The SAFT-γ Mie group-contribution equation of state is used to represent the fluid-phase behaviour of aqueous solutions of a variety of linear, branched, and cyclic amines. New group interactions are developed in order to model the mixtures characterised by alkyl primary, secondary, and tertiary amine groups (NH2, NH, N), cyclic secondary and tertiary amine groups (cNH, cN), and cyclohexylamine groups (cCHNH, cCHN). The group-interaction parameters are estimated from appropriate experimental thermodynamic data for pure amines and selected mixtures. The fluid-phase behaviour of these mixtures can then be described over broad ranges of temperature, pressure, and composition. Liquid-liquid equilibria (LLE) bounded by lower critical solution temperatures (LCSTs) have been reported experimentally and are reproduced here with SAFT-γ Mie approach. The main feature of the approach is the ability to represent accurately the experimental data employed in the parameter estimation, a to predict the phase equilibria with the same set of parameters.

Research problems in the domains of physical, engineering, biological sciences, often span multiple time and length scales, owing to the complexity of information transfer underlying mechanisms. Multiscale modeling (MSM) and high-performance computing (HPC) have emerged as indispensable tools for tackling such complex problems. We review the foundations, historical developments, and current paradigms in MSM. A paradigm shift in MSM implementations is being fueled by the rapid advances and emerging paradigms in HPC at the dawn of exascale computing. Moreover, amidst the explosion of data science, engineering, and medicine, machine learning (ML) integrated with MSM is poised to enhance the capabilities of standard MSM approaches significantly, particularly in the face of increasing problem complexity. The potential to blend MSM, HPC, and ML presents opportunities for unbound innovation and promises to represent the future of MSM and explainable ML that will likely define the fields in the 21st century.

In this study, we developed an integrated cellular automata and thermal lattice Boltzmann model to investigate the effects of different temperatures and velocities in a microbioreactor. Compared with previous studies this model accounted for the direct effects of transient temperature on biofilm growth and indirect effects caused by changes of properties. In addition, the algorithms on variations in solid boundary conditions, detachment and extra mass transport have been improved. Results showed that temperature affected both the maximum biofilm concentration and growth speed. Roughly a 10-75% increase in biofilm concentration was observed, while in some cases the time needed to reach maximum concentration decreased from 30 days to 5 days. Despite of geometrical symmetry, changes in the upper inlet characteristics were more effective on biofilm growth. This demonstrates the capability of the present model to simulate biofilm behaviour in the microbioreactor and its potential industrial and clinical applications.

The controlling nanofiller aggregation and strengthening interfacial interaction are of great scientific significance for mixed matrix membranes (MMMs). In this study, the polymer-embedded metal-organic framework (pMOF) microspheres (MSs) are designed by one-pot synthesis and employed as microfillers for improving separation performance of MMMs. Through adding polymer during solvothermal crystallization, the polymer chains are embedded into the MOF materials, and the morphologies of the MOFs are transformed from nanopaticles to polycrystalline MSs. Since the embedding of the identical polymer promotes the compatibility of polymeric matrixes and fillers, as well as the micrometer-sized porous MSs offer additionally superior and permanent transport pathways, the resulted MMMs display simultaneously enhanced selectivity and permeability for carbon capture. The CO2/CH4 selectivity and CO2 permeability of the pMOF MMMs are achieved at 1.3 and 2.2 times as those of the pure polymeric membranes, and 1.5 and 1.2 times as those of the MOF MMMs, respectively.

The majority of clinically approved therapeutics target membrane proteins (MPs), highlighting the need for tools to study this important category of proteins. To overcome limitations with recombinant MP expression, whole cell screening techniques have been developed that present MPs in their native conformations. Whereas many such platforms utilize adherent cells, here we introduce a novel suspension cell-based platform termed “biofloating” that enables quantitative analysis of interactions between proteins displayed on yeast and MPs expressed on mammalian cells, without need for genetic fusions. We characterize and optimize biofloating and illustrate its sensitivity advantage compared to an adherent cell-based platform (biopanning). We further demonstrate the utility of suspension cell-based approaches by iterating rounds of magnetic-activated cell sorting (MACS) selections against MP-expressing mammalian cells to enrich for a specific binder within a yeast-displayed antibody library. Overall, biofloating represents a promising new technology that can be readily integrated into protein discovery and development workflows.

The isosteric heat of adsorption is an important thermodynamic property used to characterise and optimise adsorption processes. In this work, analytic expressions for isosteric heats of adsorption are derived for a collection of commonly used isotherm models and a two-dimensional molecular equation of state based on the SAFT-VR approach. The use of these expressions is presented with an example of adsorption of nitrous oxide, N₂O, on biochar, which is a waste biomass charcoal that exhibits high adsorption potential. The results show that accurate fitting of the adsorption isotherms leads to consistent results obtained with different approaches, however, the predicted isosteric heat of adsorption exhibits strong variations in the regions where experimental data is insufficient such in the region of low pressure/low coverage. Convergence on the prediction of the isosteric heat of adsorption by the different models is only observed in the region where no extrapolation of experimental data is needed.

This work addresses the design of distributed model predictive control (DMPC) systems for nonlinear processes using machine learning models to predict nonlinear dynamic behavior. Specifically, sequential and iterative distributed model predictive control systems are designed and analyzed with respect to closed-loop stability and performance properties. Extensive open- loop data within a desired operating region are used to develop Long Short-Term Memory (LSTM) recurrent neural network models with a sufficiently small modeling error from the actual nonlinear process model. Subsequently, these LSTM models are utilized in Lyapunov- based DMPC to achieve efficient real-time computation time while ensuring closed-loop state boundedness and convergence to the origin. Using a nonlinear chemical process network exam- ple, the simulation results demonstrate the improved computational efficiency when the process is operated under sequential and iterative DMPCs while the closed-loop performance is very close to the one of a centralized MPC system.

This study investigates the application of a recently developed construct, the Uniform Trigonometrization Method (UTM), to the singular control problems in chemical engineering. The UTM involves minimal modifications to the original problem, thereby generating near-singular control solutions that can be used for conceptual design and serve as an alternate to direct techniques like nested and simultaneous approaches. Eight classical singular control problems with known analytical solutions and three complex singular control problems from chemical engineering domain are solved in this study. The results obtained using the UTM for these problems are found to match well with the literature and are of higher resolution as compared to the results obtained using a direct pseudospectral based solver. The ability of the UTM to handle complex chemical engineering problems with both singular controls and state path constraints has also been demonstrated in this study.

The present work analyses the behaviour of aqueous solutions of N,N-dimethylethylenediamine as chemical solvent for carbon dioxide separation by gas-liquid absorption. The interest of this molecule is centred on the presence of different types of amino centres that confer the capability to act as typical solvent based on mixtures of amines taking into account the suitable behaviour of some mixtures. For this reason a comparison between diamine and amine blend solvents has been carried out in order to understand the differences between these solvents using absorption and NMR studies. This experimental work analyses the influence of amine type, concentration and ratio between different amines. Also, the effect of gas flow rate used in the bubble column reactor upon the absorption kinetics has been analysed.

This work proposes a novel population-balance based model for a bubbling fluidized bed reactor. This model considers two continuum phases: bubble and emulsion. The evolution of the bubble size distribution was modeled using a population balance, considering both axial and radial motion. This sub-model involves a new mathematical form for the aggregation frequency, which predicts the migration of bubbles from the reactor wall towards the reactor center. Additionally, the reacting particles were considered as a lagrangian phase, which exchanges mass with the emulsion phases. For each particle, the variation of the pore size distribution was also considered. The model presented here accurately predicted the experimental data for biochar gasification in a lab-scale bubbling fluidized bed reactor. Finally, the aggregation frequency is shown to serve as a scaling parameter.

This research investigated comprehensively the fundamental science of estimating sorption, diffusion, and permeation of 1,3-butadiene and nitrogen in polydimethylsiloxane (PDMS), and zeolitic imidazolate frameworks (ZIFs) filled mixed-matrix membranes (MMMs). ZIF-8 and Ni-ZIF-8 nanocrystals were synthesized at room temperature and incorporated into PDMS at different loadings. The physical and chemical properties of both ZIFs have been studied by XRD, FTIR, SEM, EDX, and TGA, respectively. Synthesized Ni-ZIF-8/PDMS MMMs demonstrated homogeneous particle dispersion, improved particle-polymer adhesion, and better butadiene permeation under various testing conditions. 15% Ni-ZIF-8/PDMS MMM exhibits 1,3-butadiene permeance 323 GPU, which improved by 60% compared with PDMS, and 32% with 15% ZIF-8/PDMS MMM, while BD/N2 ideal selectivity of 19.5was higher 81% than PDMS, and 32% than 15% ZIF-8/PDMS MMM at 40°C and 1.5 bar. Additionally, Ni-ZIF-8/PDMS MMMs have improved the solution-diffusion properties of butadiene and decreased sorption enthalpy, entropy change, activation energies of diffusion, and permeability from PDMS and ZIF-8/PDMS.

Multiple functional and hard-to-quantify sensorial product attributes that can be satisfied by a large number of cosmetic ingredients are required in the design of cosmetics. To overcome this challenge, a new optimization-based approach for expediting cosmetic formulation is presented. It exploits the use of a hierarchy of models in an iterative manner to refine the search for creating the highest-quality cosmetic product. First, a systematic procedure is proposed for optimization problem formulation, where the cosmetic formulation problem is defined, design variables are specified, and a set of models for sensorial perception and desired product properties are identified. Then, a solution strategy that involves iterative model adoption and two numerical techniques (i.e., generalized disjunctive programming reformulation and model substitution) is applied to improve the efficiency of solving the optimization problem and to find better solutions. The applicability of the proposed procedure and solution strategy is illustrated with a perfume formulation example.

The article investigates the efficacy of gemini surfactant/polymer/nanoparticle flooding on chemical EOR. Initially, physicochemical behavior of aqueous chemical fluids were investigated via interfacial tension reduction, wettability alteration, adsorption, viscosity moderation and oil displacement experiments. During compositional analysis, Cartesian model with specified grid properties, injection flow-rate, well pattern, and rock-fluid characteristics was developed using CMG-STARS tool. Contour map analyses showed that oil saturation decreased from ~80% (initial) to 31.96%, 30.68% and 29.30% after {14-6-14 GS + chase water}, {14-6-14 GS + PHPA + chase water} and {14-6-14 GS + PHPA + SiO2 chase water} flooding respectively. Tertiary recoveries of 15-19% were achieved, depending on injected fluid composition. Experimental data were history matched via CMOST tool to achieve good matching of simulated results. The CMG flooding simulator provides a holistic approach to investigate oil displacement profiles, assess flooding recovery capabilities with near-accuracy and predict the feasibility of proposed chemical EOR projects.

In this study, we deployed a modified interfacial polymerization process to incorporate multifunctional crown ethers (CEs) into thin-film composite (TFC) polyamide membranes. These CE additives acted as both the phase-transfer catalyst and co-solvent to facilitate the diffusion of amine monomers into the organic phase and also enhanced the free volume content of the selective layer, facilitating water transport and inhibiting the diffusion of draw solutes. Various characterization techniques were employed to elucidate the modification mechanism as a function of CE chemical and physical properties on the microstructure of resultant TFC membranes and consequently separation performances. Compared to TFC membranes produced from traditional interfacial polymerization method, CE-modified membranes exhibited a 146% water flux enhancement and 59% lower reverse salt fluxes with a suitable draw solution. CE-modified membranes also exhibited improved antifouling performance with a lower flux drop (34% decline) and a higher flux recovery ratio (38% improvement).

In this study, a Schulze Ring Shear Tester and the Discrete Element Method (DEM) are employed to investigate the effect of polydispersity on the binary shear flows. Both experimental results and DEM simulations show that the pre-shear stresses are greater for binary blends than for monodispersed particles. The flowability of these mixtures is strongly affected by the solid fraction, with minimal flow function values correlating to maximum packing fraction. However, minimum flow function values are not observed at the same packing fractions where the maximum pre-shear stress occurs. The powder friction has a slightly higher contribution to powder strength than powder adhesion, and the frictional component follows the same trend as shear stress with mixture composition. Using DEM, it is demonstrated that the addition of small adhesive particles reduces the averaged angular velocity of the larger particles, which makes a contribution to the larger shear stress for binary blends.

Separation of higher hydrocarbons from methane is an important and energy-intensive operation in natural gas processing. We present a detailed investigation of thin and oriented MFI zeolite membranes fabricated from 2D MFI nanosheets on inexpensive α-alumina hollow fiber supports, particularly for separation of n-butane, propane, and ethane (“natural gas liquids”) from methane. The present MFI membranes display high permeances and selectivities for C2-C4 hydrocarbons over methane, driven primarily by stronger adsorption of C2-C4 hydrocarbons. We study the separation characteristics under unary, binary, ternary and quaternary mixture conditions, including the pressure dependence. The membranes are highly effective in quaternary mixture separation at elevated feed pressures, for example allowing n-butane/methane separation factors of 170–280 and n-butane permeances of 710–2700 GPU in the 1-9 bar feed pressure range. Furthermore, we parametrize and apply multicomponent Maxwell-Stefan transport equations to predict the main trends in separation behavior over a range of operating conditions.

Liquid-phase adsorption has hardly been established in micro-flow, although this constitutes an industrially vital method for product separation. A micro-flow UV-photo isomerization process converts cis-cyclooctene partly into trans-cyclooctene, leaving an isomeric mixture. Trans-cyclooctene adsorption and thus separation was achieved in a fixed-bed micro-flow reactor, packed with AgNO3/SiO2 powder, while the cis-isomer stays in the flow. The closed-loop recycling-flow has been presented as systemic approach to enrich the trans-cyclooctene from its cis-isomer. In-flow adsorption in recycling-mode has hardly been reported so that a full theoretical study has been conducted. This insight is used to evaluate three process design options to reach an optimum yield of trans-cyclooctene. These differ firstly in the variation of the individual residence times in the reactor and separator, the additional process option of refreshing the adsorption column under use, and the periodicity of the recycle flow.

During the course of a pressure relief discharge from a vessel containing a multicomponent liquid mixture, composition changes occur that affect the properties vessel contents. In this work, a model of the dynamic relief process for ternary, non-ideal, homogeneous mixtures is developed, under the assumption of vapour-only venting. Opening/re-closing of the relief valve introduces state-events which require re-initialization of the model at each state transition. The relationship between the pressure relief model and the concept of residue curves, which describe simple-distillation processes, is demonstrated. It is known that the presence of azeotropes and distillation boundaries in mixtures restricts the composition trajectories of simple-distillation processes, as well as continuous distillation columns at total reflux. In this work, the residue-curve analogy is extended to vapour-only pressure-relief, where vapour composition changes directly affect the operation of the pressure-relief device. Examples of dynamic relief processes are developed for ternary mixtures with varying non-ideality.