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The durability and strength of concrete in construction can be significantly compromised by permeability issues, which pose considerable challenges to its long-term effectiveness and reliability. By analyzing six selected articles from the Scopus database, this study meticulously synthesizes findings on the effectiveness of CAs in improving these essential properties of concrete. The research meticulously documents and analyzes key variables such as the CA dosage, water–cement ratio, evaluation duration, and treatment conditions, providing a thorough understanding of the factors that influence the performance of CAs in concrete. The results robustly indicate that CAs significantly reduce concrete permeability, thereby enhancing its resistance to water and other detrimental substances, and simultaneously boosts the compressive strength, leading to stronger and more durable concrete structures. However, the study also reveals that the impact of CAs can vary considerably depending on the specific conditions and methodologies employed in the individual studies. This underscores the importance of standardized testing procedures to ensure consistent and comparable results across different studies. This research provides valuable insights for optimizing the use of CAs in concrete formulations, ultimately aiming to improve the durability, performance, and sustainability of concrete in construction applications.

Conventional concrete causes significant environmental problems, including resource depletion, high CO2 emissions, and high energy consumption. Steel slag aggregate (SSA), a by-product of the steelmaking industry, offers a sustainable alternative due to its environmental benefits and improved mechanical properties. This study examined the predictive power of four modeling techniques—Gene Expression Programming (GEP), an Artificial Neural Network (ANN), Random Forest Regression (RFR), and Gradient Boosting (GB)—to predict the compressive strength (CS) of SSA concrete. Using 367 datasets from the literature, six input variables (cement, water, granulated furnace slag, superplasticizer, coarse aggregate, fine aggregate, and age) were utilized to predict compressive strength. The models’ performance was evaluated using statistical measures such as the mean absolute error (MAE), root mean squared error (RMSE), mean values, and coefficient of determination (R2). Results indicated that the GB model consistently outperformed RFR, GEP, and the ANN, achieving the highest R2 values of 0.99 and 0.96 for the training and testing dataset, respectively, followed by RFR with R2 values of 0.97 (training) and 0.93 (testing), GEP with R2 values of 0.85 (training) and 0.87 (testing), and ANN with R2 values of 0.61 (training) and 0.82 (testing). Additionally, the GB model had the lowest MAE values of 0.79 MPa (training) and 2.61 MPa (testing) and RMSE values of 1.90 MPa (training) and 3.95 MPa (testing). This research aims to advance predictive modeling in sustainable construction through analysis and well-defined conclusions.

Vortex generators and pin fins are conventionally used to deliver fluid mixing and improved convective heat transfer. The increased pressure loss following a fractional increase in heat transfer, as well as the complex manufacturing design, leave room for improvement. The present work proposes a novel diverging–converging base corrugation model coupled with vortex generation using simple geometrical modifications across rectangular microchannels to ensure a superior performance. The Nusselt number, friction factor, and flow phenomenon were numerically studied across a Reynolds number range of 50–1000. The optimum cross-section of the microchannel-generating vortices was determined after thorough study, and base corrugation was further added to improve heat transfer. For the vortex–corrugation modeling, the heat transfer enhancement was verified in two optimized cases: (1) curved corrugated model, (2) interacting corrugated model. In the first case, an optimized curve generating Dean vortices was coupled with base corrugation. An overall increase in the Nusselt number of up to 32.69% and the thermal performance of “1.285 TPF” were observed at a high Reynolds number. The interacting channels with connecting bridges of varying width were found to generate vortices in the counter-flow configuration. The thermal performance of “1.25 TPF” was almost identical to the curved corrugated model; however, a major decrease in pressure, with a loss of 26.88%, was observed for this configuration.

The results show that Wood Chips of Acacia Nilotica trees available in Sudan lands can be successfully used in the gasification process and, on the same basis, as a bio-renewable energy resource. Simulation models were used to characterize the air gasification process integrated with a Regenerative Gas Turbine Unit. The results revealed that at a moisture content of 12%, gasification temperature of 1500 K, pressure of 20 bar, and air-like gasification medium, the biomass gasifier’s flow rate is higher at higher syngas rates. The results verified that there is an optimum ER for each syngas rate, in which the slow growth of the ER revealed the maximum gasifier biomass flow rate. For ER growth at lower levels, the specific fuel consumption (SFC) of the RGT Unit declines sharply from the maximum value reached at 0.27 kg/kW·h at an ER of 5% to the minimum value reached at 0.80 kg/kW·h at an ER of 25% for the lowest gasification temperature of 1000 K. Moreover, ER growths at low levels have a significant effect on the RGT plant’s performance, leading to increased RGT thermal efficiency. The increase in the biomass moisture content led to a sharp decrease in the RGT thermal efficiency. The RGT thermal efficiency remains high at higher gasification pressure. The results revealed that the syngas lower heating value remains high at lower produced syngas rates. At the optimum ER, the H2 mole fraction depicted a value of 1.25%, 0.85% of CO, and 10.50% of CH4 for a lower heating value of 38 MJ/kg syngas. It is shown that the gasification air entered into the gasifier decreases amid the increase in the biomass moisture content. At different syngas rates (3–10 kg/s) and optimum ER, the results predicted that the Wood Chip biomass flow rates decrease when the gasifier efficiency increases. The simulation model revealed that ER growths at lower levels have a significant effect on increasing the power of the RGT plant.

Polysulfone-based mixed matrix membranes (MMMs) incorporated with silica nanoparticles are a new generation material under ongoing research and development for gas separation. However, the attributes of a better-performing MMM cannot be precisely studied under experimental conditions. Thus, it requires an atomistic scale study to elucidate the separation performance of silica/polysulfone MMMs. As most of the research work and empirical models for gas transport properties have been limited to pure gas, a computational framework for molecular simulation is required to study the mixed gas transport properties in silica/polysulfone MMMs to reflect real membrane separation. In this work, Monte Carlo (MC) and molecular dynamics (MD) simulations were employed to study the solubility and diffusivity of CO2/CH4 with varying gas concentrations (i.e., 30% CO2/CH4, 50% CO2/CH4, and 70% CO2/CH4) and silica content (i.e., 15–30 wt.%). The accuracy of the simulated structures was validated with published literature, followed by the study of the gas transport properties at 308.15 K and 1 atm. Simulation results concluded an increase in the free volume with an increasing weight percentage of silica. It was also found that pure gas consistently exhibited higher gas transport properties when compared to mixed gas conditions. The results also showed a competitive gas transport performance for mixed gases, which is more apparent when CO2 increases. In this context, an increment in the permeation was observed for mixed gas with increasing gas concentrations (i.e., 70% CO2/CH4 > 50% CO2/CH4 > 30% CO2/CH4). The diffusivity, solubility, and permeability of the mixed gases were consistently increasing until 25 wt.%, followed by a decrease for 30 wt.% of silica. An empirical model based on a parallel resistance approach was developed by incorporating mathematical formulations for solubility and permeability. The model results were compared with simulation results to quantify the effect of mixed gas transport, which showed an 18% and 15% percentage error for the permeability and solubility, respectively, in comparison to the simulation data. This study provides a basis for future understanding of MMMs using molecular simulations and modeling techniques for mixed gas conditions that demonstrate real membrane separation.

A new micro heat exchanger was analyzed using numerical formulation of conjugate heat transfer for single-phase fluid flow across copper microchannels. The flow across bent channels harnesses asymmetric laminar flow and dean vortices phenomena for heat transfer enhancement. The single-channel analysis was performed to select the bent channel aspect ratio by varying width and height between 35–300 μm for Reynolds number and base temperature magnitude range of 100–1000 and 320–370 K, respectively. The bent channel results demonstrate dean vortices phenomenon at the bend for Reynolds number of 500 and above. Thermal performance factor analysis shows an increase of 18% in comparison to straight channels of 200 μm width and height. Alumina nanoparticles at 1% and 3% concentration enhance the Nusselt number by an average of 10.4% and 23.7%, respectively, whereas zirconia enhances Nusselt number by 16% and 33.9% for same concentrations. On the other hand, thermal performance factor analysis shows a significant increase in pressure drop at high Reynolds number with 3% particle concentration. Using zirconia for nanofluid, Nusselt number of the bent multi-channel model is improved by an average of 18% for a 3% particle concentration as compared to bent channel with deionized water.

A new micro heat exchanger was analyzed using numerical formulation of conjugate heat transfer for single-phase fluid flow across copper microchannels. The flow across bent channels harnesses asymmetric laminar flow and dean vortices phenomena for heat transfer enhancement. The single-channel analysis was performed to select the bent channel aspect ratio by varying width and height between 35-300 μm for Reynolds number and base temperature magnitude range of 100-1000 and 320-370 K, respectively. The bent channel results demonstrate dean vortices phenomenon at the bend for Reynolds number of 500 and above. Thermal performance factor analysis shows an increase of 18% in comparison to straight channels of 200 μm width and height. Alumina nanoparticles at 1% and 3% concentration enhance the Nusselt number by an average of 10.4% and 23.7%, respectively, whereas zirconia enhances Nusselt number by 16% and 33.9% for same concentrations. On the other hand, thermal performance factor analysis shows a significant increase in pressure drop at high Reynolds number with 3% particle concentration. Using zirconia for nanofluid, Nusselt number of the bent multi-channel model is improved by an average of 18% for a 3% particle concentration as compared to bent channel with deionized water.