Semiparametric identification in panel data discrete response models

This paper studies semiparametric identification in linear index discrete response panel data models with fixed effects. Departing from the classic binary response static panel data model, this paper examines identification in the binary response dynamic panel data model and the ordered response static panel data model. It is shown that under mild distributional assumptions on the fixed effect and the time-varying unobservables point-identification fails, but informative bounds on the regression coefficients can still be derived. Partial identification is achieved by eliminating the fixed effect and discovering features of the distribution of the unobservable time-varying components that do not depend on the unobserved heterogeneity. Numerical analyses illustrate how the identification bounds change as the support of the explanatory variables varies.</p

Assessing the Horizontal Homogeneity of the Atmospheric Boundary Layer (HHABL) Profile Using Different CFD Software

One of the main factors affecting the reliability of computational fluid dynamics (CFD) simulations for the urban environment is the Horizontal Homogeneity of the Atmospheric Boundary Layer (HHABL) profile—meaning the vertical profiles of the mean streamwise velocity, the turbulent kinetic energy, and dissipation rate are maintained throughout the streamwise direction of the computational domain. This paper investigates the preservation of the HHABL profile using three different commercial CFD codes—the ANSYS Fluent, the ANSYS CFD, and the Siemens STAR-CCM+ software. Three different cases were considered, identified by their different inlet conditions for the inlet velocity, turbulent kinetic energy, and dissipation rate profiles. Simulations were carried out using the RANS k-ε turbulence model. Slight variations in the eddy viscosity models, as well as in the wall boundary conditions, were identified in the different software, with the standard wall function with roughness being implemented in the Fluent applications, the scalable wall function with roughness in the CFX applications, and the blended wall function option in the STAR-CCM+ simulations. There was a slight difference in the meshing approach in the three different software, with a prism-layer option in the STAR-CCM+ software, which allowed a finer mesh near the wall/ground boundary. The results show all three software are able to preserve the horizontal homogeneity of the ABL—less than 0.5% difference between the software—indicating very similar degrees of accuracy

Modelling of Packed Bed and Coated Wall Microreactors for 6 Methanol Steam Reforming for Hydrogen Production

A Computational Fluid Dynamics (CFD) study has been conducted to assess the performance of packed bed and coated wall 9 microreactors for the steam reforming of methanol with a CuO/ZnO/Al2O3 based catalyst (BASF F3-01). The results obtained 10 were compared to experimental data from literature to assess the validity and robustness of the models, and a good 11 validation has been obtained. The performance of the packed bed and coated wall microreactors are similar at a constant 12 reforming temperature. It was found that methanol conversion is enhanced with increasing temperature, residence time, 13 steam to methanol ratio, and catalyst coating thickness. Furthermore, internal and external mass transfer phenomena were 14 investigated using the models, and it was found that there were no internal and external mass transfer resistances for this 15 reactor configurations. Further studies demonstrated that larger catalyst pellet sizes led to the presence of internal mass 16 transfer resistances, which in turn causes lower methanol conversions. The CFD models have exhibited a sound agreement 17 with the experimental data, hence they can be used to predict the steam reforming of methanol in microreactors

Enhancing CFD-LES air pollution prediction accuracy using data assimilation

It is recognised worldwide that air pollution is the cause of premature deaths daily, thus necessitating the development of more reliable and accurate numerical tools. The present study implements a three dimensional Variational (3DVar) data assimilation (DA) approach to reduce the discrepancy between predicted pollution concentrations based on Computational Fluid Dynamics (CFD) with the ones measured in a wind tunnel experiment. The methodology is implemented on a wind tunnel test case which represents a localised neighbourhood environment. The improved accuracy of the CFD simulation using DA is discussed in terms of absolute error, mean squared error and scatter plots for the pollution concentration. It is shown that the difference between CFD results and wind tunnel data, computed by the mean squared error, can be reduced by up to three order of magnitudes when using DA. This reduction in error is preserved in the CFD results and its benefit can be seen through several time steps after re-running the CFD simulation. Subsequently an optimal sensors positioning is proposed. There is a trade-off between the accuracy and the number of sensors. It was found that the accuracy was improved when placing/considering the sensors which were near the pollution source or in regions where pollution concentrations were high. This demonstrated that only 14% of the wind tunnel data was needed, reducing the mean squared error by one order of magnitude

Process Simulation Modelling of the Catalytic Hydrodeoxygenation of 4-Propylguaiacol in Microreactors

A process simulation model was created using Aspen Plus to investigate the hydrodeoxygenation of 4-propylguaiacol, a model component in lignin-derived pyrolysis oil, over a presulphided NiMo/Al2O3 solid catalyst. Process simulation modelling methods were used to develop the pseudo-homogeneous packed bed microreactor. The reaction was conducted at 400 °C and an operating pressure of 300 psig with a 4-propylguaiacol liquid flow rate of 0.03 mL·min−1 and a hydrogen gas flow rate of 0.09 mL·min−1. Various operational parameters were investigated and compared to the experimental results in order to establish their effect on the conversion of 4-propylguaiacol. The parameters studied included reaction temperature, pressure, and residence time. Further changes to the simulation were made to study additional effects. In doing so, the operation of the same reactor was studied adiabatically, rather than isothermally. Moreover, different equations of state were used. It was observed that the conversion was enhanced with increasing temperature, pressure, and residence time. The results obtained demonstrated a good model validation when compared to the experimental results, thereby confirming that the model is suitable to predict the hydrodeoxygenation of pyrolysis oil

CO2 capture using membrane contactors: a systematic literature review

With fossil fuel being the major source of energy, CO2 emission levels need to be reduced to a minimal amount namely from anthropogenic sources. Energy consumption is expected to rise by 48% in the next 30 years, and global warming is becoming an alarming issue which needs to be addressed on a thorough technical basis. Nonetheless, exploring CO2 capture using membrane contactor technology has shown great potential to be applied and utilised by industry to deal with post- and pre-combustion of CO2. A systematic review has been conducted to analyse and assess CO2 removal using membrane contactors for capturing techniques in industrial processes. The review began with a total of 2650 papers, which were obtained from three major databases, and then were excluded down to a final number of 525 papers following a defined set of criteria. The results showed that hollow fibre membranes have demonstrated popularity, as well as the use of amine solvents for CO2 removal. This current systematic review in CO2 removal and capture is an important milestone in the acknowledgment of up to date research with the potential to serve as a benchmark databank for further research in similar areas of work. This study provides the first systematic enquiry in the evidence to research further sustainable methods to capture and separate CO2

Process Simulation Modelling of the Catalytic Hydrodeoxygenation of 4-Propylguaiacol in Microreactors

A process simulation model was created using Aspen Plus to investigate the hydrodeoxygenation of 4-propylguaiacol, a model component in lignin-derived pyrolysis oil, over a presulphided NiMo/Al2O3 solid catalyst. Process simulation modelling methods were used to develop the pseudo-homogeneous packed bed microreactor. The reaction was conducted at 400 °C and an operating pressure of 300 psig with a 4-propylguaiacol liquid flow rate of 0.03 mL·min−1 and a hydrogen gas flow rate of 0.09 mL·min−1. Various operational parameters were investigated and compared to the experimental results in order to establish their effect on the conversion of 4-propylguaiacol. The parameters studied included reaction temperature, pressure, and residence time. Further changes to the simulation were made to study additional effects. In doing so, the operation of the same reactor was studied adiabatically, rather than isothermally. Moreover, different equations of state were used. It was observed that the conversion was enhanced with increasing temperature, pressure, and residence time. The results obtained demonstrated a good model validation when compared to the experimental results, thereby confirming that the model is suitable to predict the hydrodeoxygenation of pyrolysis oil

Common Cause Versus Dynamic Mutualism: An Empirical Comparison of Two Theories of Psychopathology in Two Large Longitudinal Cohorts

Mental disorders are among the leading causes of global disease burden. To respond effectively, a strong understanding of the structure of psychopathology is critical. We empirically compared two competing frameworks, dynamic-mutualism theory and common-cause theory, that vie to explain the development of psychopathology. We formalized these theories in statistical models and applied them to explain change in the general factor of psychopathology (p factor) from early to late adolescence ( N = 1,482) and major depression in middle adulthood and old age ( N = 6,443). Change in the p factor was better explained by mutualism according to model-fit indices. However, a core prediction of mutualism was not supported (i.e., predominantly positive causal interactions among distinct domains). The evidence for change in depression was more ambiguous. Our results support a multicausal approach to understanding psychopathology and showcase the value of translating theories into testable statistical models for understanding developmental processes in clinical sciences

A domain decomposition non-intrusive reduced order model for turbulent flows

In this paper, a new Domain Decomposition Non-Intrusive Reduced Order Model (DDNIROM) is developed for turbulent flows. The method works by partitioning the computational domain into a number of subdomains in such a way that the summation of weights associated with the finite element nodes within each subdomain is approximately equal, and the communication between subdomains is minimised. With suitably chosen weights, it is expected that there will be approximately equal accuracy associated with each subdomain. This accuracy is maximised by allowing the partitioning to occur through areas of the domain that have relatively little flow activity, which, in this case, is characterised by the pointwise maximum Reynolds stresses.A Gaussian Process Regression (GPR) machine learning method is used to construct a set of local approximation functions (hypersurfaces) for each subdomain. Each local hypersurface represents not only the fluid dynamics over the subdomain it belongs to, but also the interactions of the flow dynamics with the surrounding subdomains. Thus, in this way, the surrounding subdomains may be viewed as providing boundary conditions for the current subdomain.We consider a specific example of turbulent air flow within an urban neighbourhood at a test site in London and demonstrate the effectiveness of the proposed DDNIROM