Estimation Of Kinetic Parameters Research Articles

Pyrolysis kinetics, physicochemical characteristics and thermal decomposition behavior of agricultural wastes using thermogravimetric analysis

This study specifically focuses on pyrolysis kinetics and thermochemical decomposition of important nonedible agricultural wastes (orange filter cake, pomegranate filter cake, poplar branch, corn stalk, sugarcane bagasse, olive pomace, hazelnut glume, hazelnut shell, walnut shell and pistachio shell) with high renewable energy generation potentials. The novelty of this study is to analyze utilization of common agricultural wastes in Turkey for energy applications by evaluating their thermal characteristics and kinetics through pyrolysis from a holistic perspective. Pyrolysis experiments were conducted using a thermogravimetric analyzer (TGA) for the estimation of kinetic parameters by Coats–Redfern method. Differential thermogravimetric analysis (DTG) was carried out to study thermal degradation of biomass components. Ultimate analysis showed that poplar branch had the highest hydrogen/carbon atomic ratio (H/C=1.3) while hazelnut shell had the lowest H/C ratio of 0.9. Fixed carbon content was the lowest in pistachio shell (16.1%) and the highest in pomegranate filter cake (26.8%). The lowest ash content (1.4%) was found for sugarcane bagasse while the highest ash content (9%) was determined for corn stalk. Orange filter cake had the lowest activation energy (Ea) value of 17.8 kJ/mol and the lowest lignin content (4.9%). Poplar branch and sugarcane bagasse had less charring tendency in which char yields at 900 °C were 10.8 and 11.5%, respectively, suggesting that in case of fast pyrolysis where low char is favored, poplar branch and sugarcane bagasse would be better options among the other agricultural wastes studied.

Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function

PurposeIn Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function (AIF) has been shown to be a significant contributor to uncertainty in the estimation of kinetic parameters. This study is to assess the feasibility of using a deep learning network to estimate local Capillary Input Function (CIF) to estimate blood-brain barrier (BBB) permeability, while reducing the required scan time. Materials and MethodA total of 13 healthy subjects (younger (<40 y/o): 8, older (> 67 y/o): 5) were recruited and underwent 25-min DCE-MRI scans. The 25 min data were retrospectively truncated to 10 min to simulate a reduced scan time of 10 min. A deep learning network was trained to predict the CIF using simulated tissue contrast dynamics with two vascular transport models. The BBB permeability (PS) was measured using 3 methods: (i) Ca-25min, using DCE-MRI data of 25 min with individually sampled AIF (Ca); (ii) Ca-10min, using truncated 10min data with AIF (Ca); and (iii) Cp-10min, using truncated 10 min data with CIF (Cp). The PS estimates from the Ca-25min method were used as reference standard values to assess the accuracy of the Ca-10min and Cp-10min methods in estimating the PS values. ResultsWhen compared to the reference method(Ca-25min), the Ca-10min and Cp-10min methods resulted in an overestimation of PS by 217 ± 241 % and 48.0 ± 30.2 %, respectively. The Bland Altman analysis showed that the mean difference from the reference was 8.85 ± 1.78 (x10−4 min−1) with the Ca-10min, while it was reduced to 1.63 ± 2.25 (x10−4 min−1) with the Cp-10min, resulting in an average reduction of 81%. The limits of agreement also reduced by up to 39.2% with the Cp-10min. We found a 75% increase of BBB permeability in the gray matter and a 35% increase in the white matter, when comparing the older group to the younger group. ConclusionsWe demonstrated the feasibility of estimating the capillary-level input functions using a deep learning network. We also showed that this method can be used to estimate subtle age-related changes in BBB permeability with reduced scan time, without compromising accuracy. Moreover, the trained deep learning network can automatically select CIF, reducing the potential uncertainty resulting from manual user-intervention.

Study on the Operational Modes Using Both Growing and Resting Cells for Succinic Acid Production from Xylose Kinetic Modelling

Succinic acid (SA) is one of the most prominent C4 biomass-based platform chemicals that can be biologically obtained. This article verifies, for the first time, the possibility of producing succinic acid with fed-batch or repeated batch operations with Actinobacillus succinogenes in a resting state, that is, in the absence of a nitrogen source. In this work it is possible to optimise separately the stages of cell growth and production in the fed-batch or repeated batch modes, minimising the costs associated with the nitrogen source and facilitating the subsequent purification of SA. These experiments were carried out with xylose, the most abundant monosaccharide in hemicelluloses, with the results subsequently being compared to those obtained in equivalent operations carried out with cells in a state of growth. First, a cost-effective synthetic growth medium was proposed and successfully employed for SA production. Biocatalysts’ reutilisation showed that the bioprocess can be carried out successfully in repeated batch and fed-batch modes. The best mode for growing cells is repeated batch, achieving a maximum productivity of 0.77 g‧L−1‧h−1, a selectivity of 53% and a yield of 51% with respect to xylose consumed. In contrast, the fed-batch mode was found to be the most convenient mode with resting cell biocatalyst, reaching a maximum productivity of 0.83 g‧L−1‧h−1, a selectivity of 0.78 g‧g−1 and a yield of 68% with respect to the xylose consumed. In addition, by-product formation is significantly reduced when employing resting cells. An unstructured non-segregated kinetic model was developed for both biocatalysts, capable of simulating cell growth, xylose consumption, SA production and by-product generation, with successful estimation of kinetic parameters supported by statistical criteria.

Short-term PET-derived kinetic estimation for the diagnosis of hepatocellular carcinoma: a combination of the maximum-slope method and dual-input three-compartment model

BackgroundKinetic estimation provides fitted parameters related to blood flow perfusion and fluorine-18-fluorodeoxyglucose (18F-FDG) transport and intracellular metabolism to characterize hepatocellular carcinoma (HCC) but usually requires 60 min or more for dynamic PET, which is time-consuming and impractical in a busy clinical setting and has poor patient tolerance.MethodsThis study preliminarily evaluated the equivalence of liver kinetic estimation between short-term (5-min dynamic data supplemented with 1-min static data at 60 min postinjection) and fully 60-min dynamic protocols and whether short-term 18F-FDG PET-derived kinetic parameters using a three-compartment model can be used to discriminate HCC from the background liver tissue. Then, we proposed a combined model, a combination of the maximum-slope method and a three-compartment model, to improve kinetic estimation.ResultsThere is a strong correlation between the kinetic parameters K1 ~ k3, HPI and {{varvec{V}}}_{{varvec{b}}} in the short-term and fully dynamic protocols. With the three-compartment model, HCCs were found to have higher k2, HPI and k3 values than background liver tissues, while K1, k4 and {{varvec{V}}}_{{varvec{b}}} values were not significantly different between HCCs and background liver tissues. With the combined model, HCCs were found to have higher HPI, K1 and k2, k3 and {{varvec{V}}}_{{varvec{b}}} values than background liver tissues; however, the k4 value was not significantly different between HCCs and the background liver tissues.ConclusionsShort-term PET is closely equivalent to fully dynamic PET for liver kinetic estimation. Short-term PET-derived kinetic parameters can be used to distinguish HCC from background liver tissue, and the combined model improves the kinetic estimation.Clinical relevance statementShort-term PET could be used for hepatic kinetic parameter estimation. The combined model could improve the estimation of liver kinetic parameters.Graphical

Synergic interactions, kinetic and thermodynamic analyses of date palm seeds and cashew shell waste co-pyrolysis using Coats–Redfern method

Date palm and cashew shell wastes are abundant byproducts of the agriculture industry in the UAE, but they are often underutilized and not adequately managed, resulting in environmental problems. For the first time, co-pyrolysis of these wastes was studied to investigate their physicochemical properties, synergistic interaction, thermal degradation behavior, and estimate kinetic and thermodynamic parameters using thermogravimetric analysis with non-isothermal heating rates from 20 to 800 °C and a heating rate of 10 °C/min. The Coats and Redfern method, utilizing twenty-one solid-state reaction mechanisms, was used to perform analyses. The three diffusion models showed the best linear regression with the experimental thermogravimetric data. Co-pyrolysis of cashew shells with date seeds significantly lowered the activation energy (Ea) and produced stable biochar, providing an opportunity to obtain pyrolysis products at better energy efficiency. The estimated Ea for 100% date seeds, 100% cashew shells, and their blend (50:50) were 109, 124, and 113 kJ/mol, respectively. The thermodynamic parameters (ΔH, ΔG, and ΔS) indicated that the pyrolysis process was endothermic but not spontaneous. The novelty of this work lies in investigating the potential of utilizing two underutilized wastes together to produce pyrolysis products. This study is essential for advancing co-pyrolysis of date seeds and cashew shell wastes, optimizing product yields, and understanding their pyrolysis behavior towards experimental pyrolysis.

Modelling Self-Heating and Self-Ignition Processes during Biomass Storage

A mathematical model was developed to predict the self-heating and self-ignition processes of relatively dry biomass during storage, considering in detail the effects of moisture exchange behaviour, low-temperature oxidation reaction and associated heat and mass transfer. Basket heating tests on fir pellets and powder at temperatures of 180–200 °C were conducted to observe the heating process and determine the kinetics of low-temperature chemical oxidation for model validation. As a result, it was demonstrated that the developed model could reasonably represent the self-heating and spontaneous combustion processes of biomass storage. Furthermore, the numerical study and model sensitivity analysis revealed that reasonably describing the low-temperature oxidation and associated heat and mass transfer process with reliable estimations of kinetic and thermophysical parameters of the biomass material is necessary for predicting the self-ignition, considering the effect of water exchange behaviour is essential to predict the self-heating process even for relatively dry biomass, such as pellets, with the moisture content up to 15–20%.

Modeling and simulation of trickle bed reactors for the purification of 1-butene

Abstract In this contribution, a mathematical model of an industrial trickle-bed reactor employed in the purification of a C4 cut by selective hydrogenation of acetylenic or dienes compounds to obtain high purity 1-butene is presented. A reaction network of ten reactions is included in the model, with kinetics expressions and parameter estimation obtained from previous experimental studies on a commercial catalyst. Internal mass transfer resistances in the catalyst particles are significant; therefore the reaction-diffusion equations must be solved. External mass transfer resistances in the liquid phase were retained, while those in the vapor phase were negligible. The model was employed to analyze the reactor behavior by varying the inlet molar flow rate of hydrogen, the operating pressure, inlet temperature and the level of activity of the catalyst, taking into account its deactivation. It was demonstrated that the mass transfer resistances, inside and outside the catalyst particles, have a significant impact on the selectivity, but a careful operation of the reactor can improve the selectivity and extent the catalyst life. On the other hand, an alternative system was proposed, with two beds and a distributed input of H2, which led to a significant improvement in the selectivity.

Design of a generic method for single dual-tracer PET imaging acquisition in clinical routine

Using different tracers in positron emission tomography (PET) imaging can bring complementary information on tumor heterogeneities. Ideally, PET images of different tracers should be acquired simultaneously to avoid the bias induced by movement and physiological changes between sequential acquisitions. Previous studies have demonstrated the feasibility of recovering separated PET signals or parameters of two or more tracers injected (quasi-)simultaneously in a single acquisition. In this study, a generic framework in the context of dual-tracer PET acquisition is proposed where no strong kinetic assumptions nor specific tuning of parameters are required. The performances of the framework were assessed through simulations involving the combination of [18F]FCH and [18F]FDG injections, two protocols (90 and 60 min acquisition durations) and various activity ratios between the two injections. Preclinical experiments with the same radiotracers were also conducted. Results demonstrate the ability of the method both to extract separated arterial input functions (AIF) from noisy image-derived input function and to separate the dynamic signals and further estimate kinetic parameters. The compromise between bias and variance associated with the estimation of net influx rate K i shows that it is preferable to use the second injected radiotracer with twice the activity of the first for both 90 min [18F]FCH+[18F]FDG and 60 min [18F]FDG+[18F]FCH protocols. In these optimal settings, the weighted mean-squared-error of the estimated AIF was always less than 7%. The K i bias was similar to the one of single-tracer acquisitions; below 5%. Compared to single-tracer results, the variance of K i was twice more for 90 min dual-tracer scenario and four times more for the 60 min scenario. The generic design of the method makes it easy to use for other pairs of radiotracers and even for more than two tracers. The absence of strong kinetic assumptions and tuning parameters makes it suitable for a possible use in clinical routine.

Bayesian kinetic modeling for tracer-based metabolomic data

BackgroundStable Isotope Resolved Metabolomics (SIRM) is a new biological approach that uses stable isotope tracers such as uniformly 13C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{13}C$$\\end{document}-enriched glucose (13C6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{13}C_6$$\\end{document}-Glc) to trace metabolic pathways or networks at the atomic level in complex biological systems. Non-steady-state kinetic modeling based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively characterize the dynamic behavior of metabolic networks. It has been increasingly used to understand the regulation of normal metabolism and dysregulation in the development of diseases. However, fitting a kinetic model is challenging because there are usually multiple sets of parameter values that fit the data equally well, especially for large-scale kinetic models. In addition, there is a lack of statistically rigorous methods to compare kinetic model parameters between different experimental groups.ResultsWe propose a new Bayesian statistical framework to enhance parameter estimation and hypothesis testing for non-steady-state kinetic modeling of SIRM data. For estimating kinetic model parameters, we leverage the prior distribution not only to allow incorporation of experts’ knowledge but also to provide robust parameter estimation. We also introduce a shrinkage approach for borrowing information across the ensemble of metabolites to stably estimate the variance of an individual isotopomer. In addition, we use a component-wise adaptive Metropolis algorithm with delayed rejection to perform efficient Monte Carlo sampling of the posterior distribution over high-dimensional parameter space. For comparing kinetic model parameters between experimental groups, we propose a new reparameterization method that converts the complex hypothesis testing problem into a more tractable parameter estimation problem. We also propose an inference procedure based on credible interval and credible value. Our method is freely available for academic use at https://github.com/xuzhang0131/MCMCFlux.ConclusionsOur new Bayesian framework provides robust estimation of kinetic model parameters and enables rigorous comparison of model parameters between experimental groups. Simulation studies and application to a lung cancer study demonstrate that our framework performs well for non-steady-state kinetic modeling of SIRM data.

Neural network estimation of kinetic parameters in distributed activation energy model (DAEM) without a priori assumptions for parallel reaction system

In this study, a new estimation method for the kinetic parameters in a distributed activation energy model (DAEM) was designed and developed. In the proposed method, the conversion estimation by the DAEM is regarded as a feedforward computation of a three-layer neural network, and the kinetic parameters of the DAEM are estimated by optimization of the neural network. The proposed method does not require an a priori assumption of the kinetic parameters or mechanism of a parallel reaction system. First, we created reaction data using numerical simulations, and a kinetic analysis using the neural network was performed. The neural network predicted conversion X very accurately; however, reactions with low contributions to the parallel reaction system also appeared. Next, we carried out a kinetic analysis using the neural network with the lower limit on the contribution of the ith reaction Vi*/V*. The lower limit on Vi*/V* did not influence the prediction accuracy of X and had a significant effect on reducing the reactions with low Vi*/V* and the estimation accuracies of the kinetic parameters in the DAEM. The optimal value of the lower limit on Vi*/V* was determined to be 1.0×10−5–1.0×10−4 when using the neural network with 64 hidden layer nodes. Moreover, the prediction accuracy of the proposed method was compared with those of conventional methods — the single-Gaussian method (1-DAEM), double-Gaussian method (2-DAEM), and Miura and Maki method. The kinetic parameters estimated using the proposed method were closer to the true values than those obtained using conventional methods. Moreover, the X value predicted by the proposed method was more accurate than that predicted by conventional methods. The influence of approximation on training data creation was also examined. The estimation accuracy of the neural network was still high but slightly deteriorated when the neural network was optimized using the reaction data created without the approximation.

Nucleation of Biomolecular Condensates from Finite-Sized Simulations.

The nucleation of protein condensates is a concentration-driven process of assembly. When modeled in the canonical ensemble, condensation is affected by finite-size effects. Here, we present a general and efficient route for obtaining ensemble properties of protein condensates in the macroscopic limit from finite-sized nucleation simulations. The approach is based on a theoretical description of droplet nucleation in the canonical ensemble and enables estimation of thermodynamic and kinetic parameters, such as the macroscopic equilibrium density of the dilute protein phase, the surface tension of the condensates, and nucleation free energy barriers. We apply the method to coarse-grained simulations of NDDX4 and FUS-LC, two phase-separating disordered proteins with different physicochemical characteristics. Our results show that NDDX4 condensate droplets, characterized by lower surface tension, higher solubility, and faster monomer exchange dynamics compared to those of FUS-LC, form with negligible nucleation barriers. In contrast, FUS-LC condensates form via an activated process over a wide range of concentrations.

Combining 13C‐NMR Triad Sequence Data with Joint Molecular Weight and Composition Data to Estimate Parameters in a Gas‐Phase Polyethylene Reactor Model

AbstractA three‐site metallocene catalyst is used in a gas‐phase semi‐batch reactor to produce ethylene/hexene copolymers. At the end of each batch, polyethylene (PE) is collected and analyzed to determine the carbon‐13 nuclear magnetic resonance (13C‐NMR) triad sequence distribution. Joint molecular weight (MW) and composition distribution data are obtained using gel permeation chromatography with an infrared detector (GPC‐IR). Data from ten experimental runs are used for kinetic parameter estimation. Using a mean‐squared error (MSE) selection methodology, 23 of the 36 model parameters are selected for estimation using the available polymerization rate and PE characterization data. The remaining parameters are held at initial guesses to avoid overfitting. Addition of the triad data to the parameter estimation problem allows for one additional parameter to be estimated and results in improved parameter estimates. Standard deviations of all but one of the estimated parameters decreased due to inclusion of triad data. The updated parameter estimates result in good fits for the triad data and for joint MW and composition data. The model accurately predicts four validation data sets not used for parameter estimation. The new model and its updated parameter estimates will be valuable for scaling up new polymer grades from laboratory‐scale to commercial‐scale.

Abstract WMP61: Vulnerable And Stabilized States After Cerebral Ischemic Events: Implications Of Kinetic Modeling In The POINT, SOCRATES, And THALES Trials

Background: Trials of acute secondary prevention following initial minor stroke or TIA, such as POINT, SOCRATES, and THALES, demonstrate a high rate of initial recurrence of ischemic stroke, quickly dropping to a sustained lower rate. For the POINT trial, this was shown to support a two-state kinetic model, in which after the index event subjects are initially in a vulnerable state, with a high rate of stroke recurrence, and then transition to a stabilized state at lower risk for stroke recurrence. This allows for estimation of the distinct kinetic rates characterizing these events. We aimed to extend such a kinetic analysis to the SOCRATES and THALES trials, and to compare estimates of kinetic rates across these trials. Methods: Kaplan-Meier estimates of survival curves in POINT, THALES, and SOCRATES trials were constructed and fitted by a non-linear two-state model, producing estimates of kinetic parameters, using nonparametric bootstrap with repetitive resampling to estimate standard errors. Results: The two-state model closely fit survival curves in all the trials, with similar kinetic parameters across trials. Estimated kinetic rates for stroke recurrence in the vulnerable state were about 100-fold higher than rates in the stabilized state for subjects in the control groups. The transition rates from the vulnerable to stabilized state were still more rapid. Active treatment groups showed similar kinetics, with reduced rates of stroke in the vulnerable state. Conclusion: Across multiple trials of acute secondary stroke prevention following minor stroke or TIA, in both control and active treatment groups, recurrence of stroke is well-described by a two-state kinetic model postulating separate vulnerable and stabilized states. The distinct kinetic states may be revealing distinct pathobiologies with implications about differential impact of therapies delivered immediately or in a delayed fashion.

Explicit Solutions for Real-time Reversible Inhibition Kinetics using Lambert W Function: Towards Progress Curve Analysis

Accurate estimation of kinetic parameters is challenging due to the dynamics and mathematical nature of the chemical systems. This paper presents simple, yet efficient closed-form solutions for the enzymatic conversion of the substrate to the product in real-time derived using the Lambert W function. The real values of the Lambert W function were calculated from the Lambert package as implemented in MATLAB. The expressions exhibit remarkable robustness in estimating the parameters for randomized data at 1% to 4% variation in noise levels. Furthermore, unlike the initial rates method, the expressions estimate chemical kinetic parameters utilizing a full range of experimental data, thus minimizing the risk of missing information that would be detected at an extended time-span. Thus, the implementation of closed-form solutions presented in this paper for the estimation of kinetic parameters eliminates common pitfalls imposed by the initial rates and double reciprocal methods.&#x0D; Keywords: Enzyme, Lambert W function, Reversible inhibition, Closed-form solutions

Comparison of parallel and in-series reaction schemes for kinetic modeling of VGO hydrocracking

An analysis of the reaction pathways developed during the hydrocracking of vacuum gas oil (VGO) was carried out using kinetic models based on parallel and in-series reaction schemes. A methodology that considers diverse crucial factors was applied for the estimation of kinetic parameters based on experimental data reported for VGO hydrocracking using a Ni-Mo-F3.6/ASA-Al2O3 catalyst in a temperature range of 380–410 °C and 10 mPa. The results of a kinetic model that considers parallel reaction pathways presented a better fit than kinetic parameters reported in the literature. While, models based on in-series reaction schemes indicated that some reaction pathways present considerable values of the kinetic coefficients. Every set of kinetic parameters was analyzed by sensitivity analysis, parity plot, and residual analysis to ensure that these values are the most suitable for the experimental data and reaction scheme used. Each kinetic model presented an adequate fit with the experimental data, obtaining values of AAE less than 5 %.

Kinetic Parameters Estimation of Thermal and Co-Pyrolysis of Groundnut De-oiled Cake and Polyethylene Terephthalate (PET) Waste

The physicochemical characterization and kinetic evaluation of the thermal and co-pyrolysis of groundnut de-oiled cake (GDC) and PET plastic is examined in this present study. A bomb calorimeter, proximate/CHNS analysis, and a thermogravimetric analyzer were used to study the physicochemical characteristics of the biomass and plastic. By using a FTIR analysis, it was found that both samples had distinct functional groups. Iso-conversional models, such as Friedman’s, the Kissinger–Akhaira–Sunose, the Ozawa–Flynn–Wall, Starink’s, and the distributed activation energy models were employed in the calculation of the kinetic parameters. The physicochemical characterization provided valuable insights into the pyrolysis characteristics. The rate at which the feedstock was heated were 10, 20, and 30 °C min−1, and were used to study the thermal breakdown behavior of the GDC and PET by the TGA. The following temperatures are the active pyrolysis zones for the thermal pyrolysis and the co-pyrolysis: for the groundnut de-oiled cake, T = 150–650 °C; for the PET, T = 375–600 °C; and for the co-pyrolysis, T = 175–550 °C. For the thermal pyrolysis (for GDC, E = 127.49 kJ mol−1; PET, E = 201.45 kJ mol−1); and the co-pyrolysis (E = 175.86 kJ mol−1), Kissinger–Akhaira–Sunose revealed low activation energy.

KinMod database: a tool for investigating metabolic regulation.

The ability of current kinetic models to simulate the phenotypic behaviour of cells is limited since cell metabolism is regulated at different levels including enzyme regulation. The small molecule regulation network (SMRN) enables cells to respond rapidly to environmental fluctuations by controlling the activity of enzymes in metabolic pathways. However, SMRN is not as well studied relative to metabolic networks. The main contributor to the lack of knowledge on this regulatory system is the sparsity of experimental data and the absence of a standard framework for representing available information. In this paper, we introduce the KinMod database that encompasses more than 2 million data points on the metabolism and metabolic regulation network of 9814 organisms KinMod database employs a hierarchical data structure to: (i) signify relationships between kinetic information obtained through in-vitro experiments and proteins, with an emphasis on SMRN, (ii) provide a thorough insight into available kinetic parameters and missing experimental measurements of this regulatory network and (iii) facilitate machine learning approaches for parameter estimation and accurate kinetic model construction by providing a homogeneous list of linked omics data. The hierarchical ontology of the KinMod database allows flexible exploration of data attributes and investigation of metabolic relationships within- and cross-species. Identifying missing experimental values suggests additional experiments required for kinetic parameter estimation. Linking multi-omics data and providing data on SMRN encourages the development of novel machine learning techniques for predicting missing kinetic parameters and promotes accurate kinetic model construction of cells metabolism by providing a comprehensive list of available kinetic measurements. To illustrate the value of KinMod data, we develop six analyses to visualize associations between data classes belonging to separate sections of the metabolism. Through these analyses, we demonstrate that the KinMod database provides a unique framework for biologists and engineers to retrieve, evaluate and compare the functional metabolism of species, including the regulatory network, and discover the extent of available and missing experimental values of the metabolic regulation. Database URL: https://lmse.utoronto.ca/kinmod/KINMOD.sql.gz

Advanced Electrochemical Impedance Analysis Using Distribution of Relaxation Times for in Operando Mechanistic Insights of Fuel Cell and Water Electrolyzer Designs

The development of sustainable and carbon-neutral alternative energy frameworks and chemical feedstocks requires rapid production of scalable water electrolyzer designs for hydrogen production.[1] Coupling electrolyzers to renewable energy supplies can provide a ‘green’ hydrogen production pathway, enabling clean production of chemical feedstocks as well as an energy storage framework. Current acid-based electrolyzer designs, however, integrate precious metals for stable operation, where drastic reductions in iridium use and increased cell durability are required for scalable deployment.[2] This requires the ability to monitor changes to the cell in operando for rapid diagnostics during initial and long-term operation under sustained or intermittent profiles. One technique proposed is electrochemical impedance spectroscopy (EIS), which can provide a breakdown of the cell resistances based on the timescale of the process.[3] Further analysis by circuit modeling, however, requires significant insight into the system for accurate interpretations. By coupling conventional EIS methods with distribution of relaxation times (DRT) analysis, the number of processes impacting cell operation can be determined without a priori knowledge of the system.[4] This has improved circuit modeling analysis of Li-ion batteries and solid oxide fuel cells.[5] Here, we demonstrate the power of EIS-coupled DRT analysis by analyzing the operation porous cathode and anode films of Nafion-based electrolyzer cells in half-cell and full cell configuration. Analysis of the electrodes in half-cell configurations provides estimates of kinetic parameters, active area, ionic conductivity, and diffusion coefficients associated with the electrode from a single EIS spectrum that are comparable to values obtained from in situ values.[6] Further analysis of the full cell operation with variable cathode gas composition provides insight as to the effect of the cathode gas composition on both the cathode and anode operation and stability. The work presented here will show the versatility and limitations of DRT-coupled EIS analysis of novel fuel cell and electrolyzer designs as well as present key findings for improving electrolyzer performance and stability.[1]Ayers, K. et al. Annu. Rev. Chem. Biomolec. Eng. 2019, 10, 219-239.[2]Pham, C. et al Adv. Energy Mater. 2021, 11, 2101998.[3]Liu, H. et al. J. Phys. Chem. Lett. 2022, 13, 6520-6531.[4]Wan, T. et al. Electrochimica Acta 2015, 184, 483-499.[5]Dierickx, S., Ivers-Tiffee, E. Electrochimica Acta 2020, 355, 136764.[6]Giesbrecht, P.K., Freund, M.S. J. Phys. Chem. C 2022, 126, 132-150.

Inactivation kinetics of Bacillus atrophaeus in liquid hydrogen peroxide for aseptic package sterilization

Liquid hydrogen peroxide is used as a common sterilant for the aseptic packaging materials. Based on the Food Safety Objective (FSO) for specific products, a surrogate organism, Bacillus atrophaeus, is used to validate the sterilization processes. This study aims to develop a methodology for the kinetic inactivation parameter estimation of B. atrophaeus. Spores of B. atrophaeus were challenged with various peroxide concentrations (20%, 28%, and 33%; v/v) and temperatures such as 23, 55, 71.1, and 82.2 °C. The log-linear and Weibull models were used for the inactivation models for liquid peroxide at different temperatures. The parameters were estimated at a reference temperature of 80 °C for log-linear and Weibull models. With peroxide concentrations of 20, 28, and 33%, the log-linear model estimated D values were 0.95, 0.47, and 0.08 min and z values were 94.67, 86.86, and 41 °C, respectively. The Weibull model δ values were 0.31, 0.23, and 0.05 min with a z value of 85.18, 81.23, and 41.39 °C for 20, 28, and 33% peroxide, respectively. The RMSE values varied from 0.60 to 0.79 log CFU/ml, the sum of the squares was between 27.3 and 39.7, and the parameters' relative errors were less than 6%.

Modeling nutrient removal and energy consumption in an advanced activated sludge system under uncertainty

Activated sludge models are widely used to simulate, optimize and control performance of wastewater treatment plants (WWTP). For simulation of nutrient removal and energy consumption, kinetic parameters would need to be estimated, which requires an extensive measurement campaign. In this study, a novel methodology is proposed for modeling the performance and energy consumption of a biological nutrient removal activated sludge system under sensitivity and uncertainty. The actual data from the wastewater treatment plant in Slupsk (northern Poland) were used for the analysis. Global sensitivity analysis methods accounting for interactions between kinetic parameters were compared with the local sensitivity approach. An extensive procedure for estimation of kinetic parameters allowed to reduce the computational effort in the uncertainty analysis and improve the reliability of the computational results. Due to high costs of measurement campaigns for model calibration, a modification of the Generalized Likelihood Uncertainty method was applied considering the location of measurement points. The inclusion of nutrient measurements in the aerobic compartment in the uncertainty analysis resulted in percentages of ammonium, nitrate, ortho-phosphate measurements of 81%, 90%, 78%, respectively, in the 95% confidence interval. The additional inclusion of measurements in the anaerobic compartment resulted in an increase in the percentage of ortho-phosphate measurements in the aerobic compartment by 5% in the confidence interval.The developed procedure reduces computational and measurement efforts, while maintaining a high compatibility of the observed data and model predictions. This enables to implement activated sludge models also for the facilities with a limited availability of data.