scholarly journals Energy Consumption Modeling for Underwater Gliders Considering Ocean Currents and Seawater Density Variation

2021 ◽  
Vol 9 (11) ◽  
pp. 1164
Author(s):  
Yang Song ◽  
Huangjie Ye ◽  
Yanhui Wang ◽  
Wendong Niu ◽  
Xu Wan ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Density Variation ◽  
Ocean Currents ◽  
Sea Trial ◽  
Multiple Parameters ◽  
Underwater Gliders ◽  
Diving Depth ◽  
Energy Consumption Model ◽  
The Relationship ◽  
Energy Consumption Modeling

Energy management is a critical and challenging factor required for efficient and safe operation of underwater gliders (UGs), and the energy consumption model (ECM) is indispensable. In this paper, a more complete ECM of UGs is established, which considers ocean currents, seawater density variation, deformation of the pressure hull, and asymmetry of gliding motion during descending and ascending. Sea trial data are used to make a comparison between ECMs with and without the consideration of ocean currents, and the results prove that the ECM that considers the currents has a significantly higher accuracy. Then, the relationship between energy consumption and multiple parameters, including gliding velocity relative to the current, absolute gliding angle, and diving depth, is revealed. Finally, a simple example is considered to illustrate the effects of the depth-averaged current on the energy consumption.

scholarly journals Refined Node Energy Consumption Modeling in a LoRaWAN Network

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6398
Author(s):  
Sébastien Maudet ◽  
Guillaume Andrieux ◽  
Romain Chevillon ◽  
Jean-François Diouris
Keyword(s):  
Energy Consumption ◽  
Collision Probability ◽  
Maintenance Cost ◽  
Important Research Topic ◽  
Energy Consumption Model ◽  
Probability Of Collision ◽  
Consumption Model ◽  
Improved Model ◽  
Energy Consumption Modeling

LPWAN technologies such as LoRa are widely used for the deployment of IoT applications, in particular for use cases requiring wide coverage and low energy consumption. To minimize the maintenance cost, which can become significant when the number of sensors deployed is large, it is essential to optimize the lifetime of nodes, which remains an important research topic. For this reason, it is necessary that it is based on a fine energy consumption model. Unfortunately, many existing consumption models do not take into account the specifications of the LoRaWAN protocol. In this paper, a refined energy consumption model based on in-situ measurements is provided for a LoRaWAN node. This improved model takes into account the number of nodes in the network, the collision probability that depends on the density of sensors, and the number of retransmissions. Results show the influence of the number of nodes in a LoRaWAN network on the energy consumption of a node and demonstrate that the number of sensors that can be integrated into a LoRaWAN network is limited due to the probability of collision.

scholarly journals Energy Consumption Modeling and Sensitivity Analysis for Deep-Argo Otarriinae Profiling Float

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Weiwei Si ◽  
Yifan Xue ◽  
Yanjun Liu ◽  
Zhitong Li ◽  
Gang Xue
Keyword(s):  
Sensitivity Analysis ◽  
Energy Consumption ◽  
Working Time ◽  
Global Ocean ◽  
Design Parameters ◽  
Important Indicator ◽  
Profiling Float ◽  
Diving Depth ◽  
Energy Consumption Modeling ◽  
Argo Profiling Float

Deep-Argo Otarriinae profiling float is a new type of Argo profiling float that has a maximum diving depth of more than 4,000 m. It can collect ocean scientific data all-weather and uninterruptedly, which provides reliable data support for the global ocean scientific research. The working time of Deep-Argo profiling float is an important indicator of its practicality and economy, and it is clear that the energy consumption is a key factor in determining its working time. In this paper, the single profile energy consumption model with 19 parameters of Deep-Argo Otarriinae is established and the main effect indices and total effect indices of the energy consumption parameters to energy consumption are calculated using Sobol’ sensitivity analysis method, aiming to find the parameters that have the greatest impact on energy consumption. The results show that the gliding angle, the diving depth, and the gliding speed have a significant impact on energy consumption of Deep-Argo Otarriinae. The results of simulation have a good match with the actual application and have certain reference significance for the determination of the design parameters and the selection of the navigation parameters. This paper also provides a new idea of multiparameter energy consumption modeling for underwater equipment using buoyancy regulation.

Energy consumption model for wastewater treatment process control

2013 ◽  
Vol 67 (3) ◽  
pp. 667-674 ◽  
Author(s):  
Xiaoqi Huang ◽  
Honggui Han ◽  
Junfei Qiao
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Treatment Process ◽  
Wastewater Treatment Process ◽  
Elman Neural Network ◽  
Effluent Quality ◽  
Treatment Processes ◽  
Energy Consumption Model ◽  
Consumption Model ◽  
The Relationship

Wastewater treatment must satisfy discharge requirements under specified constraints and have minimal operating costs (OC). The operating results of wastewater treatment processes (WWTPs) have significantly focused on both the energy consumption (EC) and effluent quality (EQ). To reflect the relationship between the EC and EQ of WWTPs directly, an extended Elman neural network-based energy consumption model (EENN-ECM) was studied for WWTP control in this paper. The proposed EENN-ECM was capable of predicting EC values in the treatment process. Moreover, the self-adaptive characteristic of the EENN ensured the modeling accuracy. A performance demonstration was carried out through a comparison of the EC between the benchmark simulation model No.1 (BSM1) and the EENN-ECM. The experimental results demonstrate that this EENN-ECM is more effective to model the EC of WWTPs.

scholarly journals Empirical Research on the Relationship amongst Renewable Energy Consumption, Economic Growth and Foreign Direct Investment in Kazakhstan and Uzbekistan

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 332
Author(s):  
Janusz Grabara ◽  
Arsen Tleppayev ◽  
Malika Dabylova ◽  
Leonardus W. W. Mihardjo ◽  
Zdzisława Dacko-Pikiewicz
Keyword(s):  
Economic Growth ◽  
Foreign Direct Investment ◽  
Renewable Energy ◽  
Energy Consumption ◽  
Direct Investment ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Renewable Energy Consumption ◽  
Integration Test ◽  
The Relationship

In this contemporary era, environmental problems spread at different levels in all countries of the world. Economic growth does not just depend on prioritizing the environment or improving the environmental situation. If the foreign direct investment is directed to the polluting industries, they will increase pollution and damage the environment. The purpose of the study is to consider the relationship between foreign direct investment in Kazakhstan and Uzbekistan and economic growth and renewable energy consumption. The study is based on data obtained from 1992 to 2018. The results show that there is a two-way link between foreign direct investment and renewable energy consumption in the considered two countries. The Granger causality test approach is applied to explore the causal relationship between the variables. The Johansen co-integration test approach is also employed to test for a relationship. The empirical results verify the existence of co-integration between the series. The main factors influencing renewable energy are economic growth and electricity consumption. To reduce dependence on fuel-based energy sources, Kazakhstan and Uzbekistan need to attract energy to renewable energy sources and implement energy efficiency based on rapid progress. This is because renewable energy sources play the role of an engine that stimulates the production process in the economy for all countries.

scholarly journals Machine Learning-Based Energy System Model for Tissue Paper Machines

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 655
Author(s):  
Huanhuan Zhang ◽  
Jigeng Li ◽  
Mengna Hong
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Gradient Boosting ◽  
Paper Machine ◽  
Steam Consumption ◽  
Tissue Paper ◽  
Paper Machines ◽  
Energy Consumption Model ◽  
Extreme Gradient Boosting ◽  
Consumption Model

With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines.

scholarly journals Practical Model for Energy Consumption Analysis of Omnidirectional Mobile Robot

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1800
Author(s):  
Linfei Hou ◽  
Fengyu Zhou ◽  
Kiwan Kim ◽  
Liang Zhang
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Mobile Robot ◽  
Load Capacity ◽  
Working Environment ◽  
Energy Consumption Analysis ◽  
Mecanum Wheel ◽  
Save Energy ◽  
Energy Consumption Modeling ◽  
Robot Platform

The four-wheeled Mecanum robot is widely used in various industries due to its maneuverability and strong load capacity, which is suitable for performing precise transportation tasks in a narrow environment. While the Mecanum wheel robot has mobility, it also consumes more energy than ordinary robots. The power consumed by the Mecanum wheel mobile robot varies enormously depending on their operating regimes and environments. Therefore, only knowing the working environment of the robot and the accurate power consumption model can we accurately predict the power consumption of the robot. In order to increase the applicable scenarios of energy consumption modeling for Mecanum wheel robots and improve the accuracy of energy consumption modeling, this paper focuses on various factors that affect the energy consumption of the Mecanum wheel robot, such as motor temperature, terrain, the center of gravity position, etc. The model is derived from the kinematic and kinetic model combined with electrical engineering and energy flow principles. The model has been simulated in MATLAB and experimentally validated with the four-wheeled Mecanum robot platform in our lab. Experimental results show that the accuracy of the model reached 95%. The results of energy consumption modeling can help robots save energy by helping them to perform rational path planning and task planning.

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