Extending the UNIFAC model for ionic liquid–solute systems by combining experimental and computational databases

Considering that the predictive UNIFAC model is highly valuable for the solvent selection, process design and optimization of separation tasks, a large extension of this model to ionic liquid (IL)–solute systems is presented by combining experimental and COSMO-RS derived databases. The experimental infinite dilution activity coefficient (γ^∞) data of different solutes in ILs are first collected exhaustively to extend UNIFAC-IL to cover all involved IL and conventional functional groups. Afterwards, the experimental and COSMO-RS calculated γ^∞ are compared for different types of solutes to evaluate the potential of using COSMO-RS predictions as quasi-experimental data for further UNIFAC-IL extension. In the cases where COSMO-RS can provide quantitatively accurate predictions after calibration, additional γ^∞ database is specifically generated to regress more group interaction parameters in the UNIFAC-IL model. Finally, a large experimental liquid–liquid and vapor–liquid equilibria database is collected and employed to evaluate the predictive performance of the obtained γ^∞-based UNIFAC-IL model.     

Output feedback robust model predictive control with unmeasurable model parameters and bounded disturbance☆

The output feedback model predictive control (MPC), for a linear parameter varying (LPV) process system including unmeasurable model parameters and disturbance (all lying in known polytopes), is considered. Some previously developed tools, including the norm-bounding technique for relaxing the disturbance-related constraint handling, the dynamic output feedback law, the notion of quadratic boundedness for specifying the closed-loop stability, and the el ipsoidal state estimation error bound for guaranteeing the recursive feasibility, are merged in the control design. Some previous approaches are shown to be the special cases. An example of continuous stirred tank reactor (CSTR) is given to show the effectiveness of the proposed approaches.     

Statistical machine-learning–based predictive control of uncertain nonlinear processes

In this study, we present machine-learning–based predictive control schemes for nonlinear processes subject to disturbances, and establish closed-loop system stability properties using statistical machine learning theory. Specifically, we derive a generalization error bound via Rademacher complexity method for the recurrent neural networks (RNN) that are developed to capture the dynamics of the nominal system. Then, the RNN models are incorporated in Lyapunov-based model predictive controllers, under which we study closed-loop stability properties for the nonlinear systems subject to two types of disturbances: bounded disturbances and stochastic disturbances with unbounded variation. A chemical reactor example is used to demonstrate the implementation and evaluate the performance of the proposed approach.     

Development of a model for thin-film stability and spreading in solid–liquid–liquid systems

The presence of thin aqueous films and their stability has a profound effect on reservoir rock–fluids interactions involved in spreading and adhesion. The stability of thin wetting aqueous films on rock surfaces is governed by several variables including pH, brine and crude oil compositions, and capillary pressure. These variables govern the wetting states in the solid–liquid–liquid systems. The wetting states influence the residual oil saturation and the oil-water relative permeabilities and, consequently, the oil recovery. The objective of this study was to deduce a functional dependence of thin-film stability on the above parameters by considering intermolecular and surface interactions in rock–crude oil–brine systems. The surface forces are manifested as disjoining pressure in thin films. The disjoining pressure isotherms for the selected solid–liquid–liquid systems have been computed in terms of the bulk properties of the media. The equilibrium contact angles have also been computed from the integration of the Young–Laplace equation, which relates contact angle to the capillary pressure and disjoining pressure isotherm of the system. The contact-angle data obtained from sessile-drop experiments have been compared with the calculated results, as well as with other published results. Adhesion maps, which relate the film stability to brine pH and molarity, have been developed. The rock–fluids systems considered for this study consisted of smooth glass, quartz and Yates reservoir fluids. The DLVO theory has been used to model the intermolecular forces. The structural forces are incorporated to overcome the limitations of the DLVO theory. A charge regulation model has been used to analyze the crude oil–brine and glass–brine interfaces. The effects of multivalent ions have been incorporated using an equivalent molarity concept. The overall computational model developed in this study is aimed at providing a priori prediction capability of rock-fluids interactions in petroleum reservoirs for inclusion in reservoir simulators.     

Modified COSMO-UNIFAC model for ionic liquid–CO2 systems and molecular dynamic simulation

A new predictive molecular thermodynamic model (i.e., modified COSMO-SAC-UNIFAC) was first proposed and extended to predict the solubility of CO₂ in pure and mixed ionic liquids (ILs) at the temperatures down to 263.2 K. It is interesting to discover that with equimolar amounts, the solubility of CO₂ in such 1:1 IL pairs, that is, [A₁][B₁] + [A₂][B₂] and [A₁][B₂] + [A₂][B₁], was consistent at the same temperature and pressure in the case of exchanging their respective cations and anions. The molecular dynamic (MD) simulation for CO₂ + mixed ILs was performed to deeply analyze and explain this intriguing phenomenon. Not only the CO₂ gas drying experiment with the ILs ([C2mim][OAc], [C2mim][dca], and [C2mim][OAc] + [C2mim][dca]) as absorbents but also the corresponding process simulation and optimization were made to stress the effectiveness and applicability of the new thermodynamic model. Thus, this work ranges from molecular level to systematic scale.     

Process parameter optimization for ionic liquids as gas separation membranes based on a GWO–BPNN model

Gas separation membranes offer a cost-effective solution for capturing greenhouse gases, mitigating the global greenhouse effect. Ionic liquids (ILs) have emerged as one of the promising materials for greenhouse gas separation due to their strong affinity for CO₂. In this study, we propose a laboratory-scale method for preparing IL–PVDF blend membranes with high CO₂/N₂ selectivity. The separation performance of the membranes was evaluated using a custom gas permeation measurement system. The effects of casting solution composition, solidification method, and film-forming processes on separation performance were experimental investigated, and the obtained experimental data were used to train a back propagation neural network (BPNN) optimized by the Gray Wolf Optimizer (GWO) algorithm. This hybrid GWO–BPNN model was utilized to predict separation membrane efficiency, optimize the film-forming process, and identify the optimal range of process parameters. Notably, the GWO–BPNN model demonstrated a 2.76% higher prediction accuracy compared to a standalone BPNN. The results indicated that the GWO–BPNN algorithm has a great potential to accurately predict membrane separation efficiency and apply in optimal membrane process design (OMPD), and this method can significantly reduce the number of experimental trials required to achieve OMPD.     

Preparation of Pt–Co alloy catalysts by electrodeposition for oxygen reduction in PEMFC

Direct current (DC) and pulse current (PC) electrodeposition of Pt–Co alloy onto pretreated electrodes has been conducted to fabricate catalyst electrodes for oxygen reduction reaction (ORR) in proton exchange membrane fuel cells (PEMFC). The effect of plating mode and pulse plating parameters on the Pt–Co alloy catalyst structure, composition and electroactivity for the ORR in PEMFC has been investigated. The electrodeposited Pt–Co alloy catalyst indicates higher electrocatalytic activity towards the ORR than the electrodeposited Pt catalyst. The activity of the electrodeposited Pt–Co catalysts is further improved by applying the current in a pulse waveform pattern. The electrodeposition mode and the pulse plating parameters do not have the significant effect on the Pt:Co composition of deposited catalysts, but show the substantial effect on the deposit structures produced. The Pt–Co catalysts prepared by PC electrodeposition have finer structures and contain smaller Pt–Co catalyst particles compared to that produced by DC electrodeposition. By varying the Pt concentration in deposition solution, the Pt:Co composition of the electrodeposited catalyst that exhibits the highest activity is found. The Pt–Co alloy catalyst with the Pt:Co composition of 82:18 obtained at the charge density of 2 C cm⁻², the pulse current density of 200 mA cm⁻², 5% duty cycle and 1 Hz was found to yield the best electrocatalytic activity towards the ORR in PEMFC.     

CFD modelling of two-phase stirred bioreaction systems by segregated solution of the Euler–Euler model

An advanced study of a bioreactor system involving a Navier–Stokes based model has been accomplished. The model allows a more realistic impeller induced flow image to be combined with the Monod bioreaction kinetics reported previously. The time-course of gluconic acid production by Aspergillus niger strain is simulated at kinetic conditions proposed in the literature. The simulation is based on (1) a stepwise solution strategy resolving first the fluid flow field, further imposing oxygen mass transfer and bioreaction with subsequent analysis of flow interactions, and (2) a segregated solution of the model replacing the multiple iterations per grid cell with single iterations. The numerical results are compared with experimental data for the bioreaction dynamics and show satisfactory agreement. The model is used for assessment of the viscosity effect upon the bioreactor performance. A 10-fold viscosity rise results in 2-fold decrease of K_La and 25% decrease of the specific gluconic acid production rate. The model allows better understanding of the mechanism of the important bioprocess.     

A deposition efficiency model for fiber–filler flocculation by microparticle retention system

A deposition efficiency model was developed in order to predict the effect of the dosage of retention aids (polymer and bentonite) on filler retention and validated with a pilot machine trial. The deposition efficiency is a function of the surface coverage of adsorbed and transferred polymer on solids (fiber and filler) and the surface coverage of microparticles on the polymer-adsorbed layer. The model includes the effect of bimodal particles (i.e., fiber and filler) and the polymer transfer from fiber to filler. In addition, the interaction between bare surfaces was included in the model and different bond strength of each interaction was considered. It was shown that an increase in the deposition efficiency leads to an increase in the filler retention. The dosage of CPAM is more predominant variable affecting deposition efficiency and thus filler retention than that of bentonite.     

Reduced graphene oxide for Li–air batteries: The effect of oxidation time and reduction conditions for graphene oxide

Reduced graphene oxide (rGO) has shown great promise as an air-cathode for Li–air batteries with high capacity. In this article we demonstrate how the oxidation time of graphene oxide (GO) affects the ratio of different functional groups and how trends of these in GO are extended to chemically and thermally reduced GO. We investigate how differences in functional groups and synthesis may affect the performance of Li–O₂ batteries. The oxidation timescale of the GO was varied between 30 min and 3 days before reduction. Powder X-ray diffraction, micro-Raman, FE-SEM, BET analysis, and XPS were used to characterize the GO’s and rGO’s. Selected samples of GO and rGO were analyzed by solid state ¹³C MAS NMR. These methods highlighted the difference between the two types of rGO’s, and XPS indicated how the chemical trends in GO are extended to rGO. A comparison between XPS and ¹³C MAS NMR showed that both techniques can enhance the structural understanding of rGO. Different rGO cathodes were tested in Li–O₂ batteries which revealed a difference in overpotentials and discharge capacities for the different rGO’s. We report the highest Li–O₂ battery discharge capacity recorded of approximately 60,000 mAh/g_carbon achieved with a thermally reduced GO cathode.     

Model predictive control of Kuramoto–Sivashinsky equation with state and input constraints

This work focuses on the model predictive control design methodology that successfully accounts for the state and input constraints applied in the context of highly dissipative Kuramoto–Sivashinsky (KS) partial differential equation (PDE) describing stability of a thin film thickness in the two-phase annular flow in vertical pipes. The evolution of a linear dissipative KSE PDE state is given by an abstract evolution equation in an appropriate functional space. The proposed constrained predictive control law utilizes a low order modal representation in the optimization functional, while higher modes are included only in the PDE state constraints. Simulation results demonstrate a successful application of the proposed predictive control technique that achieves optimal stabilization of a spatially-uniform unstable steady state of Kuramoto–Sivashinsky equation in the presence of input and state constraints.     

Effect of contact time parameter on wettability and interface phenomena of B4C substrate by Ni–Cr–Si–Fe–B–C filler alloy

In the present research, the wettability of boron carbide ceramic by BNi-1 filler alloy at various contact times from 10 to 40 min has been studied. The results of sessile drop wetting tests showed that the BNi-1 filler alloy could spread well on the B₄C surface at 10–40 min. With the increase of the contact time from the lowest time (10 min) to the highest time (40 min), the contact angle stably reduced, showing the enhancement of the spreading. However, by the increase of the contact time from 30 to 40 min, a slight change was observed in the wetting angle (from 21° to 19°). Overall, the appropriate spreading behavior of BNi-1 filler alloy on the B₄C substrate can be attributed to the tendency of nickel for the reaction with B₄C along with the simultaneous availability of silicon and chromium in the composition of this alloy. The maximum wetting angle of 48° was attained for the specimen with 10 min contact time and the minimum angle of 19° was achieved for the specimen with 40 min contact time. Due to the results, different compounds such as Ni₄B₃, CrB₂, CrB, SiC, and Ni₂Si have been observed at the system's interface. Moreover, the higher contact times can lead to the intensification of the system's interactions which can subsequently result in the higher penetration of the elements, the reacted area enlargement, and the formation of diverse microstructures and phases. The wetting experiments' results confirmed the spreading ratio calculations.     

Maxwell relaxation time for nonexponential α-relaxation phenomena in glassy systems

We examine the mean relaxation time predicted by the Maxwell relation for stress and structural α-relaxation phenomena. We express this relation using the Markov network framework and present an expression for the average relaxation time under equilibrium and nonequilibrium conditions that is rooted in the energy landscape of a material. We show that structural relaxation times calculated using the Maxwell relation must systematically underpredict the relaxation time. Finally, we report experimental evidence suggesting that the relaxation time obtained from shear viscosity measurements must correspond to a stress relaxation time.     

Oxygen control technology in applications of liquid lead and lead–bismuth systems for mitigating materials corrosion

Oxygen control technology has been successfully applied in liquid lead and lead–bismuth coolant systems for mitigating materials corrosion. In the present study, the development of the oxygen control technology is reviewed. The corrosion mitigation mechanisms, the oxygen control methods, and the oxygen concentration measurements are discussed. The study also analyzes the current technology issues and near future studies needed. For comparisons, the study also reviews other corrosion inhibitors that have also been applied for mitigating corrosion by liquid lead and lead–bismuth.     

Meerwein–Ponndorf–Verley reduction of cyclohexanone catalyzed by partially crystalline zirconosilicate

Partially crystalline zirconosilicate was prepared at low crystallization temperature of 120 °C using amorphous zirconosilicate as solid sources of both zirconium and silicon. Partially crystalline zirconosilicate showed relatively high activity in the Meerwein–Ponndorf–Verley (MPV) reduction of cyclohexanone with 2-propanol. Lewis acid amount, mesopore volume and surface hydrophobicity are the three critical factors for its catalytic activity. Furthermore, partially crystalline zirconosilicate exhibited certain water tolerance ability, most probably related to the surface hydrophobicity that originates from the zeolitic units.     

A coupled mechanical and chemical damage model for concrete affected by alkali–silica reaction

To model the complex degradation phenomena occurring in concrete affected by alkali–silica reaction (ASR), we formulate a poro-mechanical model with two isotropic internal variables: the chemical and the mechanical damage. The chemical damage, related to the evolution of the reaction, is caused by the pressure generated by the expanding ASR gel on the solid concrete skeleton. The mechanical damage describes the strength and stiffness degradation induced by the external loads. As suggested by experimental results, degradation due to ASR is considered to be localized around reactive sites. The effect of the degree of saturation and of the temperature on the reaction development is also modeled. The chemical damage evolution is calibrated using the value of the gel pressure estimated by applying the electrical diffuse double-layer theory to experimental values of the surface charge density in ASR gel specimens reported in the literature. The chemo-damage model is first validated by simulating expansion tests on reactive specimens and beams; the coupled chemo-mechanical damage model is then employed to simulate compression and flexure tests results also taken from the literature.     

The refined e-NRTL model applied to CO2–H2O–alkanolamine systems

The electrolyte NRTL (e-NRTL) model by Chen (1982) and Chen and Evans (1986) is perhaps the most commonly used activity coefficient based thermodynamic model for industrial systems. It has been shown by Bollas et al. (2008) that the original e-NRTL model is inconsistent for systems with multiple cations and/or anions, in the same work the model equations for the so-called refined e-NRTL model were given. In this work the refined e-NRTL model is applied to CO₂–H₂O–alkanolamine systems. The interaction parameters of the refined e-NRTL model are regressed to partial pressure of CO₂, binary vapour–liquid-equilibrium, freezing point depression data and excess enthalpy data. The model is in the end used to predict partial pressures and speciation for the CO₂–H₂O–MEA and CO₂–H₂O–MDEA systems.     

Novel Pd–Au/TiO2 catalyst for the selective catalytic reduction of NOx by H2

Novel Pd–Au/TiO₂ catalyst exhibited high catalytic activity with a wide temperature window for the selective catalytic reduction of NO_x by H₂ in the presence of oxygen. The synergetic effect between Pd and Au contributes to the formation of Pd⁰ and Pd–Au alloy, thus promoting the NO_x reduction to proceed.