Modeling temporal and spatial variations in dissolved oxygen in Amite River

A watershed-based modeling framework is developed in this dissertation for simulating temporal and spatial variations in DO in lowland rivers with organic-rich fine-grained sediment. The modeling framework is based on three major contributions/new models, including (1)VART-DO model for improved estimation of reaeration coefficient (K2) in natural streams, (2)VART-DOS model for simulation of temporal variations in DO in response to sediment resuspension, and (3)VART DO-3L model for simulation of spatial variations in DO. A major advantage of VART-DO model is the capability of simulating DO exchange across the water-sediment interface through the hyporheic exchange mechanism in addition to the air-water exchange. Simulation results from VART-DO model revealed that hyporheic exchange can reduce K2 by 30% while longitudinal dispersion increases K2 by 50%. VART-DOS model is developed for simulation of temporal variations in DO particularly due to sediment resuspension effect during high flow. Application results of VART-DOS model to the Amite River in Louisiana showed that 83% of DO consumption in water column in July 1990 was because of sediment resuspension. A novel feature of VART DO-3L model is that a fine-grained stream with the flocculent layer can be vertically modeled with three layers: overlying water column, an advection-dominated storage zone, and a diffusion-dominated storage zone in relatively consolidated stream bed-sediment. While the importance of flocculent layer to instream DO has been widely reported, VART-DO-3L model is the first modeling tool that incorporates the flocculent layer into DO modeling. This is a unique feature of VART-DO-3L model, making it possible for determining both longitudinal and vertical profiles of DO in streams. Results of VART-DO-3L for the Amite River indicated that the DO level decreases longitudinally from 7.9mg/L at the Denham Springs station to 2.89mg/L at the Port Vincent station. Vertically, DO level drops rapidly from overlying water column to the advection-dominated storage zone and further to the diffusive layer. The DO level in the advective layer is about 40% of that in water column. The thickness of the diffusive layer varies between 0-10mm, depending on effective diffusion coefficient. Developed models in this dissertation are also applicable to sandy/gravel rivers

Aqueous & Non-Aqueous Phase Tracer Migration Through Differing Soil Textures

The National Grid Transco Company sponsored this project in order to promote the understanding of NAPL migration through b-horizon soils and retarding effects upon non aqueous species migration. Soil structure and texture was also studied using conservative (Bromide) and non-conservative (Phosphate) tracers. Experimental data was produced using a laboratory ½ metre scale automated lysimeter designed and constn1cted at Plymouth. The tracers were compared before oil injection, to calibrate differences in soil texture, and after oil injection to detect any changes in the flow patterns caused by the oil injection. It was found that the Crediton, Sollom and Conway soils respectively offered least resistance to the tracers with the non-conservative tracer behaving much more unpredictably than the conservative tracer. After oil injection it could be seen that the oil had heavily retarded the ability of the tracers to migrate from the injection site. This retardation was identified as analogous to perturbations of the soil structure. Statistical analysis of the data showed that the experiments were all internally self consistent and visible patterns could be seen in the corrected data caused by inclusion of oil in the injection site. Methods of dispersal for the oil and tracer are suggested in the concluding chapter with references to the work of previous authors. Development of a hazard assessment framework was facilitated by the simulation of soil structures using a pedo transfer function developed at the National Soils Resource Institute. To allow the modelling of soils the Pore-Cor software had an annealed simplex algorithm integrated into the data inversion engine to allow the simulation of 3-D soil structures using 2-D data from pedo transfer functions or experimentally derived water retention curves. An extensive sensitivity analysis upon the model highlighted limitations, due to the data set the current pedo transfer function is based upon. It was suggested that inclusion of choices of different pedo transfer functions could be used to overcome this problem. A suitable framework was derived for the identification of priority soils using a validated computer model. Experimental data was compared to the simulated data in order to try and develop an understanding of practical upscaling of the data. The use of the "Scaleway" method is discussed in the concluding Chapter.Tbe National Grid Transco pl

Optimal design and operation of compact simulated moving bed processes for enantioseparations

Simulated moving bed (SMB) chromatography is attracting more and more attention since it is a powerful technique for complex separation tasks. Nowadays, more than 60% of preparative SMB units are installed in the pharmaceutical and in the food in- dustry [SDI, Preparative and Process Liquid Chromatography: The Future of Process Separations, International Strategic Directions, Los Angeles, USA, 2002. http://www. strategicdirections.com]. Chromatography is the method of choice in these ¯elds, be- cause often pharmaceuticals and ¯ne-chemicals have physico-chemical properties which di®er little from those of the by-products, and they may be thermally instable. In these cases, standard separation techniques as distillation and extraction are not applicable. The noteworthiness of preparative chromatography, particulary SMB process, as a sep- aration and puri¯cation process in the above mentioned industries has been increasing, due to its °exibility, energy e±ciency and higher product purity performance. Consequently, a new SMB paradigm is requested by the large number of potential small- scale applications of the SMB technology, which exploits the °exibility and versatility of the technology. In this new SMB paradigm, a number of possibilities for improving SMB performance through variation of parameters during a switching interval, are pushing the trend toward the use of units with smaller number of columns because less stationary phase is used and the setup is more economical. This is especially important for the phar- maceutical industry, where SMBs are seen as multipurpose units that can be applied to di®erent separations in all stages of the drug-development cycle. In order to reduce the experimental e®ort and accordingly the coast associated with the development of separation processes, simulation models are intensively used. One impor- tant aspect in this context refers to the determination of the adsorption isotherms in SMB chromatography, where separations are usually carried out under strongly nonlinear conditions in order to achieve higher productivities. The accurate determination of the competitive adsorption equilibrium of the enantiomeric species is thus of fundamental importance to allow computer-assisted optimization or process scale-up. Two major SMB operating problems are apparent at production scale: the assessment of product quality and the maintenance of long-term stable and controlled operation. Constraints regarding product purity, dictated by pharmaceutical and food regulatory organizations, have drastically increased the demand for product quality control. The strict imposed regulations are increasing the need for developing optically pure drugs.(...

Model-data interaction in groundwater studies: Review of methods, applications and future directions

This manuscript version is made available under the CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0/ which permits use, distribution and reproduction in any medium, provided the original work is properly cited. This author accepted manuscript is made available following 24 month embargo from date of publication (Sept 2018) in accordance with the publisher’s archiving policyWe define model-data interaction (MDI) as a two way process between models and data, in which on one hand data can serve the modeling purpose by supporting model discrimination, parameter refinement, uncertainty analysis, etc., and on the other hand models provide a tool for data fusion, interpretation, interpolation, etc. MDI has many applications in the realm of groundwater and has been the topic of extensive research in the groundwater community for the past several decades. This has led to the development of a multitude of increasingly sophisticated methods. The progress of data acquisition technologies and the evolution of models are continuously changing the landscape of groundwater MDI, creating new challenges and opportunities that must be properly understood and addressed. This paper aims to review, analyze and classify research on MDI in groundwater applications, and discusses several related aspects including: (1) basic theoretical concepts and classification of methods, (2) sources of uncertainty and how they are commonly addressed, (3) specific characteristics of groundwater models and data that affect the choice of methods, (4) how models and data can interact to provide added value in groundwater applications, (5) software and codes for MDI, and (6) key issues that will likely form future research directions. The review shows that there are many tools and techniques for groundwater MDI, and this diversity is needed to support different MDI objectives, assumptions, model and data types and computational constraints. The study identifies eight categories of applications for MDI in the groundwater literature, and highlights the growing gap between MDI practices in the research community and those in consulting, industry and government.Behzad Ataie-Ashtiani and Craig T. Simmons acknowledge support from the National Centre for Groundwater Research and Training, Australia. Behzad Ataie-Ashtiani also appreciates the support of the Research Office of the Sharif University of Technology, Iran

Source localization within a uniform circular sensor array

Traditional source localization problems have been considered with linear and planar antenna arrays. In this research work, we assume that the sources are located within a uniformly spaced circular sensor array. Using a modified Metropolis algorithm and Polak-Ribière conjugate gradients, a hybrid optimization algorithm is proposed to localize sources within a two dimensional uniform circular sensor array, which suffers from far field attenuation. The developed algorithm is capable of accurately locating the position of a single, stationary source within 1% of a wavelength and 1° of angular displacement. In the single stationary source case, the simulated Cramer-Rao Lower Bound has also shown low noise susceptibility for a reasonable signal to noise ratio. Additionally, the localization of multiple stationary sources within the array is presented and tracking capabilities for a slowly moving non-stationary source is also demonstrated. In each case, results are presented, analyzed and discussed. Furthermore, the proposed algorithm has also been validated through hardware experimentation. The design and construction of four microstrip patch antennas and a wire antenna have been completed to emulate a circular sensor array and the enclosed source, respectively. Within this array, data has been collected at the four sensors from several fixed source positions and fitted into the proposed algorithm for source localization. The convergence of the algorithm with both simulated data and data collected from hardware are compared and sources of error and potential improvements are proposed

Generalized Clusterwise Regression for Simultaneous Estimation of Optimal Pavement Clusters and Performance Models

The existing state-of-the-art approach of Clusterwise Regression (CR) to estimate pavement performance models (PPMs) pre-specifies explanatory variables without testing their significance; as an input, this approach requires the number of clusters for a given data set. Time-consuming ‘trial and error’ methods are required to determine the optimal number of clusters. A common objective function is the minimization of the total sum of squared errors (SSE). Given that SSE decreases monotonically as a function of the number of clusters, the optimal number of clusters with minimum SSE always is the total number of data points. Hence, the minimization of SSE is not the best objective function to seek for an optimal number of clusters. In previous studies, the PPMs were restricted to be either linear or nonlinear, irrespective of which functional form provided the best results. The existing mathematical programming formulations did not include constraints that ensured the minimum number of observations required in each cluster to achieve statistical significance. In addition, a pavement sample could be associated with multiple performance models. Hence, additional modeling was required to combine the results from multiple models. To address all these limitations, this research proposes a generalized CR that simultaneously 1) finds the optimal number of pavement clusters, 2) assigns pavement samples into clusters, 3) estimates the coefficients of cluster-specific explanatory variables, and 4) determines the best functional form between linear and nonlinear models. Linear and nonlinear functional forms were investigated to select the best model specification. A mixed-integer nonlinear mathematical program was formulated with the Bayesian Information Criteria (BIC) as the objective function. The advantage of using BIC is that it penalizes for including additional parameters (i.e., number of clusters and/or explanatory variables). Hence, the optimal CR models provided a balance between goodness of fit and model complexity. In addition, the search process for the best model specification using BIC has the property of consistency, which asymptotically selects this model with a probability of ‘1’. Comprehensive solution algorithms – Simulated Annealing coupled with Ordinary Least Squares for linear models and All Subsets Regression for nonlinear models – were implemented to solve the proposed mathematical problem. The algorithms selected the best model specification for each cluster after exploring all possible combinations of potentially significant explanatory variables. Potential multicollinearity issues were investigated and addressed as required. Variables identified as significant explanatory variables were average daily traffic, pavement age, rut depth along the pavement, annual average precipitation and minimum temperature, road functional class, prioritization category, and the number of lanes. All these variables were considered in the literature as the most critical factors for pavement deterioration. In addition, the predictive capability of the estimated models was investigated. The results showed that the models were robust without any overfitting issues, and provided small prediction errors. The models developed using the proposed approach provided superior explanatory power compared to those that were developed using the existing state-of-the-art approach of clusterwise regression. In particular, for the data set used in this research, nonlinear models provided better explanatory power than did the linear models. As expected, the results illustrated that different clusters might require different explanatory variables and associated coefficients. Similarly, determining the optimal number of clusters while estimating the corresponding PPMs contributed significantly to reduce the estimation error