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Binary energy production from pineapple peel waste and optimized by statistical and machine learning approaches

Fuel 2024年 372卷

作者： Chen, Wei-Hsin Liu, Li-Xuan Sheen, Herng-Kuang Culaba, Alvin B. Shiong Khoo, Kuan Lim, Steven Department of Aeronautics and Astronautics National Cheng Kung University Tainan 701 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung 407 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung 411 Taiwan International Master Degree Program on Energy National Cheng Kung University Tainan 701 Taiwan Sugar Business Division Taiwan Sugar Corporation Tainan 701 Taiwan Department of Mechanical Engineering De La Salle University Manila Philippines Department of Chemical Engineering and Materials Science Yuan Ze University Taoyuan Taiwan Department of Chemical Engineering Universiti Tunku Abdul Rahman Malaysia

This study uses a two-stage process to treat pineapple peel waste (PPW), including alkaline ionized water (AIW) pretreatment and microwave-assisted heating (MAH) hydrolysis. It also contains two objective functions: hydrochar yield and hydrolysis ratio. The experimental design utilizes the Taguchi method to determine optimal conditions by calculating the signal-to-noise ratio. The designed experimental results are used to predict optimal conditions through artificial neural networks (ANN). In the first step, AIW pretreatment reduces the lignin content of PPW by 17.5 % and increases the cellulose content by 14.8 %. SEM analysis reveals a roughening of its surface, while BET analysis identifies a slightly porous structure. These characteristics provide an increased efficiency for subsequent hydrolysis. In the second step, thermogravimetric analysis indicates that a substantial hemicellulose loss is observed when the hydrolysis concentration or temperature increases. This finding aligns with the ranking of factor impacts obtained from the Taguchi method. This study also demonstrates the potential of utilizing PPW for energy production. The hydrochar, hydrolyzed under optimal conditions, increases its calorific value by 10.11 % to 18.29 MJ‧kg⁻¹, making it suitable for use as solid fuel. The hydrolysate has 52.85 g‧L^-1 reducing sugar, which can be fermented to produce ethanol and is much higher than those reported in the literature. Through the Taguchi method and ANN, the optimal experimental factor combination prediction reveals the significant impact of acid hydrolysis compared to AIW pretreatment. The optimal hydrochar yield and hydrolysis ratio in the Taguchi method are 38.75 % and 63.62 %, respectively. In ANN, they are 41.51 % and 58.38 %, respectively, with relative errors of 7.08 % and 8.23 % compared to the experimental results. This research helps turn solid waste into green fuels and approach a circular bioeconomy. © 2024 Elsevier Ltd

关键词： Alkaline ionized water (AIW) Artificial neural network (ANN) Hydrochar Microwave-assisted heating (MAH) Pineapple peel waste (PPW) Taguchi method

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A Review of Carbon dots (CDs) Application in Sensing and Removing Medical Waste 5

A Review of Carbon dots (CDs) Application in Sensing and Rem...

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5th International Conference on Energy, Environmental and Information System, ICENIS 2020

作者： Alkian, Ilham Sutanto, Heri Aprimanti Utami, Bella Rafika Duri, Inten A'Yuni, Dewi Qurrota Hadiyanto, H Master Program of Environmental Science School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Smart Materials Research Center Diponegoro University Semarang Indonesia

Carbon dots materials (CDs) have the capability in the management of medical waste problem because of its advantage in the detection and degradation of pollutants. The synthesized CDs through various processes and raw materials have been applied to medical wastes, especially antibiotics such as tetracycline (TC), amoxicillin (AMX), para-Nitrophenol (p-NP), doxycycline (DC), ciprofloxacin (CIP), and oxytetracycline (OTC). These antibiotics are detected by utilizing changes in the luminescence property of CDs from the turn-off and turn-on mechanisms. Afterward, the degradation process is conducted using CDs as support materials agent semiconductors in the photosynthesis scheme. In general, CDs can be referred to as multifunction smart materials with the ability to detecting the presence of the antibiotics in simple, sensitive, and selective ways, and potency of medical pollutant removal clearly and effectively. © The Authors, published by EDP sciences, 2020.

关键词： Antibiotics

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Multimaterial 3D Nanoprinting of a 2 Fr Soft Robotic Microcatheter for Transarterial Chemoembolization of Liver Cancer

Multimaterial 3D Nanoprinting of a 2 Fr Soft Robotic Microca...

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IEEE International Conference on Soft Robotics (RoboSoft)

作者： Bailey M. Felix Jordi T. Andreou Sarah McHugh Jack E. McCann Olivia M. Young Molly A. Carton Dheeraj Gandhi Miroslaw Janowski Mark Fuge Axel Kreiger Clifford R. Weiss Christopher R. Bailey Ryan D. Sochol Fischell Department of Bioengineering University of Maryland College Park MD USA Department of Mechanical Engineering University of Maryland College Park MD USA Department of Diagnostic Radiology and Nuclear Medicine Program in Image Guided Neurointerventions University of Maryland School of Medicine Baltimore MD USA Department of Mechanical Engineering ETH Zurich Zurich Switzerland Department of Mechanical Engineering Johns Hopkins University Baltimore MD USA The Russell H. Morgan Department of Radiology and Radiological Science Division of Vascular and Interventional Radiology Johns Hopkins University School of Medicine Baltimore MD USA Maryland Robotics Center University of Maryland College Park MD USA Robert E. Fischell Institute for Biomedical Devices University of Maryland College Park MD USA

ISBN: (数字)9798331520205

ISBN: (纸本)9798331520212

Transarterial chemoembolization (TACE)—i.e., the delivery of chemotherapeutics directly into the vasculature of liver tumors—necessitates the use of guidewire-microcatheter systems to navigate tortuous vascular networks. These guide-wires and microcatheters typically lack effective steering controls, making successful navigation through geometrically complex anatomy challenging. Recent advancements in the area of soft microrobotics could offer a pathway to remotely steerable microcatheters at size scales relevant to TACE procedures; however, the difficulties associated with fabricating such systems have represented a critical hurdle. To overcome this barrier, here we present a multimaterial additive nanomanufacturing— or "three-dimensional (3D) nanoprinting"—strategy to achieve steerable soft robotic microcatheters that not only facilitate on-demand regulation of catheter tip deflection, but also comprise 2 French (Fr) outer dimensions (i.e., ⌀ = 666 μm) that are suitable for clinical chemoembolization of liver cancers. Specifically, we use "Two-Photon Direct Laser Writing (DLW)" to 3D nanoprint 1.5 mm-tall microcatheters capable of unidirectional tip bending of approximately 140° as well as mediating delivery of a fluidic payload. Experimental results for navigating the soft robotic microcatheter through a vascular phantom to deliver fluid payloads to target sites suggest the presented strategy holds promise for TACE as well as additional endovascular interventions and minimally invasive surgeries.

关键词： Liver cancer Actuators Navigation Deformation Phantoms Liver Soft robotics Bending Three-dimensional printing Payloads

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Prediction of Hydrogen Production Rate in Anaerobic Fermentation Using Grey Relation Analysis and Machine Learning

SSRN

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SSRN 2023年

作者： Wang, Yifan Wu, Jinghui Tseng, Yu-Yao Zhao, Chunliang Li, Keqing Wang, Xianze Wang, Ming-Hung Lay, Chyi-How Huo, Mingxin Engineering Research Center of Low-Carbon Treatment Green Development of Polluted Water in Northeast China Ministry of Education Changchun130117 China Science and Technology Innovation Center for Municipal Wastewater Treatment and Water Quality Protection Northeast Normal University Changchun130117 China Key Laboratory of Songliao Aquatic Environment Ministry of Education Jilin Jianzhu University Changchun130118 China Master's Program of Green Energy Science and Technology Feng Chia University Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan

Anaerobic fermentation for hydrogen production has many environmental factors that limit microbial activity, but machine learning has enormous potential in handling the complexity of biological processes. This study explores the potential of machine learning in predicting hydrogen production rates (HPR) from anaerobic fermentation of biomass energy. Grey relation analysis was conducted to determine the correlation between operational parameters and HPR. Five machine learning algorithms, such as decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and K-nearest neighbor (KNN), were then paired with operating conditions and water quality performance as features and HPR as a label, with mean squared error (MSE) and R2 as evaluation indexes. Butyric acid, oxidation-reduction potential (ORP), and volatile suspended solids (VSS) were found to play crucial roles in hydrogen production from sucrose anaerobic fermentation. XGBoost had the highest accuracy with R2 of 0.91 and MSE of 0.0052. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Multivariate analysis of carcinogenic equivalence (CEQ) to characterize carcinogenic VOC emissions in a typical petrochemical industrial park in Taiwan

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Environment International 2024年 186卷 108548页

作者： Chen, Wei-Hsiang Chang, Chin-Fa Lai, Chin-Hsing Peng, Yen-Ping Su, Yu-Jih Chen, Guan-Fu Institute of Environmental Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Aerosol Science and Research Center National Sun Yat-sen University Kaohsiung804 Taiwan Department of Public Health Kaohsiung Medical University Kaohsiung807 Taiwan Master and Doctoral Degree Program in Toxicology Kaohsiung Medical University Kaohsiung807 Taiwan Center for Emerging Contaminants Research National Sun Yat-Sen University Kaohsiung804 Taiwan Department of Environmental Engineering and Science Fooyin University Kaohsiung831 Taiwan College of Medicine Chang Gung University Taoyuan333 Taiwan Department of Internal Medicine Division of Rheumatology Allergy and Immunology Kaohsiung Chang Gung Memorial Hospital Kaohsiung807 Taiwan Center for Mitochondrial Research and Medicine Kaohsiung Chang Gung Memorial Hospital Kaohsiung807 Taiwan Institute of Biopharmaceutical Science National Sun Yat-Sen University Kaohsiung804 Taiwan

Large industrial emissions of volatile organic compounds (VOCs) from the petrochemical industry are a critical concern due to their potential carcinogenicity. VOC emissions vary in composition depending on the source and occur in mixtures containing compounds with varying degrees of toxicity. We proposed the use of carcinogenic equivalence (CEQ) and multivariate analysis to identify the major contributors to the carcinogenicity of VOC emissions. This method weights the carcinogenicity of each VOC by using a ratio of its cancer slope factor to that of benzene, providing a carcinogenic equivalence factor (CEF) for each VOC. We strategically selected a petrochemical industrial park in southern Taiwan that embodies the industry's comprehensive nature and serves as a representative example. The CEQs of different emission sources in three years were analyzed and assessed using principal component analysis (PCA) to characterize the major contributing sectors, vendors, sources, and species for the carcinogenicity of VOC emissions. Results showed that while the study site exhibited a 20.7 % (259.8 t) decrease in total VOC emissions in three years, the total CEQ emission only decreased by 4.5 % (15.9 t), highlighting a potential shift in the emitted VOC composition towards more carcinogenic compounds. By calculating CEQ followed by PCA, the important carcinogenic VOC emission sources and key compounds were identified. More importantly, the study compared three approaches: CEQ followed by PCA, PCA followed by CEQ, and PCA only. While the latter two methods prioritized sources based on emission quantities, potentially overlooking less abundant but highly carcinogenic compounds, the CEQ-first approach effectively identified vendors and sources with the most concerning cancer risks. This distinction underscores the importance of selecting the appropriate analysis method based on the desired focus. Our study highlighted how prioritizing CEQ within the analysis framework empowered

关键词： Principal component analysis

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FinGAT: Financial graph attention networks for recommending top-K profitable stocks

arXiv

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arXiv 2021年

作者： Hsu, Yi-Ling Tsai, Yu-Che Li, Cheng-Te Master Program in Statistics National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Institute of Data Science National Cheng Kung University Tainan Taiwan

Financial technology (FinTech) has drawn much attention among investors and companies. While conventional stock analysis in FinTech targets at predicting stock prices, less effort is made for profitable stock recommendation. Besides, in existing approaches on modeling time series of stock prices, the relationships among stocks and sectors (i.e., categories of stocks) are either neglected or pre-defined. Ignoring stock relationships will miss the information shared between stocks while using pre-defined relationships cannot depict the latent interactions or influence of stock prices between stocks. In this work, we aim at recommending the top-K profitable stocks in terms of return ratio using time series of stock prices and sector information. We propose a novel deep learning-based model, Financial Graph Attention Networks (FinGAT), to tackle the task under the setting that no pre-defined relationships between stocks are given. The idea of FinGAT is three-fold. First, we devise a hierarchical learning component to learn short-term and long-term sequential patterns from stock time series. Second, a fully-connected graph between stocks and a fully-connected graph between sectors are constructed, along with graph attention networks, to learn the latent interactions among stocks and sectors. Third, a multi-task objective is devised to jointly recommend the profitable stocks and predict the stock movement. Experiments conducted on Taiwan Stock, S&P 500, and NASDAQ datasets exhibit remarkable recommendation performance of our FinGAT, comparing to state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Profitability

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Cryptanalysis of quantum private comparison with six-particle maximally entangled states

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Modern Physics Letters A 2023年第7期38卷

作者： Tsai, Chia-Wei Yang, Chun-Wei Wang, Chun-Hsiang Lin, Jason Department of Computer Science and Information Engineering National Taichung University of Science and Technology No. 129 Sec. 3 Sanmin Rd. North District Taichung 404 Taiwan Master Program for Digital Health Innovation College of Humanities and Sciences China Medical University No. 100 Sec. 1 Jingmao Rd. Beitun Dist. Taichung 406040 Taiwan Department of Computer Science and Engineering National Chung Hsing University No. 145 Xingda Rd. South District Taichung 40227 Taiwan

The measurement property of the six-particle maximally entangled state was used by Sun [Mod. Phys. Lett. A 37, 2250149 (2022)] to design a quantum private comparison (QPC) protocol. However, this study points out that the entanglement property of the six-particle maximally entangled state is inconsequential as a security measure;that is, participants waste resources to store the entanglement state and perform the Bell measurement without obtaining any additional security advantage. Although a third party (TP) cannot obtain information regarding the participants' secret messages through an attack, an attack renders the six-particle entanglement redundant. Accordingly, this study proposed an improved QPC protocol to prevent third-party attacks. © 2023 World Scientific Publishing Company.

关键词： entanglement state quantum key distribution quantum private comparison Secure multiparty computation

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Water can trigger nuclear reaction to produce energy and isotope gases

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Scientific Reports 2024年第1期14卷 1-13页

作者： Huang, Bin-Juine Pan, Yu-Hsiang Wu, Po-Hsien Yeh, Jong-Fu Tso, Ming-Li Liu, Ying-Hung Wu, Litu Huang, Ching-Kang Chen, I-Fee Lin, Che-Hao Tseng, T.R. Kang, Fang-Wei Tsai, Tan-Feng Lan, Kuan-Che Chen, Yi-Tung Liao, Mou-Yung Xu, Li Chen, Sih-Li Greenyer, Robert William Chair Professor Vehicle and Energy Engineering Program National Taiwan Normal University Taipei Taiwan Advanced Thermal Devices (ATD) Inc. Konglin Group New Taipei City Taiwan Department of Mechanical Engineering National Taiwan University Taipei Taiwan Mastek Technologies Inc. New Taipei City Taiwan Institute of Nuclear Engineering and Science National Tsing Hua University Hsinchu Taiwan Department of Mechanical Engineering University of Nevada Las Vegas NV United States Martin Fleischmann Memorial Project Worthing United Kingdom

This paper reports the discovery that water can trigger a peculiar nuclear reaction and produce energy. Cavitation may induce unusual reactions through implosion of water vapor bubbles. Many of this research has been published formally or informally. We have conducted experiments using two reactor types made from multiple-pipe heat exchanger and found that the heat exchange process of water produces peculiar excess heat and abnormally high pressure leading to rupture of the reactor. Recently, we have tested another eight reactors. Interestingly, these reactors produce non-condensable gas. We suspected that they include ²²Ne and CO₂. We used a mass spectrometer (MS) to analyze 14 gas samples collected from 8 reactors, including ten samples showing a coefficient of performance COP_x > 1.05 (with excess heat) and four having COP_x < 1.05 (without excess heat). Several methods were adopted to identify the gas content. For CO₂ identification, two methods are employed. For ²²Ne identification, three methods are employed. All the results confirm that isotope ²²Ne and regular CO₂ really exist in the output gas from reactors determined to have excess heat. We conjecture a possible mechanism to produce ²²Ne and CO₂ and find out that ¹²C and isotope ¹⁷O are the intermediate. They finally form isotope gases containing ¹⁷O, including H₂O-17 (heavy-oxygen water), isotope O₂ (¹⁶O–¹⁷O), and isotope CO₂ (¹²C–¹⁶O–¹⁷O). In the excess heat producing reactors, all these gasses were detected by MS in the absence of ²⁰Ne and ²¹Ne. The observed isotope gases produced from reactors having excess heat verifies that water can trigger a peculiar nuclear reaction and produce energy. © 2024, The Author(s).

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Particle size effect of La2CuO4 nanoparticles

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Interactions 2024年第1期245卷 212-212页

作者： Putri, Anita Eka Kurniawan, Budhy Sari, Dita Puspita Syakuur, Muhammad Abdan Watanabe, Isao Meson Science Laboratory RIKEN Nishina Center 2-1 Hirosawa Saitama Wako351-0198 Japan Physics Departement Universitas Indonesia West Java Depok16424 Indonesia Innovative Global Program College of Engineering Shibaura Institute of Technology Saitama Omiya337-8570 Japan Department of Chemistry Padjadjaran University Sumedang Jatinangor45363 Indonesia Nuclear Structure Research Group RIKEN Nishina Center 2-1 Hirosawa Saitama Wako351-0198 Japan

A particle size effect to magnetic properties of the mother material of the La-based high-Tc superconducting oxides, La2CuO4 (LCO), was investigated using muon spin relaxation (μSR) measurement in the zero-field condition. A decrease in the Nèel temperature was observed in LCO nanoparticles with a decreasing particle size. The magnetic ground state was found to be an antiferromagnetically ordered state as observed in the bulk state. Two muon sites were confirmed, which showed different temperature dependence of internal fields. It is guessed that oxygen molecules are trapped among nanoparticles, forming a domain-like structure [1]. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Nanoparticles

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Utilizing Educational Robotics for Learning About Sustainability Issues

Utilizing Educational Robotics for Learning About Sustainabi...

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IEEE Latin American Robotics Symposium, LARS

作者： Andre Carlos Teixeira de Vasconcelos Almir Silva Silveira Raphael Melo de Oliveira Lucas Tejedor da Silva Giselle Medeiros Mesiara João Roberto de Toledo Quadros Master's Degree Program in Computer Science Celso Suckow da Fonseca Federal Technological Education Center Rio de Janeiro Brazil Department of Computer Science Celso Suckow da Fonseca Federal Technological Education Center Rio de Janeiro Brazil Sense & Learn (SELEA)21 Copenhagen University Denmark Department of Electronic Engineering/Pos-Graduate Program in Computer Science Celso Suckow da Fonseca Federal Technological Education Center Rio de Janeiro Brazil

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

Nowadays, schools are trying to insert knowledge and learning about sustainability issues and environmental conservation in the academic context from an early age. It is also observed the use of information technology with new didactic and ludic resources that can encourage elementary and high school students to obtain knowledge about sustainability. To this end, this research presents some results using Educational Robotics, Gestalt, and information technology, which students received from a Brazilian public elementary school. In this study, after attending workshops on sustainability, the students developed robotic resources to help them with sustainability-related knowledge. The resources were tested according to their objectives: a mobile robot that recognizes the food and shows its footprint and a box with gas sensors to identify if food is in consumable condition or should be recycled. The experiment was very successful and is in a continuous application phase.

关键词： Conferences Education Robot sensing systems Object recognition Mobile robots Sustainable development Information technology

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