Image-based evaluation methods are a valuable tool for source rock characterization. The time and resources needed to obtain images has spurred development of machine-learning generative models to create synthetic images of pore structure and rock fabric from limited image data. While generative models have shown success, existing methods for generating 3D volumes from 2D training images are restricted to binary images and grayscale volume generation requires 3D training data. Shale characterization relies on 2D imaging techniques such as scanning electron microscopy (SEM), and grayscale values carry important information about porosity, kerogen content, and mineral composition of the shale. Here, we introduce RockFlow, a method based on generative flow models that creates grayscale volumes from 2D training data. We apply RockFlow to baseline binary micro-CT image volumes and compare performance to a previously proposed model. We also show the extension of our model to 2D grayscale dat... [more]

This study focuses on the use of cloud motion vectors (CMV) and fast radiative transfer models (FRTM) in the prospect of forecasting downwelling surface solar irradiation (DSSI). Using near-real-time cloud optical thickness (COT) data derived from multispectral images from the spinning enhanced visible and infrared imager (SEVIRI) onboard the Meteosat second generation (MSG) satellite, we introduce a novel short-term forecasting system (3 h ahead) that is capable of calculating solar energy in large-scale (1.5 million-pixel area covering Europe and North Africa) and in high spatial (5 km over nadir) and temporal resolution (15 min intervals). For the operational implementation of such a big data computing architecture (20 million simulations in less than a minute), we exploit a synergy of high-performance computing and deterministic image processing technologies (dense optical flow estimation). The impact of clouds forecasting uncertainty on DSSI was quantified in terms of cloud modifi... [more]

Spectral induced polarization (SIP) is a non-intrusive geophysical method that collects chargeability information (the ability of a material to retain charge) in the time domain or its phase shift in the frequency domain. Although SIP is a temporal method, it cannot measure the dynamics of flow and solute/species transport in the subsurface over long times (i.e., 10−100 s of years). Data collected with the SIP technique need to be coupled with fluid flow and reactive-transport models in order to capture long-term dynamics. To address this challenge, PFLOTRAN-SIP was built to couple SIP data to fluid flow and solute transport processes. Specifically, this framework couples the subsurface flow and transport simulator PFLOTRAN and geoelectrical simulator E4D without sacrificing computational performance. PFLOTRAN solves the coupled flow and solute-transport process models in order to estimate solute concentrations, which were used in Archie’s model to compute bulk electrical conductivitie... [more]

Prediction of the timing and location of condensate build-up around the wellbore in gas condensate reservoirs is essential for the selection of appropriate methods for condensate recovery from these challenging reservoirs. The present work focuses on the use of a novel phase change tracking approach in monitoring the formation of condensate blockage in a gas condensate reservoir. The procedure entails the simulation of tight, low and high permeability reservoirs using global and local grid analysis in determining the size and timing of three common regions (Region 1, near wellbore; Region 2, condensate build-up; and Region 3, single-phase gas) associated with single and two-phase gas and immobile and mobile gas condensate. The results show that permeability has a significant influence on the occurrence of the three regions around the well, which in turn affects the productivity of the gas condensate reservoir studied. Predictions of the timing and location of condensate in reservoirs w... [more]

Experimental and numerical modeling of a heat pipe included with a phase change heat transfer was developed to assess the effects of three parameters of nanofluid, heat pipe inclination angles, and input heating power. Distilled water (DW) and 1-pyrene carboxylic-acid (PCA)-functionalized graphene nanofluid (with concentrations of 0.06 wt%) were used as working fluids in the heat pipe. A computational fluid dynamic (CFD) model was developed for evaluation of the heat transfer and two-phase flow through the steady-state process of the heat pipe. It was found that inclination significantly affects the heat transfer of the heat pipe. Maximum increment of thermal performance in the heat pipe reached 49.4% by using 0.06 wt% of PCA-functionalized graphene as working fluids. The result associated with this comparison indicates that the highest deviation is less than 6%, consequently confirming that the CFD model was successful in reproducing the heat and mass transfer processes in the DW and... [more]

The traditional Enhanced Oil Recovery (EOR) processes allow improving the performance of mature oilfields after waterflooding projects. Chemical EOR processes modify different physical properties of the fluids and/or the rock in order to mobilize the oil that remains trapped. Furthermore, combined processes have been proposed to improve the performance, using the properties and synergy of the chemical agents. This paper presents a novel simulator developed for a combined surfactant/polymer flooding in EOR processes. It studies the flow of a two-phase, five-component system (aqueous and organic phases with water, petroleum, surfactant, polymer and salt) in porous media. Polymer and surfactant together affect each other’s interfacial and rheological properties as well as the adsorption rates. This is known in the industry as Surfactant-Polymer Interaction (SPI). The simulations showed that optimum results occur when both chemical agents are injected overlapped, with the polymer in the fi... [more]

In this paper, an accurate model to simulate the dynamics of the flow of synthetic jets (SJ) in quiescent flow is proposed. Computational modeling is an effective approach to understand the physics involved in these devices, commonly used in active flow control for several reasons. For example, SJ actuators are small; hence, it is difficult to experimentally measure pressure changes within the cavity. Although computational modeling is an advantageous approach, experiments are still the main technique employed in the study of SJs due to the lack of accurate computational models. The same aspect that represents an advantage over other techniques also represents a challenge for the computational simulations, such as capturing the unsteady phenomena, localized compressible effects, and boundary layer formation characteristic of this complex flow. One of the main challenges in the simulation of SJs is related to the fact that the spatial and temporal scales of the actuator and the correspo... [more]

HT-ATES (high-temperature aquifer thermal energy storage) systems are a future option to shift large amounts of high-temperature excess heat from summer to winter using the deep underground. Among others, water-bearing reservoirs in former hydrocarbon formations show favorable storage conditions for HT-ATES locations. This study characterizes these reservoirs in the Upper Rhine Graben (URG) and quantifies their heat storage potential numerically. Assuming a doublet system with seasonal injection and production cycles, injection at 140 °C in a typical 70 °C reservoir leads to an annual storage capacity of up to 12 GWh and significant recovery efficiencies increasing up to 82% after ten years of operation. Our numerical modeling-based sensitivity analysis of operational conditions identifies the specific underground conditions as well as drilling configuration (horizontal/vertical) as the most influencing parameters. With about 90% of the investigated reservoirs in the URG transferable i... [more]

Renewable energy sources will represent the only alternative to limit fossil fuel usage and pollution. For this reason, photovoltaic (PV) power plants represent one of the main systems adopted to produce clean energy. Monitoring the state of health of a system is fundamental. However, these techniques are time demanding, cause stops to the energy generation, and often require laboratory instrumentation, thus being not cost-effective for frequent inspections. Moreover, PV plants are often located in inaccessible places, making any intervention dangerous. In this paper, we propose solAIr, an artificial intelligence system based on deep learning for anomaly cells detection in photovoltaic images obtained from unmanned aerial vehicles equipped with a thermal infrared sensor. The proposed anomaly cells detection system is based on the mask region-based convolutional neural network (Mask R-CNN) architecture, adopted because it simultaneously performs object detection and instance segmentatio... [more]

The assessment of regional bioenergy potentials from different types of natural land cover is an integral part of simulation tools that aim to assess local renewable energy systems. This work introduces a new workflow, which evaluates regional bioenergy potentials and its impact on water demand based on geographical information system (GIS)-based land use data, satellite maps on local crop types and soil types, and conversion factors from biomass to bioenergy. The actual annual biomass yield of crops is assessed through an automated process considering the factors of local climate, crop type, soil, and irrigation. The crop biomass yields are validated with historic statistical data, with deviation less than 7% in most cases. Additionally, the resulting bioenergy potentials yield between 10.7 and 12.0 GWh/ha compared with 13.3 GWh/ha from other studies. The potential contribution from bioenergy on the energy demand were investigated in the two case studies, representing the agricultural... [more]

The gas sulfur reduction of phosphogypsum in the acid co-production of sulfoaluminate cement clinker is a new process for treating phosphogypsum. The reduction furnace of this system was studied and analyzed by combining computational fluid dynamics (CFD) and experimental validation. The effects of n(CaSO4)/n(S2), particle residence time, and kiln tail flue gas temperature on the performance of the reduction furnace were obtained. A second-order response model based on the response surface methodology was developed using a three-factor Box−Behnken design (BBD). The results show that the comparison error between the simulation and test data of the reduction furnace is acceptable. The above three conditions arranged in order of significance in terms of their effect on the performance of the reduction furnace is n(CaSO4)/n(S2) > particle residence time > kiln tail gas temperature. Finally, by optimizing the response surface model, the predicted optimal operation parameter combination is n... [more]

The resource endowment structure of being coal-rich and oil-poor makes China’s production of coal-based ammonia and urea, with a low production cost and a good market, a competitive advantage. However, the process suffers from high CO2 emissions and low energy efficiency and carbon utilization efficiency due to the mismatch of hydrogen-to-carbon ratio between raw coal and chemicals. Based on the coal-to-urea (CTU) process and coal-based chemical looping technology for urea production processes (CTUCLAS&H), a novel urea synthesis process from a coal and coke-oven gas-based co-feed chemical looping system (COG-CTUCLAS&H) is proposed in this paper. By integrating chemical looping air separation and chemical looping hydrogen production technologies and the synergies between coal gasification, low-energy consumption CO2 capture and CO2 utilization are realized; the excess carbon emissions of the CTU process are avoided through coupling the pressure swing adsorption of COG, and the low carbo... [more]

Hydrolysis of 2-Chlorobenzal chloride is the primary production method of 2-chlobrobenzaldehyde in industry, but the reactor technologies and reaction processes of this method are traditional and underdeveloped, in which the dichotomous leaf hydrolysis agitator is commonly used, resulting in low efficiency during the hydrolysis process. In this article, ANSYS software was utilized to enhance the hydrolytic reactors used in industrial production by simulating and analyzing the dispersion characteristics of droplets in the reactor. The particle size distribution of dispersed phase droplets and the Sauter mean diameter (D32) were used to characterize the dispersion effect, and the dispersion characteristics of the dichotomous leaf agitator and the three-bladed back-curved impeller, three-bladed propeller impeller, open turbine impeller, and crescent impeller were compared and studied. The results showed that there was a log-linear relationship between the D32 of the dispersed phase and th... [more]

A mechanistic model on catalyst deactivation by coke formation in a continuous stirred tank reactor (CSTR) has been developed in the paper. Catalyst deactivation by coke formation was treated as a surface reaction. Four reaction mechanisms representing coke formation through different routes were proposed. The evolved system of ordinary differential equations (ODEs) was solved numerically using MATLAB. This approach was validated by applying it to the skeletal isomerization of 1-pentene over ferrierite. Simulation results were compared qualitatively to those obtained from the literature. Simulation results indicated that coke formation is an extremely rapid process with fast formation of coke components on the strongest acid sites leading to final coke. The coke deposition is slower at higher residence times resulting in more stable product formation and weaker deactivation. The results obtained from this work revealed that the developed model is indeed able to successfully demonstrate... [more]

Precision medicine—of which precision prescribing is a core component—is becoming a new frontier in today’s healthcare. Both artificial intelligence (AI) and machine learning (ML) have the potential to enhance our understanding of data and therefore our ability to accurately diagnose and treat patients. By leveraging these technologies and processes, we can uncover associations between a person’s genomic makeup and their health, identify biomarkers associated with diseases, fine-tune patient selection for clinical trials, reduce costs, and accelerate drug discovery and vaccine development. Although real-world data pose challenges in terms of collection, representation, and missing or inaccurate data sets, the integration of precision medicine into healthcare is critical. Clearly, precision medicine can benefit from health information innovations that empower decision-making at the patient level. is an example of an innovative framework and process [K Zhai et al. ECKM 2022, 20(3), pp. 1... [more]

As one of the basic industries in the manufacturing industry, the modeling and evaluation of resource utilization efficiency in the machining process is the premise of energy conservation and consumption reduction in the manufacturing industry. Mechanical processing is the process of using resources to change the shape and performance of the blank to form the workpiece and generate emissions. However, the current research on the utilization of machining process resources, whether focusing on energy efficiency or emissions, cannot provide a comprehensive solution to this problem. Therefore, this paper proposes a Data Envelopment Analysis (DEA) model with a slacks-based measure (SBM) to evaluate the resource utilization efficiency of a machining process with non-expected output. Through the relative effectiveness of DEA, the resource utilization efficiency of each processing process can be compared, which can provide a feasible and specific method for enterprises to evaluate their existi... [more]

Melanoma is a cancer that threatens life and leads to death. Effective detection of skin lesion types by images is a challenging task. Dermoscopy is an effective technique for detecting skin lesions. Early diagnosis of skin cancer is essential for proper treatment. Skin lesions are similar in their early stages, so manual diagnosis is difficult. Thus, artificial intelligence techniques can analyze images of skin lesions and discover hidden features not seen by the naked eye. This study developed hybrid techniques based on hybrid features to effectively analyse dermoscopic images to classify two datasets, HAM10000 and PH2, of skin lesions. The images have been optimized for all techniques, and the problem of imbalance between the two datasets has been resolved. The HAM10000 and PH2 datasets were classified by pre-trained MobileNet and ResNet101 models. For effective detection of the early stages skin lesions, hybrid techniques SVM-MobileNet, SVM-ResNet101 and SVM-MobileNet-ResNet101 wer... [more]

A series of computational fluid dynamics−discrete element method (CFD-DEM) simulations were applied to seed flow in horizontal-vertical 90-degree elbows. The performance of one-way and two-way CFD-DEM coupling methods was compared. Additionally, simulated seed velocities were compared to the current pneumatic conveying theory for each coupling method. Simulated field peas (Pisum sativum) were pneumatically conveyed to study the effect of air velocity (20, 25, and 30 m/s), seed rate (0.07, 0.21, and 0.42 kg/s), elbow diameter, D, (48.3, 60.3, and 72.4 mm), and elbow bend radius (1.5D, 2.5D, 3.5D, and 4.5D) on seed attributes (trajectory, velocity, and force). Results showed that seed velocity was significantly different between one-way and two-way coupling. Both methods resulted in nearly identical seed trajectory and force. Overall, simulated seed velocities had a strong correlation to values calculated through the current pneumatic conveyance theory. Dimensional analysis revealed that... [more]

In some pumped storage stations, water spray from the air hole occurs during load rejection. In order to avoid this phenomenon, it is necessary to study the change of the air hole water level during the transient process of the power station. A transient simulation of a pumped storage power station was carried out to study the variation in the maximum water level of the air hole and gate shaft. The transition process of a power station with or without a surge tank was analyzed, and the changes in the water level of the gate shaft and air hole were compared based on different gate shaft areas, and the influence of the gate shaft location on the changes in the water level of the gate shaft and air hole was determined. The following results were obtained: when the power station has a surge tank, the maximum water levels of the gate shaft and air hole are basically consistent; when the station has no surge tank, the maximum water level difference between the gate shaft and air hole gradual... [more]

In this work, a numerical simulation study on the mixing characteristics of multiphase flow in an autoclave was carried out using CFD technology. The Eulerian−Eulerian model and discrete phase model (DPM) were employed to investigate the solid holdup, critical suspension speed, nonuniformity of solid suspension, gas holdup distribution, bubble tracks, and residence time during stirring leaching in the autoclave. Experiments validate the accuracy of the numerical model, and the experimental values correspond well with the simulation results. The numerical simulation results show that the solid−liquid mixing is mainly affected by the axial flow, the best agitation speed is 400 rpm, and increasing the speed further cannot make the mixture more homogenous and buildup occurred above the autoclave. The calculated critical suspension speed is 406 rpm, which is slightly lower than that obtained from the empirical formula. The gas phase is mainly concentrated in the vortex area above the blade.... [more]

Blends combine advantageous characteristics of each species, resulting in products with different flavors and nutritional substances. Moreover, transforming them into powder provides numerous advantages. This work evaluated the properties of three blended foam formulations made from the pulps of tropical red fruits (acerola, guava and pitanga) to determine the foam layer drying kinetics and thermodynamic properties. The foam formulations were prepared by mixing the three pulps in equal proportions (1:1:1), all added with 6% albumin and 1% stabilizing agent. The foams were analyzed for density, volumetric expansion, stability and porosity in six mixing times. Subsequently, they were subjected to drying in an oven with forced air circulation at 4 temperatures, with a layer 0.5 cm thick. Seven mathematical models were fitted to the drying kinetics experimental data to determine the effective diffusivity and thermodynamic properties of the samples. The best mixing times were 5 min for the... [more]

This paper proposed a gas emission prediction method based on feature selection and improved machine learning, as traditional gas emission prediction models are neither accurate nor universally applicable. Through analysis, this paper identified 12 factors that affected gas emissions. A total of 30 groups of typical data for gas outflow were standardized, after which a full subset regression feature selection method was used to categorize 12 influencing factors into different regular patterns and select 18 feature parameter sets. Meanwhile, based on nuclear principal component analysis (KPCA), an optimized gas emission prediction model was constructed where the dimensionality of the original data was reduced. An optimized algorithm set was constructed based on the hybrid kernel extreme learning machine (HKELM) and the least squares support vector machine (LSSVM). The performance of feature parameters adopted in the prediction algorithm was evaluated according to certain metrics. By com... [more]

Cellular response to inflammatory stimuli leads to the production of eicosanoids—prostanoids (PRs) and leukotrienes (LTs)—and signaling molecules—cytokines and chemokines—by macrophages. Quantitative modeling of the inflammatory response is challenging owing to a lack of knowledge of the complex regulatory processes involved. Cybernetic models address these challenges by utilizing a well-defined cybernetic goal and optimizing a coarse-grained model toward this goal. We developed a cybernetic model to study arachidonic acid (AA) metabolism, which included two branches, PRs and LTs. We utilized a priori biological knowledge to define the branch-specific cybernetic goals for PR and LT branches as the maximization of TNFα and CCL2, respectively. We estimated the model parameters by fitting data from three experimental conditions. With these parameters, we were able to capture a novel fourth independent experimental condition as part of the model validation. The cybernetic model enhanced ou... [more]

Packed-bed bioreactors are often used for aerobic solid-state fermentation, since the forced aeration supplies O2 and removes metabolic heat from the bed. Motivated by the potential for applications in biorefineries, we review studies conducted on packed-bed bioreactors over the last decade, evaluating the insights these studies provide into how large-scale packed beds should be designed and operated. Many studies have used low superficial air velocities and suffer from preferential airflow, such that parts of the bed are not properly aerated. Moreover, some studies have proposed ineffective strategies, such as reversing the direction of the airflow or introducing air through perforated pipes within the bed. Additionally, many studies have used narrow water-jacketed packed-bed bioreactors, but these bioreactors do not reflect heat removal in wide large-scale packed beds, in which heat removal through the side walls makes a minor contribution. Finally, we conclude that, although some at... [more]

Facility layout in a manufacturing system is a complex production activity because decisions on layout design are influenced by numerous, ambiguous, and competing factors. This study proposes a method for determining and choosing an ideal layout using a hybridized Fuzzy Analytic Hierarchy Process (F-AHP) with the Fuzzy Technique for Order of Preference by Similarity to the Ideal Solution (F-TOPSIS). The F-AHP is used, in this case, because of its ability to generate design criteria weight. The railcar industrial case study results indicate that the developed model can effectively lead to selection of the most suitable facility layout design. The Discrete Event Simulation model is used to evaluate the performance of the suggested layout concepts with the purpose of determining quantitative criteria for use when selecting the most optimal concept by the proposed Fuzzy AHP-TOPSIS model. The proposed methodology demonstrated that a framework is a logical way to solve problems. The proposed... [more]