Set Of Kinetic Data Research Articles

Machine learning approach for the prediction of biomass pyrolysis kinetics from preliminary analysis

The pyrolytic behavior of lignocellulosic biomass is highly complex, and its kinetic behavior varies with operating conditions and the type of biomass. To reduce timescales, cost and rigorous calculations associated with new set of experimentation used for the estimation of kinetic parameters, model-based predictions are recommended. In the present work, Artificial Neural Network (ANN) based machine learning models are developed to predict the biomass pyrolysis kinetics. Data sets of thermogravimetric analysis and feedstock characterization from a diverse range of biomass were used to develop and test the networks. Four models were developed in this study based on proximate analysis (ANN-1), ultimate analysis (ANN-2), combined proximate and ultimate analysis (ANN-3) and the combined proximate, ultimate, and biochemical analysis (ANN-4). A total of 704 kinetic datasets were extracted and recalculated with the Coats-Redfern Method from which 662, 585, 465 and 133 datasets were used to develop models sequentially. The developed models, in particular ANN-3 and ANN-4 have shown a competitive prediction capability (R2 ~ 0.99, RRMSE <10.0%, and MAE < 0.071). Relative importance of each input (biomass properties & heating rate) on outputs (kinetic parameters) was also studied. Biochemical analysis was found to have higher contribution (~38%) in comparison to ultimate (~29%) followed by proximate analysis (~22%) and the pyrolysis kinetics were found to be affected by the heating rate to the extent of ~10%. The developed models were found to be accurate enough in predicting the pyrolysis kinetics for any new biomass feedstocks based on preliminary analysis.

Assessment of a catalytic plate reactor with in-situ sampling capabilities by means of CFD modeling and experiments

A catalytic plate reactor with a small movable sampling capillary enables us to gather a significantly larger set of kinetic data for the parameter estimation than using a typical steady-state packed-bed reactor with end-of-pipe measurement. In this study, a reactive three-dimensional CFD model developed with catalyticFOAM is validated by experimental axial gas species concentration profiles and then used to investigate the flow dynamics and study the effect of the immersed capillary for various operating conditions for the CO2 methanation reaction over Ni/Al2O3 catalyst. The results confirmed that the presence of the capillary (diameter 0.5 mm, continuous vs. open-ended) inside the rectangular channel (5 × 40 × 100 mm), the position of the orifice (diameter 0.25 mm), and the suction (2 to 8 ml/min) did not affect the quality of the kinetic data collected. For the current design of the reactor inlet, the flow fully develops within the first 10–25 mm of the reactor domain depending on the total flow rate (100 to 300 mlN/min). Hence, up to 90% of the reactor’s length can be used for in-situ measurements. This work demonstrates that our channel reactor is very suitable for collecting kinetic data, especially for fast and exothermic heterogeneous catalyzed reactions, with a high spatial resolution for gas composition and catalyst surface temperature.

Scaling up extractive deacidification of waste cooking oil

Biodiesel produced from waste feedstocks can play a significant role in fighting climate change, improperly disposed waste and growing energy demand. Waste feedstocks such as used cooking oil have a great potential for energy production. However, they often have to be purified from free fatty acids prior to biodiesel production. Extractive deacidification with deep eutectic solvents is a promising alternative to conventional purification methods.To evaluate the process of extractive deacidification of waste cooking oil, a full set of physical, hydrodynamic and kinetic data were experimentally determined on a laboratory scale. Hydrodynamic and kinetic experiments were performed in three geometrically similar jacketed agitated vessels. Vessels were equipped with axial flow impeller (four pitched blade impeller). Physical properties (density, viscosity and surface tension) were experimentally determined. Preliminary hydrodynamic experiments involved several model systems without mass transfer. As a result, correlation between power number and Reynolds number as well as scale-up criterion was developed. Obtained dependencies were correlated with the physical properties. Mixing intensity for achieving complete dispersion was determined. Second stage of investigation involved two sets of experiments, hydrodynamic and kinetic, with interphase mass transfer (the extraction of free fatty acids from waste cooking oil with deep eutectic solvent, potassium carbonate:ethylene glycol, 1:10). Obtained results enabled understanding interphase mass transfer and prediction of mass transfer coefficient from the derived dimensionless correlations. The values of volumetric mass transfer coefficients were smaller for the dispersed phase, indicating that the prevailing mass transfer resistance was within the droplets. The working hypothesis was that the same process result should be achieved at the same dispersion rate, and that hypothesis was confirmed – at all scales extraction efficiency was 97.9 ± 0.1%.

A model-free sparse approximation approach to robust formal reaction kinetics

Accurate and transferable models of reaction kinetics are of key importance for chemical reactors on both laboratory and industrial scale. Usually, setting up such models requires a detailed mechanistic understanding of the reaction process and its interplay with the reactor setup. We present a data driven approach which analyzes the influence of process parameters on the reaction rate to identify locally approximated effective rate laws without prior knowledge and assumptions. The algorithm we propose determines relevant model terms from a polynomial ansatz employing well established statistical methods. For the optimization of the model parameters special emphasize is put on the robustness of the results by taking not only the quality of the fit but also the distribution of errors into account in a multi-objective optimization. We demonstrate the flexibility of this approach based on artificial kinetic data sets from microkinetic models. This way, we show that the kinetics of both the classical HBr reaction and a prototypical catalytic cycle are automatically reproduced. Further, combining our approach with experimental screening designs we illustrate how to efficiently explore kinetic regimes by using the example of the catalytic oxidation of CO. • Data driven approach for robust kinetic modeling. • Algorithm automatically determines functional form of model to avoid bias. • Effective kinetic models do not require mechanistic understanding. • Automatic discovery of kinetic regimes.

Micro-Attention Branch, a Flexible Plug-In That Enhances Existing 3D ConvNets

In the field of video motion recognition, with the increase in network depth, there is an asymmetry in the amount of parameters and their accuracy. We propose the structure of micro-attention branches and a method of integrating attention branches in multi-branches 3D convolution networks. Our proposed attention branches can improve this asymmetry problem. Attention branches can be flexibly added to 3D convolution network in the form of plug-in, without changing the overall structure of the original network. Through this structure, the newly constructed network can fuse the attention features extracted by attention branches in real time in the process of feature extraction. By adding attention branches, the model can focus on the action subject more accurately, so as to improve the accuracy of the model. Moreover, in the case that there are multiple sub-branches in the construction module of the existing network, 3D micro-attention branches can well adapt to this scenario. In the kinetics dataset, we use our proposed micro-attention branch structure to construct a deep network, which is compared with the original network. The experimental results show that the recognition accuracy of the network with micro-attention branches is improved by 3.6% compared with the original network, while the amount of parameters to be trained is only increased by 0.6%.

Kinetic isotope effect in siderite growth: Implications for the origin of banded iron formation siderite

The widespread occurrence of siderite (FeCO3) at Earth's modern surface and in sedimentary rocks has led to its frequent use as a tool for palaeoenvironmental reconstruction. Isotopic studies of siderite associated with Archaean-Palaeoproterozoic banded iron formations show negative δ13C values, which coupled with δ56Fe values, have been considered to support an important role of dissimilatory iron reduction (DIR) in the genesis of iron formations.Facies-specific analyses show that texturally and petrographically syndepositional and/or early diagenetic, finely laminated microsparitic (≤10 µm in diameter) siderite exhibits δ13C between −3 and −7‰. This siderite δ13C range can be interpreted in three ways: (1) precipitation of siderite from dissolved inorganic carbon (DIC) produced by DIR coupled to partial oxidation of organic carbon with δ13C < −25‰, (2) precipitation from a mixed hydrothermal-seawater fluid bearing mantle-derived DIC with δ13C ≅ −6.5‰, and (3) precipitation from seawater-derived DIC and expression of a kinetic isotope effect (KIE) associated with siderite growth.We present the carbon isotopic composition of siderite formed in abiotic seeded growth experiments over a wide range of solution siderite saturation at 25 °C and 1 bar. With a complete set of chemical kinetic data, we develop and apply a model of disequilibrium siderite precipitation to constrain the carbon KIE as a function of growth rate. Sampling of model parameters from distributions that represent uncertainty in the parameter values, we find best-fit values of equilibrium and kinetic fractionation factors: 103lnαeq = −0.2 and 103lnαf = −14.9.These constraints allow us to assess the origins of δ13C values in microsparitic siderite in iron formations. For example, a moderate level of siderite supersaturation (e.g., Ωsid = 100) in the hydrothermal-seawater mixing fluids (source of dissolved iron) would have induced a carbon KIE of ∼−8‰ in fluid-buffered early diagenetic siderite growth; a range that encompasses essentially all negative δ13C values reported from microsparitic siderite in Campbellrand-Kuruman iron formations, Transvaal Supergroup, South Africa. We suggest that a straightforward, abiotic explanation for the range of δ13C values in microsparitic siderite in Archaean-Palaeoproterozoic banded iron formations is pulsed deposition of iron-rich sediments associated with intense hydrothermal activity. Specifically, the siderite-δ13C range is well explained by a partial expression of carbon KIE associated with siderite growth from supersaturated solutions, and from a bottom-water DIC reservoir with near-zero δ13C values (seawater with a possible contribution of mantle carbon).

Creating Ion Channel Kinetic Models Using Cloud Computing.

Computational modeling of ion channels provides key insight into experimental electrophysiology results and can be used to connect channel dynamics to emergent phenomena observed at the tissue and organ levels. However, creation of these models requires substantial mathematical and computational background. This tutorial seeks to lower the barrier to creating these models by providing an automated pipeline for creating Markov models of an ion channel kinetics dataset. We start by detailing how to encode sample voltage-clamp protocols and experimental data into the program and its implementation in a cloud computing environment. We guide the reader on how to build a containerized instance, push the machine image, and finally run the routine on cluster nodes. While providing open-source code has become more standard in computational studies, this tutorial provides unprecedented detail on the use of the program and the creation of channel models, starting from inputting the raw experimental data. © 2022 Wiley Periodicals LLC. Basic Protocol: Creation of ion channel kinetic models with a cloud computing environment Alternate Protocol: Instructions for use in a standard high-performance compute cluster.

Skeleton Motion Recognition Based on Multi-Scale Deep Spatio-Temporal Features

In the task of human motion recognition, the overall action span is changeable, and there may be an inclusion relationship between action semantics. This paper proposes a novel multi-scale time sampling module and a deep spatiotemporal feature extraction module, which strengthens the receptive field of the feature map and strengthens the extraction of spatiotemporal-related feature information via the network. We study and compare the performance of three existing multi-channel fusion methods to improve the recognition accuracy of the network on the open skeleton recognition dataset. In this paper, several groups of comparative experiments are carried out on two public datasets. The experimental results show that compared with the classical 2s-AGCN algorithm, the accuracy of the algorithm proposed in this paper shows an improvement of 1% on the Kinetics dataset and 0.4% and 1% on the two evaluating indicators of the NTU-RGB+D dataset, respectively.

Vibrational-Specific Model of Simultaneous and Relaxation Under Postshock Conditions

The present paper discusses the development of an aerothermochemistry model that can be efficiently coupled with fluid dynamics simulations and retains the accuracy of the quantum-mechanical and molecular dynamic principles from which it is derived. The present model allows simulations of thermochemical nonequilibrium in nitrogen without relying on the existence of a vibrational temperature. A series of verification tests suggest that the vibrational-specific master equation model is sufficiently accurate and computationally efficient when applied to the modeling of dissociating nitrogen flows under adiabatic conditions. Toward this end, a kinetic dataset describing vibrational energy transfer and dissociation in and collisions is obtained on a high-fidelity potential energy surface, and curve fit coefficients are reported. Validation against shock-tube data indicates that the inclusion of electronically excited and ionic species is potentially required when computing and comparing relaxation parameters with the experimental data derived from the radiative signatures of nitrogen shock flows.

Sound Can Help Us See More Clearly.

In the field of video action classification, existing network frameworks often only use video frames as input. When the object involved in the action does not appear in a prominent position in the video frame, the network cannot accurately classify it. We introduce a new neural network structure that uses sound to assist in processing such tasks. The original sound wave is converted into sound texture as the input of the network. Furthermore, in order to use the rich modal information (images and sound) in the video, we designed and used a two-stream frame. In this work, we assume that sound data can be used to solve motion recognition tasks. To demonstrate this, we designed a neural network based on sound texture to perform video action classification tasks. Then, we fuse this network with a deep neural network that uses continuous video frames to construct a two-stream network, which is called A-IN. Finally, in the kinetics dataset, we use our proposed A-IN to compare with the image-only network. The experimental results show that the recognition accuracy of the two-stream neural network model with uesed sound data features is increased by 7.6% compared with the network using video frames. This proves that the rational use of the rich information in the video can improve the classification effect.

A Deep Learning Approach to Design and Discover Sustainable Cementitious Binders: Strategies to Learn From Small Databases and Develop Closed-form Analytical Models

To reduce the energy-intensity and carbon footprint of Portland cement (PC), the prevailing practice embraced by concrete technologists is to partially replace the PC in concrete with supplementary cementitious materials [SCMs: geological materials (e.g., limestone); industrial by-products (e.g., fly ash); and processed materials (e.g., calcined clay)]. Chemistry and content of the SCM profoundly affect PC hydration kinetics; which, in turn, dictates the evolutions of microstructure and properties of the [PC + SCM] binder. Owing to the substantial diversity in SCMs’ compositions–plus the massive combinatorial spaces, and the highly nonlinear and mutually-interacting processes that arise from SCM-PC interactions–state-of-the-art computational models are unable to produce a priori predictions of hydration kinetics or properties of [PC + SCM] binders. In the past 2 decades, the combination of Big data and machine learning (ML)—commonly referred to as the fourth paradigm of science–has emerged as a promising approach to learn composition-property correlations in materials (e.g., concrete), and capitalize on such learnings to produce a priori predictions of properties of materials with new compositions. Notwithstanding these merits, widespread use of ML models is hindered because they: 1) Require Big data to learn composition-property correlations, and, in general, large databases for concrete are not publicly available; and 2) Function as black-boxes, thus providing little-to-no insights into the materials laws like theory-based analytical models do. This study presents a deep learning (DL) model capable of producing a priori, high-fidelity predictions of composition- and time-dependent hydration kinetics and phase assemblage development in [PC + SCM] pastes. The DL is coupled with: 1) A fast Fourier transformation algorithm that reduces the dimensionality of training datasets (e.g., kinetic datasets), thus allowing the model to learn intrinsic composition-property correlations from a small database; and 2) A thermodynamic model that constrains the model, thus ensuring that predictions do not violate fundamental materials laws. The training and outcomes of the DL are ultimately leveraged to develop a simple, easy-to-use, closed-form analytical model capable of predicting hydration kinetics and phase assemblage development in [PC + SCM] pastes, using their initial composition and mixture design as inputs.

Multiscale Modeling as a Tool for the Prediction of Catalytic Performances: The Case of n-Heptane Hydroconversion in a Large-Pore Zeolite

The optimization of predictive kinetic models for catalytic processes is a topical challenge. In the present work, a predictive multiscale single-event microkinetic model based on the data obtained by density functional theory (DFT) calculation for n-heptane hydroconversion in large-pore zeolites has been obtained. It was validated by a large set of kinetic data obtained by high-throughput kinetic experiments, performed with a well-balanced Pt/Beta zeolite catalyst. DFT calculations show that secondary cations are much less stable than tertiary cations and adsorbed alkenes. This is of prime importance in the quantification of type B isomerization reaction barriers depending on the type of the carbenium ion. Cracking reaction barriers are also strongly affected by the nature of the cation that cracks and that of the cracking products. The agreement between simulated and experimental kinetic data is satisfactory, showing the reliability of the multiscale kinetic approach. Only a few parameters were adjusted to improve the correspondence with experiments. The analysis of the simulated coverage demonstrates a very low proportion of acidic sites involved in the adsorption and further reactions in the relevant experimental conditions. When these occur, tertiary carbenium ion intermediates appear in a significantly higher concentration with respect to other species. This works opens the route to a better prediction of the catalytic performance of large-pore zeolites in alkane hydroconversion.

Enhanced descriptor identification and mechanism understanding for catalytic activity using a data-driven framework: revealing the importance of interactions between elementary steps

A data-driven framework was developed which used ML surrogate model to extract activity controlling descriptors from kinetics dataset. It enhanced mechanic understanding and predicted catalytic activities more accurately than derivate-based method.

Progress towards machine learning reaction rate constants.

Quantum and classical reaction rate constant calculations come at the cost of exploring potential energy surfaces. Due to the "curse of dimensionality", their evaluation quickly becomes unfeasible as the system size grows. Machine learning algorithms can accelerate the calculation of reaction rate constants by predicting them using low cost input features. In this perspective, we briefly introduce supervised machine learning algorithms in the context of reaction rate constant prediction. We discuss existing and recently created kinetic datasets and input feature representations as well as the use and design of machine learning algorithms to predict reaction rate constants or quantities required for their computation. Amongst these, we first describe the use of machine learning to predict activation, reaction, solvation and dissociation energies. We then look at the use of machine learning to predict reactive force field parameters, reaction rate constants as well as to help accelerate the search for minimum energy paths. Lastly, we provide an outlook on areas which have yet to be explored so as to improve and evaluate the use of machine learning algorithms for chemical reaction rate constants.

Human Action Recognition of Spatiotemporal Parameters for Skeleton Sequences Using MTLN Feature Learning Framework

Human action recognition (HAR) by skeleton data is considered a potential research aspect in computer vision. Three-dimensional HAR with skeleton data has been used commonly because of its effective and efficient results. Several models have been developed for learning spatiotemporal parameters from skeleton sequences. However, two critical problems exist: (1) previous skeleton sequences were created by connecting different joints with a static order; (2) earlier methods were not efficient enough to focus on valuable joints. Specifically, this study aimed to (1) demonstrate the ability of convolutional neural networks to learn spatiotemporal parameters of skeleton sequences from different frames of human action, and (2) to combine the process of all frames created by different human actions and fit in the spatial structure information necessary for action recognition, using multi-task learning networks (MTLNs). The results were significantly improved compared with existing models by executing the proposed model on an NTU RGB+D dataset, an SYSU dataset, and an SBU Kinetic Interaction dataset. We further implemented our model on noisy expected poses from subgroups of the Kinetics dataset and the UCF101 dataset. The experimental results also showed significant improvement using our proposed model.

Analyzing the causes of method-to-method variability among Rubisco kinetic traits: from the first to the current measurements.

Due to the importance of Rubisco in the biosphere, its kinetic parameters have been measured by different methodologies in a large number of studies over the last 60 years. These parameters are essential to characterize the natural diversity in the catalytic properties of the enzyme and they are also required for photosynthesis and cross-scale crop modeling. The present compilation of Rubisco kinetic parameters in model species revealed a wide intraspecific laboratory-to-laboratory variability, which was partially solved by making corrections to account for differences in the assay buffer composition and in the acidity constant of dissolved CO2, as well as for differences in the CO2 and O2 solubilities. Part of the intraspecific variability was also related to the different analytical methodologies used. For instance, significant differences were found between the two main methods for the determination of the specificity factor (Sc/o), and also between Rubisco quantification methods, Rubisco purification versus crude extracts, and single-point versus CO2 curve measurements for the carboxylation turnover rate (kcatc) determination. Causes of the intraspecific laboratory-to-laboratory variability for Rubisco catalytic traits are discussed. This study provides a normalized kinetic dataset for model species to be used by the scientific community. Corrections and recommendations are also provided to reduce measurement variability, allowing the comparison of kinetic data obtained in different laboratories using different assay conditions.

Human Activity Recognition using Resnet-34 Model

Activity recognition has been an emerging field of research since the past few decades. Humans have the ability to recognize activities from a number of observations in their surroundings. These observations are used in several areas like video surveillance, health sectors, gesture detection, energy conservation, fall detection systems and many more. Sensor based approaches like accelerometer, gyroscope, etc., have been discussed with its advantages and disadvantages. There are different ways of using sensors in a smartly controlled environment. A step-by-step procedure is followed in this paper to build a human activity recognizer. A general architecture of the Resnet model is explained first along with a description of its workflow. Convolutional neural network which is capable of classifying different activities is trained using the kinetic dataset which includes more than 400 classes of activities. The videos last around tenth of a second. The Resnet-34 model is used for image classification of convolutional neural networks and it provides shortcut connections which resolves the problem of vanishing gradient. The model is trained and tested successfully giving a satisfactory result by recognizing over 400 human actions. Finally, some open problems are presented which should be addressed in future research.

Toward ICP-SIFT mass spectrometry and atomic cation ligation as a probe of relativistic effects-A personal journey.

The ICP-SIFT mass spectrometer at York University, a derivative of flowing afterglow (FA) and selected-ion flow tube (SIFT) mass spectrometers, has provided a powerful technique to measure the chemistry and kinetics of atomic cation-molecule reactions. Here, I focus on periodic trends in the kinetics of ligation reactions of atomic ions with small molecules. I examine trends in ammonia ligation kinetics across the first two rows of the atomic transition metal cations and their correlation with ligand bond enthalpies and ligand field stabilization energies. Also explored are trends down Groups 1 and 2 in the kinetics of noncovalent electrostatic ligand bonding and the tendency for s electron solvation of the atomic alkaline-earth cations with ammonia. Finally, I briefly review trends observed with 12 different ligands in the ligation rate down the periodic table with Group 9-12 transition atomic metal cations. These trends provide a compelling probe for the presence of relativistic effects that influence the strengths of the metal-ion ligand bonds that are formed. There is a clear third-row rate enhancement with Ir+ , Pt+ , Au+ , and Hg+ , the extent of which depends on the nature of the ligand. This large set of kinetic data provides an unprecedented broad perspective of relativistic effects in ligand bonding. With CS2 as a ligand, the third-row relativistic effect is apparent in the formation of both the first and the second ligand bond with the Groups 10 and 11 atomic cations as predicted by our quantum chemical calculations of ligation energies.

Microfluidic-based in-situ determination for reaction kinetics of hydrogen peroxide decomposition

In this work, a microfluidic approach was presented to study the reaction kinetics of hydrogen peroxide (H2O2) decomposition. The production rate of oxygen correlated to the volume change of bubbles in microchannel via in-situ visualization in order to characterize H2O2 decomposition. Kinetic models were established to determine the reaction rate constant and activation energy of H2O2 decomposition reaction as a function of temperature, initial mass fraction of H2O2, light intensity, and pH value. Meanwhile, the reaction density functional theory calculation indicated a good agreement with the conclusions from the experimental observations. This method provides a rapid, accurate and common approach to determine the kinetics of liquid-to-gas decomposition reactions. Furthermore, the set of kinetic data are beneficial to the process optimization of reactions involving H2O2.

Synthetic Humans for Action Recognition from Unseen Viewpoints

Although synthetic training data has been shown to be beneficial for tasks such as human pose estimation, its use for RGB human action recognition is relatively unexplored. Our goal in this work is to answer the question whether synthetic humans can improve the performance of human action recognition, with a particular focus on generalization to unseen viewpoints. We make use of the recent advances in monocular 3D human body reconstruction from real action sequences to automatically render synthetic training videos for the action labels. We make the following contributions: (1) we investigate the extent of variations and augmentations that are beneficial to improving performance at new viewpoints. We consider changes in body shape and clothing for individuals, as well as more action relevant augmentations such as non-uniform frame sampling, and interpolating between the motion of individuals performing the same action; (2) We introduce a new data generation methodology, SURREACT, that allows training of spatio-temporal CNNs for action classification; (3) We substantially improve the state-of-the-art action recognition performance on the NTU RGB+D and UESTC standard human action multi-view benchmarks; Finally, (4) we extend the augmentation approach to in-the-wild videos from a subset of the Kinetics dataset to investigate the case when only one-shot training data is available, and demonstrate improvements in this case as well.