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Lozada, A., Calderon, F., Kasaneva, J. N., Borquez-Paredes, D., Olivares, R., Beghelli, A., et al. (2021). Impact of Amplification and Regeneration Schemes on the Blocking Performance and Energy Consumption of Wide-Area Elastic Optical Networks. IEEE Access, 9, 134355–134368.
Abstract: This paper studies the physical layer's impact on the blocking probability and energy consumption of wide-area dynamic elastic optical networks (EONs). For this purpose, we consider five network configurations, each named with a network configuration identifier (NCI) from 1 to 5, for which the Routing, Modulation Level, and Spectrum Assignment (RMLSA) problem is solved. NCI 1-4 are transparent configurations based on all-EDFA, hybrid Raman/EDFA amplifiers (with different Raman gain ratio Gamma(R)), all-DFRA, and alternating span configuration (EDFA and DFRA). NCI 5 is a translucent configuration based on all-EDFA and 3R regenerators. We model the physical layer for every network configuration to determine the maximum achievable reach of optical signals. Employing simulation, we calculate the blocking probability and the energy consumption of the different network configurations. In terms of blocking, our results show that NCI 2 and 3 offer the lowest blocking probability, with at least 1 and 3 orders of magnitude of difference with respect to NCI 1 and 5 at high and low traffic loads, respectively. In terms of energy consumption, the best performing alternatives are the ones with the worst blocking (NCI 1), while NCI 3 exhibits the highest energy consumption with NCI Gamma(R) = 0.75 following closely. This situation highlights a clear trade-off between blocking performance and energy cost that must be considered when designing a dynamic EON. Thus, we identify NCI 2 using Gamma(R) = 0.25 as a promising alternative to reduce the blocking probability significantly in wide-area dynamic EONs without a prohibitive increase in energy consumption.
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Montane, M., Ruiz-Valero, L., Labra, C., Faxas-Guzman, J. G., & Girard, A. (2021). Comparative energy consumption and photovoltaic economic analysis for residential buildings in Santiago de Chile and Santo Domingo of the Dominican Republic. Renew. Sust. Energ. Rev., 146, 111175.
Abstract: This research compares the building energy consumption and the photovoltaic economic analysis between residential buildings in Santiago de Chile and Santo Domingo of the Dominican Republic. The methodology considered thermal simulation, sizing of a solar PV system, an economic analysis and CO2 emissions given the solar resources of both countries. A scenario where the constructive systems are switched between the countries was also analyzed. A comparison of the energy performances of the houses exposed to other climate conditions. Results show that housing in Santiago de Chile required less energy than housing in Santo Domingo due to the fact that the thermal transmittance of the enclosures of the Chilean housing has better thermal behavior, compared to the materials of the Dominican housing. Dominican houses need a higher amount of electricity for air cooling due to the high temperatures in the tropic. Meanwhile, Chilean countries requires a higher amount of gas for heating purposes. The Dominican Republic lacks thermal regulation for construction material, and applying Chilean standards in Dominican houses, helped to lower the yearly electricity demand by 19%. Dominican constructions materials improvement could have an important impact in the country's overall goal to lower CO2 emission and in-house energy savings. The economic analysis showed that the Dominican Republic renewable energies incentives contribute to the development of very attractive PV projects, meanwhile in Chile, the use of net metering instead of net billing could increase by 11 times the net present value of PV projects.
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Otsuki, A., & Jang, H. (2022). Prediction of Particle Size Distribution of Mill Products Using Artificial Neural Networks. Chemengineering, 6(6), 92.
Abstract: High energy consumption in size reduction operations is one of the most significant issues concerning the sustainability of raw material beneficiation. Thus, process optimization should be done to reduce energy consumption. This study aimed to investigate the applicability of artificial neural networks (ANNs) to predict the particle size distributions (PSDs) of mill products. PSD is one of the key sources of information after milling since it significantly affects the subsequent beneficiation processes. Thus, precise PSD prediction can contribute to process optimization and energy consumption reduction by avoiding over-grinding. In this study, coal particles (-2 mm) were ground with a rod mill under different conditions, and their PSDs were measured. The variables studied included volume% (vol.%) of feed (coal particle), vol.% rod load, and grinding time. Our supervised ANN models were developed to predict PSDs and trained by experimental data sets. The trained models were verified with the other experimental data sets. The results showed that the PSDs predicted by ANN fitted very well with the experimental data after the training. Root mean squared error (RMSE) was calculated for each milling condition, with results between 0.165 and 0.965. Also, the developed ANN models can predict the PSDs of ground products under different milling conditions (i.e., vol.% feed, vol.% rod load, and grinding time). The results confirmed the applicability of ANNs to predict PSD and, thus the potential contribution to reducing energy consumption by optimizing the grinding conditions.
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