Heating Process Characteristics and Kinetics of Biomass at Different Oxygen Concentrations

2017 ◽  
Vol 15 (4) ◽  
Author(s):  
Xingxing Cheng ◽  
Yilan Xu ◽  
Junge Li ◽  
Zhiqiang Wang ◽  
Chunyuan Ma
Keyword(s):  
Kinetic Model ◽  
Oxygen Diffusion ◽  
Combustion Reaction ◽  
Oxygen Effect ◽  
Heating Process ◽  
Model Parameters ◽  
Biomass Fuels ◽  
Char Combustion ◽  
Process Characteristics ◽  
Oxygen Concentrations

Abstract Mass transfer is very important for the combustion of biomass fuels, especially pellets. The oxygen diffusion, which results in a nonuniform distribution of oxygen in the pellets, should be considered in the modeling of pellet combustion. In this study, the effect of oxygen on thermal degradation of biomass is studied by thermogravimetric (TG) experiments and then a kinetic model considering oxygen effect is developed. The fuels investigated are spruce, sophora, wheat straw, and peanut straw. TG curves are taken under different oxygen concentrations, ranging from 0 to 20 %. Oxygen concentration has little effect on the devolatilization but is critical for the behavior of char combustion. Reaction mechanism is proposed based on the observation of decomposition at different reaction conditions. The kinetic model consists of three devolatilization reactions (for cellulose, hemicellulose, and lignin) and one char combustion reaction. In the devolatilization steps, char and gas are formed as products and oxygen is not involved in the reactions. A power law dependence of oxygen is assumed for the char combustion stage. The model parameters are fitted by the experimental TG data and good agreement is observed between the experimental and modeled data at various oxygen concentrations and for different biomass fuels. It is expected that the developed kinetic model could be applied for the modeling of pellets combustion considering oxygen diffusion process.

scholarly journals Constrained Parameter Estimation for a Mechanistic Kinetic Model of Cobalt–Hydrogen Electrochemical Competition during a Cobalt Removal Process

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 387
Author(s):  
Yiting Liang ◽  
Yuanhua Zhang ◽  
Yonggang Li
Keyword(s):  
Parameter Estimation ◽  
Kinetic Model ◽  
Model Parameters ◽  
Hydrogen Ions ◽  
Removal Process ◽  
Cobalt Removal ◽  
Estimation Scheme ◽  
Zinc Hydrometallurgy ◽  
The Impact ◽  
Parameter Estimation Scheme

A mechanistic kinetic model of cobalt–hydrogen electrochemical competition for the cobalt removal process in zinc hydrometallurgical was proposed. In addition, to overcome the parameter estimation difficulties arising from the model nonlinearities and the lack of information on the possible value ranges of parameters to be estimated, a constrained guided parameter estimation scheme was derived based on model equations and experimental data. The proposed model and the parameter estimation scheme have two advantages: (i) The model reflected for the first time the mechanism of the electrochemical competition between cobalt and hydrogen ions in the process of cobalt removal in zinc hydrometallurgy; (ii) The proposed constrained parameter estimation scheme did not depend on the information of the possible value ranges of parameters to be estimated; (iii) the constraint conditions provided in that scheme directly linked the experimental phenomenon metrics to the model parameters thereby providing deeper insights into the model parameters for model users. Numerical experiments showed that the proposed constrained parameter estimation algorithm significantly improved the estimation efficiency. Meanwhile, the proposed cobalt–hydrogen electrochemical competition model allowed for accurate simulation of the impact of hydrogen ions on cobalt removal rate as well as simulation of the trend of hydrogen ion concentration, which would be helpful for the actual cobalt removal process in zinc hydrometallurgy.

The New Equations for “Rate-Determining Chemisorption of Coal”

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
Cemil Koyunoğlu
Keyword(s):  
Coal Combustion ◽  
Oxygen Diffusion ◽  
Coal Particle ◽  
Reaction Rate ◽  
Volatile Matter ◽  
Temperature Level ◽  
Char Combustion ◽  
Controlling Mechanism ◽  
Combustion Equations ◽  
Determining Factor

The purpose of the new formulas, Cml, CmlK, and CmlY, which express the slowest char combustion rate, is to show the controlling mechanism of single coal burning. Oxygen diffusion through the boundary layer (as a result of releasing volatile matter from coal) to the char surface is the slowest step rate and can also represent as the rate determining. This step has not yet been taken into account in the literature and may effect incomparable decisions between numerical and experimental results of coal combustion studies. In the 1920s, Wilhelm Nusselt found the coal combustion equation for a single coal, which is based on initial coal diameter, and its burning time, or Nusselt square law (NSL). Also, the burning constant in NSL expressed oxygen partial pressure and the ambient temperature level. Nevertheless, recent studies according to char combustion have explained the effect of coal density on char combustion. Consequently, to help understand the slowest rate of char combustion, NSL as well as ordinary char combustion equations can be used together to establish the rate-determining factor. For this purpose, in this study, the slowest step of the char reaction rate is given as “Cml” of stable position for single coal particle, “CmlK” and “CmlY” for a coal particle in a motion.

scholarly journals Experimental and Computational Analysis of NOx Photocatalytic Abatement Using Carbon-Modified TiO2 Materials

Catalysts ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1366
Author(s):  
Tatiana Zhiltsova. ◽  
Nelson Martins ◽  
Mariana R. F. Silva ◽  
Carla F. Da Silva ◽  
Mirtha A. O. Lourenço ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Kinetic Model ◽  
Photocatalytic Oxidation ◽  
Cfd Simulation ◽  
Emission Spectra ◽  
Chemical Kinetic ◽  
Solar Light ◽  
Model Parameters ◽  
Chemical Kinetic Model ◽  
Simulated Solar Light

In the present study, two photocatalytic graphene oxide (GO) and carbon nanotubes (CNT) modified TiO2 materials thermally treated at 300 °C (T300_GO and T300_CNT, respectively) were tested and revealed their conversion efficiency of nitrogen oxides (NOx) under simulated solar light, showing slightly better results when compared with the commercial Degussa P25 material at the initial concentration of NOx of 200 ppb. A chemical kinetic model based on the Langmuir–Hinshelwood (L-H) mechanism was employed to simulate micropollutant abatement. Modeling of the fluid dynamics and photocatalytic oxidation (PCO) kinetics was accomplished with computational fluid dynamics (CFD) approach for modeling single-phase liquid fluid flow (air/NOx mixture) with an isothermal heterogeneous surface reaction. A tuning methodology based on an extensive CFD simulation procedure was applied to adjust the kinetic model parameters toward a better correspondence between simulated and experimentally obtained data. The kinetic simulations of heterogeneous photo-oxidation of NOx carried out with the optimized parameters demonstrated a high degree of matching with the experimentally obtained NOx conversion. T300_CNT is the most active photolytic material with a degradation rate of 62.1%, followed by P25-61.4% and T300_GO-60.4%, when irradiated, for 30 min, with emission spectra similar to solar light.

scholarly journals Optimization of Dose Fractionation for Radiotherapy of a Solid Tumor with Account of Oxygen Effect and Proliferative Heterogeneity

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1204
Author(s):  
Maxim Kuznetsov ◽  
Andrey Kolobov
Keyword(s):  
Tumor Cells ◽  
Solid Tumor ◽  
Tissue Damage ◽  
Normal Tissue ◽  
Dose Fractionation ◽  
Oxygen Effect ◽  
Model Parameters ◽  
Time And Space ◽  
Fractionated Radiotherapy ◽  
Normal Tissue Damage

A spatially-distributed continuous mathematical model of solid tumor growth and treatment by fractionated radiotherapy is presented. The model explicitly accounts for three time and space-dependent factors that influence the efficiency of radiotherapy fractionation schemes—tumor cell repopulation, reoxygenation and redistribution of proliferative states. A special algorithm is developed, aimed at finding the fractionation schemes that provide increased tumor cure probability under the constraints of maximum normal tissue damage and maximum fractional dose. The optimization procedure is performed for varied radiosensitivity of tumor cells under the values of model parameters, corresponding to different degrees of tumor malignancy. The resulting optimized schemes consist of two stages. The first stages are aimed to increase the radiosensitivity of the tumor cells, remaining after their end, sparing the caused normal tissue damage. This allows to increase the doses during the second stages and thus take advantage of the obtained increased radiosensitivity. Such method leads to significant expansions in the curative ranges of the values of tumor radiosensitivity parameters. Overall, the results of this study represent the theoretical proof of concept that non-uniform radiotherapy fractionation schemes may be considerably more effective that uniform ones, due to the time and space-dependent effects.

Predicting overall survival (OS) and overall response (OR) following durvalumab treatment in patients with multiple cancer types using a hybrid modeling strategy.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15161-e15161
Author(s):  
Ting Chen ◽  
Yanan Zheng ◽  
Lorin Roskos ◽  
Donald E Mager
Keyword(s):  
Kinetic Model ◽  
Kinetic Parameters ◽  
Rate Constant ◽  
Tumor Size ◽  
Hybrid Modeling ◽  
Tumor Growth Rate ◽  
Growth Rate Constant ◽  
Model Parameters ◽  
Cancer Types ◽  
Modeling Strategy

e15161 Background: This study aimed to predict OS/OR and identify key predictors in patients with diverse cancer types treated with durvalumab, a PD-L1 targeting monoclonal antibody, using a hybrid modeling strategy that combines population pharmacodynamic (PD) modeling and machine learning (ML) algorithms. Methods: Individual longitudinal tumor size measurements and OS/OR data were available for 855 patients who received durvalumab therapy (10 mg/kg Q2W or 20 mg/kg Q4W; NCT01693562). Nine cancer types included non-small cell lung cancer (NSCLC), bladder cancer (BC), microsatellite instability-high (MSI-H) cancer, hepatocellular carcinoma (HCC), squamous cell carcinoma of the head and neck (SCCHN), gastroesophageal cancer (GEC), ovarian cancer (OC), pancreatic adenocarcinoma (PDAC) and triple-negative breast cancer (TNBC). A tumor kinetic model was developed to characterize diverse temporal profiles using a population-based modeling approach. Individual estimated tumor kinetic model parameters and patient demographic/physiological factors were used as inputs for predicting OS/OR using several ML approaches. Results: The final tumor kinetic model with liver metastasis (LM), neutrophil/lymphocyte ratio (NLR), tumor size at baseline (TBSL) and cancer types as covariates characterized the temporal tumor size data well. HCC and MSI-H cancer have the slowest tumor growth rate constant (kg), while GEC, SCCHN and TNBC have the fastest kg. BC, NSCLC and OC have the highest tumor killing rate constant. The most important predictors of OS identified by ML approach were tumor kinetic parameters (kg, fraction of drug-sensitive cells, time-delay in immune response), along with baseline disease factors, including hemoglobin (HGBBL), albumin (ALB), and NLR. Decision tree-based algorithms showed the best performance in predicting OR with accuracy above 90%. In addition to tumor kinetic parameters, PD-L1 expression on tumor cells (TC) and ALB were the most important predictors of OR. Conclusions: A combined population PD/ML approach showed good predictions of OS/OR in patients with different cancer types treated with durvalumab. LM, NLR,TBSL and cancer types were found to be important factors for tumor kinetics. In addition to tumor kinetic parameters, HGBBL, ALB, and NLR were found to be important predictors of OS, and TC and ALB were found to be important predictors of OR. These findings could provide a guidance on patient selection in future clinical trials.

A new char combustion kinetic model – Part 2: Empirical validation

Fuel ◽  
2012 ◽  
Vol 96 ◽  
pp. 168-175 ◽  
Author(s):  
Andrés Rojas ◽  
Juan Barraza ◽  
Richelieu Barranco ◽  
Edward Lester
Keyword(s):  
Kinetic Model ◽  
Empirical Validation ◽  
Char Combustion
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