Md Mostoba Rafid, Md. Elias Uddin, Sadia Mim
et al.
Competing ions and organic matters may alter the surface charge or block active sites of the adsorbents in real tannery effluent and reduce their effectiveness compared to synthetic solutions. Thus, the purpose of this work was to use graphene oxide-zinc oxide (GO-ZnO) nanocomposite to remove Cr ions from real tannery wastewater. The solvothermal method was used in this work to create decorated zinc oxide (ZnO) nanoparticles on graphene oxide (GO) nanoparticles. Fourier Transform Infrared (FTIR), UV–visible, X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Energy Dispersive X-ray Spectroscopy (EDX) techniques were used to characterize the surface deposition of ZnO on GO. To assess the impact of operational parameters, equilibrium isotherm, adsorption kinetics, and thermodynamics, batch adsorption analysis was performed. As time and adsorbent dosage increase, so does the removal efficiency of Cr (III). The results indicated that the maximal adsorption capacity of Cr (III) reached 99.97% with the presence of 2.5 g/L GO-ZnO under 25 min of contact time at pH 8.0. The Cr (III) concentration and clarity of the effluent were ensured via atomic absorption spectrometry and UV–visible spectroscopic analyses. The adsorption of Cr (III) could be well described using the Freundlich equation, and obeyed the pseudo-second order kinetic expression. The thermodynamic spontaneity and viability of a chemical reaction mechanism are represented by the negative value of Gibbs’ Free Energy. The results of biodegradation and antibacterial tests suggested the GO-ZnO nanocomposite to be non-toxic and environmentally safe. This work could provide an effective reference for the application of GO-ZnO nanocomposite for the adsorption of Cr (III) from tannery wastewater.
As the demand for alternative and renewable energy solutions increases, particularly in developing nations facing unreliable power supply, optimizing biomass gasification processes for power generation has become a critical challenge. Syngas, composed primarily of carbon monoxide (CO), hydrogen (H₂), and carbon dioxide (CO₂), plays a pivotal role in driving gas turbine power generation. However, the impact of varying feedstock types, thermodynamic conditions, and syngas quality on power output is still not well understood. This study addresses this knowledge gap by investigating the effects of feedstock composition (C1 to C4 alkanes), temperature, and pressure on syngas production and gas turbine efficiency. Using process simulations with DWSim and optimization techniques such as response surface methodology (RSM), we identify optimal syngas compositions for maximizing gas turbine duty (GTD). The results demonstrate that a balanced syngas mixture (CO = 4 kmol/h, H₂ = 4 kmol/h, CO₂ = 4 kmol/h) yields a GTD of 48.2 kW, significantly enhancing power generation efficiency. Our findings underscore the critical role of CO₂ in stabilizing combustion, improving thermal efficiency, and ensuring stable turbine operation, while CO and H₂ contribute directly to the energy conversion process. This research provides valuable insights for optimizing bioenergy systems, offering predictive models that can guide the development of more efficient and sustainable biomass-based power generation technologies.
Energy industries. Energy policy. Fuel trade, Renewable energy sources
Anjali Thomas, Adrian McDonald, James Renwick
et al.
This study quantifies the influences of anthropogenic forcing to date on precipitation over Aotearoa New Zealand (ANZ). Large ensembles of simulations from the weather@home regional climate model experiments are analysed under two scenarios, a natural (NAT) or counter-factual scenario which excludes human-induced changes to the climate system and an anthropogenic (ANT) or factual scenario. The impacts of anthropogenic forcing on precipitation are analysed in the context of large-scale circulation types characterized using an existing Self Organizing Map classification. The combined effect of both thermodynamics and dynamics are compared with values expected from the Clausius–Clapeyron (C–C) relation. Changes in the precipitation intensity due to greenhouse gas-forced temperature rise are lower than the expected C–C value. However extreme precipitation changes approach the C–C value for some circulation types. Specifically westerly flows enhance precipitation change across ANZ relative to the C–C rate, particularly over the West Coast. Conversely, northwesterly flows reduce the change over the North Island relative to the C–C value. Moreover, the wet day frequency generally reduces in the ANT scenario relative to NAT, reductions are largest on the West Coast of the South Island for westerly flows. Additionally, the frequency of days with extreme precipitation rises over ANZ for most circulation patterns, except in Northland and for northwesterly flows. This underscores the combined influence of dynamics and thermodynamics in shaping both precipitation intensity and frequency patterns across ANZ.
Abstract The Last Glacial Maximum (LGM) provides an opportunity to estimate how extreme precipitation may respond to large radiative forcing and hence the constraints for its future behavior. Using daily outputs from the Paleoclimate Modeling Intercomparison Project, we illustrate a decrease in extreme precipitation intensity over the global monsoon regions during the LGM relative to preindustrial, accompanied by increased extreme precipitation frequency, although regional differences exist. The weakened extreme precipitation is dominated by the thermodynamics (∼92%) linked to change in temperature, with the dynamic component linked to change in vertical velocity contributing to regional differences. Furthermore, we show a ∼3.6% decrease in extreme precipitation per 1°C cooling during the LGM, lower than the sensitivity (∼5%/°C) in future warming scenarios. Our results are in line with the proxies‐based drier conditions and lower equilibrium climate sensitivity during the LGM, and may advance understanding of extreme precipitation variation in a warmer future.
Atomistic simulations using ab initio density functional theory and machine-learned potentials have been employed to map the structural, thermodynamic, and kinetic properties of the T-WOx system (x = 0 to 3). The simulations reveal that the T permeability is low in WO2, intermediate in W, and relatively high in WO3. Diffusion of T is slowest in WO2. Vacancies and self-interstitials are strong traps for T. Oxygen vacancies in WO2 are very strong traps for a few T atoms, while vacancies in bulk W can trap up to ten T atoms. Segregation to WO2 surfaces is energetically favourable. However, segregation of T to WO3 surfaces is energetically unfavourable at high surface coverage.
We looked at the (3+1)-dimensional fractional Kadomtsev–Petviashvili–Boussinesq (KP-B) equation, which comes up in fluid dynamics, plasma physics, physics, and superfluids, as well as when connecting the optical model and hydrodynamic domains. Furthermore, unlike the Kadomtsev–Petviashvili equation (KPE), which permits the modeling of waves traveling in both directions, the zero-mass assumption, which is required for many scientific applications, is not required by the KP-B equation. In several applications in engineering and physics, taking these features into account allows researchers to acquire more precise conclusions, particularly in studies pertaining to the dynamics of water waves. The foremost purpose of this manuscript is to establish diverse solutions in the form of exponential, trigonometric, hyperbolic, and rational functions of the (3+1)-dimensional fractional (KP-B) via the application of four analytical methods. This KP-B model has fruitful applications in fluid dynamics and plasma physics. Additionally, in order to better explain the potential and physical behavior of the equation, the relevant models of the findings are visually indicated, and 2-dimensional (2D) and 3-dimensional (3D) graphics are drawn.
Farzaneh Mohammadi, G. Reza Vakili-Nezhaad, Nabeel Al-Rawahi
et al.
Increasing environmental pollution is a major concern, driven by fossil fuel consumption and growing energy demands. Energy production from various waste materials such as wastewater and sludge, could be proposed as a practical and appropriate solution to the energy crisis and waste disposal simultaneously. This approach not only addresses waste management but also generates different forms of bioenergy through biological treatment processes.Bioelectricity, a renewable and cost-effective energy source, is generated by electric potentials and currents that are produced or exist inside living microorganisms. In fact, bioelectricity is obtained from bioelectric potentials in various biological processes and through the conversion of chemical energy into electrical energy. Recently, the application of microbial electrochemical systems (MESs) has gained significant importance as a promising means of bioelectricity production. Various MESs leading to bioelectricity generation include microbial fuel cells (MFCs) and microbial desalination cells (MDCs).This paper explores the definitions of MESs, their detailed mechanisms, configurations, operational parameters, benefits and limitations, as well as the kinetics and thermodynamics associated with bioelectricity generation technologies. Additionally, it investigated the use of various types of artificial intelligence algorithms for modeling and optimizing biological processes for bioelectricity generation.
Clément Loiseau, Stéphane Mimouni, Didier Colmont
et al.
The CFD numerical study of the flash boiling phenomenon of a water film was conducted using an Euler–Euler method, and compared to the experiments on the flashing of a water film. The water film is initially heated at temperatures ranging from 34 to 74 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>C (frim 1 to 41 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>C superheat), and the pressure is decreased from 1 bar to 50 mbar during the experiments. This paper shows that the experiments could not be correctly modelled by a simple liquid/bubble model because of the overestimation of the drag force above the water film (in the gas/droplet region). The generalised large interface model (GLIM), however, a multi-regime approach implemented in the version 7.0 of the neptune_cfd software, is able to differentiate the water film, where liquid/bubble interactions are predominant from the gas region where gas/droplet interactions are predominant, and gives nice qualitative results. Finally, this paper shows that the interfacial heat transfer model of Berne for superheated liquids could accurately predict the evolution of the water temperature over time.
Thermodynamics, Descriptive and experimental mechanics
Ervin Kaminski Lenzi, Luiz Roberto Evangelista, Luciano Rodrigues da Silva
We investigated two different approaches, which can be used to extend the standard quantum statistical mechanics. One is based on fractional calculus, and the other considers the extension of the concept of entropy, i.e., the Tsallis statistics. We reviewed and discussed some of the main properties of these approaches and used the thermal Green function formalism to perform the developments, simultaneously allowing us to analyze each case’s dynamics and thermodynamics aspects. In particular, the results allow us to understand how the extensions change the behavior of some quantities, particularly fluctuations related to the system.
Jana Korte, Thomas Rauwolf, Jan-Niklas Thiel
et al.
Purpose: The analysis of pathological human left ventricular hemodynamics using high-resolved image-based blood flow simulations shows a major potential for examining mitral valve insufficiency (MI) under exercise conditions. Since capturing and simulating the patient-specific movement of the left ventricle (LV) during rest and exercise is challenging, this study aims to propose a workflow to analyze the hemodynamics within the pathologically moving LV. Methods: Patient-specific ultrasound (US) data of ten patients with MI in different stages were captured with three-dimensional real-time echocardiography. US measurements were performed while patients were resting and while doing handgrip exercise (2–4 min work). Patient-specific hemodynamic simulations were carried out based on the captured ventricular wall movement. Velocity and kinetic energy were analyzed for rest and exercise and for the different MI stages. Results: The results reveal a dependency of the kinetic energy over time in the ventricular volume curves. Concerning the comparison between rest and exercise, the left ventricular function reveals lower systolic kinetic energy under exercise (kinetic energy normalized by EDV; mean ± standard deviation: rest = 0.16 ± 0.14; exercise = 0.06 ± 0.05; <i>p</i>-value = 0.04). Comparing patients with non-limiting (MI I) and mild/moderate (MI II/III) MI, lower velocities (mean ± standard deviation: non-limiting = 0.10 ± 0.03; mild/moderate = 0.06 ± 0.02; <i>p</i>-value = 0.01) and lower diastolic kinetic energy (kinetic energy normalized by EDV; mean ± standard deviation: non-limiting = 0.45 ± 0.30; mild/moderate = 0.20 ± 0.19; <i>p</i>-value = 0.03) were found for the latter. Conclusion: With the proposed workflow, the hemodynamics within LVs with MI can be analyzed under rest and exercise. The results reveal the importance of the patient-specific wall movement when analyzing intraventricular hemodynamics. These findings can be further used within patient-specific simulations, based on varying the imaging and segmentation methods.
Thermodynamics, Descriptive and experimental mechanics
Probabilistic machine learning and data-driven methods gradually show their high efficiency in solving the forward and inverse problems of partial differential equations (PDEs). This paper will focus on investigating the forward problem of solving time-dependent nonlinear delay PDEs with multi-delays based on multi-prior numerical Gaussian processes (MP-NGPs), which are constructed by us to solve complex PDEs that may involve fractional operators, multi-delays and different types of boundary conditions. We also quantify the uncertainty of the prediction solution by the posterior distribution of the predicted solution. The core of MP-NGPs is to discretize time firstly, then a Gaussian process regression based on multi-priors is considered at each time step to obtain the solution of the next time step, and this procedure is repeated until the last time step. Different types of boundary conditions are studied in this paper, which include Dirichlet, Neumann and mixed boundary conditions. Several numerical tests are provided to show that the methods considered in this paper work well in solving nonlinear time-dependent PDEs with delay, where delay partial differential equations, delay partial integro-differential equations and delay fractional partial differential equations are considered. Furthermore, in order to improve the accuracy of the algorithm, we construct Runge–Kutta methods under the frame of multi-prior numerical Gaussian processes. The results of the numerical experiments prove that the prediction accuracy of the algorithm is obviously improved when the Runge–Kutta methods are employed.
Svatava Polsterová, Martin Friák, Monika Všianská
et al.
We present a quantum-mechanical study of silver decahedral nanoclusters and nanoparticles containing from 1 to 181 atoms in their static atomic configurations corresponding to the minimum of the <i>ab initio</i> computed total energies. Our thermodynamic analysis compares T = 0 K excess energies (without any excitations) obtained from a phenomenological approach, which mostly uses bulk-related properties, with excess energies from <i>ab initio</i> calculations of actual nanoclusters/nanoparticles. The phenomenological thermodynamic modeling employs (i) the bulk reference energy, (ii) surface energies obtained for infinite planar (bulk-related) surfaces and (iii) the bulk atomic volume. We show that it can predict the excess energy (per atom) of nanoclusters/nanoparticles containing as few as 7 atoms with the error lower than 3%. The only information related to the nanoclusters/nanoparticles of interest, which enters the phenomenological modeling, is the number of atoms in the nanocluster/nanoparticle, the shape and the crystallographic orientation(s) of facets. The agreement between both approaches is conditioned by computing the bulk-related properties with the same computational parameters as in the case of the nanoclusters/nanoparticles but, importantly, the phenomenological approach is much less computationally demanding. Our work thus indicates that it is possible to substantially reduce computational demands when computing excess energies of nanoclusters and nanoparticles by <i>ab initio</i> methods.
We explore the cloaking of a complex shape by either the neutral inclusion or the transformation thermodynamics (TT) methods. Thin cloaks are built and the heat cloaking efficiency is investigated for both the steady-state and the transient regimes. We show that the neutral inclusion cloak is more efficient in both regimes, though it has the drawback that the thermal conductivity of the cloaked shape must be known. In practice, the neutral inclusion method is more flexible and easier to implement than the coordinate transformation method, especially for complex shapes.