Editorial – The third “Futures” issueThis month’s volume of AIChE Journal is the third “Futures” issue. I think now we’ve established a tradition. This is my favorite issue each year.Each year we invite a group of early career researchers to contribute their pioneering works. I have sought the input of the AIChE Journal editorial team and consulting editorial board to identify contributors. The criterion is that the prospective author be seven or less years removed from her or his initial appointment as an academic, industrial, or national lab researcher.During the publication year we have a session at the Annual AIChE meeting where several of the authors have the opportunity to present their research. (This being the year of COVID-19, these presentations will unfortunately be virtual.)The “Futures” issue parallels the annual “Founders” issue; while the “Founders” issue celebrates the scholarly legacies of the greats from our profession, the “Futures” issue recognizes the research of emerging scholars.I hope that you enjoy this third installment of the “Futures” series.On behalf of the AIChE Journal editorial team, we look forward to your input and suggestions. As always, thanks for your support of
The existing methods of flexibility index are mainly based on mixed-integer linear or nonlinear programming methods, making it difficult to readily deal with complex mathematical models. In this article, a novel solution strategy is proposed for finding a reliable upper bound of the flexibility index where the process model is implemented in a black box that can be directly executed by a commercial simulator, and also avoiding the need for calculating derivatives. Then, the flexibility index problem is formulated as a sequence of univariate derivative-free optimization (DFO) models. An external DFO solver based on trust-region methods can be called to solve this model. Finally, after calculating the critical point of the model parameters, the vertex enumeration method and two gradient approximation methods are proposed to evaluate the impact of process parameters and to evaluate the flexibility index. A reaction model is studied to show the efficiency of the proposed algorithm.
Aging effects of off-gas streams including dry air and humid air on reduced silver exchanged mordenite (Ag0Z) were studied. Aged Ag0Z was prepared by exposing Ag0Z to dry air and humid air at different aging temperatures, time, and water vapor concentrations. Iodine loading capacity on the aged Ag0Z was obtained through a continuous-flow adsorption system. Significant iodine loading capacity losses were observed after the Ag0Z was exposed to dry air and humid air. Physical and chemical analyses were conducted to observe the physical and chemical changes of Ag0Z after being aged. From iodine adsorption data and sample analyses, it was found that iodine loading capacity on the aged Ag0Z in dry air and humid air decreases with increasing aging temperatures, time and water vapor concentrations. The pseudo reaction model describes experimental data well and the oxidation of Ag0 is the rate determining step in the aging process.
Abstract: This treatment describes the details of a methodical three step algorithm for determining the optimal operating conditions for the recrystallization separations of solid mixtures. Our algorithm was applied to optimally separate a representative pharmaceutical product (Caffeine) from a related pharmaceutical product (Theophylline). The limitations of such calculations with currently available, widely used predictive methods for computing solution thermodynamics without experimental data are directly examined. Also presented here is a novel two stage recrystallization procedure which can potentially dramatically improve the recovery yields of the desired products. The systematic optimization calculations described herein should enable researchers to quickly screen many potential solvent systems and operating conditions and concentrate experimental efforts only on the most promising candidates for such purifications.
Biotechnological application of multiple enzymes in different phases for target compounds synthesis poses a significant challenge for industrial process development. At the same time, a growing demand for natural flavors and fragrances opens up possibilities for novel biotechnological processes to replace current chemical synthesis routes, with additional advantages such as avoiding harsh reaction conditions and toxic chemicals, and less by-products in the system. Within complex biotechnological processes, the key for unfolding their industrial application potential in bioprocess engineering lies in their mathematical modeling. In this contribution, a multi-enzyme cascade reaction in a two-phase system implemented in a miniplant-scale reactor setup is mathematically modeled for the example of the flavoring agent cinnamyl cinnamate. Using our validated model and a mathematical optimization tool based on a genetic algorithm, optimization runs are performed to demonstrate the potential of computer-aided process development for complex biotechnological processes.
High performance thin-film composite (TFC) hollow fiber membranes have been developed for pervaporation dehydration by second interfacial polymerization (SIP) modification with 3 kinds of amine-functionalized β-cyclodextrin (amine-CDs), which were synthesized by modifying β-CD with ammonia, ethylenediamine (EDA) and tris(2-aminoethyl)amine, respectively. The chemical properties of amine-CDs and SIP-modified TFC membranes were characterized by various techniques. The effects of amine-CD type and SIP parameters (pH or concentration of CD-EDA solution) were studied systematically to acquire the optimized selective layer of TFC membranes for ethanol dehydration. Among all SIP-modified TFC membranes, the one with SIP by 2 wt% CD-EDA aqueous solution (pH=2) exhibited the most outstanding separation performance with a ultra-high permeation flux (3018.0±12.0 g/m2.h) and permeate concentration (98.7±0.2 wt% water) at 50 °C (equivalent to separation factor of 415), contributed by the effectively incorporated CD with rich hydrophilic functional groups and intrinsic nanocavities facilitating the passage of water molecules.
This study presents a novel model to predict gas-water two-phase transport behaviors in shale microfractures by incorporating a mobile water film with varying thickness according to the extended Derjaguin-Landau-Verwey-Overbeek (DLVO) theory as well as multiple fluid transport mechanisms (i.e., real gas transport controlled by the Knudsen number and water slippage). This model is implemented in real shale microfractures via digital-core imaging. A gas-water displacement process is modelled by the invasion percolation theory, while a local multiphase distribution is determined by combining disjoining pressure with capillary force. Key findings reveal that gas relative permeability (RP) decreases by 17% and water RP enhances by 33.5%, when the mean aperture decreases from 1.67 to 0.0418μm. Neglecting water film brings a decrease in water RP and an overestimation of gas transport ability. Moreover, two critical microfracture apertures are determined, which enhances an understanding of the water film impact on gas-water transport properties in application.
Community detection decomposes large-scale, complex networks ‘optimally’ into sets of smaller sub-networks. It finds sub-networks that have the least inter-connections and the most intra-connections. This article presents an efficient community detection algorithm that detects community structures in a weighted network by solving a multi-objective optimization problem. The whale optimization algorithm is extended to enabe it to handle multi-objective optimization problems with discrete variables and to solve the problems on parallel processors. To this end, the population’s positions are discretized using a transfer function that maps real variables to discrete variables, the initialization steps for the algorithm are modified to prevent generating unrealistic connections between variables, and the updating step of the algorithm is redefined to produce integer numbers. To identify the community configurations that are Pareto optimal, the non-dominated sorting concept is adopted. The proposed algorithm is tested on the Tennessee Eastman process to show its application and performance.
For the ionic liquid (IL)-solute systems of broad interest, a deep neural network based recommender system (RS) for predicting the infinite dilution activity coefficient (γ∞) is proposed and applied for a large extension of the UNIFAC model. In the RS, neural network entity embeddings are employed for mapping each IL and solute and neural collaborative filtering is utilized to handle the nonlinearities of IL-solute interactions. A comprehensive experimental γ∞ database covering 215 ILs and 112 solutes (totally 41,553 data points) is established for training the RS, where the cross-validation and test are performed based on a rigorous dataset split by IL-solute combinations. The obtained RS shows superior performance than the state-of-the-art γ∞ models and is thus taken to complete the solute-in-IL γ∞ matrix. Based on the completed γ∞ database, a large extension of the UNIFAC-IL model is finally presented, filling all the parameters between involved groups.
Carbon quantum dots (C-QDs) show great potential to replace traditional semiconductive quantum dots as the next generation of fluorescent probes. We demonstrate here a new C-QD production process using lignin, a high-volume but low market-value industrial waste and/or environmental hazards, as the starting carbon source. By adding a small amount of inorganic acid, the rich phenolic components in lignin were successfully converted to C-QDs through a coking formation mechanism similar to what happens on solid acid catalysts in traditional fossil fuel cracking process. The aqueous solution presence of the received lignin C-QDs is beneficial for brain cell imaging applications, attributing to their fast internalization, low toxicity, tunable photoluminescence by appropriate acidity and reaction temperature during hydrothermal synthesis. This method not only provides a low-cost C-QDs production route, but also helps gain extra profit and/or improve environment for many small agricultural business and paper and pulp industry located in rural area.
Zwitterionic materials have attracted increasing attentions in the underwater super-oleophobic applications for its strong hydration via electrostatic interactions. Herein, molecular dynamics simulations were used to investigate the hydration and underwater oleophobicity of sulfobetaine-terminated self-assembled monolayers (SB-SAMs) with different carbon spacer lengths (CSL) between oppositely charged groups of SB molecules. Simulation results show that the hydration of SB-SAMs is positively dependent on CSL; the underwater oleophobicity is strengthened and then weakened with the increase of CSL, reaching optimal performance when CSL = 3; Adhesion force of oil droplet on SB-SAMs is inversely correlated with their contact angles, reaching the minimum value when CSL = 3. Moreover, the addition of NaCl can weaken the self-association of SB molecules resulted from interactions between cationic and anionic groups, which promotes hydration and enhances underwater oleophobicity of SB-SAMs. These results will benefit for the design of novel zwitterion-based materials for anti-fouling and oil-water separation applications.
Acetylene, an important petrochemical feedstock, is the starting chemical to produce many polymer products. Separating C2H2 from its by-product mixtures is still an energy-consuming process and remains challenging. Here, we present a metal-organic framework[Zn2(bpy)(btec)], with a desirable pore geometry and highly stable framework, which demonstrated a high separation performance of C2H2 from simulated mixtures. With the desirable pore dimension and hydrogen bonding sites, Zn2(bpy)(btec) shows by far the both highest C2H2/CO2 and C2H2/CO2 uptake ratios, very high adsorption selectivities and moderately C2H2 uptake of 93.5 cm3•cm−3 under 298 K and 1 atm. Not only straightforwardly produced high purity of C2H4, but also recovered high purity of C2H2 (>98%) in the regeneration process (>92% recovery). More notably, Zn2(bpy)(btec) can be straightforwardly synthesized at a large scale under environmentally friendly conditions, and its good water/chemical stability, thermostability, and cyclic stability highlight the promise of this molecular sieving material for industrial C2H2 separation.
The cell free system has been paid more attention due to its potential of facilitating more eﬃcient catalysis of multistep reactions. In this study, an efficient enzymatic cascade of GSH production was developed through the evolution of bifunctional glutathione synthetase (GshF), coupled with polyphosphate kinase (PPK). First, the stability and activity of GshF were enhanced by loop interchange and site-directed mutagenesis. As a result, the GshF half-value period increased 163.3-fold, and its activity raised 18 %. PPK from Jhaorihella thermophile (PPKJT) was characterized and used to regenerate ATP in the GSH synthesis, with hexametaphosphate (PolyP(6)) as the phosphate donor. After the process optimization, 99.9 mM GSH and 7.6 mM oxidized glutathione (GSSG) were produced within 2 h. The molar yield was 95.9 mol/mol based on the amino acid added, while the productivities of GSH achieved 49.95 mM/h, which was the highest yield and productivity ever reported about GSH synthesis.
Since chromatographic separation is a dynamic process, with the interactions between the drug and the chiral stationary phase mediated by the solvent, no single interacting structure, such as could be found by minimizing the energy, could possibly describe and account for the ratio of residence times in the chromatographic column for the enantiomeric pair. We describe the use of explicit-solvent fully atomistic molecular dynamics simulations, permitting all the interactions between the atoms constituting the chiral stationary phase, solvent molecules and the drug molecule. This allows us to better understand the molecular dynamic chiral recognition that provides the discrimination which results in the separation of enantiomers by high performance liquid chromatography. It also provides a means of predicting, for a given set of conditions, which enantiomer elutes first and an estimate of the expected separation factor. In this review we consider the use of molecular dynamics towards this understanding and prediction.
Vapor recompressed batch distillation (VRBD) is an energy-integrated configuration which works on the principle of a heat pump. Operation of such a column is challenging due to unsteady, nonlinear dynamics and strong interplay between separation and energy efficiency. In this paper, a two-step approach is proposed for optimal operation and control of such a column. Initially, an openloop optimal operation policy is generated for maximization of an overall performance index using offline optimization. To this end, three performance indices are proposed to capture interplay between separation and energy efficiency. Subsequently, a model-based output feedback controller is designed to track this optimal performance trajectory. The effectiveness of the proposed approach is demonstrated using a benzene-toluene separation case study wherein it is shown that the proposed approach helps to achieve optimal operation in the presence of operational disturbances.
The growth of silica nanoparticles by agglomeration and viscous flow sintering is studied from free molecular to transition regime at high temperatures by discrete element method simulations. The effect of temperature on the aggregate mobility and gyration radii, particle morphology and collision frequency function is elucidated as function of the number of primary particles. The ratio between the characteristic sintering time and characteristic collision time controls the particle size and structure, quantified by the mass fractal dimension. The effect of this ratio of characteristic times on aggregate morphology is illustrated at various temperatures. Finally, when sintering is negligible, the overall collision frequency is 90% larger than that predicted by the classic Fuchs collision kernel for monodisperse agglomerates in the near free molecular and transition regime. For comparable coagulation and sintering rates, where aggregates with sinter bonds are formed, the overall collision frequency increases an enhancement of <90% is observed.
Massive amounts of gas hydrates occur naturally in the pores of sediments or fractures in permafrost regions and beneath the oceans. For hydrate formation in confinement, the equilibrium condition can shift to harsher conditions, lowering the water activity, and subsequently depressing the hydrate freezing temperature at a given pressure. Conversely, the nucleation and rate of hydrate formation, as well as hydrate conversion can be increased in confinement. Therefore, reliable assessment of the hydrate distribution in nature requires accurate thermodynamic and kinetic models of hydrate formation; however, these models tend to be based upon the properties of bulk hydrates. Hydrate formation and growth promotion in confinement are potentially interesting for hydrate technological applications, such as gas separation, energy storage, and flow assurance. This paper reviews the thermodynamic and kinetic properties and their interrelations of gas hydrates in confined spaces.