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To improve the thermal performance of base fluid, nanoparticles of three types are dispersed in the base fluid. A novel theory of non-Fourier heat transfer is used for design and development of models. The thermal performance of sample fluids is compared to determine which types of combination of nanoparticles are the best for an optimized enhancement in thermal performance of fluids. This article aims to: (i) investigate the impact of nanoparticles on thermal performance; and (ii) implement the Galerkin finite element method (GFEM) to thermal problems.

The mathematical models are developed using novel non-Fourier heat flux theory, conservation laws of computational fluid dynamics (CFD) and no-slip thermal boundary conditions. The models are approximated using thermal boundary layer approximations, and transformed models are solved numerically using GFEM. A grid-sensitivity test is performed. The accuracy, correction and stability of solutions is ensured. The numerical method adopted for the calculations is validated with published data. Quantities of engineering interest, i.e. wall shear stress, wall mass flow rate and wall heat flux, are calculated and examined versus emerging rheological parameters and thermal relaxation time.

The thermal relaxation time measures the ability of a fluid to restore its original thermal state, called thermal equilibrium and therefore, simulations have shown that the thermal relaxation time associated with a mono nanofluid has the most substantial effect on the temperature of fluid, whereas a ternary nanofluid has the smallest thermal relaxation time. A ternary nanofluid has a wider thermal boundary thickness in comparison with base and di- and mono nanofluids. The wall heat flux (in the case of the ternary nanofluids) has the most significant value compared with the wall shear stresses for the mono and hybrid nanofluids. The wall heat and mass fluxes have the highest values for the case of non-Fourier heat and mass diffusion compared to the case of Fourier heat and mass transfer.

An extensive literature review reveals that no study has considered thermal and concentration memory effects on transport mechanisms in fluids of cross-rheological liquid using novel theory of heat and mass [presented by Cattaneo (Cattaneo, 1958) and Christov (Christov, 2009)] so far. Moreover, the finite element method for coupled and nonlinear CFD problems has not been implemented so far. To the best of the authors’ knowledge for the first time, the dynamics of wall heat flow rate and mass flow rate under simultaneous effects of thermal and solute relaxation times, Ohmic dissipation and first-order chemical reactions are studied.

Fluid flows through rotatory disks are encountered in industrial and practical engineering processes, such as computer storage devices, gas turbine rotators, rotating machinery, air cleaning machines, etc. The primary purpose of this research is to examine the combined aspects of variable electrical conductivity, thermal radiation, Soret and Dufour effects on a magnetohydrodynamic Maxwell single-walled carbon nanotubes–graphene oxide–multi-walled carbon nanotubes–copper (SWCNT–GO–MWCNT–Cu)/sodium alginate tetra-hybrid nanofluid flow through a stretchable rotatory disk.

The modeled administrative equations of the present flow problem are converted to a non-dimensional system of ordinary differential equations by applying suitable similarity conversion and then solved numerically by implementing the bvp4c method. The impressions of noteworthy dimensionless parameters on velocity, temperature, concentration distributions, Nusselt number, skin friction and Sherwood number are reported via graphs and tables.

The authors figured out that the developed values of the rotation parameter diminish the temperature but enhance both the radial and angular velocities. Further, the mass and heat transmission rates are better for tetra-hybrid nanofluids than for ternary and hybrid nanofluids.

The present study emphasizes a special type of fluid called the tetra-hybrid nanofluid. The existing literature has not discussed the Maxwell tetra hybrid nanofluid flow through a stretchable rotatory disk with variable electrical conductivity. Besides, the novel aspects of magnetohydrodynamics, thermal radiation, Soret and Dufour effects are also incorporated into the present flow problem.

This study aims to explore entropy evaluation in the bi-directional flow of Casson hybrid nanofluids within a stagnated domain, a topic of significant importance for optimizing thermal systems. The aim is to investigate the behavior of unsteady, magnetized and laminar flow using a parametric model based on the thermo-physical properties of alumina and copper nanoparticles.

The research uses boundary layer approximations and the Keller-box method to solve the derived ordinary differential equations, ensuring numerical accuracy through convergence and stability analysis. A comparison benchmark has been used to authenticate the accuracy of the numerical outcomes.

Results indicate that increasing the Casson fluid parameter (ranging from 0.1 to 1.0) reduces velocity, the Bejan number decreases with higher bidirectional flow parameter (ranging from 0.1 to 0.9) and the Nusselt number increases with higher nanoparticle concentrations (ranging from 1% to 4%).

This study has limitations, including the assumption of laminar flow and the neglect of possible turbulent effects, which could be significant in practical applications.

The findings offer insights for optimizing thermal management systems, particularly in industries where precise control of heat transfer is crucial. The Keller-box simulation method proves to be effective in accurately predicting the behavior of such complex systems, and the entropy evaluation aids in assessing thermodynamic irreversibilities, which can enhance the efficiency of engineering designs.

These findings provide valuable insights into the thermal management of hybrid nanofluid systems, marking a novel contribution to the field.

Scientists have been conducting trials to find ways to reduce fuel consumption and enhance heat transfer rates to make heating systems more efficient and cheaper. Adding solid nanoparticles to conventional liquids may greatly improve their thermal conductivity, according to the available evidence. This study aims to examine the influence of external magnetic flux on the flow of a mixed convective Maxwell hybrid non-Newtonian nanofluid over a linearly extending porous flat plate. The investigation considers the effects of thermal radiation, Dufour and Soret.

The mathematical model is formulated based on the fundamental assumptions of mass, energy and momentum conservation. The implicit models are epitomized by a set of interconnected nonlinear partial differential equations, which include a suitable and comparable adjustment. The numerical solution to these equations is assessed for approximate convergence by the Runge−Kutta−Fehlberg method based on the shooting technique embedded with the MATLAB software.

The findings are presented through graphical representations, offering a visual exploration of the effects of various dynamic parameters on the flow field. These parameters encompass a wide range of factors, including radiation, thermal and Brownian diffusion parameters, Eckert, Lewis and Soret numbers, magnetic parameters, Maxwell fluid parameters, Darcy numbers, thermal and solutal buoyancy factors, Dufour and Prandtl numbers. Notably, the authors observed that nanoparticles with a spherical shape exerted a significant influence on the stream function, highlighting the importance of nanoparticle geometry in fluid dynamics. Furthermore, the analysis revealed that temperature profiles of nanomaterials were notably affected by their shape factor, while concentration profiles exhibited an opposite trend, providing valuable insights into the behavior of nanofluids in porous media.

A distinctive aspect of the research lies in its novel exploration of the impact of external magnetic flux on the flow of a mixed convective Maxwell hybrid non-Newtonian nanofluid over a linearly extending porous flat plate. By considering variables such as solar radiation, external magnetic flux, thermal and Brownian diffusion parameters and nanoparticle shape factor, the authors ventured into uncharted territory within the realm of fluid dynamics. These variables, despite their significant relevance, have not been extensively studied in previous research, thus underscoring the originality and value of the authors’ contribution to the field.

The hybrid nanofluid flow due to a rotating disk has numerous applications, including centrifugal pumps, paper production, polymers dying, air filtration systems, automobile cooling and solar collectors. This study aims to investigate the convective heat transport and magnetohydrodynamics (MHD) hybrid nanofluid flow past a stretchable rotating surface using the Yamada-Ota and Xue models with the impacts of heat generation and thermal radiation.

The carbon nanotubes such as single-wall carbon nanotubes and multi-wall carbon nanotubes are suspended in a base fluid like water to make the hybrid nanofluid. The problem’s governing partial differential equations are transformed into a system of ordinary differential equations using similarity transformations. Then, the numerical solutions are found with a bvp4c function in MATLAB software. The impacts of pertinent parameters on the flow and temperature fields are depicted in tables and graphs.

Two solution branches are discovered in a certain range of unsteadiness parameters. The fluid temperature and the rate of heat transport are enhanced when the thermal radiation and heat generation effects are increased. The Yamada-Ota model has a higher temperature than the Xue model. Furthermore, it is observed that only the first solution remains stable when the stability analysis is implemented.

To the best of the authors’ knowledge, the results stated are original and new with the investigation of MHD hybrid nanofluid flow with convective heat transfer using the extended version of Yamada-Ota and Xue models. Moreover, the novelty of the present study is improved by taking the impacts of heat generation and thermal radiation.

The performance of solid fins inside a differentially heated cubical cavity is numerically studied in this paper. The main purpose of the study is to make an optimization to reach the maximum heat transfer in the enclosure having the solid fins with studied parameters.

The considered domain of interest is a differentially heated cube having heat-conducting solid fins placed on the heated wall while an opposite wall is a cooled one. Other walls are adiabatic. Governing equations describing natural convection in the fluid filled cube and heat conduction in solid fins have been written using non-dimensional variables such velocity and vorticity taking into account the Boussinesq approximation for the buoyancy force and ideal solid/fluid interfaces between solid fins and fluid. The formulated equations with appropriate initial and boundary conditions have been solved by the finite difference method of the second of accuracy. The developed in-house computational code has been validated using the mesh sensitivity analysis and numerical data of other authors. Analysis has been performed in a wide range of key parameters such as Rayleigh number (Ra = 10⁴–10⁶), non-dimensional fins length (l = 0.2–0.8), non-dimensional location of fins (d = 0.2–0.6) and number of fins (n = 1–3).

From numerical methods point of view the used non-primitive variables allows to perform numerical simulation of convective heat transfer in three-dimensional (3D) regions with two advantages, namely, excluding difficulties that can be found using vector potential functions and reducing the computational time compared to primitive variables and SIMPLE-like algorithms. From a physical point of view, it has been shown that using solid fins can intensify the heat transfer performance compared to cavities without any fins. Fins located close to the bottom wall of the cavity have a better heat transfer rate than those placed close to the upper cavity surface. At high Rayleigh numbers, increasing the fins length beyond 0.6 leads to a reduction of the average Nusselt number, and one solid fin can be used to intensify the heat transfer.

The present numerical study is based on hybrid approach for numerical analysis of convective heat transfer using velocity and vorticity that has some mentioned advantages. Obtained results allow intensifying the heat transfer using solid fins in 3D chambers with appropriate location and length.

Bangladesh is one of the leading countries that has been facing serious air pollution issues, with an exponentially higher death rate attributed to it than other environmental pollution. This study aims to identify the sources and dynamics of particulate matter (PM) pollution across different micro-environments in Rajshahi City.

PMs’ concentration data were collected from 60 sampling stations, located across the six micro-environments of the study area, throughout the year using “HT 9600 Particle Counter.” To assess the level of pollution, the air quality index (AQI) was calculated, and different methods, including observation, group discussion, interview and questionnaire survey, were used to identify the pollution sources.

Both PM_2.5 and PM₁₀ exhibit varied concentrations in different micro-environments, and the area covered by different AQI classes differs considerably throughout the year. The monthly average concentration of PM_2.5 and PM₁₀ was highest in January, 200 and 400 µg/m³ and was lowest in September, 46 and 99 µg/m³, respectively. Among the total 1,440 observations, 853 observations (59.24%) exceeded the national standard. Based on the pollution level, different months and micro-environments in the city have been ranked in descending order as January > December > February > March > April > November > October > May > June > July > August > September and traffic > commercial > industrial > residential > green cover > riverine environment.

Although numerous research has been conducted on air pollution in Bangladesh, the authors are certain that no attempt has been made to address the issue from a multi- micro-environmental perspective. This makes the methodology and findings truly unique and significant in the context of air pollution research in Bangladesh.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

Brewer's spent grain (BSG) is the main by-product of the brewing industry, holding significant potential for biomass applications. The purpose of this paper was to determine the effective thermal conductivity (k_eff) of BSG and to develop an Artificial Neural Network (ANN) to predict k_eff, since this property is fundamental in the design and optimization of the thermochemical conversion processes toward the feasibility of bioenergy production.

The experimental determination of k_eff as a function of BSG particle diameter and heating rate was performed using the line heat source method. The resulting values were used as a database for training the ANN and testing five multiple linear regression models to predict k_eff under different conditions.

Experimental values of k_eff were in the range of 0.090–0.127 W m⁻¹ K⁻¹, typical for biomasses. The results showed that the reduction of the BSG particle diameter increases k_eff, and that the increase in the heating rate does not statistically affect this property. The developed neural model presented superior performance to the multiple linear regression models, accurately predicting the experimental values and new patterns not addressed in the training procedure.

The empirical correlations and the developed ANN can be utilized in future work. This research conducted a discussion on the practical implications of the results for biomass valorization. This subject is very scarce in the literature, and no studies related to k_eff of BSG were found.

The purpose of this study is to present a methodical procedure on how to prepare event logs and analyse them through process mining, statistics and visualisations. The aim is to derive roots and patterns of quality deviations and non-conforming finished products as well as best practice facilitating employee training in the food processing industry. Thereby, a key focus is on recognising tacit knowledge hidden in event logs to improve quality processes.

This study applied process mining to detect root causes of quality deviations in operational process of food production. In addition, a data-ecosystem was developed which illustrates a continuous improvement feedback loop and serves as a role model for other applications in the food processing industry. The approach was applied to a real-case study in the processed cheese industry.

The findings revealed practical and conceptional contributions which can be used to continuously improve quality management (QM) in food processing. Thereby, the developed data-ecosystem supports production and QM in the decision-making processes. The findings of the analysis are a valuable basis to enhance operational processes, aiming to prevent quality deviations and non-conforming finished products.

Process mining is still rarely used in the food industry. Thereby, the proposed method helps to identify tacit knowledge in the food processing industry, which was shown by the framework for the preparation of event logs and the data ecosystem.