A thermodynamically consistent model for the carbon dioxide (CO2) absorption in aqueous alkanolamine system is of great importance in the research and development of a CO2 capture process. To facilitate the development of thermodynamic models, linear Gibbs free energy, enthalpy, and heat capacity relationships using well-known amines as reference are used to correlate the standard reference state properties of ionic species with those of molecular species in the electrolyte system, which has been approved to provide a reliable and consistent way to estimate required parameters when there is minimal or no appropriate experimental data available. The proposed relationships have been applied to the development of an electrolyte Non-Random Two Liquid (NRTL) activity coefficient model for CO2 absorption in aqueous 1-amino-2-propanol (A2P) solution, as an example to demonstration the methodology. With limited vapor-liquid equilibrium data and other thermodynamic properties, the parameters in the electrolyte NRTL model are identified with good accuracy.
This work aims to study the gas phase hydrodynamics in a stirred tank with a surface-aerated long-short blades agitator by the Eulerian‒Eulerian approach coupled with population balance model. Predicted local gas holdup and bubble size distribution agree well with those measured by a conductivity probe technique. The predictions demonstrate that the pressure depression in the center is the main driving force for gas suction and the downward flow carries the bubbles down to redistribute in the whole tank. The gas phase has higher gas holdup with large bubble size in the upper part and lower gas holdup but with small bubble size in the lower part of the tank. The predicted gas-liquid mass transfer coefficients agree well with our previous experimental results and just depends on the power consumption per unit volume when the aspect ratio of the liquid height to the tank diameter varies from 1.1 to 2.0.
The performance of advanced controllers depends on the selection of several tuning parameters that can affect the closed-loop control performance and constraint satisfaction in highly nonlinear and nonconvex ways. There has been a significant interest in auto-tuning of complex control structures using Bayesian optimization (BO). However, an open challenge is how to deal with uncertainties in the closed-loop system that cannot be attributed to a lumped, small-scale noise term. This paper develops an adversarially robust BO (ARBO) method that is suited to auto-tuning problems with significant time-invariant uncertainties in a plant simulator. ARBO uses a Gaussian process model that jointly describes the effect of the tuning parameters and uncertainties on the closed-loop performance. ARBO uses an alternating confidence-bound procedure to simultaneously select the next candidate tuning and uncertainty realizations, implying only one expensive closed-loop simulation is needed at each iteration. The advantages of ARBO are demonstrated on two case studies.
The mass transfer between a rising bubble and the surrounding liquid is mainly determined by an extremely thin layer of dissolved gas forming at the liquid side of the gas-liquid interface. Resolving this concentration boundary layer in numerical simulations is computationally expensive. Subgrid-scale models mitigate the resolution requirements enormously and allow approximating the mass transfer in industrially relevant flow conditions with high accuracy. However, the development and validation of such models is difficult as only integral mass transfer data for steady-state conditions are available. Therefore, it is difficult to assess the validity of the sub-grid models in transient conditions. In this contribution, we compare the local and global mass transfer of an improved subgrid-scale model for rising bubbles (Re = 72-569 and Sc = 10^2-10^4) to a single-phase simulation approach, which maps the two-phase flow field to a highly-resolved mesh comprising only the liquid phase.
The work reported in this investigation involves the determination of the hydrodynamic properties of the Trickle Bed Reactor which has been loaded in various ways to mark the effect of the loading methodologies employed to pack the catalyst pellets. The bed structure of a packed three-phase reactor is critical to study as it provides the essential contact between the phases and provides the catalytic sites where the reaction takes place. Depending on the structural properties of the bed such as local void structure, liquid distribution, two-phase pressure drop, and holdup of fluids gets affected. The study aims to envelop the catalyst bed characteristics such as the local void structure, the length of the catalyst bed, flow characteristics such as liquid and gas flow rate, and liquid distributor at the top of the catalyst bed to gauge and quantify their effect on the hydrodynamics of a trickle bed reactor.
Yttrium-doped barium cerate (BaCe0.8Y0.2O3-δ, BCY) is the most widely studied proton conducting material and is frequently fabricated as dense membranes for hydrogen separation. However, the difficulty to prepare dense BCY membranes is the extremely high sintering temperature, normally higher than 1500 oC. Herein, the BCY 7-channel hollow fiber membrane was prepared by one-step thermal processing (OSTP). It proved that adding Co2O3 as sintering aid is beneficial to the densification and 1wt% Co2O3 was the optimum addition to form a homogeneous phase structure. The dense sintering temperature was greatly reduced from over 1500 to 1350 oC. The hydrogen permeation flux of the BCY hollow fiber membrane reached up to 0.34 ml min-1cm-2 at 900 oC. The long-term stability test last for 300 h. The properties of OSTP samples were demonstrated to be essentially higher than samples made by conventional ceramic hollow fiber fabrication methods.
Multifluid model (MFM) simulations have been carried out on liquid-solid fluidized beds (LSFB) consisting of binary and higher-order polydisperse particle mixtures. The role of particle-particle interactions was found to be as crucial as the drag force under laminar and homogenous LSFB flow regimes. The commonly used particle-particle closure models are designed for turbulent and heterogeneous gas-solid flow regimes and thus exhibit limited to no success when implemented for LSFB operating under laminar and homogenous conditions. A need is perceived to carry out Direct Numerical Simulations of liquid-solid flows and extract data from them to develop rational closure terms to account for the physics of LSFB. Finally, a recommendation flow regime map signifying the performance of the MFM has been proposed. This map will act as a potential guideline to identify whether or not the bed expansion characteristics of a given polydisperse LSFB can be correctly simulated using MFM closures tested.
In this paper, we propose a novel solution strategy to explicitly describe the design space in which no recourse is considered for the realization of the parameters. First, to smooth the boundary of the design space, the Kreisselmeier-Steinhauser (KS) function is applied to aggregate all inequality constraints, and project them into the design space. Next, for creating a surrogate polynomial model of the KS function, we focus on finding the sampling points on the boundary of KS space. After testing the feasibility of Latin hypercube sampling points, two methods are presented to efficiently extend the set of boundary points. Finally, a symbolic computation method, cylindrical algebraic decomposition, is applied to transform the surrogate model into a series of explicit and triangular subsystems that can be further converted to describe the KS space. Two case studies are considered to show the efficiency of the proposed algorithm.
To enhance the catalytic performance of H2SO4-catalyzed alkylation, various catalytic additives have drawn considerable attention. Herein, the effects of deep eutectic solvents additives (DESs) on catalytic performance and interfacial properties of H2SO4 alkylation were systematically investigated using experimental methods and molecular dynamics (MD) simulation. Experimental results indicate that DESs additives with the optimal concentration about 1.0 wt% can efficiently improve C8 selectivity and research octane number (RON) of alkylate. However, DESs additives contribute less to the quality of alkylate at low temperature and to the lifetime of H2SO4. MD results reveal that the phenyl molecules of DESs additives play a major role in enhancing interfacial properties of H2SO4 alkylation, including enlargement of interfacial thickness, promotion of isobutane relative solubility and diffusion to butene, which is probably the main reason for the better quality of alkylate. This work gives a good guideline for the design of novel DESs for H2SO4 alkylation.
This work explores the model predictive controller design of the continuous pulp digester process consisting of the co-current zone and counter-current zone modelled by a set of nonlinear coupled hyperbolic partial differential equations (PDE). The distributed parameter system of interest is not spectral and slow-fast dynamic separation does not hold. To address this challenge, the nonlinear continuous-time model is linearized and discretized in time utilizing the Cayley-Tustin discretization framework, which ensures system theoretic properties and structure preservation without spatial discretization or model reduction. The discrete model is used in the full state model predictive controller design, which is augmented by the Luenberger observer design to achieve the output constrained regulation. Finally, a numerical example is provided to demonstrate the feasibility and applicability of the proposed controller designs.
The gas-liquid two-phase flow pattern, absorption rate and pressure drop of CO2 absorbed into the aqueous solution of the task-specific ionic liquid (1-aminopropyl-3-methylimidazole tetrafluoroborate [Apmim][BF4] and 1- hydroxyethyl-3-methylimidazole tetrafluoroborate [OHemim][BF4]) and halide-free ionic liquid 1- butyl -3-methylimidazolium methylsulfate [Bmim][CH3SO4] were investigated in a microreactor. The absorption mechanism of the three ionic liquids was analyzed employing the 13C NMR spectroscopy. The [Apmim][BF4] was found to have the best ability of CO2 capture compared to the other two ionic liquids, as chemical absorption occurred between [Apmim][BF4] and CO2, while only physical absorption took place between [OHemim][BF4] / [Bmim][CH3SO4] and CO2. The sequence of CO2 absorption rate in three ionic liquid aqueous solutions is: [Apmim][BF4] > [Bmim][CH3SO4] >[OHemim][BF4]. Furthermore, the effects of gas-liquid flow rate and ionic liquids concentration on CO2 absorption rate and pressure drop were studied, the pressure drop models based on various flow patterns were proposed.
This paper describes a machine learning guided framework for screening the potential toxicity impact of amine chemistries used in the synthesis of hybrid organic-inorganic perovskites. Using a combination of a probabilistic molecular fingerprint technique that encodes bond connectivity (MinHash) coupled to non-linear data dimensionality reduction methods (UMAP), we develop an “Amine Atlas’. We show how the Amine Atlas can be used to rapidly screen the relative toxicity levels of amine molecules used in the synthesis of 2D and 3D perovskites and help identify safer alternatives. Our work also serves as a framework for rapidly identifying molecular similarity guided, structure-function relationships for safer materials chemistries that also incorporate sustainability/ toxicity concerns.
The dynamic pH response resulting from acid-base transport of interfacial reactions greatly influences the kinetic performance and process mechanism, but its theoretical foundation is lacked. Herein, a generalized acid-base transport model is established owing to the success in deriving buffer transport equations and is experimentally through the relationships of buffer transport limiting current versus solution pH and buffer concentration (CB). The relationships bring forth the parameter determination methods of buffers with the superiority of facile survey of practical parameter values. Based on model calculations, the dynamic pH response is drawn as a j‒pH diagram to show the buffer transport law in the full pH range, highlighting the rate-limiting effect. The buffer operation principles are graphically presented as CB‒ΔpH diagrams to aid economic buffer applications. This study has laid the foundation for quantification and regulation of dynamic pH response and is of wide interest to the chemistry encompassing interfacial processes.
The binary fluidization of Geldart-D type non-spherical wood particles and spherical LDPE particles was investigated in a laboratory-scale bed. The experiment was performed for varying static bed height, wood particles count, as well as superficial gas velocity. The LDPE velocity field were quantified using Particle Image Velocimetry (PIV). The wood particles orientation and velocity are measured using Particle Tracking Velocimetry (PTV). A machine learning pixel-wise classification model was trained and applied to acquire wood and LDPE particle masks for PIV and PTV processing, respectively. The results show significant differences in the fluidization behavior between LDPE only case and binary fluidization case. The effects of wood particles on the slugging frequency, mean, and variation of bed height, and characteristics of the particle velocities/orientations were quantified and compared. This comprehensive experimental dataset serves as a benchmark for validating numerical models.
The degree of rate control quantitatively identifies the kinetically relevant (sometimes known as rate-limiting) steps of a complex reaction network. This concept relies on derivatives which are commonly implemented numerically, e.g. with finite differences. Numerical derivatives are tedious to implement, and can be problematic, and unstable or unreliable. In this work, we demonstrate the use of automatic differentiation in the evaluation of the degree of rate control. Automatic differentiation libraries are increasingly available through modern machine learning frameworks. Compared to the finite differences, automatic differentiation provides solutions with higher accuracy with lower computational cost. Furthermore, we illustrate a hybrid local-global sensitivity analysis method, the distributed evaluation of local sensitivity analysis (DELSA), to assess the importance of kinetic parameters over an uncertain space. This method also benefits from automatic differentiation to obtain high-quality results efficiently.
The droplet size distribution in liquid-liquid dispersions is a complex convolution of impeller speed, impeller type, fluid properties, and flow conditions. In this work, we present three a priori modeling approaches for predicting the droplet diameter distributions as a function of system operating conditions. In the first approach, called the two-fluid approach, we use high-resolution solutions to the Navier-Stokes equations to directly model the flow of each phase and the corresponding droplet breakup/coalescence events. In the second approach, based on an Eulerian-Lagrangian model, we describe the dispersed fluid as individual spheres undergoing ongoing breakup and coalescence events per user-defined interaction kernels. In the third approach, called the Eulerian-Parcel model, we model a sub-set of the droplets in the Eulerian-Lagrangian model to estimate the overall behavior of the entire droplet population. We discuss output from each model within the context of predictions from first principles turbulence theory and measured data.
High dimensional models typically require a large computational overhead for multiphysics applications, which hamper their use for broad-sweeping domain interrogation. Herein, we develop a modeling framework to capture the through-plane fluid dynamic response of electrodes and flow fields in a redox flow cell, generating a computationally inexpensive two-dimensional (2D) model. We leverage a depth averaging approach that also accounts for variations in out-of-plane fluid motion and departures from Darcy’s law that arise from averaging across three-dimensions (3D). Our Resulting depth-averaged 2D model successfully predict the fluid dynamic response of arbitrary in-plane flow field geometries, with discrepancies of < 5% for both maximum velocity and pressure drop. This corresponds to reduced computational expense, as compared to 3D representations (< 1% of duration and 10% of RAM usage), providing a platform to screen and optimize a diverse set of cell geometries.
Machine learning (ML) models are valuable research tools for making accurate predictions. However, ML models often unreliably extrapolate outside their training data. We propose an uncertainty quantification method for ML models (and generally for other nonlinear models) with parameters trained by least squares regression. The uncertainty measure is based on the multiparameter delta method from statistics, which gives the standard error of the prediction. The uncertainty measure requires the gradient of the model prediction and the Hessian of the loss function, both with respect to model parameters. Both the gradient and Hessian can be readily obtained from most ML software frameworks by automatic differentiation. We show that the uncertainty measure is larger for input space regions that are not part of the training data. Therefore this method can be used to identify extrapolation and to aid in selecting training data or assessing model reliability.