Artificial intelligence-based forecasting model for incinerator in sulfur recovery units to predict SO2 emissions.

Pollutant emissions from chemical plants are a major concern in the context of environmental safety. A reliable emission forecasting model can provide important information for optimizing the process and improving the environmental performance. In this work, forecasting models are developed for the prediction of SO2 emission from a Sulfur Recovery Unit (SRU). Since SRUs incorporate complex chemical reactions, first-principle models are not suitable to predict emission levels based on a given feed condition. Accordingly, artificial intelligence-based models such as standard machine learning (ML) algorithms, multi-layer perceptron (MLP), long short-term memory (LSTM), one-dimensional convolution (1D-CNN), and CNN-LSTM models were tested, and their performance was evaluated. The input features and hyperparameters of the models were optimized to achieve maximum performance. The performance was evaluated in terms of mean squared error (MSE) and mean absolute percentage Error (MAPE) for 1 h, 3 h and 5 h ahead of forecasting. The reported results show that the CNN-LSTM encoder-decoder model outperforms other tested models, with its superiority becoming more pronounced as the forecasting horizon increased from 1 h to 5 h. For the 5-h ahead forecasting, the proposed model showed a MAPE advantage of 17.23%, 4.41%, and 2.83%, respectively over the 1D-CNN, Deep LSTM, and single-layer LSTM models in the larger dataset.

Exploring nanoparticle dynamics in binary chemical reactions within magnetized porous media: a computational analysis.

Artificial Neural Networks are incredibly efficient at handling complicated and nonlinear mathematical problems, making them very useful for tackling these challenges. Artificial neural networks offer a special computational architecture that is extremely valuable in disciplines like biotechnology, biological computing, and computational fluid dynamics. The present work investigates the applicability of back-propagation artificial neural networks in conjunction with the Levenberg-Marquardt algorithm for evaluating heat transmission in hybrid nanofluids. This work focuses on the computational analysis of a MgO + GO/EG hybrid nanofluid's steady mixed convection flow over an exponentially stretched sheet, considering multiple slip boundary conditions, thermal conductivity, heat generation, and thermal radiation. A nonlinear system of ordinary differential equations is produced from the basic associated partial differential system by performing the proper exponential similarities modifications. For generating benchmark datasets, the resulting ordinary differential equations are processed employing the bvp4c method. Considering benchmark datasets set aside for training (70%), testing (15%), and validation (15%), the Levenberg-Marquardt algorithm, which employs back-propagation in artificial neural networks, is implemented. The accuracy of the suggested strategy for handling nonlinear problems is verified utilizing mean squared error, error histograms, and regression analysis, which are all used to evaluate the methodology. Outstanding agreement is seen when ANN outputs are compared to numerical results. The flow properties, including temperature, velocity, and concentration profiles, are shown graphically and numerically. For practical purposes, it is therefore essential to analyze the flow and heat transfer in hybrid nanofluids over exponentially extending and shrinking surfaces under mixed convection and heat source scenarios. Hybrid nanofluid problems have a wide range of practical and industrial applications, such as medication delivery, manufacturing, microelectronics, nuclear plant cooling, and marine engineering.

Entropy optimization and heat flux analysis of Maxwell nanofluid configurated by an exponentially stretching surface with velocity slip.

Hybrid nanofluids are extremely important in field of engineering and technology due to their higher heat transportation performance resulting in increased heat transfer rates. In the presence of thermal heat flux, the effect of a slanted MHD with velocity slip condition on a CNTs hybrid nanocomposite across a gradually extending surface is investigated. In present analysis, Maxwell nanofluid is embedded with SWCNT and MWCNT (single and multiple wall carbon nanotubes) nanoparticles. The nanomaterials transformation framework is obtained by employing Xue modified theoretical model. Various factors like dissipation, thermal radiations and Ohmic heat influences are adequately implemented in heat formulation. The physical features of thermodynamical mechanism of irreversibility are explored. The thermodynamics second law is used to produce the entropy optimization formulation. In addition, entropy is utilized to assess the energy aspects of a heat exchanger. Utilizing appropriate parameters, the model nonlinear PDEs are transformed to ODEs. The HAM technique is used to compute the solution of nonlinear ODEs. For both types of CNTs, the variations of entropy rate, Bejan number, velocity and temperature field versus key technical parameters is analyzed. The Nu and Cf computational result for both CNTs are examined in tabulated and chart form. Velocity is inversely proportional to magnetic and solid volume nanoparticle parameters. The Br and Rd accelerates NG and Be for both nanocomposites. Additionally, a comparison of the HAM result and the numerical result is validated.

Develop the artificial neural network approach to predict thermal transport analysis of nanofluid inside a porous enclosure.

This study explores the impacts of heat transportation on hybrid (Ag + MgO) nanofluid flow in a porous cavity using artificial neural networks (Bayesian regularization approach (BRT-ANN) neural networks technique). The cavity considered in this analysis is a semicircular shape with a heated and a cooled wall. The dynamics of flow and energy transmission in the cavity are influenced by various features such as the effect of magnetize field, porosity and volume fraction of nanoparticles. To explore the outcomes of these features on hybrid nanofluid thermal and flow transport, a BRT-ANN model is developed. The ANN model is trained using a dataset generated through numerical scheme. The trained ANN model is then used to predict the heat and flow transport characteristics for various input parameters. The accuracy of the ANN simulation is confirmed through comparison of the predicted results with the results obtained through numerical simulations. By maintaining the corrugated wall uniformly heated, we inspected the levels of isotherms, streamlines and heat transfer distribution. A graphical illustration highlights the characteristics of the Hartmann and Rayleigh numbers, permeability component in porous material, drag force and rate of energy transport. According to the percentage analysis, nanofluids (Ag + MgO/H2O) are prominent to enhance the thermal distribution of traditional fluids. The study demonstrates the potential of ANNs in predicting the impacts of various factors on hybrid nanofluid flow and heat transport, which can be useful in designing and optimizing heat transfer systems.

CFD microscale modelling of flow behavior in different parts of a rotating packed bed.

Process intensification (PI) is playing a key role in alleviating the challenge of reducing carbon footprint of many chemical processes and bringing down their development costs. Over the years, many PI technologies have been investigated with rotating packed bed (RPB) technology receiving much of the attention for its potential of significant intensification in terms of capital expenditure, operating costs, and hardware size. In this study, microscale CFD simulations of a rotating packed bed were conducted, and the results were validated with experimental data. The results show the strong relation between the reverse flow at the packing outer periphery and the gas maldistribution factor. The latter is mainly caused by the accelerating flow in the outer cavity. Inside the wire mesh packing, the gas flow is found to be almost fully uniform for nearly half of the total packing depth. Also, turbulent kinetic energy (TKE) levels at the packing outer edge are strongly linked to the slip tangential velocity component, while at its inner edge, they depend mainly on the radial packing velocity. The so-called gas end effect zone is detected by observing the TKE profiles near the packing outer edge. The latter accounts for less than 10% of the total packing depth. The validity of the widely used porous media model in RPBs' packing for both radial and tangential directions is confirmed by the obtained results, but this excludes the packing inner and outer edges. In the inner cavity region, gas exhibits two distinctive behaviors and transits from free vortex flow to swirling flow as the flow becomes close to the vortex core. As a result of this transition, the increase in shear stress accelerates the decrease in the gas tangential velocity in the vortex core and help speed up the favorable pressure gradient and flow establishment beyond the vortex core.

Features of flow and heat transport of MoS2+GO hybrid nanofluid with nonlinear chemical reaction, radiation and energy source around a whirling sphere.

The current investigation employs a numerical simulation to demonstrate the impact of hall current on unsteady free convective flow caused by hybrid-nanofluid over a revolving sphere approaching the stagnation point. The prominent characteristics of Lorentz force as a result of magnetic field coupling with hybrid nanofluid is also explored. The process of energy and mass transmission is inspected with nonlinear thermal radiations, non-uniform energy supply, dissipation and nonlinear chemical reaction. In current flow model, a unique class of nanofluid known as the hybrid nanofluid is being used, which contain GO (graphene oxide) and MoS 2 (molybdenum disulfide) with water. The angular speed of both the sphere and free stream changes frequently with time. Employing adequate dimensionless variables, the partial-differential patterns strongly non-linear that represent the situational analysis are morphed into non-linear ordinary differential patterns. The analytical outcomes of ordinary differential pattern have been developed via OHAM technique. Utilizing tables and graphs, various aspects of such controllable physical characteristics have been highlighted and explored in depth. For varying values of M , φ , δ , S c and K n ,the variations in C f x , C f z , N u and S h in MoS 2 -GO/H 2 O are the greatest as contrasted to MoS 2 /H 2 O. The results are also compared to those reported existing literature and they are noticed to be in very close agreement.

Unsteady mix convectional stagnation point flow of nanofluid over a movable electro-magnetohydrodynamics Riga plate numerical approach.

The flow at a time-independent separable stagnation point on a Riga plate under thermal radiation and electro-magnetohydrodynamic settings is examined in this research. Two distinct base fluids-H2O and C2H6O2 and TiO2 nanostructures develop the nanocomposites. The flow problem incorporates the equations of motion and energy along with a unique model for viscosity and thermal conductivity. Similarity components are then used to reduce these model problem calculations. The Runge Kutta (RK-4) function yields the simulation result, which is displayed in graphical and tabular form. For both involved base fluid theories, the nanofluids flow and thermal profiles relating to the relevant aspects are computed and analyzed. According to the findings of this research, the C2H6O2 model heat exchange rate is significantly higher than the H2O model. As the volume percentage of nanoparticles rises, the velocity field degrades while the temperature distribution improves. Moreover, for greater acceleration parameters, TiO2/ C2H6O2has the highest thermal coefficient whereas TiO2/ H2O has the highest skin friction coefficient. The key observation is that C2H6O2 base nanofluid has a little higher performance than H2O nanofluid.

Simulation of the water-based hybrid nanofluids flow through a porous cavity for the applications of the heat transfer.

This study looks at the natural convections of Cu + Al2O3/H2O nanofluid into a permeable chamber. The magnetic field is also executed on the flow field and the analysis has been approached numerically by the control volume method. The study of hybrid nanofluid heat in terms of the transfer flux was supplemented with a wide range of parameters of hybrid nanofluid fractions, Rayleigh numbers Hartmann numbers and porosity factor. It's also determined that the flow and thermal distribution are heavily affected by the concentration of the nanoparticles. The concentration of nanoparticles increases the transport of convective energy inside the enclosure. The primary findings demonstrate that a rise in both the Rayleigh number and Darcy number leads to an improvement in convective heat transfer within the enclosure. However, the porosity has a negligible effect. Additionally, the rotation in a clockwise direction has a beneficial impact on the dispersion of heat transfer throughout the cavity. Furthermore, it is concluded that hybrid nanofluids are more reliable than conventional fluids in improving thermal properties.

Lattice Boltzmann-based numerical analysis of nanofluid natural convection in an inclined cavity subject to multiphysics fields.

This research conducts a study of natural convection heat transfer (NCHT) in a nanofluid under a magnetic field (MF). The nanofluid is in a cavity inclined at an angle of 45°. The MF can take different angles between 0° and 90°. Radiative heat transfer is present in the cavity in volumetric form. There are two hot semicircles, similar to two half-pipes, on the bottom wall. The top wall is kept cold. The side walls and parts of the bottom wall, except the pipes, have been insulated. The lattice Boltzmann method has been used for the simulation. The studied parameters are the Rayleigh number (in the range 103-106), magnetic field angle, radiation parameter (in the range 0-2), and nanoparticle volume fraction (in the range 0-5%). The generated entropy has been studied as the NCHT. The results indicate that adding nanoparticles improves heat transfer rate (HTR). Moreover, the addition of volumetric radiation to the cavity enhances the Nusselt number by 54% and the generated entropy by 12.5%. With an augmentation in the MF angle from 0° to 90°, HTR decreases and this decrease is observed mostly at higher Rayleigh numbers. An augmentation in the Ra increases NCHT and entropy generation. Indeed, a rise in the Ra from 103 to 106 increases HTR by almost sixfold.

Assessment of economic, thermal and hydraulic performances a corrugated helical heat exchanger filled with non-Newtonian nanofluid.

Improved heat transfer efficiency with considering economic analysis in heating systems is an interesting topic for researchers and scientists in recent years. This research investigates the heat transfer rate (HTR) and flow of non-Newtonian water-Carboxyl methyl cellulose (CMC) based Al2O3 nanofluid in a helical heat exchanger equipped with common and novel turbulators using two-phase model. The requirements for dimensions and cost reduction and also energy saving in thermal systems are the main goal of this study. According to gained results usage of corrugated channel in helical heat exchanger has a considerable influence on thermal and hydraulic performance evaluation criteria (THPEC) index of helical heat exchanger and can improve the THPEC index. Thus, Re = 5000 is obtained as an optimum value, in which the maximum THPEC value is achieved. As it is found in this paper, in case of using novel heat exchanger instead of the basic smooth system, the thermal properties (by considering Nusselt number) increases about 210%, the hydraulic performance (friction factor) reduces about 28%, performance evaluation criteria index increases about 57% and the material consumption (in case of similar THPEC) decreases about 31%. In another word, with considering economic analysis for the basic and novel system which has same efficiencies, the novel one has lower length and consequently 31% lower material.

Experimental measurements and large eddy simulation of expiratory droplet dispersion in a mechanically ventilated enclosure with thermal effects.

Understanding of droplet transport in indoor environments with thermal effects is very important to comprehend the airborne pathogen infection through expiratory droplets. In this work, a well-resolved Large Eddy Simulation (LES) was performed to compute the concentration profiles of monodisperse aerosols in non-isothermal low-Reynolds turbulent flow taking place in an enclosed environment. Good care was taken to ensure that the main dynamical features of the continuous phase were captured by the present LES. The particle phase was studied in both Lagrangian and Eulerian frameworks. Steady temperature and velocity were measured prior to droplet emission. Evolution of aerosol concentration was measured by a particle counter. Results of the present LES were to compare reasonably well with the experimental findings for both phases.