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Solar Panel Recycling from Circular Economy Viewpoint: A Review

Applied Solar Energy (English translation of Geliotekhnika) 2024年第2期60卷 328-345页

作者： K Sivagami Bose, Siddharth Vinayak, Anil Kumar Sreenivas, Malavika Ghosh, Ahana Narasimhan, Mukundan Gurumoorthy, Anand V P School of Chemical Engineering Vellore Institute of Technology Vellore Tamil Nadu India Chemical and Materials Engineering Department University of Alberta 116 St NW Edmonton Canada Playsolar Systems Pvt Ltd Manuvana Vijaya Nagar Bengaluru India

Abstract: Solar energy has emerged as a prominent contender in this arena, attracting significant attention across the globe. Governments worldwide have undertaken extensive efforts to encourage the adoption of renewable energy, increasing the usage of solar panels. Despite its benefits, the deployment of photovoltaic (PV) modules generates significant waste, thereby posing a major environmental challenge. This study explores several recycling techniques, including physical, thermal, and chemical methods, that could be employed to manage solar panel waste. An in-depth analysis of separation techniques presently employed and underdevelopment was studied and compared to determine the physical treatment necessary for the separation of glass and aluminium. Extraction of rare earth metals cadmium, copper and tellurium requires chemical treatments using organic and inorganic solvents along with thermal treatment at 500–600°C to remove the EVA polymer. Recovery of silicon wafers and rare metals through various metal extraction processes is further examined. Europe was concluded as a frontrunner in solar waste management policies after analysis of the governmental policies of developed and developing nations of the world. The circular economy model developed portrayed a systematic approach for the removal of different components of a solar panel and reintegration into the manufacturing process. The implementation of a robust circular economy for renewable energy systems is conditional upon the optimization of resource recovery while minimizing energy consumption and this serves as the governing framework of this review. © Allerton Press, Inc. 2024. ISSN 0003-701X, Applied Solar Energy, 2024, Vol. 60, No. 2, pp. 328–345. Allerton Press, Inc., 2024.

关键词： Solar panels

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Towards atom-level understanding of metal oxide catalysts for the oxygen evolution reaction with machine learning

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npj Computational Materials 2024年第1期10卷 2430-2440页

作者： Jaclyn R.Lunger Jessica Karaguesian Hoje Chun Jiayu Peng Yitong Tseo Chung Hsuan Shan Byungchan Han Yang Shao-Horn Rafael Gómez-Bombarelli Department of Materials Science and Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Center for Computational Science and Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Department of Chemical and Biomolecular Engineering Yonsei UniversitySeoul 03722Republic of Korea Department of Computational and Systems Biology Massachusetts Institute of TechnologyCambridgeMA 02139USA Department of Materials Science and Engineering University of TorontoTorontoON M5SCanada Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Research Laboratory of Electronics Massachusetts Institute of TechnologyCambridgeMA 02139USA

Green hydrogen production is crucial for a sustainable future,but current catalysts for the oxygen evolution reaction(OER)suffer from slow kinetics,despite many efforts to produce optimal designs,particularly through the calculation of descriptors for *** this study,we develop a dataset of density functional theory calculations of bulk and surface perovskite oxides,and adsorption energies of OER intermediates,which includes compositions up to quaternary and facets up to(555).We demonstrate that per-site properties of perovskite oxides such as Bader charge or band center can be tuned through element substitution and faceting,and develop a machine learning model that accurately predicts these properties directly from the local chemical *** leverage these per-site properties to identify promising perovskites with high theoretical OER *** identified design principles and promising materials provide a roadmap for closing the gap between current artificial catalysts and biological enzymes such as photosystem II.

关键词： perovskite oxides kinetics

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Accelerating Factor Xa inhibitor discovery with a de novo drug design pipeline

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Chinese Journal of chemical engineering 2024年第8期72卷 85-94页

作者： Yujing Zhao Qilei Liu Jian Du Qingwei Meng Liang Sun Lei Zhang State Key Laboratory of Fine Chemicals Frontiers Science Center for Smart Materials Oriented Chemical EngineeringInstitute of Chemical Process Systems EngineeringSchool of Chemical EngineeringDalian University of TechnologyDalian 116024China Ningbo Institute of Dalian University of Technology Ningbo 315016China Shenzhen Shuli Tech Co. LtdShenzhen 518126China Department of Physics City University of Hong KongTat Chee AvenueKowloonHong Kong SARChina

Small-molecule drugs are essential for maintaining human health. The objective of this study is to identify a molecule that can inhibit the Factor Xa protein and be easily procured. An optimization-based de novo drug design framework, Drug CAMD, that integrates a deep learning model with a mixed-integer nonlinear programming model is used for designing drug candidates. Within this framework, a virtual chemical library is specifically tailored to inhibit Factor Xa. To further filter and narrow down the lead compounds from the designed compounds, comprehensive approaches involving molecular docking,binding pose metadynamics(BPMD), binding free energy calculations, and enzyme activity inhibition analysis are utilized. To maximize efficiency in terms of time and resources, molecules for in vitro activity testing are initially selected from commercially available portions of customized virtual chemical libraries. In vitro studies assessing inhibitor activities have confirmed that the compound EN300-331859shows potential Factor Xa inhibition, with an IC_(50)value of 34.57 μmol·L^(-1). Through in silico molecular docking and BPMD, the most plausible binding pose for the EN300-331859-Factor Xa complex are identified. The estimated binding free energy values correlate well with the results obtained from biological assays. Consequently, EN300-331859 is identified as a novel and effective sub-micromolar inhibitor of Factor Xa.

关键词： chemical product design Mathematical programming method Deep learning Binding affinity Factor Xa inhibitor

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Steady state modeling of autotrophic membrane bioreactor – a new approach to quantify biomass

Arab Gulf Journal of Scientific Research

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Arab Gulf Journal of Scientific Research 2024年第3期42卷 920-941页

作者： Gasmi, Aicha Heran, Marc Elboughdiri, Noureddine Kolsi, Lioua Ghernaout, Djamel Hannachi, Ahmed Grasmick, Alain Department of Chemical Engineering Laboratory of Engineering Processes and Industrial Systems National School of Engineering of Gabes-Tunisia Gabes Tunisia European Institute of Membrane Montpellier II France Department of Chemical Engineering Process National School of Engineers Gabes Tunisia Department of Mechanical Engineering College of Engineering Ha’il Saudi Arabia Department of Chemical Engineering College of Engineering Ha’il Saudi Arabia Laboratory of Process Engineering and Industrial Systems Gabes Tunisia European Membrane Institute Montpellier II France

Purpose: The main purpose of this study resides essentially in the development of a new tool to quantify the biomass in the bioreactor operating under steady state conditions. Design/methodology/approach: Modeling is the most relevant tool for understanding the functioning of some complex processes such as biological wastewater treatment. A steady state model equation of activated sludge model 1 (ASM1) was developed, especially for autotrophic biomass (XBA) and for oxygen uptake rate (OUR). Furthermore, a respirometric measurement, under steady state and endogenous conditions, was used as a new tool for quantifying the viable biomass concentration in the bioreactor. Findings: The developed steady state equations simplified the sensitivity analysis and allowed the autotrophic biomass (XBA) quantification. Indeed, the XBA concentration was approximately 212 mg COD/L and 454 mgCOD/L for SRT, equal to 20 and 40 d, respectively. Under the steady state condition, monitoring of endogenous OUR permitted biomass quantification in the bioreactor. Comparing XBA obtained by the steady state equation and respirometric tool indicated a percentage deviation of about 3 to 13%. Modeling bioreactor using GPS-X showed an excellent agreement between simulation and experimental measurements concerning the XBA evolution. Originality/value: These results confirmed the importance of respirometric measurements as a simple and available tool for quantifying biomass. © 2023, Aicha Gasmi, Marc Heran, Noureddine Elboughdiri, Lioua Kolsi, Djamel Ghernaout, Ahmed Hannachi and Alain Grasmick.

关键词： Activated sludge model 1 (ASM1) Bioreactor Respirometric tool State variable Steady state modeling

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Data-driven intelligent modeling framework for the steam cracking process

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Chinese Journal of chemical engineering 2023年第9期61卷 237-247页

作者： Qiming Zhao Kexin Bi Tong Qiu Department of Chemical Engineering Tsinghua UniversityBeijing 100084China Beijing Key Laboratory of Industrial Big Data Systems and Applications Tsinghua UniversityBeijing 100084China School of Chemical Engineering Sichuan UniversitySichuan 610065China Department of Bioprocess Engineering Institute of BiotechnologyTechnische Universität BerlinBerlin 10623Germany

Steam cracking is the dominant technology for producing light olefins,which are believed to be the foundation of the chemical *** models of the cracking process can boost production efficiency and profit *** advancements in machine learning research have recently enabled data-driven solutions to usher in a new era of process ***,its practical application to steam cracking is still hindered by the trade-off between prediction accuracy and computational *** research presents a framework for data-driven intelligent modeling of the steam cracking *** data preparation and feature engineering techniques provide computational-ready datasets for the framework,and feedstock similarities are exploited using k-means *** propose LArge-Residuals-Deletion Multivariate Adaptive Regression Spline(LARD-MARS),a modeling approach that explicitly generates output formulas and eliminates potentially outlying *** framework is validated further by the presentation of clustering results,the explanation of variable importance,and the testing and comparison of model performance.

关键词： Mathematical modeling Data-driven modeling Process systems Steam cracking Clustering Multivariate adaptive regression spline

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Reward-Based Energy-Aware Routing Protocols in Wireless Sensor Networks

Reward-Based Energy-Aware Routing Protocols in Wireless Sens...

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International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-AIoT 2024

作者： Salama, Ramiz Alturjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Cyprus Artificial Intelligence Software and Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Cyprus

ISBN: (纸本)9783031782756

Smart cities, industrial automation, healthcare, and environmental monitoring are just a few of the industries that have seen revolutionary changes as a result of Wireless Sensor Networks’ (WSNs) explosive growth. WSNs are significantly hampered by the limited energy resources of their sensor nodes, which are usually battery-powered and placed in inaccessible locations, despite their wide-ranging applications. As a result, energy efficiency is now a crucial component of these networks’ operation and design. Though these protocols frequently fail to adjust to the dynamic and unpredictable nature of WSNs, traditional energy-aware routing methods have been created to decrease energy consumption by choosing the best paths for data transfer. By allowing sensor nodes to learn from their surroundings and make wise routing decisions, the machine learning field of reinforcement learning (RL) provides a viable answer to these problems. In contrast to conventional techniques, RL-based protocols provide continuous energy optimization by dynamically modifying their routing strategies in response to real-time feedback. This paper investigates how to include RL methods—specifically, Q-learning and SARSA—into WSN energy-aware routing protocols. The suggested method creates a more distributed and scalable solution by enabling each node to independently learn and modify its routing choices. The effectiveness of RL-based routing protocols is compared to traditional methods through comprehensive simulations. The findings show that by slowing down the rate at which energy is being used up by each node, RL not only increases energy efficiency but also lengthens the network's operational life. In particular, Q-learning demonstrates a notable improvement in sustaining data delivery rates and network connectivity under various circumstances. Although a little more computationally demanding, the SARSA algorithm offers greater flexibility in situations where network topology changes often. Th

关键词： Multi agent systems

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Using Gamification to Increase Healthcare Professionals’ Cybersecurity Awareness

Using Gamification to Increase Healthcare Professionals’ Cy...

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International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-AIoT 2024

作者： Salama, Ramiz Alturjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Cyprus Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Cyprus

ISBN: (纸本)9783031782756

Cybersecurity knowledge is essential in today’s increasingly digital healthcare environment to protect sensitive patient data and guarantee the integrity of healthcare systems. But conventional cybersecurity training approaches frequently fail to hold healthcare professionals’ attention, which results in inadequate application and retention of the material. In order to increase cybersecurity awareness among healthcare professionals, this study investigates the use of gamification—a technological technique that incorporates game design aspects into non-gaming contexts—as an innovative method. A mixed-methods research methodology is used in the study to assess the effectiveness of a cybersecurity training program that is gamified. To encourage user involvement and motivation, the program incorporates aspects like leaderboards, interactive scenarios, badges, and points. The study included a total of 100 healthcare professionals from various departments, offering a thorough understanding of the program’s effects across a range of positions and experience levels. Pre- and post-training surveys were used to gather quantitative data on participant changes in knowledge, attitudes, and behaviors pertaining to cybersecurity procedures. Furthermore, semi-structured interviews yielded qualitative insights that further elucidated the participants’ perspectives and experiences with the gamified training method. Comparing gamification to conventional training approaches, the results show that gamification dramatically improves engagement and knowledge retention. The willingness of the participants to acquire and use cybersecurity techniques in their daily lives has reportedly increased. Additionally, the gamified components strengthened the training objectives by fostering a competitive yet cooperative learning environment. The potential of gamification as a game-changing instrument for cybersecurity education in healthcare settings is highlighted by this study. Healthcare busines

关键词： Cybersecurity

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Segmentation of Retina Vessels in 2D OCT-Reconstructed Fundus Images with 3D UNet 26

Segmentation of Retina Vessels in 2D OCT-Reconstructed Fundu...

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26th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Marciniak, Tomasz Stankiewicz, Agnieszka Zaradzki, Przemyslaw Poznan University of Technology Division of Electronic Systems and Signal Processing Institute of Automatic Control and Robotics Department of Control Robotics and Electrical Engineering Poznan Poland

ISBN: (纸本)9798350304985

This paper presents an analysis of the effectiveness of blood vessel segmentation in human retinal images. The segmentation process was carried out for three-dimensional optical coherence tomography (OCT) scans using the author's publicly available CAVRI-C database. A 3D version of the U-Net artificial neural network was used. Various dimensions of the input blocks were investigated to determine their influence on the segmentation process. The effectiveness of the proposed solutions was evaluated using various metrics and the results obtained were compared with the 2D solution, also based on neural networks. The use of the proposed solutions allowed us to improve the segmentation even by 15% (in the case of the F1-score). © 2023 Division of Signal Processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： Optical tomography

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Fast yet predictable braking manoeuvers for real-time robot control

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arXiv 2023年第2期56卷 9984-9991页

作者： Hamad, Mazin Gutierrez-Moreno, Jesus Kussaba, Hugo T.M. Mansfeld, Nico Abdolshah, Saeed Swikir, Abdalla Burgard, Wolfram Haddadin, Sami Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany Franka Emika GmbH Munich Germany Department of Electrical and Electronic Engineering Omar Al-Mukhtar University Libya Department of Engineering University of Technology Nuremberg Germany Germany

This paper proposes a framework for generating fast, smooth and predictable braking manoeuvers for a controlled robot. The proposed framework integrates two approaches to obtain feasible modal limits for designing braking trajectories. The first approach is real-time capable but conservative considering the usage of the available feasible actuator control region, resulting in longer braking times. In contrast, the second approach maximizes the used braking control inputs at the cost of requiring more time to evaluate larger, feasible modal limits via optimization. Both approaches allow for predicting the robot's stopping trajectory online. In addition, we also formulated and solved a constrained, nonlinear final-time minimization problem to find optimal torque inputs. The optimal solutions were used as a benchmark to evaluate the performance of the proposed predictable braking framework. A comparative study was compiled in simulation versus a classical optimal controller on a 7-DoF robot arm with only three moving joints. The results verified the effectiveness of our proposed framework and its integrated approaches in achieving fast robot braking manoeuvers with accurate online predictions of the stopping trajectories and distances under various braking settings. © 2023, CC BY-NC-ND.

关键词： Braking

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Centralizing Data Warehouse Platform for Mining Management Using AWS Computing Instance

Centralizing Data Warehouse Platform for Mining Management U...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Sathaporn, Posathip Chaowalittawin, Vasutorn Krungseanmuang, Woranidtha Benjangkaprasert, Chawalit Purahong, Boonchana School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Robotics and Computational Intelligent Systems Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Computer Engineering Department Bangkok Thailand

ISBN: (纸本)9798350350821

Mining industry is one of significant industry in the world. In order to increase efficiency, safety, sustainability and environmental impacts in mining process. This article presents a centralized data platform for mining process by design based on microservices architecture which can be supported various of input data source such as manual form, excel file and internet of things (IoT) device and notification when the value is abnormal. Our platform implementation on Amazon Web Service (AWS) cloud. For development of productivity in process and controlling quality the mine environment. The results of the platform operations with various source of data acquisition with response time is less than 1.5 seconds per each request. © 2024 IEEE.

关键词： Cloud Computing Data Warehouse Internet of Things Microservices Architecture Mining Industry

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