Display omitted
The development of nanofluid as an innovative class of thermal fluid subsequently inspired use in their engineering applications. As a result, the necessity of experimental work to ...determine the thermophysical properties of nanofluids affecting heat transfer such as specific heat capacity, viscosity, thermal conductivity and density. Theoretical models are used in numerical studies of engineering applications to calculate thermophysical properties. This study intends to develop a new correlation for calculating the effective viscosity of nanofluids. In the model, we considered an effect of interfacial layer on the nanoparticle, the interfacial layer on nanoparticle works as a solid like layer in between the base fluid and nanoparticle surface. When nanoparticles are suspended in the base fluid, Brownian motion occurs due to the relative velocity of the base fluid and nanoparticles, which is also incorporated in this model. The correlation developed successfully express in outcome advance the viscosity of a variety of nanofluids, (Al2O3, Fe, hexagonal boron nitride (hBN), ZnO)-Ethylene Glycol, (Al2O3, hBN, SiC)-Ethylene Glycol Water mixture, (CuO, Al2O3, Fe3O4, TiO2, hBN, Graphite, Single-wall carbon nanotube (SWCNT))-water, (Fe3O4)-Toluene. The new correlation was derived from 501 viscosity values of nanofluid, 75% of them are within the correlation coefficient 0.78–1 and mean deviation less than 5%.
Thermochemical processes, which include pyrolysis, torrefaction, gasification, combustion, and hydrothermal conversions, are perceived to be more efficient in converting waste biomass to energy and ...value-added products than biochemical processes. From the chemical point of view, thermochemical processes are highly complex and sensitive to numerous physicochemical properties, thus making reactor and process modeling more challenging. Nevertheless, the successful commercialization of these processes is contingent upon optimized reactor and process designs, which can be effectively achieved via modeling and simulation. Models of various scales with numerous simplifying assumptions have been developed for specific applications of thermochemical conversion of waste biomass. However, there is a research gap that needs to be explored to elaborate the scale of applicability, limitations, accuracy, validity, and special features of each model. This review study investigates all above mentioned important aspects and features of the existing models for all established industrial thermochemical conversion processes with emphasis on waste biomass, thus addressing the research gap mentioned above and presenting commercial-scale applicability in terms of reactor designing, process control and optimization, and potential ways to upgrade existing models for higher accuracy.
Wind energy harvesting for electricity generation has a significant role in overcoming the challenges involved with climate change and the energy resource implications involved with population growth ...and political unrest. Indeed, there has been significant growth in wind energy capacity worldwide with turbine capacity growing significantly over the last two decades. This confidence is echoed in the wind power market and global wind energy statistics. However, wind energy capture and utilisation has always been challenging. Appreciation of the wind as a resource makes for difficulties in modelling and the sensitivities of how the wind resource maps to energy production results in an energy harvesting opportunity. An opportunity that is dependent on different system parameters, namely the wind as a resource, technology and system synergies in realizing an optimal wind energy harvest. This paper presents a thorough review of the state of the art concerning the realization of optimal wind energy harvesting and utilisation. The wind energy resource and, more specifically, the influence of wind speed and wind energy resource forecasting are considered in conjunction with technological considerations and how system optimization can realise more effective operational efficiencies. Moreover, non-technological issues affecting wind energy harvesting are also considered. These include standards and regulatory implications with higher levels of grid integration and higher system non-synchronous penetration (SNSP). The review concludes that hybrid forecasting techniques enable a more accurate and predictable resource appreciation and that a hybrid power system that employs a multi-objective optimization approach is most suitable in achieving an optimal configuration for maximum energy harvesting.
Municipal solid waste (MSW) has ranked among the most detrimental global issues of the decade, where it has been induced by the population trends, urbanization, and economic growth. The majority of ...conventional pollution treatment methods involve high capital and maintenance costs with sophisticated instruments and technology. Biomass valorization and phytoremediation has been described to be an effective and practicable alternative for expensive, conventional engineering techniques in managing MSW and remediating contamination. Modern biomass valorization methods are promising technologies that provide effective MSW reduction, at the same time providing measures for removing pollutants from leachate with its particular focus on biochar, which is resulted by torrefaction of the perishable waste. The simultaneous ability of phytoremediation to remove many types of contaminants in leachate by significant amounts is emphasized in the context with considerations to the challenges in the sector. Phytoremediation is limited by several factors such as contaminant specificity, time consumption, and some external factors, while biochar applications are limited due to substrate specificity. The study aimed to review scientific literature to provide a platform for biomass valorization and phytoremediation integration for developing economy context.
Graphical Abstract
A sufficient amount of air is needed for the optimum combustion of fuel. The correct amount of air supply assures optimal flue gas temperature to maximise heat utilisation with fewer pollutants. The ...quantity of air requirement depends on the fuel type. Equivalence Ratio (ER) requirement has been defined empirically for different fuel types. Wood combustion requires a high degree of excess air requirement compared to other fuels. Data on ER requirement is essential for the industrial operation of wood combustion systems. One of the factors which affect the amount of ER is the size of fuel which has not been given sufficient attention. The effect of particle size on the ER requirement in packed bed combustion of thermally thick wood particles is studied in this research through numerical modelling. Computational fluid dynamics simulations were carried out for the particle sizes of 25 mm, 38 mm, and 63 mm wood particles under air flow velocity of 0.12 ms
−1
. Simulation results show that the ER value for smaller particle sizes is less than that for the larger particle sizes under the same volumetric air flow rate. CFD simulations were used to decide the optimum ER which maximises the flue gas temperature with minimum possible CO fraction for particle sizes of 25 mm, 38 mm, and 63 mm.
This study focuses on developing a dynamic two dimensional Computational Fluid Dynamics (CFD) model of a moving bed updraft biomass gasifier. The model uses inlet air at room temperature as the ...gasifying medium and a fixed batch of biomass. The biomass batch is initially ignited by a heat source which is removed after a certain amount of time. This model operates by the heat emitted by combustion reactions, until the fuel is finished. Since the operation is batch wise, model is transient and takes into consideration the effect of bed movement as a result of shrinkage. The CFD model is capable of simulating the movement of interface between solid packed bed and gas free board and this motion is also presented. The model is validated by comparing the simulation results with experimental data obtained from a laboratory scale updraft gasifier operated in batch mode with Gliricidia. The developed model is used to find the optimum air flow rate that maximizes the cumulative CO production. It is found that from the simulation study for the particular experimental gasifier, a flow rate of 7 m3/h maximizes the CO production. The maximum cumulative CO production was 6.4 m3 for a 28 kg batch of Gliricidia.
•Mathematical model is developed for the biomass gasification process.•Computational Fluid Dynamics (CFD) model is used to analyses the system.•CFD model is validated by experimental data from a lab scale updraft gasifier unit.•Optimal air flow rate is evaluated by simulations of CFD model.
•Multi Objective dynamic optimization of seeded suspension polymerization process.•Incorporating ELM-RBF ANNs in a genetic algorithm to optimize temperature profile.•Using a SVM classifier to impose ...nonlinear inequality constraints.•Using a hybrid SVR-(ELM-RBF based search algorithm) for narrowing down 44 inputs.•Significant reduction in computational effort required for dynamic optimization.
The temperature profile of the seeded suspension polymerization process was optimized to maximize molecular weight, shell thickness and monomer conversion ratio of core–shell polymer particles. Extreme learning machine radial basis function neural networks with R2 values greater than 0.93 were developed, to predict polymer properties at any point in time, using data generated by a computational fluid dynamics model. The optimal combination of input parameters for each neural network was selected from a pool of 44 variables, by using a weight-based method that uses a support vector regression model, and a global exhaustive search algorithm, consecutively. The neural networks developed were incorporated into a genetic algorithm that maximizes the molecular weight, monomer conversion ratio and shell thickness. The optimum temperature profile generated by the algorithm satisfactorily maximized all target polymer properties. This study also demonstrates that a support vector machine classifier could be reliably used for imposing nonlinear inequality constraints for solving dynamic optimization problems.
Landfills and anaerobic digesters in the waste treatment processes generate biogas. Biogas can be used as a fuel and excess biogas is typically burned in a flare to reduce the greenhouse effect. ...However, burning biogas produces several pollutants, including CO
2
, NO
x
, and SO
2
. To minimize these emissions, the amount of excess air used in the combustion process needs to be considered, which has a significant impact on NO
x
emissions. This study developed a Computational Fluid Dynamics (CFD) model to simulate a small-scale biogas combustion system and analyses the effect of excess air on heat output and NO
x
emissions during biogas combustion. The GRI-Mech reaction mechanism was used to simulate reactions, and the model was validated by comparing it to experimental data from the DLR-Stuttgart CH
4
/H
2
/N
2
Jet Flame. To reduce computational costs, a Tabulation of Dynamic Adaptive Chemistry (TDAC) algorithm was used to dynamically adapt the reaction mechanism in real time. Turbulence in the DLR flame was simulated using Reynolds-Averaged Navier–Stokes (RANS). The CFD model used a co-flow of a natural draft to provide additional air, while the air was premixed with fuel. The CFD model was used to simulate various premixed equivalent ratios, and the resulting emissions and heat outputs were compared. The study found that the optimal premixed equivalent ratio for the studied system was between 0.85 and 1.1, as this range produced the highest temperature and lowest NO
x
emissions. This model facilitates emission analysis of gas-phase combustion systems.
•Modeling seeded suspension polymerization using computational fluid dynamics.•Predicting Case II diffusion & phase separation using CFD.•Using K-means clustering, Hansen solubility parameters & ANNs ...to estimate unknowns.•Sensitivity analysis of swelling & reaction parameters to phase separation by DOE.
The current study devises a methodology to illustrate phase separation occurring in core–shell structured polymer particles by comprehensively modeling Case II diffusion and reaction kinetics of seeded suspension polymerization using computational fluid dynamics. The model predicted weight and number average molecular weights (3022 kg/mol and 1326 kg/mol) were comparable to actual values (3086 kg/mol and 1387 kg/mol). Dynamic simulations of molecular weight, monomer conversion ratio and particle radius complied with observations reported by experimental studies. Unavailable values were estimated using clustering techniques, neural network models and Hansen solubility parameters. The sensitivity of process parameters on phase separation was examined by Design of Experiments methodology based on model simulations. The percentage sensitivity (which varied between 1% and 12%) of process parameters pertaining to monomer swelling of polymer seeds depended on the radial position in the particle. Reaction temperature demonstrated the highest percentage sensitivity (60%) to phase separation.
In the present work, the drying and pyrolysis process of a thermally thick single wood particle has been investigated. A novel approach has been made considering the two phases gas and solid inside ...the particle are not in thermal equilibrium. Mathematical relationship was built to determine distinct temperatures of solid and gas boundaries. An unsteady three-dimensional (3D) model is developed and simulated in Computational Fluid Dynamics (CFD) framework. The Euler-Euler approach for modelling of single biomass particle has been succeeded with the help of C++ CFD toolbox in OpenFOAM. The 3D model can simulate the thermochemical conversion process of different particle types, particularly for different shapes to examine the spatial variations during the process. The model was validated by comparing the simulation results with data obtained by experiments conducted using a single particle reactor.
•Three-dimensional CFD model was developed for single biomass particle pyrolysis.•Temperature and mass loss simulation results well agreed with experimental data.•Developed lump model determines gas and solid boundary temperatures precisely.•The model predicts accurate results for changing particle shapes in particular.
PDF
