Machine learning based soft sensor and long-term calibration scheme: A solid oxide fuel cell system case

Zhao, Weiqi; Jiang, Jianhua; Qin, Hongchuan; Li, Xi; Li, Jian

doi:10.1016/j.ijhydene.2021.02.134

Cited by 12 publications

(5 citation statements)

References 37 publications

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“…However, in field conditions where more than one variable influences the measurement result, the SLR is often not sufficient to achieve high measurement accuracy. In these cases, Multivariate Linear Regression (MLR) [66], high-dimensional models [57] or hybrid models are used for calibration [40,52,59]. Since there is a strong linearity between the raw and reference data of the measurements obtained in our study, it was calibrated with the SLR method.…”

Section: Resultsmentioning

confidence: 99%

A low-cost air quality monitoring system based on Internet of Things for smart homes

Taştan

2022

AIS

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Global climate change and COVID-19 have changed our social and business life. People spend most of their daily lives indoors. Low-cost devices can monitor indoor air quality (IAQ) and reduce health problems caused by air pollutants. This study proposes a real-time and low-cost air quality monitoring system for smart homes based on Internet of Things (IoT). The developed IoT-based monitoring system is portable and provides users with real-time data transfer about IAQ. During the COVID-19 period, air quality data were collected from the kitchen, bedroom and balcony of their home, where a family of 5 spend most of their time. As a result of the analyzes, it has been determined that indoor particulate matter is mainly caused by outdoor infiltration and cooking emissions, and the CO2 value can rise well above the permissible health limits in case of insufficient ventilation due to night sleep activity. The obtained results show that the developed measuring devices may be suitable for measurement-based indoor air quality management. In addition, the proposed low-cost measurement system compared to existing systems; It has advantages such as modularity, scalability, low cost, portability, easy installation and open-source technologies.

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Section: Resultsmentioning

confidence: 99%

A low-cost air quality monitoring system based on Internet of Things for smart homes

Taştan

2022

AIS

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“…Machine learning, renowned for its efficacy in solving nonlinear problems across various fields [168,169], has been increasingly applied to soft robotics. Its applications extend to soft sensor calibration [170,171], soft actuator position control [172,173], and more intricate tasks like grasping and in-hand manipulation [69]. Research indicates that machine learning-based methods have successfully mitigated many of the current challenges faced by soft robotic hands.…”

Section: Machine Learning In Soft Handsmentioning

confidence: 99%

Anthropomorphic Soft Hand: Dexterity, Sensing, and Machine Learning

Wang,

Hao,

Liu

et al. 2024

Actuators

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Humans possess dexterous hands that surpass those of other animals, enabling them to perform intricate, complex movements. Soft hands, known for their inherent flexibility, aim to replicate the functionality of human hands. This article provides an overview of the development processes and key directions in soft hand evolution. Starting from basic multi-finger grippers, these hands have made significant advancements in the field of robotics. By mimicking the shape, structure, and functionality of human hands, soft hands can partially replicate human-like movements, offering adaptability and operability during grasping tasks. In addition to mimicking human hand structure, advancements in flexible sensor technology enable soft hands to exhibit touch and perceptual capabilities similar to humans, enhancing their performance in complex tasks. Furthermore, integrating machine learning techniques has significantly promoted the advancement of soft hands, making it possible for them to intelligently adapt to a variety of environments and tasks. It is anticipated that these soft hands, designed to mimic human dexterity, will become a focal point in robotic hand development. They hold significant application potential for industrial flexible gripping solutions, medical rehabilitation, household services, and other domains, offering broad market prospects.

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“…When the model is built, the training data have 614 h and the test data have 68 h. In order to test the DAG model effectiveness proposed in this study, we also use other common methods to compare predictive effect with ANN [19], BP neural network [35], GA-RBF neural network [36], GA-BP [37], and LS-SVM [38]. Compared with the experimental results of the DAG method in this study, the specific index effect is shown in Figure 8.…”

Section: Sofc System Thermoelectric Efficiency Predictive Modeling Re...mentioning

confidence: 99%

A Novel Adaptive Neural Network-Based Thermoelectric Parameter Prediction Method for Enhancing Solid Oxide Fuel Cell System Efficiency

Wu,

et al. 2023

Sustainability

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Efficiency prediction plays a crucial role in the ongoing development of electrochemical energy technology. Our industries heavily depend on a reliable energy supply for power and electricity, and solid oxide fuel cell (SOFC) systems stand out as renewable devices with immense potential. SOFCs, as one of the various types of fuel cells, are renowned for their capability of combined heat and power generation. They can achieve an efficiency of up to 90% in operation. Furthermore, due to the fact that water is the byproduct of their electricity generation process, they are extremely environmentally friendly, contributing significantly to humanity’s sustainable development. With the advancement of renewable energy technologies and the increasing emphasis on sustainable development requirements, predicting and optimizing the efficiency of SOFC systems is gaining importance. This study leverages data collected from an SOFC system and applies an improved neural network structure, specifically the dendritic network (DN) architecture, to forecast thermoelectric efficiency. The key advantage of this method lies in the adaptive neural network algorithm based on the dendritic network structure without manually setting hidden nodes. Moreover, the predicted model of thermoelectric efficiency is validated using 682 and 1099 h of operational data from the SOFC system, and the results are compared against a conventional machine learning method. After comparison, it is found that when the novel method with adaptive characteristics proposed was used for SOFC system efficiency prediction, the MAE and RMSE values were both lower than 0.014; the result is significantly better than from other traditional methods. Additionally, this study demonstrated its effectiveness in predicting the thermoelectric efficiency of SOFC systems through secondary experiments. This study offers guidance on enhancing SOFC systems thermoelectric efficiency. Therefore, this study provides a foundation for the future industrialization of fuel cell systems.

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Machine learning based soft sensor and long-term calibration scheme: A solid oxide fuel cell system case

Cited by 12 publications

References 37 publications

A low-cost air quality monitoring system based on Internet of Things for smart homes

A low-cost air quality monitoring system based on Internet of Things for smart homes

Anthropomorphic Soft Hand: Dexterity, Sensing, and Machine Learning

A Novel Adaptive Neural Network-Based Thermoelectric Parameter Prediction Method for Enhancing Solid Oxide Fuel Cell System Efficiency

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