A multiobjective optimization of an organic Rankine cycle (ORC) evaporator, operating with toluene as the working fluid, is presented in this paper for waste heat recovery (WHR) from the exhaust gases of a 2 MW Jenbacher JMS 612 GS-N.L. gas internal combustion engine. Indirect evaporation between the exhaust gas and the organic fluid in the parallel plate heat exchanger (ITC2) implied irreversible heat transfer and high investment costs, which were considered as objective functions to be minimized. Energy and exergy balances were applied to the system components, in addition to the phenomenological equations in the ITC2, to calculate global energy indicators, such as the thermal efficiency of the configuration, the heat recovery efficiency, the overall energy conversion efficiency, the absolute increase of engine thermal efficiency, and the reduction of the break-specific fuel consumption of the system, of the system integrated with the gas engine. The results allowed calculation of the plate spacing, plate height, plate width, and chevron angle that minimized the investment cost and entropy generation of the equipment, reaching 22.04 m 2 in the heat transfer area, 693.87 kW in the energy transfer by heat recovery from the exhaust gas, and 41.6% in the overall thermal efficiency of the ORC as a bottoming cycle for the engine. This type of result contributes to the inclusion of this technology in the industrial sector as a consequence of the improvement in thermal efficiency and economic viability.The optimization of equipment used for waste heat recovery (WHR) has been studied by many researchers using different methods and formulations. Technical challenges, such as the high acquisition costs and entropy generation inside the heat exchanger, represent an improvement opportunity to increase ORC performance [4]. In these cases, mathematical tools can be used to find the best solutions through a stochastic search according to the objective selected. Holland [5] and De Jong [6] introduced the concept of genetic algorithms in publications, although these were not applied to the heat transfer field of knowledge.Several researchers have applied optimization techniques to design industrial equipment using thermodynamic and economic approaches, specifically in heat exchangers in the last years. Martin et al.[7] used a dimensionless function proportional to the sum of annual investment costs and operating costs, where the minimum of this function made it possible to determine the optimal Reynolds number, which depends on the type of heat exchanger chosen. In other research, Niclout et al. [8] describe an optimization problem in which objective functions, such as manufacturing cost and heat exchanger volume, as well as operating and manufacturing constraints were studied considering as decision variables the geometric parameters of the fins. To solve this problem, the author developed nonlinear programming of mixed integer numbers, like other options of the solution. Nevertheless, his work is limited by not considering the...

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Many studies applying Brain-Computer Interfaces (BCIs) based on Motor Imagery (MI) tasks for rehabilitation have demonstrated the important role of detecting the Event-Related Desynchronization (ERD) to recognize the user's motor intention. Nowadays, the development of MI-based BCI approaches without or very few calibration stages session-by-session for different days or weeks is still an open and emergent scope. In this work, a new scheme is proposed by applying Convolutional Neural Networks (CNN) for MI classification, using an end-to-end Shallow architecture that contains two convolutional layers for temporal and spatial feature extraction. We hypothesize that a BCI designed for capturing event-related desynchronization/synchronization (ERD/ERS) at the CNN input, with an adequate network design, may enhance the MI classification with less calibration stage. The proposed system using the same architecture was tested on three public datasets through multiple experiments, including both subject-specific and nonsubject-specific training. Comparable and also superior results with respect to the state-of-the-art were obtained. On subjects whose EEG data were never used in the training process, our scheme also achieved promising results with respect to existing non-subject-specific BCIs, which is other progress to facilitate the clinical application.

The electrical sector in the Caribbean region of Colombia is currently facing problems that affect its reliability. Many thermo-electric plants are required to fill the gap and ensure energy supply. This paper thus proposes a hybrid renewable energy generation plant that could supply a percentage of the total energy demand and reduce the environmental impact of conventional energy generation. The hybrid plant works with a photovoltaic (PV) system and wind turbine systems, connected in parallel with the grid to supply a renewable fraction of the total energy demand. The investigation was conducted in three steps: the first stage determined locations where the energy system was able to take advantage of renewable sources, the second identified a location that could work more efficiently from an economic perspective, and finally, the third step estimated the number of PV solar panels and wind turbines required to guarantee optimal functioning for this location using, as a main method of calculation, the software HOMER pro® for hybrid optimization with multiple energy resources. The proposed system is expected to not only limit environmental impacts but also decrease total costs of electric grid consumption from thermoelectric plants. The simulations helped identify Puerto Bolivar, Colombia, as the location where the hybrid plant made the best use of non-conventional resources of energy. However, Rancho Grande was found to offer the system more efficiency, while generating a considerable amount of energy at the lowest possible cost. An optimal combination was also obtained—441 PV arrays and 3 wind turbines, resulting in a net present cost (NPC) of $11.8 million and low CO2 production of 244.1 tons per year.

Although public participation (PP) has influenced some integrated coastal zone management (ICZM) programs around the world, researchers have rarely analyzed this component specifically inside an ICZM cycle. Furthermore, the approach for integrating environmental energy planning tools within the ICZM Programs for the Small Island Developing States (SIDS) has presented an ongoing challenge for specialists involved in management issues. In Cuba, plans for coastal development are supported by land use planning activities and environmental planning tools. However, the functions and outputs of those tools are "non-obvious", precluding sufficient integration among them. As these aforementioned actions have not been systematically carried out in the Cuban territory, and the systems of inter-institutional relationships with local communities have presented some insufficiencies, the national marine-terrestrial interphase has suffered some negative environmental impacts impossible to be solved by the national authorities. Designing a new methodology that incorporates PP and environmental energy planning tools in the stages of an ICZM program is the objective of this article. The methodology was named MePuPa and has improved current tools for land use planning and ICZM in Cuba. Previously selected "Local Indicators of Environmental Sustainability", applied in two geo-systemic units in the southeastern region of Cuba, were used to demonstrate the methodology. The qualitative and qualitative methods in the proposed MePuPa were also applied. Finally, the MePuPa methodology was tested for four of its five stages. Six advantages and five learned lessons were identified during the Preparation to Proposal stages. MePuPa resulted in a useful local management tool for environmental energy planning, ICZM, economic and agricultural activities, strategic ecosystems recovery, as well as improvements to the governance and decision-making processes in one SIDS.