Incorporating Hydrologic Uncertainty in Industrial Economic Models: Implications of Extreme Rainfall Variability on Metal Mining Investments

Water balance uncertainties have long been known to lead to potential environmental hazards, but their effect on economic profitability of mines is an under-studied field of research. Historical rainfall data are analyzed using the extreme value theory (EVT) and the peak over threshold method (POT). The resulting distributions are used as inputs into a system dynamics techno-economic metal mining investment profitability model, and simulation analysis is performed. The proposed methodology incorporates rainfall extremes and uncertainty into techno-economic modeling of metal mining operations. A case study with real-life historical rainfall data was used to illustrate the relationship between hydrologic uncertainty and the economic value of a metal mining investment

Tools and analysis of spatio-temporal dynamics in heterogeneous aquifers: applications to artificial recharge and forced-gradient solute transport

This thesis deals with the development of tools and analysis to characterize and predict artificial recharge and radial convergent solute transport processes in heterogeneous media. The goal is to provide new insights to understand how heterogeneity, which is the main natural source of uncertainty in decision-making processes related with groundwater applications, can be controlled and its effects predicted for practical purposes in these topics. For hydrogeological applications, accurate modeling of phenomena is needed, but it is uncertain. Uncertainty is derived from the spatio-temporal random distribution of hydrodynamic (physical, chemical and biological) variables affecting groundwater processes, which is translated into random distribution of modeling parameters and equations. Such randomness is of two types: epistemic, when it can be reduced increasing the sample frequency of an experiment; aleatory, when it cannot be reduced when more information is analyzed. Sometimes hydrodynamic processes occur at so small scales that they become impossible to characterize with traditional methods, and from a practical perspective, this is analogous to deal with aleatoric model parameters. However, if some constitutive relationship (either empirically, theoretically or physically based) can be built between processes across different scales, then small-scale processes can be reproduced by equivalent large-scale model parameters. Uncertainty becomes amenable to be treated as epistemic randomness, and large-scale characterization techniques can be used to improve the description, interpretation or prediction of these processes. This thesis deals with these topics. The manuscript is composed by two main parts (the first on artificial recharge and the second on solute transport), each of them divided into three chapters. In chapter 1 of each part, a tool is developed to obtain quantitative information to model a selected variable at coarse grid resolutions. In the case of artificial recharge, satellite images are used to model the spatial variability of the infiltration capacity on top soils with a metric-scale detail. In the case of solute transport, a new method to estimate density from particle distribution is shown. In chapters 2, it is explored what processes occurring at the fine scales can affect the interpretation of artificial recharge and solute transport processes at larger scales. In the first part, a combined method that joins satellite images and field data along with a simple clogging model is used to display the equally-possible spatio-temporal mapping of the infiltration capacity of topsoil during artificial pond flooding activities. In the second part, numerical three-dimensional models are used to simulate transport in heterogeneous media under convergent radial flow to a well at fine scale. It is shown that an appropriate model framework can reproduce similar observations on contaminant temporal distribution at controlling section similar to those obtained in the field tracer tests. It is also provided a physical explanation to describe the so-called anomalous late-time behavior on breakthrough curves which is sometimes observed in the reality at larger scales. In the chapters 3, models are used to define the uncertainty around operating parameters in the optic of prediction and management on artificial recharge and solute transport. In the first case, a probability framework is built to define the engineering risk of management of artificial recharge ponds due to random variability of the initial distribution of infiltration, which controls several important clogging factors based on theoretical approaches. In the case of solute transport, it is discussed how equivalent parameters based on mass-transfer models can be related with the geometrical distribution of hydraulic parameters in anisotropic formation, when convergent flow tracer tests are used

Device and Circuit Architectures for In‐Memory Computing

With the rise in artificial intelligence (AI), computing systems are facing new challenges related to the large amount of data and the increasing burden of communication between the memory and the processing unit. In‐memory computing (IMC) appears as a promising approach to suppress the memory bottleneck and enable higher parallelism of data processing, thanks to the memory array architecture. As a result, IMC shows a better throughput and lower energy consumption with respect to the conventional digital approach, not only for typical AI tasks, but also for general‐purpose problems such as constraint satisfaction problems (CSPs) and linear algebra. Herein, an overview of IMC is provided in terms of memory devices and circuit architectures. First, the memory device technologies adopted for IMC are summarized, focusing on both charge‐based memories and emerging devices relying on electrically induced material modification at the chemical or physical level. Then, the computational memory programming and the corresponding device nonidealities are described with reference to offline and online training of IMC circuits. Finally, array architectures for computing are reviewed, including typical architectures for neural network accelerators, content addressable memory (CAM), and novel circuit topologies for general‐purpose computing with low complexity

Optimal reconstruction of non-symmetric travel time density distributions using a new kernel density estimator

Postprint (published version

A geologically-based approach to map arsenic risk in crystalline aquifers: Analysis of the Tampere region, Finland

The study illustrates the critical role of accurate geological structural mapping to delineate crystalline aquifer zones more prone to high health risk due to elevated dissolved As in drinking wells. The analysis revisits the results from more than 1200 groundwater samples collected over ten years from domestic wells across the Tampere region (Finland). It is demonstrated that the highest dissolved As concentrations in the region (up to 2230 μg/L) are exclusively found near major faults and deformation zones (FDZs) detected via geophysical and geological surveys, and that a clear correlation exists between dissolved concentrations and the distance from the FDZs (r). Almost all values exceeding the drinking water limit (10 μg/L) occur at r < 8 km, while concentrations above 100 μg/L occur at r < 4 km. Solid-phase As concentrations in bedrock show less dependency on FDZ than aqueous concentrations. This behavior is explained considering different mechanisms, which include enhanced sulfide oxidation and fracture connectivity, promoting preferential transport of dissolved As to FDZs and mixing of waters from different redox zones, mobilizing preferentially As(III) or As(V). Fe hydro-oxides may also precipitate/dissolve preferentially because of FDZs, while residence time may influence the contact time between water and As-bearing minerals. It is concluded that the accurate mapping of FDZs, and in general of structural geology, provides an important preliminary information to identify where localized, site-specific characterization of hydrogeology and geochemistry is more urgent to reduce As-related health risk from groundwater intake. Keywords: Arsenic risk, Crystalline bedrock, Fractured aquifers, Heterogeneity, Finlan

Scale dependence of the hydraulic properties of a fractured aquifer estimated using transfer functions

We present an investigation of the scale dependence of hydraulic parameters in fractured media based on the concept of transfer functions (TF). TF methods provide an inexpensive way to perform aquifer parameter estimation, as they relate the fluctuations of an observation time series (hydraulic head fluctuations) to an input function (aquifer recharge) in frequency domain. Fractured media are specially sensitive to this approach as hydraulic parameters are strongly scale-dependent, involving nonstationary statistical distributions. Our study is based on an extensive data set, involving up to 130 measurement points with periodic head measurements that in some cases extend for more than 30 years. For each point, we use a single-porosity and dual-continuum TF formulation to obtain a distribution of transmissivities and storativities in both mobile and immobile domains. Single-porosity TF estimates are compared with data obtained from the interpretation of over 60 hydraulic tests (slug and pumping tests). Results show that the TF is able to estimate the scale dependence of the hydraulic parameters, and it is consistent with the behavior of estimates from traditional hydraulic tests. In addition, the TF approach seems to provide an estimation of the system variance and the extension of the ergodic behavior of the aquifer (estimated in approximately 500 m in the analyzed aquifer). The scale dependence of transmissivity seems to be independent from the adopted formulation (single or dual-continuum), while storativity is more sensitive to the presence of multiple continua

Infilitration tests at the Sant Vicenç dels Horts artificial recharge experimental site

Infiltration capacity is the key parameter in an artificial recharge operation site. Infiltration capacity is spatially variable, and during operation it is also temporally variable due to surface clogging processes. Double-ring infiltrometer tests were performed at an experimental site close to Barcelona city (Spain). The site is located on alluvial deposits from the Llobregat River and comprises two half hectare ponds. River water collected upstream traveled through a two km pipe before entering the settling pond. Once the pond is filled water flows to the infiltration pond. Tests were performed only in the latter, prior to and after recharging the ponds. Prior to recharge, six points were selected to estimate infiltration capacity Points were evenly distributed and chosen considering apparent soil texture at the site (coarse, medium and fine grains). All tests were performed allowing water to infiltrate for two hours and data was interpreted using the modified Kostiakov equation. Ponds were then flooded for about two months. The average infiltration rate values for the full infiltration pond before and after the flooding campaign were 5.8 m/day and 2.2 m/day, respectively. The double ring tests were then repeated at the same points, showing a reduction of the infiltration rate that varied between 7 and 90%. Control points with the initial highest infiltration rates presented the highest reduction in infiltration. Physical clogging due to particles settling appears to be the most likely cause of the diminished infiltration rates. This result is confirmed by other independent measurements during the flooding test. There is a clear tendency towards a lower infiltration rates when observing the relation through time of flow entering per volume of water on the infiltration pond at a given time.Peer ReviewedPostprint (published version

Sustainability of sunflower cultivation for biodiesel production in central Italy according to the Renewable Energy Directive methodology

The use of renewable energies as alternative to fossil fuels has value from different points of view and has effects at environmental, social and economic level. These aspects are often connected to each other and together define the overall sustainability of bioenergy. At European level, the Directive 2009/28/EC gives the basic criteria for the estimation of sustainability of biofuels and indicates a minimum threshold of 35% of greenhouse gas saving for a biofuel in order to be considered sustainable. The Directive gives the possibility to identify standard regional values for the cultivation steps that could be utilized for the certification. This paper aims to give a contribution to the definition of these values considering the RED methodology applied to the sunflower cropped in central Italy which is characterized by a hilly landscape and not-irrigated crops. To determine input and output of sunflower cultivation in the central Italy, the results of PROBIO project, carried out by the Authors, were used. The sustainability of biodiesel produced from sunflower grown in central Italy is variable and depends on the nitrogen input and seasonal climatic conditions that affect the yields. The greenhouse gases savings of the Italian chain is 40% in average, greater than the required 35% and would be possible to assign this value as standard to the biofuel chain biodiesel from sunflower cultivated in central Italy. Using an averaged regional standard value guards against the possibility of considering unsustainable harvesting in unfavourable years and seeing it overestimated in the favourable ones

Reproducing tailing in breakthrough curves: are statistical models equally representative and predictive?

Breakthrough curves (BTCs) observed during tracer tests in highly heterogeneous aquifers display strong tailing. Power laws are popular models for both the empirical fitting of these curves, and the prediction of transport using upscaling models based on best-fitted estimated parameters (e.g. the power law slope or exponent). The predictive capacity of power law based upscaling models can be however questioned due to the difficulties to link model parameters with the aquifers’ physical properties. This work analyzes two aspects that can limit the use of power laws as effective predictive tools: (a) the implication of statistical subsampling, which often render power law undistinguishable from other heavily tailed distributions, such as the logarithmic (LOG); (b) the difficulties to reconcile fitting parameters obtained from models with different formulations, such as the presence of a late-time cutoff in the power law model. Two rigorous and systematic stochastic analyses, one based on benchmark distributions and the other on BTCs obtained from transport simulations, are analyzed. It is found that a power law model without cutoff (PL) results in best-fitted exponents (αPL) falling in the range of typical experimental values reported in the literature (1.5 < αPL < 4). The PL exponent tends to lower values as the tailing becomes heavier. Strong fluctuations occur when the number of samples is limited, due to the effects of subsampling. On the other hand, when the power law model embeds a cutoff (PLCO), the best-fitted exponent (αCO) is insensitive to the degree of tailing and to the effects of subsampling and tends to a constant αCO ≈ 1. In the PLCO model, the cutoff rate (λ) is the parameter that fully reproduces the persistence of the tailing and is shown to be inversely correlated to the LOG scale parameter (i.e. with the skewness of the distribution). The theoretical results are consistent with the fitting analysis of a tracer test performed during the MADE-5 experiment. It is shown that a simple mechanistic upscaling model based on the PLCO formulation is able to predict the ensemble of BTCs from the stochastic transport simulations without the need of any fitted parameters. The model embeds the constant αCO = 1 and relies on a stratified description of the transport mechanisms to estimate λ. The PL fails to reproduce the ensemble of BTCs at late time, while the LOG model provides consistent results as the PLCO model, however without a clear mechanistic link between physical properties and model parameters. It is concluded that, while all parametric models may work equally well (or equally wrong) for the empirical fitting of the experimental BTCs tails due to the effects of subsampling, for predictive purposes this is not true. A careful selection of the proper heavily tailed models and corresponding parameters is required to ensure physically-based transport predictions

On the formation of breakthrough curves tailing during convergent flow tracer tests in three-dimensional heterogeneous aquifers

Anomalous transport in advection-dominated convergent flow tracer tests can occurs due to small-scale heterogeneities in aquifer hydraulic properties. These result in fluctuations of the groundwater velocity field and complex connectivity patterns between injection and extraction wells. While detailed characterization of heterogeneity is often not possible in practice, a proper understanding of what fundamental physical mechanisms can give rise to macroscopic behaviors that are measurable is essential for proper upscaling of solute transport processes. We analyze here how heavy-tailed breakthrough curves can arise in radially convergent flow to a well. The permeability fields are three-dimensional multi-Gaussian fields with varying statistical geometry and degrees of heterogeneity. We consider transport of conservative tracers from multiple injection locations by varying distance and angle from the extraction well. Anomalous power law tailing in breakthrough curves is attributed to a variety of features including the initial vertical stratification of the solute that arises due to a flux-weighted injection, the injection distance to the well relative to the depth of the aquifer, and the statistics of the heterogeneity field as defined by the correlation length and variance of the permeability. When certain conditions cooccur for a given injection, such as strong connectivity contrasts between aquifer layers, injection distances comparable to the horizontal heterogeneity integral scales, and large global variances, breakthrough curves tend to scale as a PL with unit slope at late time. These findings offer new insights to understand what physical processes must be understood to develop and choose appropriate upscaling approaches that might reproduce such anomalous transport in heterogeneous advection-dominated systems