We have used a combination of density functional theory, the implicit solvent model COSMO-RS and molecular dynamics simulations to predict the pKa of surface groups on calcium aluminosilicate glasses. We found that the average of pKa for deprotonation and protonation for silanols agrees well with the point of zero charge for pure silica materials. Similarly, the average of pKa for deprotonation and protonation for aluminols agrees well with the point of zero charge for pure alumina. We identified trends in the pKa related to the hydrogen bonding of the surface groups, as well as the influence of tri-briding oxygen defects.

The dissolution behavior of calcium aluminosilicate based glass fibers, such as stone wool fibers, is an important consideration in mineral wool applications for both the longevity of the mineral wool products in humid environments and limiting the health impacts of released and inhaled fibers from the mineral wool product. Balancing these factors requires a molecular-level understanding of calcium aluminosilicate glass dissolution mechanisms, details that are challenging to resolve with experiment alone. Molecular dynamics simulations are a powerful tool capable of providing complementary atomistic insights regarding dissolution; however, they require force fields capable of describing not-only the calcium aluminosilicate surface structure but also the interactions relevant to dissolution phenomena. Here, a new force field capable of describing amorphous calcium aluminosilicate surfaces interfaced with liquid water is developed by fitting parameters to experimental and first principles simulation data of the relevant oxide-water interfaces, including ab initio molecular dynamics simulations performed for this work for the wüstite and periclase interfaces. Simulations of a calcium aluminosilicate surface interfaced with liquid water were used to test this new force field, suggesting moderate ingress of water into the porous glass interface. This design of the force field opens a new avenue for the further study of calcium and network-modifier dissolution phenomena in calcium aluminosilicate glasses and stone wool fibers at liquid water interfaces.

The concentration of surfactant in solution for which micelles start to form, also known as critical micelle concentration is a key property in formulation design. The critical micelle concentration can be determined experimentally with a tensiometer by measuring the surface tension of a concentration series. In analogy with experiments, in-silico predictions can be achieved through interfacial tension calculations. We present a newly developed method, which employs first principles-based interfacial tension calculations rooted in COSMO-RS theory, for the prediction of the critical micelle concentration of a set of nonionic, cationic, anionic, and zwitterionic surfactants in water. Our approach consists of a combination of two prediction strategies for modelling two different phenomena involving the removal of the surfactant hydrophobic tail from contact with water. The two strategies are based on regular micelle formation and thermodynamic phase separation of the surfactant from water and both are required to take into account a wide range of polarity in the hydrophilic headgroup. Our method yields accurate predictions for the critical micellar concentration, within one log unit from experiments, for a wide range of surfactant types and introduces possibilities for first-principles based prediction of formulation properties for more complex compositions.

Offshore natural gas represents a significant portion of global natural gas reserves. Processing of natural gas is essential for gas upgrading before pipeline transportation and utilization. In this work, a phenomena-based approach to propose more sustainable intensified alternatives for separation of sour gases from offshore natural gas is considered. All possible process alternatives are explored systematically using a bottom-up approach. Over six hundred feasible intensified separation alternatives are generated and ranked in terms of enthalpy index. Six intensified separation alternatives are identified as novel and innovative candidates worthy of potential further investigation and possible industrial application. These alternatives are analyzed in terms of performance matrices and LCA indicators and compared against reference cases.

Today's manufacturing is based on ample fossil fuel sources, large and centralized plants, and high waste intensity. Climate change, aging infrastructure, dwindling resources, increasing population, changing geopolitical landscape, and the COVID-19 pandemic have laid bare the frailties of the current global supply chain. While there is still place for centralized production, geographic variation in renewable energy sources and sustainable feedstocks calls for a flexible approach towards smaller-scale and more decentralized production. With the pressing need for decarbonization of power generation and the chemical value chain, flexible manufacturing will play a major role in redefining the energy-chemistry nexus. Intensification and modularization are identified as the key enablers for such a transition. A sample case study based on valorization of hydrogen sulfide extracted from sour gas is presented to demonstrate the potential economic favorability of modular chemical process intensification. Our work shows that a net profit of US$97 million can be achieved over a five-year operational period when compared to a conventional process. A complementary evaluation of green solvents is also provided to further improve the sustainability of the proposed solution.

Hydrogen sulfide is a highly flammable, acutely toxic, and extremely hazardous gas that must be captured and removed from a number of important gaseous and liquid streams. This long-standing challenge of capturing H2S has seen the rise of different materials used in different types of technologies over the years. Some of the wellknown examples are alkanolamines used as absorbents and metal oxides used as adsorbents. This work presents an exhaustive review of the latest developments and emerging materials in this field, including ionic liquids, deep eutectic solvents, zeolites, carbon-based materials, metal organic frameworks, polymeric membranes, biological methods, advanced oxidation processes, etc. In addition to a detailed discussion of the state of the art, this review also provides a general technology map and identifies opportunities and challenges to guide future work.

Continuous monitoring of abnormal conditions during operation is an important requirement to increase the quality, and efficiency of chemical processes, and to optimize operating costs. In this study, fault diagnosis of abnormal conditions is considered for flocculation processes, which due to the complexity of these processes requires more attention. To this end, an unsupervised learning method is developed to diagnose the faults in chemical processes based on recurrence analysis. This method consists of two stages of pre-processing and clustering. The pre-processing stage is carried out by transferring the time series from time space to state space and converting the data into a two-dimensional recurrence plot. Quantitative parameters of recurrence analysis can be extracted from this plot. Then, in the clustering stage, the density-based spatial clustering of applications with noise (DBSCAN) method was used for clustering different operating conditions and diagnosing faults. By comparing the results with conventional methods, such as independent component analysis (ICA) and Kernel ICA (KICA) it was found that the developed method is more powerful and shows the best performance. Application of this method was illustrated throughout a laboratory scale flocculation of silica particles in water. An on-line non-invasive sampling method was used for monitoring the size distribution of particles with a dynamic image analysis sensor.

Hybrid modelling has caught renewed attention in many fields of engineering in the last two decades. By combining machine learning with first principles modelling, hybrid modelling is in many cases a more pragmatic modelling approach compared to first principles modelling, and at the same time a more robust alternative to data-driven modelling. However, quantifying uncertainty associated with hybrid models has not been investigated in detail thus far. Thereby, in practice, some models fail to reliably provide information for their performance under uncertainty. In this work, an integrated probabilistic modelling approach is presented for simultaneous modelling and uncertainty quantification using a hybrid model structure. The approach accounts for three types of uncertainty, including training data uncertainty, process stochasticity and model structure uncertainty. To demonstrate the advantages of this approach, the modelling strategy is highlighted through the modelling of a flocculation process. Here, mass and population balance models are combined with a probabilistic machine learning based kinetic model for estimating the particle phenomena kinetics. The model predictions are compared to predictions from a deterministic hybrid model counterpart.

The Quantum industry is currently at an embryonic stage. If it is to grow, it will require new markets, and, a workforce with the requisite skills and knowledge to support it. Anticipating this potential growth, this paper will explore capacity building within engineering education on the subject of Quantum Computing (QC). This work has two aims. On the one hand, it seeks to illustrate the need for developing education on the subject, inferred by trends in open literature. On the other hand, it seeks to suggest a starting point for quantum computing education in higher education. Since 2018, a sharp incline can be observed in the number of publications on topics related to QC. These publications are arising within several fields related to engineering including, but not limited to, material science, chemistry and computer science. In response to this trend, this paper will evaluate several third party educational approaches to teaching emergent technologies with a view to developing a model for teaching QC. Due to a lack of precedent in a wide range of industry applications and the current limitations in the state-of-the-art of this technology, the educational model proposed will be one that exploits imagination, as opposed to knowledge acquisition, in the pursuit of new knowledge building.

Understanding the adsorption behavior of nitrogen heterocyclic compounds (NHCs) on soil minerals underlies more effective design of wastewater treatment and soil/groundwater remediation. We investigated adsorption of quinoline, a representative NHC, on quartz sand (using Berea sandstone as a model), at 3 < pH < 9, in low (0.05 M) and high (0.7 M) ionic strength solutions where NaCl was the background electrolyte. Minor clay (kaolinite) in the sandstone contributed significantly to quinoline uptake. Adsorption peaked at pH similar to 6. It decreased from 2 equivalent monolayers at low ionic strength to a monolayer at NaCl activity approaching that of seawater. A triple layer surface complexation model fits the data well, where quartz and kaolinite contributed sites for three types of quinoline (Q) complexes: 1) innersphere SiOHQ; 2) outersphere SiO-QH(+) and 3) innersphere AlOHQ(2). Aluminol kaolinite sites promote multilayer quinoline adsorption, whereas only monolayers form on silanol sites. Site density calculations and molecular dynamics (MD) confirmed that quinoline adsorbs upright, on edge, and multilayer adsorption follows formation of the initial monolayer. Our results confirm the effectiveness of sand(stones) and clays for removing NHCs from waste and groundwaters.

Characterization of compositionally-complex aluminosilicate glass particles and fibers such as stone wool, and their interfaces with water and ions, is significant to a range of areas regarding dissolution phenomena. Knowledge of atomic level structures of these interfaces is critical to elucidating their dissolution traits. Molecular simulations can provide these details, complementing experimental efforts. However, prediction of the structure of stone wool fiber has been hampered by a lack of suitable inter-atomic potentials. Here, two candidate potentials are evaluated for their ability to recover experimental structural data of calcium aluminosilicate (CaO-Al2O3-SiO2) glass of compositions relevant to stone wool fibers. Both potentials produce structures that are broadly consistent with experimental data, including defect concentrations, aluminium avoidance, and ring size distributions, and either could provide a suitable basis for modelling dissolution of these materials.

With the advent of digitalization and industry 4.0, education in chemical and biochemical engineering has undergone significant revamping over the last two decades. However, undergraduate students sometimes do lack industrial exposure and are unable to visualise the complexity of actual process plants. Thereby, students might graduate without adequate professional hands-on experience. Similarly, in the process industry, operator training-simulators are widely used for the training of new and skilled operators. However, conventional training-simulators often fail to simulate reality and do not provide the user with the opportunity to experience unexpected and hazardous scenarios. In these regards, virtual reality appears to be a promising technology that can cater to the needs of both academia and industry. This paper discusses the opportunities and challenges for the incorporation of virtual reality into chemical and biochemical engineering education with an emphasis on the fundamental areas of technology, pedagogy and socio-economics. The paper emphasises the need for augmenting virtual reality interfaces with mathematical models to develop advanced immersive learning applications. Further, the paper stresses upon the need for novel educational impact assessment methodologies for the evaluation of virtual-reality-based learning. Finally, an ongoing case study application is presented to briefly discuss the social and economic implications, and to identify the bottlenecks involved in the adoption of virtual reality tools across chemical and biochemical engineering education.

A simple approach is introduced to locate a side-draw tray for ternary and multi-component mixtures with middle boiling component(s) present in the system at trace levels. The concept is based on a probability function defined by the thermodynamic properties of the system. The advantage of this method over existing methods is the ability to quickly and efficiently provide a feasible configuration of the distillation unit without relying on rigorous optimization or trial and error approaches. Moreover, it provides an intuitive understanding of the movements of the middle boiling components in the column.

Surface charge density can be derived from atomic force microscopy (AFM) by using Derjaguin, Landau, Vervey and Overbeek (DLVO) theory. The sub-micrometer data allows observation of local differences in charge density and changes with time or solution composition, which has interesting applications in crystal growth and inhibition, bone formation and colloid behavior. To calibrate this type of AFM data and verify DLVO assumptions, it has to be correlated with an established technique. We successfully matched AFM derived surface charge densities with zeta potential measurements on a mica surface within one order of magnitude. A reproducible difference between surface charge of the mica substrate exposed to solutions cations with monovalent and divalent charge was also observed. The results provide confidence that the AFM method is valid for obtaining local surface charge information.

The flocculation process is an important step towards product purification in downstream biomanufacturing, and for removal of organics, biomass and cell debris in wastewater treatment. Despite a broad application in various industries, the process mechanism is not well understood. Flocculation is a process that can be represented across scales, from nano-scale and all the way beyond the microscale. Due to the current lack of knowledge for modeling flocculation across the length scales, industry often resorts to manual control or no control at all of flocculation processes. In this work, it is intended to develop a hybrid systematic model-based framework, which integrates computational chemistry and stochastic modeling approaches for monitoring and control of the flocculation process above micro-scale. The intention is to reduce the time required for manual control, and to avoid potential product losses in addition to unwanted process variations during operation.

In Rh-catalysts for CH4-oxidation, Si-rich zeolite supports yield the more active Rh2O3 nanoparticle form and the highest SO2 and H2O tolerance.

The activity of Rh/ZSM-5 catalysts for methane oxidation was investigated under conditions simulating the exhaust from lean burning, natural gas fueled engines to evaluate the influence of reaction atmosphere on catalytic activity. The Rh-catalysts yield high methane conversion at conditions achievable in real exhaust systems, despite the inhibiting effects of H2O and SO2. The influence of temperature and SO2 concentration (1 - 20 ppm) on the activity were studied. The deactivation caused by SO2 was intensified with decreasing temperature and increasing SO2 concentration, and the SO2-poisoning could be modeled as the occupation of active sites following a Temkin adsorption isotherm. At 400 degrees C essentially full coverage of S-species was reached with 1 - 2 ppm of SO2, but at 500 degrees C the coverage had decreased significantly, and considerable catalytic activity was preserved in the presence of SO2. Significant activity was regenerated in SO2-free gas, but the nature of the SO2-free regeneration atmosphere was unimportant.

Phase transfer catalysis (PTC) is a general methodology with importance in intensified extraction-reaction processes, and it is applicable to a large number of chemical reactions. This technique accommodates reactions that are generally not achievable through conventional synthesis methods due to the introduction of a homogeneous catalyst for biphasic systems that can transfer a reactant species between two immiscible phases. This two-phase system offers several advantages, such as high conversion yields, high purity of products, operational simplicity, mild reacting conditions, suitability for scale-up of the process, and an environmentally benign reaction system. The economic viability and successful implementation of the large-scale process are heavily contingent on the design and modeling of these kinds of systems. Although a number of attempts have been made to develop case-specific and generalized models for PTC, the proposed models and accurate thermodynamic parameters are not fully developed. The lack of published theoretical process modeling for scale-up hurts the commercialization potential of PTC. In this study, an integrated and multi-scale modeling framework is proposed for overcoming these limitations for liquid-liquid (LL)-PTC. The framework needs little to no experimental data and combines different tools at different time and space scales to model virtually any LL-PTC system. The goal of this work is to utilize this framework for the recovery and conversion of H2S from an aqueous alkanolamine solution into value-added products as a way to improve economics and sustainability of the process, specifically at offshore oil and gas platforms.

The properties and behavior of the interface between mineral surfaces, adsorbed organic compounds, and water are important for oil recovery. Low-salinity (LS) water flooding releases more oil from sandstone reservoirs than conventional flooding with seawater or formation water. However, the role of strongly adsorbed organic material, as an anchor for oil molecules, is not yet completely understood. Here, we mimic reservoir pore surfaces using graphene oxide sheets deposited on flat silicon wafers. The LS response was quantified using atomic force microscopy (AFM) in chemical force mapping mode to directly measure the adhesion force. AFM tips were functionalized to serve as models for hydrophobic and polar oil molecules, i.e., with alkyl, -CH3, and carboxyl, -COO(H). Adhesion force, measured with -CH3 tips, was 18% lower in LS (similar to 1500 ppm) than high-salinity (HS, similar to 35 600 ppm) solutions, while for -COO(H) tips, adhesion force was 13% lower in LS than HS solutions. The Dejarguin-Landau-Verwey-Overbeek theory predicts that the difference in response to the salinity-dependent force with the -CH3 tips results from electric double layer (EDL) repulsion. The response to -COO(H) tips can be explained by combined EDL repulsion and cation bridging, which is consistent with density functional theory calculations. The absolute adhesion and the level of response agree with observations on sand grains from oil reservoirs, where other studies have demonstrated strongly bound organic compounds. Important implications of our study are that (i) oxidized graphene provides a convincing model for reservoir pore surfaces that is robust and reproducible and can be used for systematic testing for developing more effective enhanced oil recovery strategies and (ii) the new fundamental understanding about pore surfaces can also be applied over a range of disciplines, including improved remediation strategies for contaminated soil and groundwater.