeability reduction of various reservoir fluids not used in the development of the models. One of the ways in modeling such systems is using intelligent techniques, which need information about the systems, so, based on some intelligent learning methods, it can provide a suitable model. Furthermore, the performance of the model is compared with the performance of a simple model for permeability reduction prediction, a new correlation, and experimental data. Results of this comparison show that the proposed GPNN method first and then NF method is superior both in accuracy and generality, over the other models.     

Optimal control of a nonlinear fed-batch fermentation process using model predictive approach

Bioprocesses are involved in producing different pharmaceutical products. Complicated dynamics, nonlinearity and non-stationarity make controlling them a very delicate task. The main control goal is to get a pure product with a high concentration, which commonly is achieved by regulating temperature or pH at certain levels. This paper discusses model predictive control (MPC) based on a detailed unstructured model for penicillin production in a fed-batch fermentor. The novel approach used here is to use the inverse of penicillin concentration as a cost function instead of a common quadratic regulating one in an optimization block. The result of applying the obtained controller has been displayed and compared with the results of an auto-tuned PID controller used in previous works. Moreover, to avoid high computational cost, the nonlinear model is substituted with neuro-fuzzy piecewise linear models obtained from a method called locally linear model tree (LoLiMoT).     

Effect of Carbon Nanotube on Damping Properties of Epoxy

In a nanocomposite structure,it is anticipated that high damping can be achieved by taking advantage of the interfacial friction between the nanotubes and the polymer.The purpose of this paper is to investigate the structural damping characteristics of polymeric composites containing Carbon nanotubes(CNTs) with various kinds and amounts.The damping characteristics of the specimens with 0 wt% and 0.5 wt% nanotube contents were computed experimentally.Through comparing with neat resin specimens,the study show...