Chemical Engineering Applications Research Articles

Membrane flux response technology for early warning of initial surface scaling in membrane distillation

Membrane distillation (MD) is a promising green separation technology for the treatment of high salinity wastewater attributed to excellent salt rejection, which is significant in relevant environmental and chemical engineering application. However, membrane scaling is an inevitable issue during long-term membrane distillation process. Thus, we developed a membrane flux response technology with data processing method to in-situ monitor the membrane scaling according to the reduction of permeate flux. By real-time analyzing the derivative of deviation between theoretical and experimental fluxes, early warning of initial membrane surface scaling was observed at initial nucleation stage. Besides, such timely detection was also achieved under different temperatures and flow velocities of feed solution, even for the poor light transmittance aqueous solution (such as NiSO4 and CoSO4 solution), which exhibited wide application potential on diverse high salinity wastewater systems. In addition, the metastable zone width of salt solution within MD system was obtained for the efficient operation design. To sum up, membrane flux response technology provides an early warning at imminent membrane scaling, which will maintain the great separation effect for a fresh feed solution after a facile cleaning by pure water, indicating the extension of device lifetime.

Dynamics of bubbles detached from non-circular orifices: Confinement effect of orifice boundary

In practice, gas usually escapes from non-circular orifices due to machining accuracy, plugging, and other reasons, especially gas leakage in the natural environment. Compared to circular orifices, the bubble behavior and dynamics of non-circular orifices have rarely been investigated. This paper presents three-dimensional imaging system results to obtain comprehensive information on bubble dynamics generated from five elliptical orifices with equal cross-sectional areas and different aspect ratios. Computational fluid dynamics studies using the coupled level set and volume of fluid methods were adopted to assist in the flow field analysis. Experimental data were analyzed statistically to study the effects of orifice wall structure on bubble flow pattern, size, trajectory, and other dynamic parameters. The results show that the equivalent bubble diameter increases with increasing orifice aspect ratio, as does the randomness of the bubble flow field, while the bubble flow pattern complexity increases. A force analysis as the bubble is generated is also conducted. The wall structure of the orifice affects the bubble migration effect to some extent. The work presented in this article provides valuable fundamental information for a range of heater and desalination applications in chemical engineering, wastewater treatment, and immersion leakage identification.

Accurate solutions of a thin rectangular plate deflection under large uniform loading

The large deflection of a thin rectangular plate under uniform loading is a classic problem in solid mechanics. However, the equations are challenging to solve due to their strong nonlinearity. To the author’s best knowledge, existing studies have only reported solutions for rectangular plates with immovable clamped edges within the range of loadings a4q/(Dh)<500. In this study, we employ the homotopy analysis method (HAM) to address this problem and obtain accurate solutions even when the loading a4q/(Dh) reaches as high as 105. Additionally, we discover that the central deflection w(0,0)/h follows a scaling law of [a4q/(Dh)]1/3 for loadings a4q/(Dh)>104, which agrees with the scaling law for extremely large deflections. Consequently, we can utilize this scaling law to extrapolate our homotopy series solution from a4q/(Dh)=104 to even larger loadings. Our investigation also reveals that the bending and membrane stresses increase with the loading. The maximum bending stress is located at the edge, while the location of the maximum membrane stress shifts from the plate center towards the edge as the loading increases. Moreover, we find that even for large deflections, the bending stress remains significant at the edge, challenging the assumption of negligible bending stress adopted by the membrane equations. Thus, the membrane equation may over-predict the plate deflection even under large loadings. These findings advance our understanding of the large deflection of a thin rectangular plate with clamped edges, which has important applications in aerospace, ocean, chemical, and microelectronic engineering. The results presented in this paper can also be used to verify other algorithms designed for highly nonlinear problems.

Assessment of Numerical Accuracy and Parallel Performance of OpenFOAM and its Reacting Flow Extension EBIdnsFoam

OpenFOAM is one of the most widely used open-source computational fluid dynamics tools and often employed for chemical engineering applications. However, there is no systematic assessment of OpenFOAM’s numerical accuracy and parallel performance for chemically reacting flows. For the first time, this work provides a direct comparison between OpenFOAM’s built-in flow solvers as well as its reacting flow extension EBIdnsFoam with four other, well established high-fidelity combustion codes. Quantification of OpenFOAM’s numerical accuracy is achieved with a benchmark suite that has recently been established by Abdelsamie et al. (Comput Fluids 223:104935, 2021. https://doi.org/10.1016/j.compfluid.2021.104935) for combustion codes. Fourth-order convergence can be achieved with OpenFOAM’s own cubic interpolation scheme and excellent agreement with other high-fidelity codes is presented for incompressible flows as well as more complex cases including heat conduction and molecular diffusion in multi-component mixtures. In terms of computational performance, the simulation of incompressible non-reacting flows with OpenFOAM is slower than the other codes, but similar performance is achieved for reacting flows with excellent parallel scalability. For the benchmark case of hydrogen flames interacting with a Taylor–Green vortex, differences between low-Mach and compressible solvers are identified which highlight the need for more investigations into reliable benchmarks for reacting flow solvers. The results from this work provide the first contribution of a fully implicit compressible combustion solver to the benchmark suite and are thus valuable to the combustion community. The OpenFOAM cases are publicly available and serve as guide for achieving the highest numerical accuracy as well as a basis for future developments.

A trust-region-based phase envelope construction algorithm

Pressure-temperature (PT) and pressure-composition (Px) phase diagrams play an important role in various chemical and petroleum engineering applications. Lots of algorithms based on the Newton-Raphson method have been proposed to numerically construct PT/Px phase envelopes. No attempt is made to improve the computational efficiency of such Newton-Raphson solution method. This work develops a new trust-region-based algorithm to replace the Newton-Raphson method to provide more robust and efficient phase envelope construction calculations. We first convert the phase envelope construction problem into a minimization problem by defining a least-squares objective function. We then apply the trust-region optimization method with an exact subproblem solver to minimize the objective function. We follow the general strategy proposed by Michelsen (1980) to develop the revised phase envelope construction algorithm. We demonstrate the good performance of the trust-region-based algorithm by comparing it with the conventional Newton's method. The computational results indicate that the new trust-region-based algorithm is more robust and cost-effective than the Newton-based algorithm.

Characterization of pressure drop through Schwarz-Diamond triply periodic minimal surface porous media

Additive manufacturing (3D printing) is enabling novel porous structures such as triply periodic minimal surfaces (TPMS) that may help improve the efficiency of various applications in chemical engineering. In this work, a range of Schwarz-Diamond TPMS structures were examined experimentally in terms of printability and pressure drop. X-ray computed tomography scans were used to show that the shape of the structures was accurately printed, however a systematic reduction in porosity was observed compared with each designed structure. The pressure drop through the printed structures was measured for Reynolds numbers between 1 and 1000. Pressure drop reduced with increasing porosity and hydraulic diameter but there was no effect of printer type or column diameter on the measured pressure drop. These measurements were used to propose a new pressure drop correlation that can be used to predict the pressure drop for Schwarz-Diamond TPMS structures over a range of porosities and hydraulic diameters.

Pore-morphology-based pore structure characterization for various porous media

Quantitative characterization of pore structure plays an important role in predicting properties of porous media and it has significant applications in energy and chemical engineering. Mercury injection capillary pressure experiments and gas adsorption experiments are the major pore structure characterization techniques, but they are time-consuming, costly, and destructive. In this paper, a simple pore-morphology-based method is developed to simulate the mercury injection and gas pore condensation in CT image for obtaining the pore structure characteristics. The main idea of the developed algorithm is to utilize some of the morphological operations to capture the pore geometry and topology, and then combine with the invasion phase connectivity analysis to implement the mercury injection and gas pore condensation. The algorithm is designed in several ways with an improved computational speed and characterization accuracy. The algorithm is user-friendly and can be applied on 2D and 3D images. Various porous media are used to assess the performance of the algorithm and the characteristics of pore structures. The results are compared to the available results in the literature. Findings show that the algorithm can reliably quantitatively characterize the pore structure for a wide variety of porous media. The general pore radius distribution, pore throat radius distribution, and strict throat radius distribution involved in pore structure characterization are clarified. The characterization of heterogeneous pore structures and the effects of pore roughness on capillary pressure are discussed.

Electro-Responsive Aggregation and Dissolution of Cationic Polymer Using Reversible Redox Reaction of Electron Mediator.

Stimuli-responsive aggregation of polymer chains in water has found a variety of applications in polymer science, biology, and chemical engineering. To date, the majority of the phase transitions between the aggregated and dissolved forms has been observed by changing the solution temperature, and an active and precise control on the phase transition with a high time resolution has been challenging. Herein, a reversible phase transition of poly(allylamine-co-allylurea) (PAU) in an aqueous electrolyte is achieved by electrochemical redox cycling of hexacyanoferrate(II/III) ([Fe(CN)6 ]4-/3- ) ion pair. The aggregation and dissolution cycle can be completed in a high-resolution time frame of as short as 5s. The strong electrostatic interaction between the protonated primary amino group of PAU and the tetravalent [Fe(CN)6 ]4- anion induces the aggregation, while the oxidation to the trivalent [Fe(CN)6 ]3- anion reduces the attractive force, and the polymer chain redissolves in solution. The ureido group of PAU helps the chain-folding process through the formation of inter/intrachain hydrogen-bonding networks, resulting in the sharp phase transition. By using [Fe(CN)6 ]4-/3- as the electron mediator, the electrochemical control on the large transparency change of polymer aqueous solution is realized for the first time.

Significance of irregular heat source and Arrhenius energy on electro-magnetohydrodynamic hybrid nanofluid flow over a rotating stretchable disk with nonlinear radiation

For its applications in nuclear reactors, food processing, chemical engineering, water emulsions and thermal power generating systems, the significance of irregular heat source and Arrhenius energy on electro-magnetohydrodynamic hybrid nanofluid flow over a rotating stretchable disk with nonlinear radiation have been investigated. The flow problem has been modeled utilizing the modified Buongiorno model and the thermophysical characteristics of water-based Cu − F e 3 O 4 hybrid nanoliquid. Effects like passive control of nanoparticles, hydrodynamic slip and convective boundary conditions are also heeded to boost the realistic nature of this work. Further, engineering quantities like moment coefficient and pumping efficiency of the disk are also elucidated which boosts the novelty of this research work. The modeled governing equations are transmuted into a system of first-order ODEs, with the help of apposite similarity transformations, which are then numerically resolved using the finite-difference based bvp5c algorithm. It is noticed that per unit increase in the electric parameter decreases the skin friction coefficient by 41.75% and increases the heat transfer rate by 15.31%. It is also observed that the entrainment velocity is directly proportional to the changes in electric field parameter and is inversely proportional to the changes in volume fraction of copper and magnetite nanoparticles.

Mesoscale simulation investigation of droplet impacting behaviors on cylindrical surfaces

Understanding and controlling the impacting behaviors of droplets on cylindrical surfaces is a key issue for the design of the process intensification in chemical reaction engineering. In the work, the mesoscale dynamics simulation is performed to systematically investigate the effect of impact conditions on the droplet impacting behaviors. As a result, five impacting modes have been distinguished and the competition mechanisms between the impact parameters are revealed. High impact velocity and low liquid viscosity benefit to the spreading and dispersion of impacting droplets, while the increase of surface wettability can promote and inhibit droplet spreading at low and high velocities, respectively. Besides, the machine-learning models are developed to build the quantitative relation between the impacting behaviors and the impact conditions. The machine-learning models can predict droplets impacting behaviors in experiment and simulation, which is of great importance in relevant chemical engineering applications.

Hydrodynamic and mass transfer correlations for SiC ceramic foam monolithic trays used in distillation column

AbstractBACKGROUNDCellular foam materials are excellent engineering materials for many applications in chemical engineering, especially process intensification of chemical conversions and separation. Recently, a silicon carbide (SiC) ceramic foam monolithic tray was developed as an excellent mass transfer tool to improve distillation and it showed an improved operational flexibility. Previous studies have revealed the effects of open cell diameter and tray thickness on the hydraulic performance and mass transfer efficiency of SiC foam monolithic trays, but relevant correlations are still missing, limiting their practical uses.RESULTSIn this study, based on the published experimental data, empirical correlations were developed to predict the hydrodynamic performance and mass transfer efficiency of SiC ceramic foam monolithic trays. The new correlations include the influence of the tray structure parameters, such as open cell (or pore) diameter and tray thickness, and of the operation conditions (such as the liquid and vapor volumetric flow rates), and the data obtained agree well with the experimental results.CONCLUSIONThe availability of such empirical correlations can be beneficial for the chemical engineering design of practical distillation processes based on SiC foam monolithic trays. © 2023 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley &amp; Sons Ltd on behalf of Society of Chemical Industry (SCI).

How random fluctuations can generate and suppress complex oscillatory regimes in continuous stirred tank reactors

Motivated by important chemical engineering applications, we study probabilistic mechanisms of stochastic effects in a randomly forced model of the continuous stirred tank reactor. The bifurcation analysis of the deterministic model reveals the parameter zone of bistability with coexistence of the equilibrium and oscillatory regimes. It is shown that the boundary between basins of these regimes is moving as the bifurcation parameter changes. We study noise-induced transitions across this boundary leading to the stochastic generation of spiking oscillations and backward effect of the noise-induced suppression of large-amplitude spiking. In the study of these stochastic effects, we use statistics extracted from the direct numerical simulation, and an analytical approach using confidence ellipses method. An important probabilistic phenomenon of coherence resonance is revealed and discussed.

Optimization of the Geraniol Transformation Process in the Presence of Natural Mineral Diatomite as a Catalyst

Process optimization is increasingly finding applications in chemical engineering. The reason for this increase in applications is to create more efficient and sustainable technological processes. Thanks to innovative models, it is possible to plan an experiment in a given field of study without much complication and carry out the optimization of such a process, achieving goals in a much shorter time period. This paper describes the performance of optimization of the geraniol transformation process in the presence of a catalyst of natural origin—diatomite. Response surface methodology (RSM) was chosen as the method. For this purpose, the following parameters were used as variables: temperature (80, 110, and 150 °C), catalyst concentration (1 wt%, 5 wt%, and 10 wt%), and reaction time (0.25 h, 12 h, and 24 h). At the same time, the functions describing the process and response functions were the conversion of geraniol (GA) as well as the selectivity of conversion to beta-pinene (BP), respectively. The obtained results made it possible to identify the optimal set of parameters at which the highest values of GA conversion and the selectivity of conversion to BP are obtained. It turned out that the GA transformation process is best carried out at 80 °C at a diatomite concentration of 1.0 wt% and a reaction time of 0.25 h.

Structure optimization and flow field characteristics of baffle-type impinging stream reactor

The impinging stream reactor is an efficient equipment for fluid mixing that strengthens the mass and heat transfer in two fluids mixing processes, with potential for many chemical engineering and industrial applications. In this paper, the flow characteristics and concentration distribution of baffle-type impinging stream reactor are analyzed by numerical simulation to optimize the reactor. The influence of different baffle spacing W and baffle aspect ratio λ on the velocity, turbulent kinetic energy and mixing effect of the reactor are explored. It is indicated that the flow regime changed with the inserted baffle in reactor. With the decrease of the baffle spacing and the baffle aspect ratio, the velocity, turbulent kinetic energy and the fluid mixing intensity firstly increase and then decrease. The mixing intensity of the flow field with different baffle spacing is compared, it is found that W = 40mm>W = 60mm>W = 30mm>W = 80 mm. The mixing intensity with different baffle aspect ratios are found that λ=1>λ=1.5>λ=0.75. The baffle-type impinging stream reactors with W = 40 mm and λ=1 have the best mixing effect.

Automatic differentiation rules for Tsoukalas–Mitsos convex relaxations in global process optimization

With increasing digitalization and vertical integration of chemical process systems, nonconvex optimization problems often emerge in chemical engineering applications, yet require specialized optimization techniques. Typical global optimization methods proceed by progressively refining bounds on the unknown optimal value, by strategically employing convex relaxations. This article constructs a general closed-form expression for the convex subdifferentials of recent “multivariate McCormick” convex relaxations of nontrivial composite functions, by solving a previous duality formulation in all cases using nonsmooth Karush–Kuhn–Tucker conditions. Based on this subdifferential expression, new automatic differentiation rules are developed to compute gradients and subgradients for multivariate McCormick relaxations, to ultimately generate useful bounds in global optimization. Unlike established differentiation techniques for these relaxations, our new rules are expressed in closed form, do not require solving separate dual optimization problems, are efficiently carried out, and are compatible with the reverse/adjoint mode of algorithmic differentiation. Our formulations become more straightforward when the relevant functions are either smooth or piecewise smooth.

In situ micropillar compression of an anisotropic metal-organic framework single crystal

Understanding of the complex mechanical behavior of metal-organic frameworks (MOF) beyond their elastic limit will allow the design of real-world applications in chemical engineering, optoelectronics, energy conversion apparatus, and sensing devices. Through in situ compression of micropillars, the uniaxial stress-strain curves of a copper paddlewheel MOF (HKUST-1) were determined along two unique crystallographic directions, namely the (100) and (111) facets. We show strongly anisotropic elastic response where the ratio of the Young’s moduli are E(111) ≈ 3.6 × E(100), followed by extensive plastic flows. Likewise, the yield strengths are considerably different, in which Y(111) ≈ 2 × Y(100) because of the underlying framework anisotropy. We measure the fracture toughness using micropillar splitting. While in situ tests revealed differential cracking behavior, the resultant toughness values of the two facets are comparable, yielding Kc ~ 0.5 MPasqrt{{{{{{rm{m}}}}}}}. This work provides insights of porous framework ductility at the micron scale under compression and failure by bonds breakage.

A GPU-based DEM model for the pebble flow study in packed bed: Simulation scheme and validation

This study developed a GPU-based DEM (GPU-DEM) model for the pebble flow in the packed bed. The details of the GPU-DEM scheme have been illustrated. The uniform mesh neighbor-search method, unified memory addressing (UMA), and special boundary conditions were incorporated in the GPU-DEM model. Cases of the direct impact of two identical particles, the angle of repose of particles drained from a lifted hopper, and the circulating hopper to mimic a pebble flow in High-Temperature Gas-cooled Reactor (HTGR) reactors were carried out to systematically verify the applicability, computational efficiency, and accuracy of the GPU-DEM model. Related results, like the unloading speed, porosity, and velocity distribution, obtained by the GPU-DEM model have been validated too. In addition, the effects of single-precision floating-point number computation on GPU were discussed. The results demonstrated that the GPU-DEM can achieve 18– 20 times acceleration while maintaining accuracy, even when single-precision floating-point numbers were used for calculation. Therefore, it is a powerful tool to explore the particle flows in chemical engineering applications.

Magnetic Control of Water Droplet Impact onto Ferrofluid Lubricated Surfaces

Controlling the impact process of a droplet impacting a liquid film has remained a wide-open challenge. The existing passive techniques lack precise on-demand control of the impact dynamics of droplets. The present study introduces a magnet-assisted approach to control water droplets' impact dynamics. We show that by incorporating a thin, magnetically active ferrofluid film, the overall droplet impact phenomena of the water droplets could be controlled. It is found that by modifying the distribution of the magnetic nanoparticles (MNPs) present inside the ferrofluid using a permanent magnet, the spreading and retraction behavior of the droplet could be significantly controlled. In addition to that, we also show that by altering the impact Weber number (Wei), and the magnetic Bond number (Bom), the outcomes of droplet impact could be precisely controlled. We reveal the role of the various forces on the consequential effects of droplet impact with the help of phase maps. Without the magnetic field, we discovered that the droplet impact on ferrofluid film results in no-splitting, jetting, and splashing regimes. On the other hand, the presence of magnetic field results in the no-splitting and jetting regime. However, beyond a critical magnetic field, the ferrofluid film gets transformed into an assembly of spikes. In such scenarios, the droplet impact only results in no-splitting and splashing regimes, while the jetting regime remains absent. The outcome of our study may find potential applications in chemical engineering, material synthesis, and three-dimensional (3D) printing where the control and optimization of the droplet impact process are desirable.

An optimal System of Lie Subalgebras and Group-Invariant Solutions with Conserved Currents of a (3+1)-D Fifth-Order Nonlinear Model I with Applications in Electrical Electronics, Chemical Engineering and Pharmacy

AbstractHigher-dimensional nonlinear integrable partial differential equations are significant as they often describe diverse phenomena in nonlinear systems like laser radiations in a plasma, optical pulses in the glass fibres, fluid mechanics, radio waves in the ion sphere, condensed matter and electromagnetics. This article shows an analytical investigation of a (3+1)-dimensional fifth-order nonlinear model with KdV forming its main part. Lie group analysis of the model is performed through which its infinitesimal generators are obtained. These generators are engaged in the construction of an optimal system of Lie subalgebra in one dimension. Moreover, members of the system secured are utilized in reducing the underlying model to ordinary differential equations (ODEs) for possible exact solutions. In consequence, we achieve various functions, ranging from trigonometric, logarithmic, rational, to hyperbolic. In addition, the results found constitute diverse solitonic solutions such as complex, topological kink and anti-kink, trigonometric and bright. We utilize the power series technique to obtain a series solution of the most complicated ordinary differential equation with forty-four terms. In addition, we reveal the dynamics of these solutions via graphical depictions. In the end, we constructed conserved currents of the underlying equation through the use of the multiplier technique. Further, we utilize the optimal system of the underlying model to derive more conserved vectors using Ibragimov’s theorem for conservation laws.

Physics-informed recurrent neural networks and hyper-parameter optimization for dynamic process systems

Many of the processes in chemical engineering applications are of dynamic nature. Mechanistic modeling of these processes is challenging due to the complexity and uncertainty. On the other hand, recurrent neural networks are useful to be utilized to model dynamic processes by using the available data. Although these networks can capture the complexities, they might contribute to overfitting and require high-quality and adequate data. In this study, two different physics-informed training approaches are investigated. The first approach is using a multi-objective loss function in the training including the discretized form of the differential equation. The second approach is using a hybrid recurrent neural network cell with embedded physics-informed and data-driven nodes performing Euler discretization. Physics-informed neural networks can improve test performance even though decrease in training performance might be observed. Finally, smaller and more robust architecture are obtained using hyper-parameter optimization when physics-informed training is performed.