Electrical properties of iron-sand columns: Implications for induced polarization investigation and performance monitoring of iron-wall barriers

Geophysics ◽  
2005 ◽  
Vol 70 (4) ◽  
pp. G87-G94 ◽  
Author(s):  
Lee D. Slater ◽  
Jaeyoung Choi ◽  
Yuxin Wu
Keyword(s):  
Relaxation Time ◽  
Surface Area ◽  
Performance Monitoring ◽  
Electrical Response ◽  
Induced Polarization ◽  
Iron Hydroxides ◽  
Interfacial Chemistry ◽  
Reactive Iron ◽  
Volume Formula ◽  
And Performance

We investigate the electrical response (0.1–1000 Hz) of reactive iron barriers by making measurements on zero valent iron ([Formula: see text])-sand columns under the following conditions: (1) variable [Formula: see text] surface area (0.1–100% by volume [Formula: see text] under constant electrolyte chemistry; (2) variable electrolyte activity (0.01–1 mol/liter), valence (mono trivalent), and pH under constant [Formula: see text]-sand composition; and (3) forced precipitation of iron hydroxides and iron carbonates on the [Formula: see text] surface. We model the measurements in terms of conduction magnitude, polarization magnitude, and polarization relaxation time. Our key findings are: (a) Polarization magnitude exhibits a linear relation to the surface area of [Formula: see text], whereas conduction magnitude is only weakly dependent on the [Formula: see text] concentration below 30% by volume [Formula: see text]. (b) Polarization magnitude shows a power law relation to electrolyte activity, with exponents decreasing from 0.9 for monovalent solutions to 0.7 for trivalent solutions. (c) The relaxation time parameter depends on activity and valence in a manner that is partly consistent with the variation in double layer thickness predicted from theory. (d) pH exerts minor control on the electrical parameters. (e) Polarization magnitude and relaxation time both increase as a result of precipitation induced on the surface of [Formula: see text]. Our results show that induced polarization parameters systematically change in response to changes in the [Formula: see text]-electrolyte interfacial chemistry.

On the relationship between iron concentration and induced polarization in marsh soils

Geophysics ◽  
2007 ◽  
Vol 72 (1) ◽  
pp. A1-A5 ◽  
Author(s):  
Nasser Mansoor ◽  
Lee Slater
Keyword(s):  
Surface Area ◽  
Iron Concentration ◽  
Induced Polarization ◽  
Model Parameters ◽  
Soil Geochemistry ◽  
Iron Cycling ◽  
Volume Formula ◽  
Cole Model ◽  
The Relationship ◽  
Fluid Conductivity

Induced polarization (IP) measurements [Formula: see text] were conducted on seventeen clay and peat marsh soils that were subsequently analyzed for heavy metal concentrations, moisture content, organic matter, porosity, specific surface area, and pore fluid conductivity. A Cole-Cole model was fit to each sample and model parameters analyzed in terms of physicochemical properties. We found a linear relation between the normalized chargeability [Formula: see text] and estimated surface area to pore volume [Formula: see text] when iron content (ranging from 0.25% to 1.63% by volume) is accounted for as a polarizable element of the soil. In fact, the dependence of [Formula: see text] on volumetric Fe concentration per unit volume of the bulk soil is described by a linear relationship with a correlation coefficient [Formula: see text] of 0.94. As Fe concentration is a critical biogeochemical parameter, our findings suggest that IP measurements may provide a hitherto unrecognized approach to probing soil geochemistry, iron cycling and anaerobic microbial activity. Furthermore, our results yield insights into physicochemical controls on IP in natural soils.

On the relationship between induced polarization and surface area in metal-sand and clay-sand mixtures

Geophysics ◽  
2006 ◽  
Vol 71 (2) ◽  
pp. A1-A5 ◽  
Author(s):  
Lee Slater ◽  
Dimitrios Ntarlagiannis ◽  
DeBonne Wishart
Keyword(s):  
Surface Area ◽  
Gas Adsorption ◽  
Induced Polarization ◽  
Nitrogen Gas ◽  
Volume Formula ◽  
Percentage Weight ◽  
Mineral Constituent ◽  
Cole Model ◽  
The Relationship ◽  
Electrical Data

Induced polarization (IP) measurements were conducted on saturated kaolinite-, iron-, and magnetite-sand mixtures as a function of varying percentage weight of a mineral constituent: 0%–100% for iron and magnetite and 0%–32% for kaolinite. We determined the specific surface area for each mineral using nitrogen gas adsorption, where the porosity of each mixture was calculated from weight loss after drying. We fit a Cole-Cole model (Cole and Cole, 1941) to the electrical data obtained for the magnetite and iron mixtures. In contrast, the kaolinite mixtures showed a power-law dependence of phase-on frequency. The global polarization magnitude we obtained from the Cole-Cole modeling of the iron and magnetite mixtures displays a single, near-linear dependence on the ratio of surface area to pore volume ([Formula: see text]) calculated for the mixtures. A similar relationship is found using a local measure of polarization (imaginary conductivity at 1 Hz) for the clay-sand mixtures. The [Formula: see text] appears to be a critical parameter for determining IP in both metallic- and clay-containing soils. This result is not easily reconciled with traditional models of induced polarization.

Spectral induced polarization of the plant root: Experiments and numerical modeling.

2021 ◽  
Author(s):  
Kuzma Tsukanov ◽  
Nimrod Schwartz
Keyword(s):  
Relaxation Time ◽  
Surface Area ◽  
External Surface ◽  
Plant Root ◽  
Planck Equation ◽  
Induced Polarization ◽  
Plant Roots ◽  
Length Scale ◽  
External Surface Area ◽  
Spectral Induced Polarization

<p>Exploration of plant roots and monitoring their conditions during growth is of great importance. A promising method for the non-invasive investigation of plant roots is spectral induced polarization (SIP). To enhance understanding of the mechanism controlling the plant root’s induced polarization response, we have conducted a series of experiments and constructed a physical-based numerical model. We measured the SIP signal of wheat root grown in the nutrient solution. The experiments have demonstrated a relationship between the SIP parameters (chargeability and relaxation time) and the root biomass and surface area. Monitoring the SIP response of roots poisoned by cyanide has revealed that the root polarization source is the cell membrane potential. In addition, we modeled plant root as a collection of 2-dimensional individual cells surrounded by an electrolyte. The SIP signal was calculated based on the numerical solution of the Poisson-Nernst-Planck equation. The model has supported the experimental results with the correlation between the magnitude of polarization and the root surface area. According to the model, the root polarization magnitude is related to the root’s external surface area. The polarization length scale is the root’s diameter, not the cell diameter. Based on these results and data from the literature, we suggest that at the low-frequency range associated with the SIP method, passing the current through the plant results in polarization of the individual cells, a relatively high polarization and relaxation time that is related to the cell length. On the other hand, injecting current to the growing medium results in the polarization of the external surface area of the root and polarization length scale related to the root diameter.</p>

Social Technology: A Compendium of Short Cases

2015 ◽  
Vol 31 (4) ◽  
pp. 417-430
Author(s):  
Brett Considine ◽  
John Peter Krahel ◽  
Margarita M. Lenk ◽  
Diane J. Janvrin
Keyword(s):  
Financial Reporting ◽  
Internal Control ◽  
Technology Use ◽  
Performance Monitoring ◽  
Regulatory Compliance ◽  
Social Technology ◽  
Student Groups ◽  
Accounting Information Systems ◽  
And Performance ◽  
Technology Strategies

ABSTRACT Seven short cases highlight the need for organizational control of the use of social technology. Executives now consider the management of social technology strategies and risks to be their fourth highest priority, investing significant resources to develop effective social technology use policies (Carrick et al. 2013; Deloitte 2012; Feltham and Nichol 2012). Moreover, organizations vary their social technology investment choices depending on their objectives and their target audiences (AICPA 2013; Gallaugher and Ransbotham 2010; Kaplan and Haenlein 2010). A wide variety of case learning objectives involve applying internal control models, and developing and justifying opinions about how social technology uses and abuses affect operational, financial reporting and regulatory compliance objectives, risks, controls, and performance-monitoring activities. Instructors may utilize one or more of these cases at a time, either individually or in student groups, and in undergraduate or graduate financial accounting, accounting information systems, governance, or auditing courses.

Programmable Integrated Photonics

2020 ◽  
Author(s):  
José Capmany ◽  
Daniel Pérez
Keyword(s):  
Integrated Circuit ◽  
Performance Monitoring ◽  
Low Cost ◽  
Limiting Factors ◽  
External Control ◽  
Integrated Photonics ◽  
New Paradigm ◽  
Photonic Integrated Circuit ◽  
And Performance ◽  
Application Fields

Programmable Integrated Photonics (PIP) is a new paradigm that aims at designing common integrated optical hardware configurations, which by suitable programming can implement a variety of functionalities that, in turn, can be exploited as basic operations in many application fields. Programmability enables by means of external control signals both chip reconfiguration for multifunction operation as well as chip stabilization against non-ideal operation due to fluctuations in environmental conditions and fabrication errors. Programming also allows activating parts of the chip, which are not essential for the implementation of a given functionality but can be of help in reducing noise levels through the diversion of undesired reflections. After some years where the Application Specific Photonic Integrated Circuit (ASPIC) paradigm has completely dominated the field of integrated optics, there is an increasing interest in PIP justified by the surge of a number of emerging applications that are and will be calling for true flexibility, reconfigurability as well as low-cost, compact and low-power consuming devices. This book aims to provide a comprehensive introduction to this emergent field covering aspects that range from the basic aspects of technologies and building photonic component blocks to the design alternatives and principles of complex programmable photonics circuits, their limiting factors, techniques for characterization and performance monitoring/control and their salient applications both in the classical as well as in the quantum information fields. The book concentrates and focuses mainly on the distinctive features of programmable photonics as compared to more traditional ASPIC approaches.

Remote Thermal Performance Monitoring and Diagnostics: Turning Data Into Knowledge

2013 ◽  
Author(s):  
Xiaomo Jiang ◽  
Craig Foster
Keyword(s):  
Anomaly Detection ◽  
Gas Turbines ◽  
Performance Monitoring ◽  
Combined Cycle ◽  
Integrated System ◽  
Site Specific ◽  
Performance Improvements ◽  
Maintenance Schedule ◽  
Effectiveness Monitoring ◽  
And Performance

Gas turbine simple or combined cycle plants are built and operated with higher availability, reliability, and performance in order to provide the customer with sufficient operating revenues and reduced fuel costs meanwhile enhancing customer dispatch competitiveness. A tremendous amount of operational data is usually collected from the everyday operation of a power plant. It has become an increasingly important but challenging issue about how to turn this data into knowledge and further solutions via developing advanced state-of-the-art analytics. This paper presents an integrated system and methodology to pursue this purpose by automating multi-level, multi-paradigm, multi-facet performance monitoring and anomaly detection for heavy duty gas turbines. The system provides an intelligent platform to drive site-specific performance improvements, mitigate outage risk, rationalize operational pattern, and enhance maintenance schedule and service offerings via taking appropriate proactive actions. In addition, the paper also presents the components in the system, including data sensing, hardware, and operational anomaly detection, expertise proactive act of company, site specific degradation assessment, and water wash effectiveness monitoring and analytics. As demonstrated in two examples, this remote performance monitoring aims to improve equipment efficiency by converting data into knowledge and solutions in order to drive value for customers including lowering operating fuel cost and increasing customer power sales and life cycle value.

Induced polarization and pore radius — A discussion

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. D519-D526 ◽  
Author(s):  
Andreas Weller ◽  
Zeyu Zhang ◽  
Lee Slater ◽  
Sabine Kruschwitz ◽  
Matthias Halisch
Keyword(s):  
Diffusion Coefficient ◽  
Relaxation Time ◽  
Diffusion Coefficients ◽  
Pore Radius ◽  
Mercury Porosimetry ◽  
Induced Polarization ◽  
Reliable Estimate ◽  
Characteristic Relaxation Time ◽  
A Value ◽  
Apparent Diffusion

Permeability estimation from induced polarization (IP) measurements is based on a fundamental premise that the characteristic relaxation time [Formula: see text] is related to the effective hydraulic radius [Formula: see text] controlling fluid flow. The approach requires a reliable estimate of the diffusion coefficient of the ions in the electrical double layer. Others have assumed a value for the diffusion coefficient, or postulated different values for clay versus clay-free rocks. We have examined the link between a widely used single estimate of [Formula: see text] and [Formula: see text] for an extensive database of sandstone samples, in which mercury porosimetry data confirm that [Formula: see text] is reliably determined from a modification of the Hagen-Poiseuille equation assuming that the electrical tortuosity is equal to the hydraulic tortuosity. Our database does not support the existence of one or two distinct representative diffusion coefficients but instead demonstrates strong evidence for six orders of magnitude of variation in an apparent diffusion coefficient that is well-correlated with [Formula: see text] and the specific surface area per unit pore volume [Formula: see text]. Two scenarios can explain our findings: (1) the length scale defined by [Formula: see text] is not equal to [Formula: see text] and is likely much longer due to the control of pore-surface roughness or (2) the range of diffusion coefficients is large and likely determined by the relative proportions of the different minerals (e.g., silica and clays) making up the rock. In either case, the estimation of [Formula: see text] (and hence permeability) is inherently uncertain from a single characteristic IP relaxation time as considered in this study.

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