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1,403 results on '"Yang, Weihong"'

174. Integration of hydrothermal carbonization treatment for water and energy recovery from organic fraction of municipal solid waste digestate

Author

Aragon-Briceno, Christian, Pozarlik, Artur, Bramer, Eddy, Brem, Gerrit, Wang, Shule, Wen, Yuming, Yang, Weihong, Pawlak-Kruczek, Halina, Niedzwiecki, Lukasz, Urbanowska, Agnieszka, Moscicki, Krzysztof, Ploszczyca, Michal, Aragon-Briceno, Christian, Pozarlik, Artur, Bramer, Eddy, Brem, Gerrit, Wang, Shule, Wen, Yuming, Yang, Weihong, Pawlak-Kruczek, Halina, Niedzwiecki, Lukasz, Urbanowska, Agnieszka, Moscicki, Krzysztof, and Ploszczyca, Michal

Abstract

Anaerobic digestion is an efficient way of using a wet fraction of municipal solid waste (MSW) for energy purposes as it can produce biogas. The moisture content of the digestate after application of mechanical dewatering is still high, and the amount of heat needed for drying is significant. Hydrothermal carbonization (HTC) is a process that can potentially offer great benefits by improved mechanical dewatering and valorization of the digestate into a better quality solid fuel. This study focuses on the determination of the optimum HTC process conditions to recover water from the MSW digestate. Different process conditions as temperature (180, 200, and 230 °C) and residence time (30, 60, and 120 min) were tested. Furthermore, a mass and energy balance was carried out and a process model in Aspen Plus was built. Results showed that HTC treatment increased the water recovery (40–48%) during the dewatering process compared with the original feedstock (18%). The process model showed a positive energy balance of 110 kWh per ton of MSW digestate treated with an electrical efficiency of 23.9%., QC 20220421

Published
2022
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189. Integration of hydrothermal carbonization treatment for water and energy recovery from organic fraction of municipal solid waste digestate

Author

Aragon-Briceño, Christian, primary, Pożarlik, Artur, additional, Bramer, Eddy, additional, Brem, Gerrit, additional, Wang, Shule, additional, Wen, Yuming, additional, Yang, Weihong, additional, Pawlak-Kruczek, Halina, additional, Niedźwiecki, Łukasz, additional, Urbanowska, Agnieszka, additional, Mościcki, Krzysztof, additional, and Płoszczyca, Michał, additional

Published
2022
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197. Recognizing pathology of renal tumor from macroscopic cross-section image by deep learning.

Author

Lin, Zefang, Yang, Weihong, Zhang, Wenqiang, Jiang, Chao, Chu, Jing, Yang, Jing, and Yuan, Xiaoxu

Subjects
*DEEP learning, *KIDNEY tumors, *MACROSCOPIC cross sections, *RECEIVER operating characteristic curves, *CONVOLUTIONAL neural networks, *PATHOLOGY
Abstract

Objectives: This study aims to develop and evaluate the deep learning-based classification model for recognizing the pathology of renal tumor from macroscopic cross-section image. Methods: A total of 467 pathology-confirmed patients who received radical nephrectomy or partial nephrectomy were retrospectively enrolled. The experiment of distinguishing malignant and benign renal tumor are conducted followed by performing the multi-subtypes classification models for recognizing four subtypes of benign tumor and four subtypes of malignant tumors, respectively. The classification models used the same backbone networks which are based on the convolutional neural network (CNN), including EfficientNet-B4, ResNet-18, and VGG-16. The performance of the classification models was evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Besides, we performed the quantitative comparison among these CNN models. Results: For the model to differentiate the malignant tumor from the benign tumor, three CNN models all obtained relatively satisfactory performance and the highest AUC was achieved by the ResNet-18 model (AUC = 0.9226). There is not statistically significance between EfficientNet-B4 and ResNet-18 architectures and both of them are significantly statistically better than the VGG-16 model. The micro-averaged AUC, macro-averaged sensitivity, macro-averaged specificity, and micro-averaged accuracy for the VGG-16 model to distinguish the malignant tumor subtypes achieved 0.9398, 0.5774, 0.8660, and 0.7917, respectively. The performance of the EfficientNet-B4 is not better than that of VGG-16 in terms of micro-averaged AUC except for other metrics. For the models to recognize the benign tumor subtypes, the EfficientNet-B4 ranked the best performance, but had no significantly statistical difference with other two models with respect to micro-averaged AUC. Conclusions: The classification results were relatively satisfactory, which showed the potential for clinical application when analyzing the renal tumor macroscopic cross-section images. Automatically distinguishing the malignant tumor from benign tumor and identifying the subtypes pathology of renal tumor could make the patient-management process more efficient. [ABSTRACT FROM AUTHOR]

Published
2023
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