There is a trend in the industry in the digitalization of assets for generating large amounts of information and greater control and supervision over the production. With this digitalization, the possibility arises to include new technologies that can facilitate the life of the maintenance team and the operator, such as the concept of augmented operator. In this concept, the operator using mobile devices can access information in real time of the desired equipment, saving time in the acquisition of this information and allowing a greater time to analyze this data. Thus, this work aims to propose an augmented operator system for an hydroelectric power generation industry, as an example there is the possibility of visualizing information of vibration, temperature and rotation of recirculating motor pumps of the cooling system from generation units. Preliminary results indicate a wide possibility of using this concept. In addition to the actual monitoring of the asset in real time, there is the possibility of obtaining the list of spare parts and materials that help in the communication to the purchasing sector and quick replacement of failed components.
IntroductionThe delay in cardiac autonomic recovery (CAR) following physical activity is associated with increased risk of cardiovascular events. Aging is a major risk factor for cardiovascular disease, and older adults have altered resting cardiac autonomic modulation than young individuals. The intensity of exercise also influences autonomic recovery in young individuals.ObjectiveTo compare the CAR following resistance exercise (RE) in young (Y) and older (O) men, using high and low intensity RE.MethodsThe two groups (Y: n=9, age=26.3±4y, BMI=22.3±1.5Kg/m2 and O: n=9, age=64.3±4y, BMI=26.2±1.8 Kg/m2) performed 4 sets of RE, using leg press equipment, until failure. Subjects performed two different protocols; RE at 80% of 1RM (RE80) and at 30% of 1RM (RE30) in a random sequence. Heart rate was measured via a heart rate monitor and CAR was obtained through heart rate variability analysis pre and 30‐min post‐RE. The change values for heart rate variability (post 30‐min – pre) were assessed by root mean square (RMSSD) of successive RR intervals (iRR), high and low frequency in normalized units (HF and LF, respectively) and analyzed by 2‐way ANOVA (group*load).ResultsThe table shows a greater reduction of iRR, RMSSD and HF and a greater increase of LF in Y compared to O. Since the total load was higher in Y compared to O in RE80 and RE30 (Y RE30: 13767 ± 4738 kg vs. O RE30: 7812 ± 2094 kg, p < 0.05, and Y RE80: 10921 ± 4201 kg vs. O RE80: 7488 ± 2040 kg, p < 0.05), we evaluated the change in heart rate variability as a function of TL. This correction eliminated differences between Y and O for iRR and RMSSD (p > 0.05), while HF and LF remained different between Y and O (p < 0.05).ConclusionY showed delayed CAR in protocols with both RE80 and RE30. The higher TL in Y did not explain the delayed CAR in this group, thus other factors are involved. It is possible the lower heart rate variability adjustments of O is associated with limiting increases in sympathetic and reductions in parasympathetic modulation during RE but this needs further investigation.Support or Funding InformationWe thanks the support of CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico), FAEPEX (Fundo de Apoio ao Ensino, à Pesquisa e Extensão da Universidade Estadual de Campinas) and CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior).This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.
A new procedure for estimating area and capital cost targets of constrained heat exchanger networks is presented. The method allows for match constrained networks and exchangers with more than one tube pass. The procedure is based on modelling the problem as a non-linear formulation where the forbidden exchanger matches are included as constraints and the temperature difference correction due to multipass exchangers is included in the model. The difficulty of converging to a solution due to the additional non-linear constraints imposed by the multipass exchangers required the use of a two-level approach: at the inner level, the area targets for simple pass exchangers are obtained, and at the outer level the temperature difference required for multipass exchangers are computed and fed back to the inner level. The procedure is repeated until an appropriate tolerance between two iterations was achieved. A comparison between the estimated exchanger areas and costs estimated by the new procedure and the area and costs obtained from the final heat exchanger design shows a very good agreement
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