Absorption systems are a sustainable solution as solar driven air conditioning devices in places with warm climatic conditions, however, the reliability of these systems must be improved. The absorbing component has a significant effect on the cycle performance, as this process is complex and needs efficient heat exchangers. This paper presents an experimental study of a bubble mode absorption in a plate heat exchanger (PHE)-type absorber with NH3-LiNO3 using a vapor distributor in order to increase the mass transfer at solar cooling operating conditions. The vapor distributor had a diameter of 0.005 m with five perforations distributed uniformly along the tube. Experiments were carried out using a corrugated plate heat exchanger model NB51, with three channels, where the ammonia vapor was injected in a bubble mode into the solution in the central channel. The range of solution concentrations and mass flow rates of the dilute solution were from 35 to 50% weight and 11.69 to 35.46 × 10−3 kg·s−1, respectively. The mass flow rate of ammonia vapor was from 0.79 to 4.92 × 10−3 kg·s−1 and the mass flow rate of cooling water was fixed at 0.31 kg·s−1. The results achieved for the absorbed flux was 0.015 to 0.024 kg m−2·s−1 and the values obtained for the mass transfer coefficient were in the order of 0.036 to 0.059 m·s−1. The solution heat transfer coefficient values were obtained from 0.9 to 1.8 kW·m−2·K−1 under transition conditions and from 0.96 to 3.16 kW·m−2·K−1 at turbulent conditions. Nusselt number correlations were obtained based on experimental data during the absorption process with the NH3-LiNO3 working pair.

Characterization of spatial and temporal changes in the dynamic patterns of a nonstationary process is a problem of great theoretical and practical importance. On-line monitoring of large-scale power systems by means of time-synchronized Phasor Measurement Units (PMUs) provides the opportunity to analyze and characterize inter-system oscillations. Wide-area measurement sets, however, are often relatively large, and may contain phenomena with differing temporal scales. Extracting from these measurements the relevant dynamics is a difficult problem. As the number of observations of real events continues to increase, statistical techniques are needed to help identify relevant temporal dynamics from noise or random effects in measured data. In this paper, a statistically based, data-driven framework that integrates the use of wavelet-based EOF analysis and a sliding window-based method is proposed to identify and extract, in near-real-time, dynamically independent spatiotemporal patterns from time synchronized data. The method deals with the information in space and time simultaneously, and allows direct tracking and characterization of the nonstationary time-frequency dynamics of oscillatory processes. The efficiency and accuracy of the developed procedures for extracting localized information of power system behavior from time-synchronized phasor measurements of a real event in Mexico is assessed.

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In this paper, a statistically-based, data-driven framework that integrates the use of empirical orthogonal function (EOF) analysis and a time-frequency method is proposed to identify and extract, relevant dynamically independent spatio-temporal patterns from time synchronized data. Using time-frequency methods, the temporal signals at selected system locations are decomposed into modal approximations at different scales. Multi-scale EOF analysis is then used to extract cross-correlations across the measurement sites. The method allows for the extremely nonstationary behavior of interarea oscillations to be analyzed into separate frequency bands and is capable of detecting propagating features in non-stationary processes.

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