Solid-phase extraction followed by dispersive liquid-liquid microextraction for the sensitive determination of ecstasy compounds and amphetamines in biological samples

A novel approach for the determination of ecstasy and amphetamines (3,4-methylenedioxymethylamphetamine (MDMA, Ecstasy), 3,4-methylenedioxyamphetamine (MDA), 3,4-methylenedioxyethylamphetamine (MDEA) and 3,4-methylenedioxypropylamphetamine (MDPA)) in biological samples is presented. The analytes were extracted from the matrix and transferred to a small volume of a high density, water insoluble solvent using solid-phase extraction (SPE) followed by dispersive liquid-liquid microextraction (DLLME). This combination not only resulted in a high enrichment factor, but also it could be used in complex matrices (biological samples). Some important extraction parameters, such as sample solution flow rate, sample pH, type and volume of extraction and disperser solvents as well as the salt addition, were studied and optimized. Under the optimized conditions, the calibration graphs were linear in the range of 0.5-500 µg L-1 and 1.0-500 µg L-1 with detection limits in the range of 0.1-0.3 µg L-1 and 0.2-0.7 µg L-1 in urine and plasma samples, respectively. The results showed that SPE-DLLME is a suitable method for the determination of ecstasy components and amphetamines in biological and water samples. KEY WORDS: Dispersive liquid-liquid microextraction, Solid-phase extraction, Ecstasy compounds, Amphetamines, Gas chromatography, Biological samples Bull. Chem. Soc. Ethiop. 2014, 28(3), 339-348.DOI: http://dx.doi.org/10.4314/bcse.v28i3.

Preliminary study of improving reservoir quality of tight gas sands in the near wellbore region by microwave heating

The formation damages, such as water blocking and clay swelling, in tight gas reservoir have been recognized as severe problems impairing gas production. To remedy these damages, formation heat treatment (FHT) was taken as one of the effective measures by some researchers. In this paper, the effects of microwave heating on the petrophysical properties of sandstone samples has been investigated. A modified commercial microwave oven was used to heat the core plugs and aluminum tubes were used to accommodate tight sandstone samples in order to confine them and reduce their contact with air. After microwave heating, any alterations in the porosity, permeability, texture, structure, mineralogy, and pore size distribution of tight sandstones were investigated by a series of lab experiments. By subjecting tight sandstone samples to microwave, the surface temperature of sandstone can be elevated to approximately 400 °C or more. The intense heat is effective in changing the structure, texture and mineralogy of the sandstone. The shrinkage or decomposition of minerals, which are shown by XRD analysis, and generation of micro-fractures created more spaces in the samples.By employing Automated Permeameter, porosity and permeability are found increased after heating. Nuclear Magnetic Resonance (NMR) and CT numbers of all samples after microwave heating indicate the increase of porosity as well. Moreover, the NMR T2 distribution reveals the smaller pores diminished, so the incremental porosity of short NMR T2 decreased. Micro-fractures generated between grains or in grains due to decomposition of some cement minerals and clay shrinkage, so the amplitude of long NMR T2 increased. The fractures are visible both in X-ray CT images and in Scanning Electronic Microscopy (SEM) images. By comparing with NMR T2 distribution data, it is found that the presence of micro-fractures accounts for the increased population of pores with T2 larger than 10 ms. The numerical simulation of microwave heating in the borehole indicates that the microwave heating is effective to raise the temperature of reservoir rock to approximately 900 °C within 1 day and to remove the water within a distance of 25 cm from the borehole wall. The efficiency of microwave heating can be further improved by optimizing the downhole microwave device

Application of Ultrasound-assisted Emulsification Microextraction followed by Gas Chromatography for Determination of Oxadiazon in Water and Soil Samples

In this study, a simple and efficient ultrasound-assisted emulsification microextraction (USAEME) method combined with gas chromatography (GC) was developed for the preconcentration and determination of oxadiazon in water and soil samples. In this method, fine droplets of toluene were formedand dispersed in the sample with the help of ultrasonic waves which accelerated the formation of a fine cloudy solution without using disperser solvents. Several factors influencing the extraction efficiency, such as the nature and volume of organic solvent, extraction temperature, ionic strength and centrifugation time, were investigated and optimized. Using optimum extraction conditions a detection limit of 0.1 μg L–1 and a good linearity in a calibration range of 0.25–250 μg L–1 were achieved for the analyte in a river water sample. This proposed method was successfully applied to the analysis of oxadiazon in water and soil samples.KEYWORDS Utrasound-assisted emulsification microextraction, oxadiazon, gas chromatography, water samples, soil samples

State Estimation Using a Network of Distributed Observers With Unknown Inputs

State estimation for a class of linear time-invariant systems with distributed output measurements (distributed sensors) and unknown inputs is addressed in this paper. The objective is to design a network of observers such that the state vector of the entire system can be estimated, while each observer has access to only local output measurements that may not be sufficient on their own to reconstruct the entire system’s state. Existing results in the literature on distributed state estimation assume either that the system does not have inputs, or that all the system’s inputs are globally known to all the observers. Accordingly, we address this gap by proposing a distributed observer capable of estimating the overall system’s state in the presence of inputs, while each observer only has limited local information on inputs and outputs. We provide a design method that guarantees convergence of the estimation errors to zero under joint detectability conditions. This design suits undirected communication graphs that may have switching topologies and also applies to strongly connected directed graphs.We also give existence conditions that are consistent with existing results on unknown input observers. Finally, simulation results verify the effectiveness of the proposed estimation scheme for various scenarios

Sensor fault-tolerant state estimation by networks of distributed observers

We propose a state estimation methodology using a network of distributed observers. We consider a scenario in which the local measurement at each node may not guarantee the system’s observability. In contrast, the ensemble of all the measurements does ensure that the observability property holds. As a result, we design a network of observers such that the estimated state vector computed by each observer converges to the system’s state vector by using the local measurement and the communicated estimates of a subset of observers in its neighborhood. The proposed estimation scheme exploits sensor redundancy to provide robustness against faults in the sensors. Under suitable conditions on the redundant sensors, we show that it is possible to mitigate the effects of a class of sensor faults on the state estimation. Simulation trials demonstrate the effectiveness of the proposed distributed estimation scheme

A committee neural network for prediction of normalized oil content from well log data: An example from South Pars Gas Field, Persian Gulf

Normalized oil content (NOC) is an important geochemical factor for identifyingpotential pay zones in hydrocarbon source rocks. The present study proposes an optimaland improved model to make a quantitative and qualitative correlation between NOC andwell log responses by integration of neural network training algorithms and thecommittee machine concept. This committee machine with training algorithms (CMTA)combines Levenberg-Marquardt (LM), Bayesian regularization (BR), gradient descent(GD), one step secant (OSS), and resilient back-propagation (RP) algorithms. Each ofthese algorithms has a weight factor showing its contribution in overall prediction. Theoptimal combination of the weights is derived by a genetic algorithm. The method isillustrated using a case study. For this purpose, 231 data composed of well log data andmeasured NOC from three wells of South Pars Gas Field were clustered into 194modeling dataset and 37 testing samples for evaluating reliability of the models. Theresults of this study show that the CMTA provides more reliable and acceptable resultsthan each of the individual neural networks differing in training algorithms. Also CMTAcan accurately identify production pay zones (PPZs) from well logs

Almost Sure Resilient Consensus Under Stochastic Interaction: Links Failure and Noisy Channels

The resilient consensus problem over a class of discrete-time linear multiagent systems is addressed. Because of external cyber-attacks, some agents are assumed to be malicious and not following a desired cooperative behavior. Thus, the objective consists in designing a control strategy for the healthy agents to reach consensus upon their state vectors, while due to interaction among the agents, the malicious agents try to prevent them to achieve consensus. Although this problem has been investigated by some researchers, under the existing approaches in the literature, achieving consensus is only guaranteed when the information exchange among the agents is deterministic. Based on this motivation, the main contribution of the paper is on almost sure resilient consensus control of a network of healthy agents in the presence of stochastic links failure and communication noises. We design a discrete-time protocol for the set of the healthy agents, and we show that under some probabilistic conditions on interaction among the agents, achieving almost sure consensus among the healthy agents can be guaranteed. The results also are verified by numerical examples

A Committee Machine with Intelligent Systems for Estimation of Total Organic Carbon Content from Petrophysical Data: an Example from Kangan and Dalan Reservoirs in South Pars Gas Field, Iran

Total Organic Carbon (TOC) content present in reservoir rocks is one of the important parameters which could be used for evaluation of residual production potential and geochemical characterization of hydrocarbon bearing units. In general, organic rich rocks are characterized by higher porosity, higher sonic transit time, lower density, higher gamma-ray, and higher resistivity than other rocks. Current study suggests an improved and optimal model for TOC estimation by integration of intelligent systems and the concept of committee machine with an example from Kangan and Dalan Formations, in South Pars Gas Field, Iran. This committee machine with intelligent systems (CMIS) combines the results of TOC predicted from intelligent systems including fuzzy logic (FL), neuro-fuzzy (NF), and neural network (NN), each of them has a weight factor showing its contribution in overall prediction. The optimal combination of weights is derived by a genetic algorithm (GA). This method is illustrated using a case study. One hundred twenty-four data points including petrophysical data and measured TOC from three wells of South Pars Gas Field were divided into eighty-seven training sets to build the CMIS model and thirty-seven testing sets to evaluate the reliability of the developed model. The results show that the CMIS performs better than any one of the individual intelligent systems acting alone for predicting TOC