A 1D multifluid population balance model approach is presented as a compromise between computational effort and accuracy. The approach is used to test process scenarios, perform sensitivity analysis, and provide a reliable scale-up and optimization tool. The study focuses on a mini-plant batch bubble column, where the scale-up behavior in terms of bubble column height, gas flux, and composition of the liquid phase is investigated. Although simplifications were made, the model requires calibration to experimental data using different calibration methods. An optimal calibration procedure is found that minimizes experimental effort while maximizing scalability. The model was tested on various liquid-phase compositions, and it was found to reproduce experimental data accurately. However, the model cannot reproduce flow regime changes and does not perform well outside the calibrated concentration. The study shows that the applied 1D multifluid populations balance approach is a valuable and reliable tool in multiphase reactor scale-up and optimization.
With the rapid development of modern industry, it is necessary to effectively recycle precious metals such as gold and palladium from e-waste. IL (ionic liquid) is an efficient extractant with high boiling points, non-flammability, and high thermal stability. The properties of IL can be improved by modifying the n-substituted groups of their cations, enhancing the separation efficiency of noble metals. Thus, two guanidinium ILs, with the substituents of -R and -OR, were synthesized to extract Au(III) from a hydrochloric acid medium. These ILs exhibited excellent extraction properties, including high selectivities, rapid kinetics, and significant extraction capacities. The maximum extraction capacities of Au(Ⅲ) by [diMTMG][Tf2N] and [diPTMG][Tf2N] were 519.4mg/g and 427.9mg/g, respectively, achieving rapid extraction equilibrium within 30s. The introduction of o-containing functional atoms enhanced extraction capacity and selectivity. The extraction mechanism involving anion exchange was confirmed by slope method, jobs method, as well as ultraviolet and nuclear magnetic resonance analysis.
In this article, we extend a previously developed globally optimal enumeration methodology for the synthesis of Heat Exchanger Networks to the simultaneous synthesis of the network and the basic design of Heat Exchangers. Our procedure guarantees global optimality, unlike previous approaches, such as Pinch Technology, metaheuristics, or mathematical programming that do not guarantee it and sometimes do not even guarantee local optimality. The procedure is not iterative, and does not present any convergence issues. To enumerate HEN structures, we use linear methods and for the HEX design we use Set Trimming followed by sorting. In addition, because some network structures are incompatible with single shell exchangers, we use multiple shell exchangers in series. The comparison of the results of the proposed approach with two solution alternatives from the literature in two different problems indicates that considerable cost reductions may be obtained.
To overcome the limitations of empirical synthesis and expedite the discovery of new polymers, this work aims to develop a data-driven strategy for profoundly aiding in the design and screening of novel polyester materials. Initially, we collected 695 polyesters with their associated glass transition temperatures (Tgs) to develop a quantitative structure-property relationship (QSPR) model. The model underwent rigorous validation (external validation, internal validation, Y-random and application domain analysis) to demonstrate its robust predictive capabilities and high stability. Subsequently, by employing an in-silico retrosynthesis strategy, over 95000 virtual polyesters were designed, largely expanding the available space for polyester materials. External assessments highlight the good extrapolation ability of the QSPR model. Furthermore, we experimentally synthesized diverse virtual polyesters with Tgs covering a sufficient large temperature range. It is believed that this data-driven approach can drive future product development of polymer industry.
The selection of chemical reactions is directly related to the quality of synthesis pathways, a reasonable reaction evaluation index plays a crucial role in the design and planning of synthesis pathways. Since the construction of traditional reaction evaluation indicators mostly rely on the structure of molecules rather than the reactions themselves, considering the impact of reaction agents poses a challenge for traditional evaluation indicators. In this study, we first propose a chemical reaction graph descriptor that includes the mapping relationship of atoms to effectively extract reaction features. Then, through pre-training using graph contrastive learning and fine-tuning through supervised learning, we establish a model for generating the probability of reaction superiority (RSscore). Finally, to validate the effectiveness of the current evaluation index, RSscore is applied in two applications: reaction evaluation and synthesis routes analysis, which proves that the RSscore provides an important agents-considered evaluation criterion for Computer-Aided Synthesis Planning (CASP).
The rheological properties of natural polymer solutions are difficult to be modeled universally because of the strongly nonlinear relations between viscosities and the external factors and the discreteness of the rheological data owning to different molecular parameters including the molecular weights and size of clusters from different types of natural polymers and solvents. In this study, a typical natural polymer-lignin was selected and dissolved in polyethylene glycol (PEG). The rheological properties of different PEG-lignin solutions (PEG-Ls) and the molecular parameters of the pretreated lignin were tested. Subsequently, machine learning was applied to establish the generalized models considering the molecular parameters. The models were successfully developed in Newtonian and non-Newtonian regimes for PEG-Ls with correlation coefficients of 0.982 and 0.980, respectively. The models and relevant methodology can provide scenarios for further application of natural polymer solutions.
Simulation and optimization of chemical flowsheets rely on the solution of a large number of non-linear equations. Finding such solutions can be supported by constructing machine-learning based surrogates, relating features and outputs by simple, explicit functions. In order to generate training data for those surrogates computationally efficiently, schemes to adaptively sample the feature space are mandatory. In this article, we present a novel family of utility functions to favor an adaptive, Bayesian exploration of the feature space in order to identify regions that are convergent, fulfill customized inequality constraints and are Pareto-optimal with respect to conflicting objectives. The benefit is illustrated by small toy-examples as well as by industrially relevant chemical flowsheets.
Many corporations and nations have pledged to reach net-zero emissions within a few decades. Meeting such targets for greenhouse gases, plastics, etc. requires systematic methods to guide investment in technologies and value-chain alternatives, and develop roadmaps. The proposed framework is a multi-period planning model to guide optimal reforms in cradle-to-cradle life-cycle networks across the time horizon. It aims to meet environmental targets while minimizing the total annualized marginal cost of natural resources and the investment cost associated with adoption of novel technologies. This considers the evolution of technology readiness levels as S-curves or continuous time Markov-chains. Integrated Assessment models account for climate change, decarbonization due to energy mix changes, and carbon taxes. Multiple climate change scenarios and shared socioeconomic pathways are used to model the future. In addition to providing roadmaps, the outputs can also be used to identify technologies that will be robust to future scenarios.
Swelling granular media can experience size-induced percolation phenomena giving rise to segregation. In this work, the Discrete Element Method is employed to investigate the effects of size ratio and swelling kinetics on the segregation. The numerical analysis was carried out on a binary mixture consisting of coarse and fine particles and several mixing indices found in literature were adapted and tested for evaluating the mixing of expanding systems. Additionally, a relative percolation velocity was employed to quantify the percolation of fine particles. The results show that the percolation of fine particles becomes more significant as the size ratio increases. Additionally, results showed that the swelling kinetics has no impact on the segregation tendency. This research provides valuable insight into the effect of size ratio and swelling kinetics on the segregation behaviour of swelling granular materials, which can contribute to understanding percolation phenomena in various fields.
An easy-to-implement noise estimation method for tuning state estimators is proposed. It outperforms benchmark methods in terms of accuracy or computational cost both in theory and in a case study. We assume parametric uncertainty in the process model, which we transform into noise statistics using the generalized unscented transformation (GenUT). While most other methods estimate only the noise covariance, we also estimate the mean. Our tuning method is suitable for input-output models, demonstrated through a case study involving process simulators and industrial data. We present a theoretical analysis of our method, which is based on splitting one large GenUT to two smaller GenUTs. This results in two theorems: i) mean approximations for the two systems are equal and ii) covariance approximations are similar under certain mild conditions. These theorems confirm the validity of our method, and we discuss their potential to realize a numerically stable GenUT for high-dimensional systems.
In this paper, BSA surface-imprinted magnetic Fe-Ni bimetallic oxide shell nanorods (m-FeNi@MIPs@PCBMA) were prepared with the assistance of poly(3-[[2-(methacryloyloxy)ethyl]dimethylammonium] propionate (PCBMA) in connection with the surface-imprinting technique. The Fe-Ni bimetallic oxide shell layer nanorods (m-FeNi) with magnetic responsiveness simplified the separation and recovery process of adsorbed materials. The controlled introduction of PCBMA facilitated the reduction of protein non-specific adsorption. At the optimal encapsulation ratio of 1:0.75 (Wm-FeNi@MIPs: WCBMA), m-FeNi@MIPs@PCBMA could adsorb 122.98 ± 5 mg/g of BSA within 80 min, and the value of the imprinting factor (IF) was also increased from 1.68 (m-FeNi@MIPs) to 3.95. In the mixed protein adsorption and real sample separation experiments, m-FeNi@MIPs@PCBMA could selectively separate BSA. Meanwhile, after seven adsorption-desorption experiments, the loss of BSA adsorption by the imprinted nanorods was only 15.9%, which had good reusability. Therefore, m-FeNi@MIPs@PCBMA has a broader application prospect in the field of protein separation and purification.
A polar cubic equation of state (EOS) is developed by incorporating the dipolar theory of Jog and Chapman into the Soave-Redlich-Kwong (SRK) EOS. We propose simplifying assumptions in the dipolar term of Jog and Chapman to reduce the double and triple sums in the theory to single sums. The simplified version of the dipolar theory can significantly improve computational speed and can be used with either Cubic EOS or SAFT-based EOS. The proposed model, which we here call polar-SRK (P-SRK), is parametrized in a similar fashion to classical cubic EOS to exactly reproduce T_ci,P_ci,ω_i, and will self-consistently reduce to the base SRK EOS in the absence of polar interactions. Binary VLE data with a non-polar reference hydrocarbon is used to extract the polarity of the respective functional group. The model shows superior performance in capturing the phase behavior of polar mixtures compared to the base SRK.
Multiphase reactors’ performance depends on the mesoscale structures formed due to multiphase hydrodynamics. Examples of mesoscale structures include gas bubbles in a fluidized bed and particle clusters in a riser. Experimental investigation of these mesoscale structures is challenging and expensive. To this end, Computational Fluid Dynamics (CFD) simulations are extensively employed; however, post-processing CFD data to capture mesoscale structures is challenging. This work develops a DBSCAN-based methodology to capture and characterize mesoscale structures from multiphase CFD simulation data. DBSCAN is an unsupervised machine-learning algorithm, which requires the value of two hyperparameters. A simple technique to calculate these hyperparameters is provided and the performance of DBSCAN is assessed on CFD-DEM simulations of bubbling fluidized beds and particle clustering. We demonstrate the computational complexity of DBSCAN to be Ο(n log n), lower than the existing techniques, by testing its scalability on highly resolved grids (up to 100 million grid points).
Trickle bed reactors (TBR) are widely used in the chemical industries. These reactors involve gas and liquid flow through a catalyst-packed bed. For optimal TBR performance, it is crucial to achieve a uniform distribution of gas and liquid among the catalyst particles. However, in multi-tubular reactors with slender tubes, flow maldistribution near the tube walls is a common issue. Therefore, a comprehensive understanding of local phase and flow distribution is essential for designing and operating reactors with slender tubes. This study employs Magnetic Resonance Imaging (MRI) to characterize the three-dimensional distribution of the two-phase trickle flow within a slender tube. Three quantities are characterized: gas-liquid-solid distribution, particle wetting efficiency, and the flow field. Structure and flow MRI images are processed to calculate these quantities. Additionally, a novel post-processing technique is introduced to determine the liquid distribution over individual particle surfaces. This distribution is determined at several axial and radial positions.
This study examines how granular mixtures of differently shaped particles segregate in a Freeman (FT4) rheometer. The mixtures contained two sets of particles with varying shapes and relative sizes. While our main focus was on the effect of particle shape on segregation, we recognized that even slight differences in size could lead to segregation. We specifically investigated when particles of different shapes have the same effective size, exploring three cases: 1) the largest sphere within a cubic particle (inscribed sphere), 2) the smallest sphere enclosing a cubic particle (circumscribed sphere), and 3) a sphere and cube with equal volume. Our findings reveal that binary mixtures of cubical and spherical particles can significantly segregate radially in the bed. We propose that the primary mechanism for this radial segregation is percolation caused by radial centrifugal forces pushing the particles outward.
Properties of the vapor-liquid interface of 16 binary mixtures were studied using molecular dynamics simulations and density gradient theory in combination with the PCP-SAFT equation of state. All binary combinations of the heavy-boiling components (cyclohexane, toluene, acetone, and carbon tetrachloride) with the light-boiling components (methane, carbon dioxide, hydrogen chloride, and nitrogen) were investigated at 0.7 times the critical temperature of the heavy-boiling component in the whole composition range. Data on the surface tension, the enrichment, the relative adsorption, and the interfacial thickness, as well as for the vapor-liquid equilibrium and Henry’s law constant are reported. The binary interaction parameters were fitted to experimental data in a consistent way for all systems and both methods. Overall, the results from both methods agree well for all investigated properties. The interfacial properties of the different studied systems differ strongly. We show that these differences are directly related to the underlying phase equilibrium behavior.
The selective separation of CO2 from CH4-containing gases is crucial to produce clean energy gases. In this study, triazole anion-functionalized ionic liquids (TAFILs) were designed by combining low molecular weight cations with triazole anions containing N electronegative site, and further mixed with physical solvents to form physicochemical absorbents. The results indicated that [Cho][Triz]/TMS (80 wt%/20 wt%) not only absorb 0.125 g CO2/g absorbent equal to that of 30 wt% MEA solution at 40 °C and 1 bar, but also have low enthalpy of -35.76 kJ/mol less than half of 30 wt% MEA solution. Simultaneously, superhigh CO2/CH4 selectivity of 191.0 higher than most of reported absorbents is obtained for [Cho][Triz]/TMS solvents. Such great performance of CO2 separation was attributed to relatively weak chemical and physical interaction between CO2 and TAFIL binary absorbents. This study may provide novel promising IL absorbents for CO2 capture applications from clean energy gases.
With the large-scale development of drugs, understanding the drug phase behaviors in complex systems become increasingly important. Among them, the solubility of drugs in biorelevant media needs to be urgently understood. To address this challenge, new strategies based on machine learning models are proposed. First, the strategy trains five machine learning models based on fifteen molecular descriptors of the drug molecular properties. The XGboost model was identified as the best predictive model for predicting drug solubility performance in various solvents. Next, the input feature vectors were expanded for machine learning using the MACCS chemical fingerprint coupled with the XGboost model. The MACCS chemical fingerprint coupled with the XGboost model has significantly improved the prediction accuracy of drug solubility. This finding demonstrates that the proposed strategy has solubility prediction capability, which is expected to provide valid information for drug development and drug solvent screening.