Reactor Scale Model Research Articles

Mechanistic investigations into the removal of Hg0 over Pt-based sorbents by warm syngas cleanup

Commercial sorbents for Hg0 removal from syngas operate at low temperatures (<120 ºC), which impacts the thermal efficiency of integrated gasification and chemical production processes. For the first time, supported Pt-based sorbents with different supports, viz., Pt/SBA-15, Pt/S-40, Pt/ZrO2, Pt/CeO2, and Pt/TiO2 are synthesized for high-temperature (≥200 ºC) Hg0 removal. Pt/SBA-15 shows the highest cumulative Hg0 removal efficiency (>99 %) at 270 ºC in N2, which is retained for much higher durations (>20 h) compared to the best-reported Pd-based and metal oxide sorbents. Besides amalgam formation, Hg0 removal on Pt/ZrO2 and Pt/CeO2 occurs via oxidation at high temperatures (> 270 ºC), which explains the reduced negative effect of high temperature over these sorbents. In contrast to other supports, the removal efficiency over Pt/SBA-15 is above 99 % even in the presence of H2, H2O, and CO2, and is attributed to the hydrophobic mesoporous silica support. DRIFTS studies indicate CO adsorption on the active Pt sites over all sorbents. Based on the inferences obtained, a kinetic model is developed and integrated into a reactor-scale model which effectively simulates the spatio-temporal features of Hg0 sorption over Pt/SBA-15.

Multiscale Model for Hydrogen Chloride Uptake on Aluminum for Spacecraft Fire Safety

An electrical fire within spacecraft environments often produces poisonous gases, such as hydrogen chloride (HCl). These gases adsorb on the interior materials of the spacecraft, primarily treated aluminum surfaces. A safe design of the spacecraft can be facilitated by modeling the spacecraft compartment. In this study, a multiscale model is developed to predict HCl uptake by anodized aluminum, which has a thick oxide layer covering its surface compared to other treatments. X-ray photoelectron spectroscopy revealed that aluminum chloride is found deep in the oxide layer of anodized aluminum, implying that the pore-scale transport and reaction of HCl within the oxide layer covering the aluminum surface must be considered. Hence, a multiscale approach is warranted. A pore-scale model is first developed, wherein the HCl reacts with the aluminum oxide pore walls to create an aluminum chloride layer. The HCl then diffuses through that layer for further reaction, until the layer (diffusion resistance) grows too large and the uptake of HCl stops. This pore-scale model is coupled with a reactor-scale model to predict overall HCl uptake. Results show that this new multiscale approach predicts the uptake of HCl far more accurately when compared to experimental data than previously used single (reactor)-scale models.

Machine learning based prediction of biomass pyrolysis with detailed reaction kinetics for thermally-thick particles: from 1D to 0D

In reactor-scale CFD modeling of biomass pyrolysis with thermally-thick particles, zero-dimensional (0D) models coupled with lumped kinetics are commonly used, as they are simple and computationally efficient. However, intra-particle heat transfer, which cannot be directly implemented in 0D models, has significant effects on pyrolysis behaviors of thermally-thick biomass particles. Additionality, lumped kinetics usually fails to predict detailed composition of pyrolysis products. To overcome these issues, a widely-used one-dimensional (1D) model that can directly incorporate intra-particle heat transfer was employed with a detailed pyrolysis kinetics in this work to develop a corrected 0D (Cor-0D) model for accurate CFD modeling of biomass pyrolysis inside thermally-thick particles. Correction coefficients of external heat transfer, particle diameter, and pyrolysis reactions were introduced by comparing predictions of the 1D model with those of the 0D model quantitatively to reflect the effects of respective factors. The comparison demonstrates that if correction coefficients are properly determined, predictions of the developed Cor-0D model are in good agreement with experimental data as well as those of the employed 1D model under various conditions, while the 0D model overestimates mass loss rate and particle heating rate for thermally-thick biomass particles. Considering that correction coefficients are case dependent and determination of their values are tedious, artificial neural network (ANN) was used to correlate correction coefficients as functions of convective heat transfer coefficient, particle size, gas temperature, moisture content, and particle’s dimensionless temperature to derive an ANN-Cor-0D model. Results show that the ANN-Cor-0D model has the same performance as the Cor-0D model.

ANN based surrogate model for key Physico-chemical effects of cavitation

Intense and localised physico-chemical effects realised by cavitation such as generation of hydroxyl radicals, high-speed jets, and very high energy dissipation rates are being harnessed for a wide range of applications from emulsions, crystallisation, reactions to water treatment and waste valorisation. Single cavity models are typically used to quantitatively estimate such localised effects of cavity collapse. However, these models demand significant computing resources for resolving fast dynamics and therefore are very difficult, if not impossible, to integrate with CFD based cavitation device or reactor scale models. This severely limits the utility of device/ reactor scale models in simulating key applications of interest. In this work, we present, for the first time, artificial neural network (ANN) based surrogate models which accurately represent complex physico-chemical effects of cavity collapse. Recently developed cavity dynamics model was used for generating training data set encompassing both acoustic and hydrodynamic cavitation. Appropriate methodology for training ANN was developed. A shallow three hidden layer dense ANN was found to be more effective for estimating three main effects of cavity collapse: jet velocity, •OH generation and localised energy dissipation rate. The performance of trained ANN was then evaluated by comparing the predictions with the totally unseen data obtained from the cavity dynamics model. The developed ANN was shown to simulate unseen data very well not just within the range of training data (interpolation) but also beyond (extrapolation). Algebraic equations representing ANN are included to facilitate incorporation in device/ reactor scale CFD models. The presented methodology and results will be useful for developing high-fidelity CFD models of cavitation devices/ reactors based on key physico-chemical effects of cavity collapse.

Numerical Modelling on the Effect of Temperature on MOCVD Growth of ZnO Using Diethylzinc and Tertiarybutanol

The dynamic growth of MOCVD-grown ZnO thin films under temperature effect was systematically investigated by a numerical approach using computational fluid dynamics (CFD) technique. A three-dimensional (3D) reactor-scale model was developed to determine the growth rate and uniformity of ZnO thin film in the temperature range of 593 K to 713 K. The mixed-convection flow and heat transfer inside the reactor chamber were assessed. The results showed that as the temperature increased, ZnO thickness increased initially before decreasing. At 673 K, the highest deposition rate with acceptable uniformity was achieved. The admixture of transverse and longitudinal rolls was observed for the flow conditions. Temperature variations were found to directly affect the axial and lateral uniformity of deposition, but had a minor impact on the size and position of transverse rolls. Experimental verification studies were conducted, and high-quality ZnO films were successfully fabricated by using diethylzinc (DEZn) and tertiarybutanol (t-BuOH) as precursors; it was found that the comprehensive thickness and structural properties of ZnO deposited at temperature of 673 K are preferred. Experimental results and numerical simulations exhibited good agreement.

Three-scale integrated optimization model of furnace simulation, cyclic scheduling, and supply chain of ethylene plants

In order to explore the potential of profit margin improvement, a novel three-scale integrated optimization model of furnace simulation, cyclic scheduling, and supply chain of ethylene plants is proposed and evaluated. A decoupling strategy is proposed for the solution of the three-scale model, which uses our previously proposed reactor scale model for operation optimization and then transfers the obtained results as a parameter table in the joint MILP optimization of plant-supply chain scale for cyclic scheduling. This optimization framework simplifies the fundamental mixed-integer nonlinear programming (MINLP) into several sub-models, and improves the interpretability and extendibility. In the evaluation of an industrial case, a profit increase at a percentage of 3.25% is attained in optimization compared to the practical operations. Further sensitivity analysis is carried out for strategy evolving study when price policy, supply chain, and production requirement parameters are varied. These results could provide useful suggestions for petrochemical enterprises on thermal cracking production.

A zero-dimensional model of porous char gasification in supercritical water: Experiments and mathematical modeling

This work aims to develop a zero-dimensional model of porous char gasification in supercritical water which can serve as the char conversion sub-model in reactor-scale modeling and simulation. Experiments were firstly carried out to obtain the evolution of specific surface area of char, based on which the structure parameter ψ in random pore model was calculated. Then, a kinetic model with detailed coal conversion pathways in supercritical water was proposed, with the evolution of specific surface area specifically considered. This model was applied to fit the experimental results to calculate the intrinsic kinetic parameters. The obtained structure parameter ψ and kinetic parameters were finally used to develop the sub-model. The established sub-model was evaluated through model predication of 0.25–4 mm sized char particles gasified at 1000–1800 K. The results showed that this model gave a reasonable predication of the gasification transition from zone Ⅰ to zone II/III regime with the increase of temperature and particle size, and the development of particle size, apparent density and specific surface area was well described. Besides, the convection was found to obviously promote the gasification in zone II/III regimes mainly by enhancing the mass transfer process, while its effect was negligible in zone Ⅰ regime. Similarly, the effect of ash content on the burnout time became more obvious from zone Ⅰ to zone II/III regime.

A solar thermal sorption-enhanced steam methane reforming (SE-SMR) approach and its performance assessment

This paper proposes an integration of concentrating solar power (CSP) with a sorption-enhanced steam methane reforming (SE-SMR) process and assesses its overall solar-to-fuel conversion performance. A thermodynamic treatment of the SE-SMR process for H2 production is presented and evaluated in an innovative two reactors system configuration using CSP as a heat input. Four metal carbonate/metal oxide pairs are considered, and the equilibrium thermodynamics reveals that CaCO3/CaO pair is the most suitable candidate for this process. Additionally, a reactor-scale thermodynamic model is developed to determine the optimum operating conditions for the process. For the carbonation step, temperatures between 700 and 900 K and steam-to-methane ratio ≥4 are found to be the most favorable. Furthermore, an advanced process model, which utilizes operating conditions determined from the reactor-scale model, is developed to evaluate the process efficiency. The model predicts that the proposed process can achieve a solar-to-fuel efficiency ∼41% for calcination temperature of 1500 K and carbonation temperature of 800 K, without considering any solid heat recovery. An additional 2.5% increase in the process efficiency is feasible with the consideration of the solid heat recovery. This study shows the thermodynamic feasibility of integrating the SE-SMR process with CSP technologies.

Reactor scale modeling of quorum sensing induced biofilm dispersal

We present a simple reactor-scale mathematical model of active biofilm dispersal that is controlled by a quorum sensing system. The model is cast as a system of three ordinary differential equations for the dependent variables biofilm thickness, quorum sensing signal concentration in the reactor, and concentration of the growth limiting substrate in the reactor. The model is investigated with analytical and numerical techniques. We find that a single QS dispersal mechanism, depending on parameters, can lead to (i) continuous cell loss at a signal level at which the biofilm is entirely up-regulated, (ii) periodically repeating almost instantaneous cell loss with the biofilm switching between down- and up-regulated states, (iii) continuous cell loss at a signal level at which the biofilm is entirely down-regulated, or (iv) complete washout. We contrast this with a simple model of starvation induced dispersal, which is cast in the same modeling framework, but shows entirely different dynamic behavior, in that it only permits washout or asymptotic convergence to a steady state at break-even concentration, depending on parameters.

Multiscale CFD simulation of biomass fast pyrolysis with a machine learning derived intra-particle model and detailed pyrolysis kinetics

Coupling particle and reactor scale models is as essential as reactor fluid dynamics and particle motion for accurate Computational Fluid Dynamic (CFD) simulations of biomass fast pyrolysis reactors due to intraparticle heat transfer and chemical reactions controlling conversion time and product distributions. Direct online coupling of a particle model with a reactor model is computationally expensive, while offline coupling is case-dependent. In this research, solutions from a series of particle pyrolysis simulations were regressed with Artificial Neural Network (ANN). This machine learning-derived model predicted the same temperature and conversion profiles compared with particle resolved simulation while the isothermal approach overpredicted the temperature by 130 K and underpredicted the conversion time by 30 s. The ANN model was then integrated into CFD simulations of fluidized bed biomass fast pyrolysis with varied feedstocks via coupling PyTorch and MFiX. The averaged error of simulation predicted bio-oil yields with four feedstocks is 6.4%. This multi-scale approach provides an efficient tool for the coupled particle and reactor scale simulations of biomass pyrolysis.

A reactor-scale CFD model of soot formation during high-temperature pyrolysis and gasification of biomass

Soot generation is an important problem in high-temperature biomass gasification, which results in both air pollution and the contamination of gasification equipment. Due to the complex nature of biomass materials and the soot formation process, it is still a challenge to fully understand and describe the mechanisms of tar evolution and soot generation at the reactor scale. This knowledge gap thus motivates the development of a comprehensive computational fluid dynamics (CFD) soot formation algorithm for biomass gasification, where the soot precursor is modeled using a component-based pyrolysis framework to distinguish cellulose, hemicellulose and lignin. The model is first validated with pyrolysis experiments from different research groups, after which the soot generation during biomass steam gasification in a drop-tube furnace is studied under different operating temperatures (900–1200 °C) and steam/biomass ratios. Compared with the predictions based on a detailed tar conversion model, the current algorithm captures the soot generation more reasonably although a simplified tar model is used. Besides, the influence of biomass lignin content and the impact of tar and soot consumptions on the soot yield is quantitatively studied. Moreover, the impact of surface growth on soot formation is also discussed. The current work demonstrates the feasibility of the coupled multiphase flow algorithm in the prediction of soot formation during biomass gasification with strong heat/mass transfer effects. In conclusion, the model is thus a useful tool for the analysis and optimization of industrial-scaled biomass gasification.

Mathematical modeling of thermal behavior of single iron ore pellet during heat hardening oxidation

In this study, a one-dimensional generic model capable of being integrated with reactor scale models is proposed for a single pellet through solving the transient differential conservation equations. Predicted results comparison with the experimental data showed close agreement. In addition, the model was used to investigate the relevance of physical characteristics of pellet, reacting gas composition, diffusion factors, and prevailing regime. It was found that the pure magnetite pellet could achieve a temperature rise of about 245 K at oxygen concentration of 40 vol.%, whereas the maximum temperature difference inside the pellet was approximately 24 K. Moreover, increasing pellet size, the maximum attainable temperature reached a peak and then leveled out. Furthermore, by decreasing the pore diameter, the pellet size with peak temperature shifted to the smaller pellet sizes. Analyzing the numerical results also showed that for the small pellet sizes, shortening the diffusion path leads to the spreading of the reaction interface. The modeling methodology herein can be applied to any particulate processes and is not limited to the aforementioned case.

A Boudart Number for the Assessment of Irreducible Pellet-Scale Mass Transfer Limitations: Application to Oxidative Coupling of Methane

The traditional concept of the Thiele or Weisz modulus can fail to assess the relevance of pellet-scale transport limitations in the case of heterogeneously catalyzed gas-phase reactions such as oxidative coupling of methane (OCM). Irreducible transport limitations occur when surface-produced gas-phase intermediates (e.g., radicals) react fast in the pores of catalyst pellets, causing strong pellet-scale concentration profiles that directly affect the product distribution. To assess the effect of the pellet diameter on pellet-scale gradients of reactants and intermediates during OCM, a pellet-scale model is used in combination with detailed kinetic models for the gas (57 species, 317 reactions) and surface chemistry (11 species, 26 reactions). On the basis of a preliminary analysis using a skeleton reaction mechanism a so-called Boudart number, Phi(Bou), is proposed for assessment of the potential relevance of transport limitations during this type of heterogeneously catalyzed gas-phase reactions. The effect of pellet-scale gradients on product yields and selectivities is evaluated using a rigorous 1D heterogeneous reactor-scale model in combination with the above-mentioned detailed kinetic models for the gas and surface chemistry. Pellet-scale diffusion limitations are found to have a significantly negative effect on C-2 selectivities during OCM with the applied kinetics. When the Weisz-Prater criterion is satisfied for reactants and products, pellet-scale gradients of the intermediates are responsible for this drop in selectivity. For larger pellet diameters, the drop in selectivity is due to diffusion limitations of the C-2 products and the corresponding increased importance of secondary total oxidation reactions.

Heat and mass transfer inside of a monolith honeycomb: From channel to full size reactor scale

This paper is devoted to the multi-scale model of the heat and mass transfer inside of a honeycomb monolith substrate. Due to computational limitations, monoliths are modelled as a continuum in full-scale catalytic reactors models. That makes it necessary to use correlations or sub-models derived from channel scale results to account for a physically consistent heat and mass transfer inside of the substrate. In this paper detailed computational models at a channel and reactor scales are analyzed. Catalytic oxidation of CO is used as a reaction and the fluid properties are considered to be temperature-dependent. First, a channel scale model is used to analyze Nusselt, Sherwood, Lewis, and Damköhler numbers inside of the monolith channels. Secondly, sub-models obtained at a channel level are implemented in a full-scale reactor model using the continuum approach, to evaluate the impact of using detailed vs. highly simplified correlations for heat and mass transfer. The reactor scale model accounts for the transitions of the flow regime, entrance length effects, an-isotropic substrate thermal conductivity and temperature-dependent fluid properties. According to the results, the Lewis number can deviate significantly from one in the entrance length, however, it approaches asymptotically to unity as the flow develops. Regarding Nusselt and Sherwood, current interpolating methodologies are not able to predict the correct value in the entrance region when Damköhler is low, nonetheless, are reasonably accurate for the asymptotic one.

Reduced-order model for CaO carbonation kinetics measured using micro-fluidized bed thermogravimetric analysis

A reduced-order model was developed to describe the CaO carbonation kinetics by simplifying the detailed one-dimensional rate-equation-based grain model. The reduced-order model was derived using the Thiele modulus method and is an analytical model, which can be easily used in the reactor scale model or computational fluid dynamics (CFD) simulation. In the reduced-order model, physical/chemical steps occurring during the carbonation process were considered, including external diffusion of gas around the particle, intraparticle diffusion, surface reaction, product islands growth and product layer diffusion. This model can be used to investigate the complex kinetic behaviors such as reaction controlled steps, kinetics transition from chemical reaction to product layer diffusion, and the effect of particle structure change on intraparticle diffusion, such as pore plugging phenomenon. The prediction accuracy of reduced-order model was verified by the detailed one-dimensional model. In order to demonstrate the application of the reduced-order model for reactor scale analysis, the CaO carbonation kinetics under different temperatures (600–750 °C), CO2 concentrations (25–75 vol%) and particle sizes (0.2–3 mm) were measured using a novel micro-fluidized bed thermogravimetric analysis (MFB-TGA) method, which is based on real-time mass measurement of CaO sample in a fluidizing state. The reduced-order model was implemented into a K-L two-phase bubbling bed reactor model to analyze the experimental data of a micro-fluidized bed reactor. Results indicated that the two-stage behavior of carbonation kinetics can be described successfully using the model, and the effect of particle structure change on pore plugging and intraparticle diffusion can be well predicted and explained. The carbonation is first-order reaction when the CO2 partial pressure is within 75% (1 bar). The effect of particle size on carbonation kinetics shows three-zone characteristics of reaction/diffusion combined controlling mechanism, and was discussed in detail. The reduced-order model provides a promising application for describing multiscale mechanisms (surface, particle and reactor) of carbonation kinetics.

Pyrolysis of wood and PVC mixtures: thermal behaviour and kinetic modelling

Wood waste containing halogenated compounds such as polyvinyl chloride (PVC) is in abundant supply, although the pyrolysis of such waste feedstock for energy production may cause corrosion and environmental problems due to the release of HCl gas. Hence, there is a need to understand the pyrolysis behaviour of chlorine-contaminated wood in order to develop methods that minimise the impact of chloride species on pyrolysis equipment and product quality. In literature, few studies exist on the kinetic analysis of wood and PVC co-pyrolysis. The existing models assume a single-step reaction with an n-order reaction mechanism for the entire process, which may lead to large errors in the kinetic parameters estimated. Therefore, in this paper, we develop and validate a multi-step kinetic model that predicts the pyrolysis behaviour and reaction mechanism of poplar wood (PW) pellet with different contents of PVC (0, 1, 5, 10, 100 wt%). Using data from thermogravimetric analysis of the pellets at heating rates of 5, 10 and 20 °C/min, we determined the apparent kinetic parameters by combining Fraser-Suzuki deconvolution, isoconversional methods and master plot procedures. Our model fitted the experimental data well with a deviation of less than 4.5%. Our results show that the addition of 1 wt% PVC to PW decreases the activation energy of hemicellulose and cellulose pyrolysis in PW from 136.3 to 101.6 kJ/mol and from 216.7 to 108.2 kJ/mol, respectively. This demonstrates the importance of acid hydrolysis reactions between the cellulosic fibres of PW and HCl released from PVC dehydrochlorination. Furthermore, we found that a nucleation and growth mechanism best represents the rate-limiting interactions between PVC and PW, which we linked to the formation of metal chloride crystals from acid-base reactions between HCl and PW minerals. Our kinetic model is an improvement of current models for the co-pyrolysis of wood and PVC, and can be readily used in a reactor-scale model of a pyrolyser or gasifier due to its relative simplicity.

General rate equation theory for gas–solid reaction kinetics and its application to CaO carbonation

The kinetics of reactions between gases and solids are typically modeled using shrinking-core or shrinking-pore theoretical frameworks, in which the formation of a uniform solid product layer covering the entire solid surface with a sharp interface between the solid reactant and the product is assumed. However, theories involving a uniform solid product layer cannot predict the kinetic transition behavior that occurs in some gas–solid reactions. This work proposes that the growth of solid product islands occurs instead of the progressive formation of a uniform solid product layer in traditional shrinking-core and shrinking-pore models and establishes a general rate equation theory to model the kinetics of gas–solid reactions for solid reactants of various shapes. The growth of the product islands is calculated using the rate equation theory and is integrated into the shrinking-core model and shrinking-pore model, in which the microstructure of the solid reactant is also considered. Elemental steps of chemical reaction, surface diffusion and product layer diffusion are included in the general rate equation theory. The kinetic parameters included in the model are chemical reaction rate constant ks, surface diffusion coefficient Ds, and product layer diffusion coefficient Dp. The resulting model is analyzed at the limits where either the chemical reaction or product layer diffusion is the rate-controlling step. At very small or very high Ds values, the rate equation theory is equal to the kinetics-controlled regime or diffusion-controlled regime in traditional models; therefore, the traditional models represent special cases of this theoretical rate equation model. At intermediate Ds values, the so-called two-stage kinetic behavior occurs. The transition from the fast initial stage to the diffusion-controlled stage depends on the value of Ds, with the conversion at the transition point increasing with increasing Ds. The rate equation theory is demonstrated to successfully predict the CaO carbonation kinetics under a wide range of experimental conditions including temperature, CO2 concentration, sorbent type, and amount of added inert supports as well as for systems that exhibit a temperature rise during the carbonation reaction The rate equation theory is integrated into a particle model to account for the external diffusion of gas around the particle and intraparticle diffusion as well as the effect of particle structural changes on intraparticle diffusion, such as the pore plugging phenomenon. The developed rate equation theory is an analytical model that can be easily used in reactor-scale modeling or computational fluid dynamics simulations.

Analyzing the impact of implementing different approaches of the approximation of the catalyst effectiveness factor on the prediction of the performance of trickle bed reactors

Abstract A steady-state diffusion model comprising of diffusion and reaction in a catalyst pellet was used to study and understand the effect of the approximation of the catalyst effectiveness factor in the modeling of trickle bed reactors. The hydrogenation of alpha-methylstyrene was chosen as the case study reaction from literature for liquid-limited reaction conditions. The effectiveness factor values in this study were sensitive to reactor scale equations during the prediction of reactor performance. The approximation of accounting for the overall catalyst effectiveness factor in the reactor scale model equations from different models and solving the reactor scale model equations by different modeling approaches was assessed. It was observed that evaluating the overall effectiveness factor from pellet scale model equations and integrating the parameter in the reactor scale axial dispersion model at every local axial collocation point to simulate the reactor performance showed better agreement to the experimental data. This approach evaluates the effectiveness factor locally with the variation of the reactant concentration at different axial points across the reactor rather than using one value for the entire reactor. The approach of using a single effectiveness factor for the whole bed and using the effectiveness factor approximated as a fitted polynomial to the reactant concentrations did not properly predict the reactor performance.

Plasma etching of high aspect ratio features in SiO2 using Ar/C4F8/O2 mixtures: A computational investigation

Plasma etching of high aspect ratio (HAR) features, typically vias, is a critical step in the fabrication of high capacity memory. With aspect ratios (ARs) exceeding 50 (and approaching 100), maintaining critical dimensions (CDs) while eliminating or diminishing twisting, contact-edge-roughening, and aspect ratio dependent etching (ARDE) becomes challenging. Integrated reactor and feature scale modeling was used to investigate the etching of HAR features in SiO2 with ARs up to 80 using tri-frequency capacitively coupled plasmas sustained in Ar/C4F8/O2 mixtures. In these systems, the fluxes of neutral radicals to the wafer exceed the fluxes of ions by 1–2 orders of magnitude due to lower threshold energies for dissociation compared with ionization. At low ARs (&amp;lt;5), these abundant fluxes of CFx and CxFy radicals to the etch front passivate the oxide to form a complex which is then removed by energetic species (ions and hot neutrals) through chemically enhanced reactive etching, resulting in the formation of gas phase SiFx, COx, and COF. As the etching proceeds into higher ARs, the fractional contribution of physical sputtering to oxide removal increases as the fluxes of energetic species to the etch front surpass those of the conduction constrained CFx and CxFy radicals. The instantaneous etch rate of oxide decreases with increasing aspect ratio (ARDE effect) due to decreased fluxes of energetic species and decreased power delivered by these species to the etch front. As the etch rate of photoresist (PR) is independent of AR, maintaining CDs by avoiding undercut and bowing requires high SiO2-over-PR selectivity, which in turn requires a minimum thickness of the PR at the end of etching. Positive ions with narrow angular distributions typically deposit charge on the bottom of low AR features, producing a maximum in positive electric potential on the bottom of the feature. For high AR features, grazing incidence collisions of ions on sidewalls depositing charge produce electric potentials with maxima on the sidewalls (as opposed to the bottom) of the feature.

Operational performance and biofoulants in a dynamic membrane bioreactor

In this study, a mathematical model was developed to have a better understanding of the process and be used in future reactor scale models to predict its process performance. This model utilizes the Activated Sludge Model NO.1 (ASM1) framework and incorporates bioprocesses of formation and degradation of soluble microbial products (SMP) and extracellular polymeric substances (EPS). Simulation result shows the model could very well predict the bioreactor performance. The average error of COD, BOD and NH3-N removal efficiency was 0.48, 0.28 and 1.18%, respectively.