Laser Spectroscopic Sensors for the Development of Anthropomorphic Robot Sensitivity

Underwater determination of calcium and strontium ions in oilfield produced water by laser-induced breakdown spectroscopy (LIBS)

Laser-induced breakdown spectroscopy (LIBS) was applied to the determination of scaling ions in oilfield-produced water employing underwater measurements. Initially, the stability of plasma was verified using four different optical setups and expansion of the laser beam, and a combination of an achromatic lens with a meniscus lens were necessary to stabilize the plasma. Preliminary experiments demonstrated that only the determinations of Ca(II) and Sr(II) ions were feasible while the signal for the Mg(II) ion was absent and the sensitivity for Ba(II) was very low. The laser pulse repetition rate was evaluated and rates of 10 and 20 Hz provided a more stable breakdown in water compared to repetition rates of 2 to 7 Hz, besides imparting higher intense signals. The increase in salinity showed a small matrix effect, decreasing the sensitivities of the calibration curves by 8-13% when standard solutions with a salinity of 30‰ were used instead of water. Under optimized conditions with a laser pulse energy of 31 mJ, gate delay of 300 ns, gate width of 5.0 μs, repetition rate of 10 Hz, and accumulation of 500 laser shots, a linear range from 25 to 150 mg L-1 was obtained, with limits of detection of 0.58 and 0.85 mg L-1 for Ca(II) and Sr(II), respectively. The underwater determination of scaling ions in produced water by LIBS provided results that do not significantly differ from those obtained by inductively coupled plasma atomic emission spectroscopy (ICP OES) at a confidence level of 95%, with relative errors of up to 5.2%. These results demonstrate the potential of underwater LIBS measurements as an analytical tool for the determination of alkaline-earth metal ions in produced water, which can help the oil industry to overcome the problems related to scale formation.

New Solutions of Laser-Induced Fluorescence for Oil Pollution Monitoring at Sea

Laser-induced fluorescence (LIF) spectral features for oil products of different states (solutions in the seawater and thin slicks) are discussed in this article. This research was done to evaluate LIF application for the identification of oil products and the measurement of the volume of ocean pollution by bilge water disposal. It was found out that the form of LIF spectral distribution was changed depending on the oil product state (pure fuel, slick or solution). The LIF method was calibrated for the most common types of heavy and light marine fuels at the standard measurement method of solution concentrations and limit of detection (LoD) values were established for each type. The time dynamics of the solution spectra were researched, and the time change features were determined. The smallsized LIF sensor for the unmanned aerial vehicle (UAV) is described and aims to investigate the LIF for oil pollution at sea.

Application of Computational Intelligence Methods for the Automated Identification of Paper-Ink Samples Based on LIBS

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.