Continuous water-assisted flow (CWAF), where a water layer surrounds a viscous oil core, provides low energy, long distance transport of heavy oil and bitumen without requiring heating or solvent addition. In industrial applications of CWAF, the pipe wall is fouled by a thin coating of oil, an effect not considered in many studies of water-lubricated pipe flows. In the present study, a new method to model pressure loss in the water-assisted pipeline flow of heavy oil is introduced. The hydrodynamic effects produced by the wall-fouling layer are incorporated in the model as input parameters for CFD simulations. The most important of these parameters are the thickness of the wall-fouling layer and the equivalent hydrodynamic roughness it produces. The CFD methodology described here was developed on the ANSYS-CFX platform and is able to capture the effects of the wall-fouling layer, the hydrodynamic roughness produced by this layer, and the water hold-up. The new CFD model was validated using previously collected data from tests conducted in two separate pipeline loops (100 and 260 mm in diameter), using a range of oil viscosities, water fractions, and mixture velocities. Compared to existing models, the one presented here provides more accurate predictions and requires significantly fewer computing resources. Because the model was developed using a physics-based approach, it is a useful tool in evaluating the effects of pipe diameter, oil viscosity (or temperature), water cut, and mixture velocity on pressure losses in water-assisted heavy oil pipelines.

In water-lubricated pipeline transportation of heavy oil and bitumen, a thin oil film typically coats the pipe wall. A detailed study of the hydrodynamic effects of this fouling layer is critical to the design and operation of oil-water pipelines, as it can increase the pipeline pressure loss (and pumping power requirements) by 15 times or more. In this study, a parametric investigation of the hydrodynamic effects caused by the wall coating of viscous oil was conducted. A custom-built rectangular flow cell was used. A validated CFD-based procedure was used to determine the hydrodynamic roughness from the measured pressure losses. A similar procedure was followed for a set of pipe loop tests. The effects of the thickness of the oil coating layer, the oil viscosity, and water flow rate on the hydrodynamic roughness were evaluated. Oil viscosities from 3 to 21300 Pa s were tested. The results show that the equivalent hydrodynamic roughness produced by a wall coating layer of viscous oil is dependent on the coating thickness but essentially independent of oil viscosity. A new correlation was developed using these data to predict the hydrodynamic roughness for flow conditions in which a viscous oil coating is produced on the pipe wall.Keywords Pipeline transportation Á Heavy oil Á Wall fouling Á Lubricated pipe flow Á CFD simulation List of symbols

No abstract

No abstract

Applications of machine learning algorithms (MLAs) to modeling the adsorption efficiencies of different heavy metals have been limited by the adsorbate–adsorbent pair and the selection of specific MLAs. In the current study, adsorption efficiencies of fourteen heavy metal–adsorbent (HM-AD) pairs were modeled with a variety of ML models such as support vector regression with polynomial and radial basis function kernels, random forest (RF), stochastic gradient boosting, and bayesian additive regression tree (BART). The wet experiment-based actual measurements were supplemented with synthetic data samples. The first batch of dry experiments was performed to model the removal efficiency of an HM with a specific AD. The ML modeling was then implemented on the whole dataset to develop a generalized model. A ten-fold cross-validation method was used for the model selection, while the comparative performance of the MLAs was evaluated with statistical metrics comprising Spearman’s rank correlation coefficient, coefficient of determination (R2), mean absolute error, and root-mean-squared-error. The regression tree methods, BART, and RF demonstrated the most robust and optimum performance with 0.96 ⫹ R2 ⫹ 0.99. The current study provides a generalized methodology to implement ML in modeling the efficiency of not only a specific adsorption process but also a group of comparable processes involving multiple HM-AD pairs.

Water-lubricated pipe flow technology is an economic alternative for the long-distance transportation of viscous oils, such as heavy oil and bitumen. In the industrial-scale application of this technology, a thin oil film is always observed to coat the pipe wall. The natural process of wall coating during the lubrication is often referred to as wall fouling. A wall-fouling layer produces ultrahigh values of hydrodynamic roughness (∼1 mm), which have not been studied sufficiently to date. In this work, the hydrodynamic effects of a viscous wall-coating layer were experimentally investigated. A customized flow cell was used for the purpose. The equivalent sand grain (hydrodynamic) roughness was determined using a methodology involving computational fluid dynamics (CFD) simulations. The hydrodynamic roughness was also determined from the measured topology (physical roughness) of the surface. Additional verification of the method was obtained by applying it to analyze the hydrodynamic roughness produced by sandpapers and biofouling layers. The primary outcome of the present study is the validation and application of a CFD-based methodology to quantify the hydrodynamic roughness produced by any surface, including viscous oil coatings and biofouled surfaces. Additionally, it has been shown that the hydrodynamic roughness of a viscous oil coating, for the range of conditions tested here, is much more dependent on the coating thickness than on the Reynolds number. This has significant implications for the modeling of lubricated pipeline flows involving heavy oil and water.

No abstract

The continuous growth in population, urbanization, and industrial development has been increasing the generation of solid waste (SW) in the Kingdom of Saudi Arabia. Consequently, the associated greenhouse gas (GHG) emission is also following an increasing trend. The collection and use of greenhouse gases emitted from solid waste management practices are still limited. A causality analysis examined the driving factors of the emissions from solid waste management. The methane (CH4) emissions from municipal solid waste (MSW) increased with an increase in gross domestic product (GDP) per capita and urban population, and an increase in foreign direct investment (FDI) inflows and literacy rate was likely to reduce CH4 emissions from municipal solid waste and vice versa. The CH4 emission generated from industrial solid wastes was found to be positively related to GDP per capita, urban population, and FDI inflows. However, a decrease in the unemployment rate was likely to increase CH4 emissions from industrial solid wastes. The future greenhouse gas emissions were projected under different possible socio-economic conditions. The scenario analysis based on different variations of population and GDP growth revealed that methane emission from total waste would increase at an average annual rate of 5.13% between 2020 and 2050, and is projected to reach about 4000 Gg by the end of the year 2050. Although the Kingdom has been taking some initiatives towards climate change mitigation, it has significant opportunities to adopt some of the best practices in solid waste management including reduction, recycling, composting and waste-to-energy, and carbon capture and utilization. This study also put emphasis on developing appropriate policy approaches for climate change mitigation based on the circular economy which is gaining momentum in the Kingdom.