This paper presents an energetically autonomous IoT sensor powered via thermoelectric harvesting. The operation of thermal harvesting is based on maintaining a temperature gradient of at least 26.31 K between the thermoelectric-generator sides. While the hot side employs a metal plate, the cold side is attached with a phase-change material acting as an effective passive dissipative material. The desired temperature gradient allows claiming power conversion efficiencies of about 26.43%, without efficiency reductions associated with heating and soiling. This work presents the characterization of a low-cost off-the-shelf thermoelectric generator that allows estimating the production of at least 407.3 mW corresponding to 2.44 Wh of available energy considering specific operation hours—determined statistically for a given geographic location. Then, the energy production is experimentally verified with the construction of an outdoor IoT sensor powered by a passively-cooled thermoelectric generator. The prototype contains a low-power microcontroller, environmental sensors, and a low-power radio to report selected environmental variables to a central node. This work shows that the proposed supply mechanism provides sufficient energy for continuous operation even during times with no solar resource through an on-board Li-Po battery. Such a battery can be recharged once the solar radiation is available without compromising sensor operation.
Purpose The purpose of this paper is to provide a comprehensive methodology and a case study about the successful integration of FCA with continuous improvement tools for strategic decision-making processes. Reliable knowledge of the condition of tangible assets and their ability to fulfill their target activities over time are required for an assertive strategical decision process. Facility condition assessment (FCA) is a recognized methodology that allows the systematic evaluation of this performance. For those companies whose primary objective is the production of goods, decisions associated with improvements on the productive system or re-adaptation of existing assets may also require the implementation of alternative methodologies, with a direct impact on the indicators of the company and therefore on the FCA. Design/methodology/approach This study presents a methodology for the integration of FCA and lean manufacturing (LM) as a tool in strategic decision-making process that involves the integration of continuous improvement processes or significant changes in the production process, in which the condition of the installation impacts decisively the productivity of the system. Findings The results of the implementation on an insecticide and herbicide production plant indicate an increase of 33 per cent in the capacity of the formulation process and over 20 per cent reduction in the internal quality claims associated with the packaging system. Practical implications Those methodological stages are applicable to facilities in which the FCA shows the need for significant reconditioning of assets, the need to increase the efficiency and/or the production capacity. This methodology integrates elements of continuous improvement and redesign of production systems. Originality/value The original value of this paper is oriented to the capacity to integrate different FCA and LM tools through the company indicators of productivity key performance indicators and, in addition, of a comprehensive illustration based on a study case.
In this paper, an architecture based on computational intelligence for time series modeling is proposed to guarantee the automatic adjustability of trained models no matter the dynamic behavior of the modeled phenomena. The proposed method can assess the performance; and then proposes a maintenance routine for the time-series model. Thus, an auditor is devised to identify when a model must be updated before losing forecast performance. It has been determined that the MAPE (Mean Absolute Percentage Error) metric could not reveal changes in the model predicted curve contributing to invalidating the model, in particular if a non-stationary behavior is expected in the studied phenomena. Therefore, the novel rMAPE performance metric is proposed, so that the auditor does detect that the updating process does not achieve better performance; the system opts for replacing the time-series modeling techniques included in an available knowledge base. The intelligent system allows building time-series models automatically considering exogenous variables such as weather, calendar, and statistical transformations that can lead to the number of models required for a particular application. The proposed approach has been experimentally tested for power consumption and energy price via simulation. The forecasting results showed an improvement in the MAPE of up to 23% in the tests performed.INDEX TERMS. Forecasting, intelligent systems, time series modeling.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.