This study investigates a hybrid biogas-driven energy system integrating a Graz cycle, molten carbonate fuel cell, and methanol–methane synthesis units with internal CO2 recycling. The configuration enables simultaneous production of electricity and renewable fuels within a ther­modynamically consistent and carbon-neutral framework. Comprehensive thermodynamic, exergoeconomic, and environmental analyses were conducted to assess overall performance. Results indicate that the integrated system achieves a maximum exergy efficiency of 54.82 %, with maximum net electrical power of 10.773 MW. The total product cost varies between 131.14 and 193.89 $/GJ, depending on operating conditions, while CO2 emissions are reduced by more than 80% relative to conventional biogas-fired systems. The sustainability index reaches 1.5–2.2, confirming a high degree of reversibility and resource efficiency. These results demonstrate that the Graz–MCFC hybrid platform provides an effective pathway toward clean power generation and carbon-neutral synthetic fuel production from renewable biogas.

The use of carbon dioxide as a high-performance working-fluid in advanced thermodynamic cycles provides a compelling route for developing low-carbon, multi-output renewable-energy systems. The study develops and assesses an advanced hybrid solar–geothermal polygeneration facility designed to produce electricity, hydrogen, and freshwater under the real resource conditions of the Harrat Rahat geothermal zone in Saudi Arabia. The configuration combines a double-flash geothermal cycle with a Transcritical CO2 Rankine cycle, a Kalina cycle, an alkaline electrolyser, and a reverse-osmosis desalination unit, supported by parabolic trough solar thermal augmentation. A full 3E+S evaluation—covering energy, exergy, economic, and sustainability metrics—is carried out alongside multi-objective optimization using the Secretary Bird metaheuristic algorithm. Under the real resource inputs of the Harrat Rahat site—geothermal reservoir temperatures exceeding 220 °C and mean solar irradiance of ∼6.6 kWh m−2 day−1, the results show the system could deliver 3.65 MW of net electricity, 9.35 kg.h−1 of hydrogen, and 10.23 m3.h−1 of freshwater, with overall energy and exergy efficiencies of 42.7 % and 38.18 %. Optimization enhances exergy efficiency by about 1.54 % and lowers the levelized cost of energy by roughly 2.2 %, yielding an LCOE of 0.04039 USD/MJ and a sustainability index of 0.238. Exergy-destruction profiling shows that condensers (≈47 %) and the solar thermal subsystem (≈16 %) are the main contributors to irreversibility. Overall, the results indicate that integrating high-enthalpy geothermal resources with concentrated solar power and advanced thermodynamic cycles can deliver a robust, efficient, and economically competitive polygeneration pathway suited to arid regions with strong energy and water needs.

This study introduces a Deep Reinforcement Learning (DRL) framework for aerodynamic control of weakly compressible flow around an angled airfoil, aiming to optimize lift and drag through synthetic jet actuation. By combining pressure and velocity information from strategically placed sensors, the framework enhances the agent’s learning efficiency and control precision. Results demonstrate that integrating both variables improves total reward, reduces vortex shedding, stabilizes the wake, and minimizes lift fluctuations while lowering drag. The control policy, trained through 300 episodes using a Deep Q-Network (DQN) with five hidden layers of 128 neurons, achieves stable convergence and effective wake stabilization. Among the tested architectures, i.e. Traditional DQN, Double DQN, and Dueling DQN, the latter yields the most consistent learning behavior and highest performance by distinguishing state-value and advantage functions. Overall, the proposed DRL-based approach provides an efficient and robust strategy for active flow control in compressible aerodynamic applications, highlighting its potential for future engineering and aerospace systems.

This study presents a thorough characterization and validation of R410A condensation performance in split air conditioning units, offering actionable insights for industry and addressing the critical challenges of energy efficiency and climate adaptation in regions such as the Kurdistan Region of Iraq. By integrating advanced experimental instrumentation with high-fidelity Computational Fluid Dynamics (CFD) modeling, it presents a comprehensive investigation of two-phase flow dynamics across the full length of an 8.914 m copper condenser tube. Forty-one precisely calibrated temperature sensors and pressure transducers provide real monitoring of thermal and flow behavior. The Volume of Fluid (VOF) method with geometric reconstruction and level set interface tracking was implemented for CFD modeling, providing direct visualization of evolving refrigerant flow patterns. Distinct flow regimes were identified: a fully vapor, desuperheating region near the inlet (0–1.8 m), a condensation zone (1.8–6 m) marked by classic stratified and annular two-phase flow with a sharp decline in vapor void fraction and a near-constant temperature and a subcooled liquid zone (6–8.9 m) where the tube was completely filled with liquid. The presence and length of each regime were confirmed by 2D cross-sectional CFD images and experimental surface temperature profiles. Pointwise comparison of measured and simulated wall temperatures across all 41 positions yielded a Mean Absolute Error (MAE) of 1.46 °C and a Root Mean Square Error (RMSE) of 1.63 °C, validating the numerical approach. These findings demonstrate that the spatially resolved CFD model captures both the thermal and hydrodynamic developments throughout condensation, supporting more robust and energy-efficient condenser designs for the local operation.

Environmental challenges such as climate change, air pollution, and resource depletion are intensifying due to the widespread reliance on fossil fuels for energy. Addressing these problems requires a shift toward cleaner, renewable energy sources that can meet growing energy demands while minimizing environmental impact. This paper provides a comprehensive analysis, combining thermodynamic principles and machine learning, of a novel system that includes a biomass gasifier, PEM electrolyzer, geothermal energy source, thermoelectric generators, and a humidification-dehumidification (HDH) desalination unit. The biomass gasifier converts feedstock into syngas, the primary fuel for a combined power cycle. Hydrogen storage is identified as a key factor in the wider adoption of hydrogen as a clean energy source, with efficient storage methods crucial for its use in fuel cells, transportation, and various industrial applications. Geothermal energy is incorporated to supplement the system's energy needs, enhancing sustainability. Additionally, the Kalina cycle recovers waste heat from the gas turbine to generate extra electricity, further boosting the system's efficiency. Data-driven models are utilized in an integrated system to predict system behavior, enabling real-time optimization and adaptive control, and enhancing performance and resource utilization. The combined thermodynamic and machine learning analysis provides insights into the complex interactions and synergies within the integrated renewable energy system. Results demonstrate the feasibility and potential of such systems to meet energy demands sustainably while minimizing environmental footprint. Elicited optimized results are comprised of two scenarios including essential parameters such as exergy efficiency, Ẇnet (net produced work), and CPsys (cost of products).The optimized point in the first optimization scenario depicts exergy efficiency, Ẇnet, and CPsys of 47.93 %, 5958 kW, and 56.97 $/GJ with the initial parameters. In the second optimization scenario, the optimized point depicts EI, Ẇnet, and CPsys of 0.3996 kg/kWh, 5957.88 kW, and 56.90 $/GJ with the initial parameters. In the third optimization scenario, the optimized point depicts EI, exergy efficiency, and m˙ hydrogen of 0.3996 kg/kWh, 47.97 %, and 56.085 kg/h with the initial parameters.

This study addresses this knowledge gap by leveraging Ansys Fluent and the Lagrangian-Eulerian technique to simulate a micro pin fin heat sink containing a novel hybrid nanofluid, composed of MoS2-Cu3O4 nanoparticles suspended in water. The validity of the simulations is established through meticulous comparison with established experimental data documented in the literature. The hybrid nanofluid employed in the simulations is formulated with a concentration ranging from 0.1 % to 0.5 %. Thermo-fluidic characteristics of the studied cases such as thermal performance, friction factor, and Nusselt number are presented and discussed.

This study aims to investigate the influence of building height and layout on energy consumption. It also analyzes methods for reducing energy consumption in these buildings. The EnergyPlus software performs simulations under local climatic conditions for all seasons. The city is divided into several sections based on cardinal directions, and energy consumption is calculated for each section, considering the city's distinct seasonal variations. Buildings in suburban areas with more sunlight exhibited higher overall energy consumption due to the reliance on heating and cooling systems, compared to the city center where denser urban areas moderated temperature extremes. Additionally, building design and insulation played significant roles. The analysis also revealed a west-to-east trend; higher consumption at the edges compared to the center. This is attributed to factors such as building density and shade from taller structures. The study further examined the impact of varying building heights. While most buildings were 20 meters tall, specific rows ranged from 21 to 25 meters. Changing these heights resulted in decreased cooling and increased heating demands in the north-south analysis, and reduced demands for both heating and cooling in the west-east analysis. This highlights the complex interplay between building layout, height, and energy consumption.

With diminishing light crude oil reserves, the focus shifts to heavy and extra-heavy crude oil, posing challenges with high viscosity impeding flow. Water-lubricated technology addresses this issue in oil transmission lines. This study introduces a novel method integrating response surface methodology (RSM), computational fluid dynamics (CFD), and optimized machine learning (ML) models to analyze pipeline pressure gradients (PG) in oil–water two-phase flows downstream of T-junctions. The present study uses the D-optimal technique for simulation design to optimize CFD computational demands efficiently. This study breaks new ground by proposing a framework that leverages support vector machines (SVMs). The proposed framework incorporates metaheuristic optimization algorithms (genetic algorithm (GA) and particle swarm optimization (PSO)) to achieve superior PG prediction accuracy. The optimized ML models outperformed RSM models for predicting PG. Results indicated that oil-to-water viscosity ratio and oil inlet velocity significantly affect PG, followed by water inlet velocity and surface tension between phases. In contrast, the oil-to-water density ratio, oil entry angle at the T-junction, and wall contact angle have minimal impact. Furthermore, statistical metrics and visual comparison tools identified the PSO-optimized SVM model based on linear kernel function as the most effective (MAPE = 13.2 % and R = 0.9949). The hybrid methodology presented in this research holds significant promise for optimizing heavy oil transfer efficiency in applications involving water-lubricated technologies.

The prevalence of peer-to-peer operation among industrial energy hubs is considered a promoting method to enhance local energy autonomy, efficiency, and flexibility. By conducting stochastic programming, this paper aims to minimize the operating cost of multiple industrial energy hubs that trade power and heat energies with each other and utility. Concerning the important role of hydrogen systems, fuel cell-based combined heat and power (CHP) generators are employed within the hubs to increase sustainability. The presence of electric vehicles (EVs) and shiftable loads enhances the flexibility during peer-to-peer operating mode. The trading price for power and heat energies are derived at first by solving a deterministic problem and considering average values for demands and generation. However, to address the uncertainties in renewable generation, electric and heat demands, and electricity prices under operation, scenario-based stochastic programming tries to optimize the expected objective function. Furthermore, the Conditional Value-at-Risk (CVaR) approach is proposed to provide various risk-averse scheduling tactics and measure the risk of each scenario. The results show that the proposed risk-constrained approach reduces the risk and makes cautious decisions about energy resources and trades. In this regard, the expected profit has been reduced by 75%, i.e., from 1636.78 NOK under a risk-neutral case to 414.97 NOK under a risk-averse case to reduce the expected risk in operating cost.

This study investigated the impact of using modified bitumen binders and additives to enhance the physical characteristics of asphalt mixtures at high, medium, and low temperatures. Both basic and rheological characteristics of the bituminous binders were tested by adding Styrene-Butadiene-Styrene, a type of thermoplastic elastomer, as a chemical additive to asphalt mixtures at various fractions (3.5%, 4%, and 4.5%), and two kinds of modified Bitumen: Kraton styrene block copolymers and Europrene (SOL T 6302). The following methods were used to test the properties of the binders: needle penetration, ductility, softening point, resistance to hardening when exposed to heat and air, Pressure Aging Vessel test dynamic (PAV), Shear Rheometer (DSR), Bending Beam Rheometer (BBR), direct tension test, and storage stability. The results showed that the best properties of the asphalt concentration were observed when using 4% of additives, while the penetration of Bitumen was reduced to 27.2% and 30.3% when adding Kraton and Europrene SOL T6302, respectively. The influence of adding 4.0% additives decreased the bulk specific gravity to 2.395. Laboratory tests showed how the mentioned chemical additives directly affect the properties of the mixture and the binder by increasing their viscosity.

The current study is performed to identify the flow regimes of oil-gas two-phase flow experimentally in a vertical pipe has an internal diameter of 6.7 cm. It also aims to provide more details about the possibility of using Differential Pressure Transducers (DPT) for indicating flow patterns. A flow development of oil and gas has been investigated in a vertical pipe of 6 m in length and operated at atmospheric pressure. A series of experiments have been run to cover a range of inlet oil superficial velocities from 0.262 to 0.419 m/s, and inlet gas superficial velocities from 0.05 to 4.7 m/s. Wire Mesh Sensors (WMS) have been used to collect the obtained void fraction values of the flow. The Differential Pressure Transducer (DPT) is utilized to measure the pressure drop values of a one-meter along the pipe. The flow patterns are classified according to the analysis of void fractions, pressure gradients regarding time series, tomographic images, probability density functions of the void fractions, and pressure gradients. A bubbly flow is observed at low superficial velocities of gas and liquid, slug flow is observed at the lower flow rate of liquid and moderate flow rates of gas, while the churn flow pattern is recognized at the higher rates of liquid and gas. Also, the result has revealed the possibility of using Differential Pressure Transducers (DPT) to classify the gas-oil flow patterns in vertical pipes.

Air-water two-phase flow development in a vertical pipe has been investigated through service of experiments and simulations in this research. Differential Pressure Transducers (DPTs) and Wire Mesh sensors (WMSs) are used to monitor the two-phase flow in a vertical pipe of 67 mm inlet diameter and 7000 mm length. Computational Fluid Dynamic (CFD) is used to evaluate the experiments of the air-water flow in the vertical pipe using a volume of fluid (VOF) model. The operating conditions cover a range of inlet air superficial velocities from 0.05 to 5 m/s. The inlet water superficial velocity remains constant at 0.2m/s and 0.4 m/s for all experiments. The results show that the bubbly flow is noted at low superficial velocities of gas, slug flow is observed at the moderate flow rates of gas, while the churn flow pattern is observed at high rates of gas. There is no significant effect when the Usl changed from 0.2 m/s to 0.4 m/s on the vertical flow lines. Pressure drop is recorded and compared with the CFD simulations. The CFD results are over estimation compared with the experimental pressured drop with maximum absolute error of 21% at Usl of 0.2 m/s and 25% at Usl 0.4 m/s.

To improve the performance of vapor compression refrigeration systems that use vertical gravitational flash tank separators, the liquid separation efficiency of the vertical gravitational flash tank separator requires to be approved. To approach this improvement, the two-phase flow development and its behavior after the expansion device need to be investigated and predicted. For thus, this paper presents a three-dimensional computational investigation of the two-phase flow development of R134a after the expansion device in a horizontal pipe. Computational Fluid Dynamic (CFD) was used to predict the two-phase development and its behavior in the horizontal pipe. ANSYS 16.2 program was used to generates the geometry of the three-dimensional horizontal pipe of 2 meters long and 25 mm inner diameter. The hexahedral mesh was generated and it is assessed to obtain the optimum mesh size and number. Eulerian-Eulerian two-phase model was used with k-ɛ turbulence model. R134a was used as a working fluid in the horizontal pipe utilizing four different inlet diameters: 12, 12.5, 25, and 50.0 mm. Mass flux and vapor quality have been changed from 288 to 447 kg/m2.s and from 10 to 20% respectively. Results were validated against experimental results from the literature and revealed that the separation region length is affected by the initial phase velocities, inlet vapor quality, and inlet tube diameter. An empirical correlation to predict the expansion region length is proposed as a function of Froude, Webber, and Lockhart-Martinelli numbers.

The current research aims to improve the cetane number of diesel extracted from the crude oil of Tawke region-Iraq Kurdistan. A specific mixture of chemical compounds was prepared which included m-nitrophenol, 4-nitro toluene, and nitrobenzene. The components' effects were investigated with regard to the cetane number, flash point, viscosity, and refractive index of diesel. The quantity of each compound mixed with diesel was prepared based on the statistical analysis of the experiment device (Box–Behnken Designs-BBDs). The tested mixture showed a good agreement and improvement of cetane and flash point and a very low effect on viscosity and refractive index. According to the statistical analysis, the main influence on cetane number and the flashpoint was from m-nitrophenol. The investigation showed that the best results were acquired from the samples of 25PPM 4-nitro toluene and 50PPM m-nitrophenol with a cetane number of 65.3. The correlation and the interaction of the regression equation were linear with all cases. It is worth mentioning that all additives positively influenced the cetane number in the regression equation. The sulfur content was measured as well, and the obtained weight percentage of sulfur was 0.8404%.

The current investigation aimed to identify pressure gradients and to study the fully developed flow patterns of oil-gas as a blend in a pipe of internal diameter 50 mm and 6 m length with different orientations of 0, 30, and 45-degree. The study was performed at constant values of liquid superficial velocities 0.052, 0.157, 0.262, 0.314, 0.419, and 0.524 m/s, and inlet superficial velocities of gas were ranged from 0.05 to 4.7 m/s at atmospheric pressure. Two pressure transducers located up and downstream were used to measure pressure drops inside the tested pipe. Flow patterns were derived by using the correlation between pressure gradients and time series, the Probability Density Function of differential pressures, pressure gradients with gas superficial velocities, and total pressure losses with mean void fractions. The flow patterns of oil-gas were observed as a uniform stratified flow in the pipe on a 0-degree orientation at various superficial velocities. Stratified, wavy, and slug flow patterns were observed at 30-degree orientation, whereas, bubbly, slug, and churn flow patterns were observed in the pipe of 45-degree orientation. The experiment also showed that pressure drop gradients decreased with increased void fractions, gas superficial velocities, and degree rotations of the flow lines. Finally, the validation of using pressure transducers as a technique for estimating the flow patterns of two-phase flow showed acceptable results with some kind of patterns.

The air-water two-phase flow plays an important role in many applications of industry fields. Usually, a 90-degree bend is used to connect pipes for changing the direction of flow which influences the two-phase flow pattern. In this paper, the effect of 90-degree bend under different ranges of gas and liquid superficial velocities on the two-phase flow patterns in the horizontal pipe located after the bend was experimentally investigated, and then results were presented and compared in a two-phase flow pattern map. Also, tomographic images and probability density functions were used to capture the cross-section void fraction and its distribution for the two-phase flow patterns. The results revealed that at low liquid and gas flow rates, a stratified-wavy flow pattern was observed as a dominant flow pattern. While the wavy-annular and semiannular flow patterns were observed at a high range of gas flow rates in the horizontal pipe. The results also showed that at the high range of liquid flow rate, bubbly, plug, slug, stratified-wavy, and wavy-annular flow patterns were observed in the horizontal pipe when the gas flow increased. The tomographic images and probability density functions gave good agreement with the experimental observations and results.

Air-water two-phase flow in pipes introduces a noticeable challenge due to the complexity of the fluids. Thus, to estimate the best design and reasonable financing cost of the transportation pipelines where the bends are presenting a part of their accessories, the investigators should have been able to estimate the flow regime occurring in different directions. An experiment was carried out by using a 90o bend fixed with two pipes where the flow was upstream from a vertical to a horizontal pipe which were representing the bend inlet and outlet respectively. Two wire-mesh sensors were used for obtaining the data of the void fractions (α) at water superficial velocities (Usl) which changed from 0.052 to 0.419m/s, and air superficial velocities (Usg) from 0.05 to 4.7m/s. Furthermore, the characterization of flow regimes of the air-water flow at both bend inlet and outlet were competed accurately by using void fraction analysis of the time series, Power Spectral Density (PSD), tomographic images observed by the sensor program, and the Probability Density Function (PDF) method. The flow regimes of vertical flow lines at the bend inlet were observed as bubbly, cap bubble, slug, and churn flow, whereas the flow regimes of the horizontal flow line at the bend outlet was characterized as having stratified, stratified wavy, bubbly, plug, slug, wavy annular, and semi-annular flow due to the gravity and bend effects.