Crystallization of calcite from amorphous calcium carbonate: earthworms show the way

No abstract available

Soil pH governs production rate of calcium carbonate secreted by the earthworm Lumbricus terrestris

Lumbricus terrestris earthworms exposed to 11 soils of contrasting properties produced, on average, 0.8 ± 0.1 mgCaCO3 earthworm−1 day−1 in the form of granules up to 2 mm in diameter. Production rate increased with soil pH (r2 = 0.68, p < 0.01). Earthworms could be a significant source of calcite in soils

Optimal sensor placement for classifier-based leak localization in drinking water networks

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a sensor placement method for classifier-based leak localization in Water Distribution Networks. The proposed approach consists in applying a Genetic Algorithm to decide the sensors to be used by a classifier (based on the k-Nearest Neighbor approach). The sensors are placed in an optimal way maximizing the accuracy of the leak localization. The results are illustrated by means of the application to the Hanoi District Metered Area and they are compared to the ones obtained by the Exhaustive Search Algorithm. A comparison with the results of a previous optimal sensor placement method is provided as well.Postprint (author's final draft

Pumps condition assessment in water distribution networks

Postprint (published version

Leak localization in water distribution networks using a mixed model-based/data-driven approach

“The final publication is available at Springer via http://dx.doi.org/10.1016/j.conengprac.2016.07.006”This paper proposes a new method for leak localization in water distribution networks (WDNs). In a first stage, residuals are obtained by comparing pressure measurements with the estimations provided by a WDN model. In a second stage, a classifier is applied to the residuals with the aim of determining the leak location. The classifier is trained with data generated by simulation of the WDN under different leak scenarios and uncertainty conditions. The proposed method is tested both by using synthetic and experimental data with real WDNs of different sizes. The comparison with the current existing approaches shows a performance improvement.Peer ReviewedPostprint (author's final draft

Tema 1. Introducción a la fabricación automatizada

2011/201

Vitamin E as Adjuvant in Emulsified Vaccine for Chicks

Abstract Mineral oil was partially replaced with D, L-α-tocopheryl acetate (vitamin E) in bacterial and viral inactivated emulsified vaccines. Vitamin E increased the immune response to the viral antigen (Newcastle disease virus) used but not to the bacterial antigen (Escherichia coli) when its presence in the oil phase did not exceed 30%. Inoculated vitamin E may have enhanced the immune response by interacting with the immune-competent cells involved in the inflammatory reaction that followed inoculation of emulsified vaccines

Set-membership identification and fault detection using a Bayesian framework

This paper deals with the problem of set-membership identification and fault detection using a Bayesian framework. The paper presents how the set-membership model estimation problem can be reformulated from the Bayesian viewpoint in order to, first, determine the feasible parameter set in the identification stage and, second, check the consistency between the measurement data and the model in the fault-detection stage. The paper shows that, assuming uniform distributed measurement noise and uniform model prior probability distributions, the Bayesian approach leads to the same feasible parameter set than the well-known set-membership technique based on approximating the feasible parameter set using sets. Additionally, it can deal with models that are nonlinear in the parameters. The single-output and multiple-output cases are addressed as well. The procedure and results are illustrated by means of the application to a quadruple-tank process.Peer ReviewedPostprint (author's final draft

Robust fault diagnosis of proton exchange membrane fuel cells using a Takagi-Sugeno interval observer approach

In this paper, the problem of robust fault diagnosis of proton exchange membrane (PEM) fuel cells is addressed by introducing the Takagi-Sugeno (TS) interval observers that consider uncertainty in a bounded context, adapting TS observers to the so-called interval approach. Design conditions for the TS interval observer based on regional pole placement are also introduced to guarantee the fault detection and isolation (FDI) performance. The fault detection test is based on checking the consistency between the measurements and the output estimations provided by the TS observers. In presence of bounded uncertainty, this check relies on determining if all the measurements lie inside their corresponding estimated interval bounds. When a fault is detected, the measurements that are inconsistent with their corresponding estimations are annotated and a fault isolation procedure is triggered. By using the theoretical fault signature matrix (FSM), which summarizes the effects of the different faults on the available residuals, the fault is isolated by means of a logic reasoning that takes into account the bounded uncertainty, and if the number of candidate faults is more than one, a correlation analysis is used to obtain the most likely fault candidate. Finally, the proposed approach is tested using a PEM fuel cell case study proposed in the literature.Peer ReviewedPostprint (author's final draft

Leak localization in water distribution networks using Bayesian classifiers

This paper presents a method for leak localization in water distribution networks (WDNs) based on Bayesian classifiers. Probability density functions for pressure residuals are calibrated off-line for all the possible leak scenarios by using a hydraulic simulator, and considering the leak size uncertainty, demand uncertainty and sensor noise. A Bayesian classifier is applied on-line to the computed residuals to determine the location of leaks in the WDN. A time horizon based reasoning combined with the Bayesian classifier is also proposed to improve the localization accuracy. Two case studies based on the Hanoi and the Nova Icària networks are used to illustrate the performance of the proposed approach. Simulation results are presented for the Hanoi case study, whereas results for a real leak scenario are shown for the Nova Icària case study.Peer ReviewedPostprint (author's final draft