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Properties of pure magnesium produced using mechanical milling and spark-plasma sintering

Keikinzoku/Journal of Japan Institute of Light Metals 2019年第4期69卷 242-248页

作者： Kubota, Masahiro Hagino, Toshiki Department of Mechanical Engineering College of Industrial Technology Nihon University 1-2-1 Izumi-cho Narashino-shi Chiba275-8575 Japan Graduate School of Industrial Technology Nihon University Toshiba Plant Systems and Services Corporation Narashino-shi Chiba Japan

Pure magnesium powders together with different amounts of process control agent (PCA) were mechanically milled (MM) using a planetary type of ball mill with different processing times. MMed powders were consolidated into bulk materials by the spark plasma sintering (SPS). Changes in hardness and solid-state reactions of the MMed pure magnesium powders and the bulk materials have been examined by hardness measurements and an X-ray diffraction (XRD). Morphology of the MMed pure magnesium powders was characterised by scanning electron microscopy (SEM). The Vickers microhardness of the pure magnesium powders increased from 38.9 to 52.5 HV after 32 h of MM time with 1.00 g of PCA due to the combination of strain induced and minimized crystallite size. The Vickers hardness of the bulk materials fabricated from 32 h MMed pure magnesium powders with 1.00 g of PCA exhibited 51.4 HV that is similar to that of AZ31-O. © 2019 The Japan Institute of Light Metals

关键词： Spark plasma sintering

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Electricity energy price forecasting based on hybrid multi-stage heterogeneous ensemble: Brazilian commercial and residential cases

Electricity energy price forecasting based on hybrid multi-s...

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International Joint Conference on Neural Networks (IJCNN)

作者： Matheus Henrique Dal Molin Ribeiro Ramon Gomes da Silva Cristiane Canton Naylene Fraccanabbia Viviana Cocco Mariani Leandro dos Santos Coelho Industrial & Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil Department of Mathematics Federal Technological University of Parana (UTFPR) Pato Branco Parana Brazil Pato Branco University Center (UNIDEP) Pato Branco Parana Brazil Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil Department of Electrical Engineering Federal University of Parana (UFPR) Curitiba Parana Brazil

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

The development of accurate models to forecast electricity energy prices is a challenge due to the number of factors which can affect this commodity. In this paper, a hybrid multi-stage approach is proposed to forecast multi-stepahead (one, two and three-month-ahead) Brazilian commercial and residential electricity energy prices. The proposed data analysis combines the pre-processing named complementary ensemble empirical mode decomposition (CEEMD) in the first stage coupled with the coyote optimization algorithm (COA) to define the CEEMD's hyperparameters, aiming to deal with time series non-linearities and enhance the model's performance. On the next stage, four machine learning models named extreme learning machine, Gaussian process, gradient boosting machine, and relevance vector machine are employed to train and predict the CEEMD's components. Finally, in the final stage, the results of the previous step are directly integrated to compose a heterogeneous ensemble learning of components to obtain the final forecasts. In this case, a grid of models is obtained. The best model is one that has better generalization out-of-sample. Through developed comparisons, results showed that combining COA-CEEMD with a heterogeneous ensemble learning can develop accurate forecasts. The modeling developed in this paper is promising and can support decision making in electricity energy price forecasting.

关键词： Predictive models Biological system modeling Forecasting Data models Optimization Empirical mode decomposition

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Bioethanol Production from Microalgae Biomass at High Solid Loadings

SSRN

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SSRN 2022年

作者： Condor, Billriz E. de Luna, Mark Daniel G. Chang, Yu-Han Chen, Jih-Heng Leong, Yoong Kit Chen, Po-Ting Chen, Chun-Yen Lee, Duu-Jong Chang, Jo-Shu Energy Engineering Program National Graduate School of Engineering University of the Philippines Diliman Quezon City1101 Philippines Department of Chemical Engineering College of Engineering University of the Philippines Diliman Quezon City 1101 Philippines Department of Chemical Engineering National Cheng Kung University Tainan701 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung Taiwan Department of Chemical and Materials Engineering Tunghai University Taichung Taiwan Department of Biotechnology and Food Technology Southern Taiwan University of Science and Technology Tainan71005 Taiwan University Center for Bioscience and Biotechnology National Cheng Kung University Tainan701 Taiwan Department of Mechanical Engineering City University of Hong Kong Kowloon Tang Hong Kong Department of Chemical Engineering and Materials Science Yuan Ze University Chung-Li32003 Taiwan

industrial adoption of microalgae biofuel technology has always been hindered by its economic viability. To increase the feasibility of bioethanol production from microalgae, fermentation was applied to Chlorella vulgaris FSP-E biomass at high-solid-loading conditions. First, Chlorella vulgaris FSP-E was cultivated to produce microalgae biomass with high carbohydrate content. Next, different ethanol-producing microorganisms were screened. Saccharomyces cerevisiae FAY-1 showed no inhibition when fermenting high initial glucose concentrations and was selected for the fermentation experiments at high solids loading. Optimization of acid hydrolysis at high biomass loading was also performed. The fermentation of microalgal biomass hydrolysate produced a final ethanol concentration and yield higher than most reported literature using microalgae feedstock. In addition, the kinetics of bioethanol fermentation of microalgae hydrolysate under high solid loading were evaluated. These results show the potential of fermenting microalgae biomass at high solids loading in improving the viability of microalgae bioethanol production. © 2022, The Authors. All rights reserved.

关键词： Microalgae

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Inertial Microfluidic-Assisted Rapid Detection of Chemical Contaminant in Foods Using Quantum Dots-Based Molecular Imprinting Sensor

SSRN

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SSRN 2023年

作者： Feng, Shaolong Lin, Yang Xu, Jie Lu, Xiaonan Food Nutrition and Health Program Faculty of Land and Food Systems The University of British Columbia VancouverBCV6T 1Z4 Canada Department of Food Science and Agricultural Chemistry McGill University Macdonald Campus 21111 Lakeshore Road Sainte-Anne-de-BellevueQCH9X 3V9 Canada Department of Mechanical and Industrial Engineering University of Illinois at Chicago ChicagoIL60607 United States University of Rhode Island United States

Integration of sample pretreatment, separation and detection in simple steps for food chemical contaminant analysis is challenging due to the complexity of food matrices. A novel analytical approach of using quantum dots-based molecular imprinting fluorescence sensor (QDs@MIPs) combined with inertial separation microfluidic device for separation and detection of enrofloxacin is presented in this study. The microfluidic device was designed based on laminar flow principle combined with Dean-coupled inertial migration of particles in spiral microchannels. Micron-sized QDs@MIPs sensing particles were synthesized for selective separation and sensitive detection of enrofloxacin. Separation of QDs@MIPs was achieved via parallel migration from the original suspension solution to another buffer solution in the same spiral microchannel with a high separation efficiency of 97.7±1.6%. Fluorescence intensity of QDs@MIPs was quenched in the presence of enrofloxacin and showed a linearity in the range of 0.1-50 µg/mL with limit of detection of 0.059 µg/mL. On-chip analyses were performed to detect enrofloxacin in both standard samples and contaminated swine muscle. This microfluidic-assisted fluorescence sensing system provides a solution to integrate sample pretreatment, separation and detection together to realize rapid analysis of food contaminants with high selectivity and sensitivity. © 2023, The Authors. All rights reserved.

关键词： Microchannels

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Agriculture 4.0 and its Barriers in the Agricultural Production Chain Development in Southern Brazil

SSRN

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SSRN 2022年

作者： Silveira, Franco da Silva, Sabrina Letícia Couto da Machado, Filipe Molinar Barbedo, Jayme Garcia Arnal Amaral, Fernando Gonçalves Graduate Program of Industrial Engineering Federal University of Rio Grande do Sul Av. Osvaldo Aranha 99 RS Porto Alegre90035-190 Brazil Federal Institute of Education Science Technology of Rio Grande do Sul Campus Porto Alegre RS Brazil Embrapa Agricultural Digital Av. André Tosello 209 - C.P. 6041 SP Campinas13083-886 Brazil Department of Mechanical Engineering Regional Integrated University of Alto Uruguay and the Missões Av. Universidade das Missões 464 RS Santo Ângelo98802-470 Brazil

Agriculture 4.0 can drive the growth of the agricultural production chain in emerging countries like Brazil, which is known as one of the primary food and meat producers worldwide, by implementing a range of advanced technologies such as Internet of Things (IoT), Artificial Intelligence (AI), Blockchain, and Machine Learning. However, the development of agriculture 4.0 in Brazil is a complex process, and little is known about the real barriers that impact its adoption among the actors of the agricultural production chain. There is a lack of empirical studies about the perception of Brazilian farmers regarding the barriers that may compromise the successful path of agriculture 4.0. This article aims to identify and validate the barriers that hinder the development of agriculture 4.0 in the agricultural production chain of Southern Brazil. Twenty-five barriers were chosen for validation based on a Systematic Review of the existing Literature. A confirmatory factor analysis was performed using the statistical tests of Kaiser-Meyer-Olkin and Bartlett . Validation was performed through the perception of farmers ( N = 347 ) distributed among the states of Rio Grande do Sul, Santa Catarina, and Paraná. The data were collected from an online questionnaire that identified the importance of the barriers for the farmers in the sample. The most frequent and important barriers mentioned by the farmers were: lack of infrastructure , lack of solutions accessible to farmers, need to foster R&D and innovative business models , age group risk , and lack of efficacy in the data on the rural environment. © 2022, The Authors. All rights reserved.

关键词： Digital storage

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Dynamic Multi-criteria Classifier Selection for Illegal Tapping Detection in Oil Pipelines

Dynamic Multi-criteria Classifier Selection for Illegal Tapp...

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International Joint Conference on Neural Networks (IJCNN)

作者： Victor Henrique Alves Ribeiro Pedro Henrique Domingues Paulo Rodrigo Cavalin Gilberto Reynoso-Meza Helon Vicente Hultmann Ayala Luís Fernando Alzuguir Azevedo Industrial and Systems Engineering Graduate Program Pontifícia Universidade Católica do Paraná Curitiba Brazil IBM Research Rio de Janeiro Brazil Department of Mechanical Engineering Pontifícia Universidade Católica do Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Illegal tapping of fuel pipelines has recently become one of the most relevant safety problems faced by the industry. Hundreds of illegal interventions have been reported around the world, causing a significant number of deaths, relevant impacts on the environment, and capital loss. Therefore, it is important to develop systems that are able to detect such scenarios at an early stage, enabling a fast counteract. To this end, machine learning algorithms can train models on available data for detecting future issues. Most recently, ensemble learning and dynamic classifier selection (DCS) techniques have been achieving promising results in supervised learning tasks. Such models are usually trained based on a single criterion. However, it is desirable to take into account both the number of false positives (FP) and false negatives (FN) for the illegal tapping detection task, since they are conflicting and both lead to financial losses and/or accidents. Therefore, this work proposes a novel DCS technique based on multiple criteria, namely overall local class-specific accuracy (OLCA), which employs multi-criteria decision making for dynamically selecting the best classifier for a new sample given the local true positive and negative ratios. A numerical experiment is conducted for assessing the generalization performance of the proposed method in an oil pipeline, with the goal of detecting illegal taping using pressure transient signals. Results show that OLCA is able to reduce the number of both FP and FN when dynamically selecting the classifiers of a baseline Random Forest ensemble.

关键词： Pipelines Leak detection Oils Decision making Feature extraction Biological system modeling Task analysis

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A Comparative Study of Dimensional Tolerancing Capabilities and Microstructure Formation between Binder Jet Additively Manufactured Sand Molds and Olivine Green Sand Molds for Metalcasting of A356.0

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Procedia Manufacturing 2020年 48卷 338-348页

作者： C.R. Hasbrouck Joseph W. Fisher Manuel Rudy Villalpando Paul C. Lynch Department of Industrial and Manufacturing Engineering Pennsylvania State University University Park PA 16802 USA Department of Mechanical Engineering Pennsylvania State University University Park PA 16802 USA Additive Manufacturing and Design Graduate Program Pennsylvania State University University Park PA 16802 USA Department of Industrial Engineering Penn State Erie The Behrend College Erie PA 16563 USA

The development of additive manufacturing in the 1980s led to a revolution in various metalcasting processes as early as the 1990s, including the use of polymer 3D printing for the manufacture of sand casting patterns. While additive manufacturing has entered many metalcasting processes, including both expendable and permanent mold processes, this paper specifically examines the sand casting process. ExOne’s development of binder jetting technology over the last 20 years has allowed designers in the sand casting field to directly 3D print sand molds for metalcasting. Use of binder jet additively manufactured sand molds allows for quicker turnaround time for testing prototypes, new gating designs, and for producing one-off parts. Additive manufacturing of sand molds for metalcasting may implement any of the same foundry sands as green sand processes, but uses a furan binder instead of the traditional mixture of bentonite clay and water. The use of a chemical binder had led to questions about the resulting dimensional capabilities and mechanical properties of the castings produced by the mold. This study investigated the dimensional tolerancing capabilities, surface finish, mechanical properties, microstructure, and defects present in identical castings made from both a traditional olivine green sand molding process and a binder jet additively manufactured silica sand molding process. It was concluded that binder jet additively manufactured sand molds are capable of either equal or better dimensional accuracy and tolerance capabilities than traditional olivine green sand molds. The olivine green sand parts had an average of approximately 1 μm better surface finish than the binder jet sand molds; however, it is likely that both the addition of sea coal to the green sand and the difference in final part color significantly affected this result. The mean hardness of the binder jet parts was 58.9 HBW with a standard deviation of 5.6 HBW, compared to the mean of the green

关键词： Hybrid Manufacturing Additive Manufacturing Metalcasting Binder Jetting Metallurgy Tolerancing Microstructure Cooling Rate

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Implementation of Generate and Test Algorithm for Junior High School Zoning System in Malang 4

Implementation of Generate and Test Algorithm for Junior Hig...

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4th International Conference on Vocational Education and Training, ICOVET 2020

作者： Ratnasar, Novia Agustin Mawarni, Inggar Tri Nur Handayani, Anik Wibawa, Aji Prasetya Hammad, Jehad A. H Wibowotomo, Budi Sugandi, MacHmud Universitas Negeri Malang Vocational Education Program Department Indonesia Universitas Negeri Malang Electrical Engineering Dept Indonesia Al-Quds Open University Faculty of Technology and Applied Sciences Department of Computer Information Systems Palestine Universitas Negeri Malang Industrial Technology Department Malang65145 Indonesia Universitas Negeri Malang Mechanical ENgineering Department Malang65145 Indonesia

ISBN: (纸本)9781728181318

Admission of new students in 2019 through zoning still faces many obstacles, one of which is the readiness of the application to determine the distance of a student's house from the nearest recommended school. The purpose of this study is to provide better recommendations regarding the shortest and most accurate distance in the zoning system for admission of junior high school students in Malang by using the Generate and Test algorithm without having to do it manually or using other map applications. The results of this study indicate that the distance value obtained from the haversine formula corresponds to the original distance measured through a zoning application that applies the Generate and Test algorithm to measure the closest distance from the student's location. The recommended distance is given through a process of repetition and comparison of nodes. By using the generate and test algorithm for the search process the shortest distance from the user to the destination school has an average time of 6.2MB, the level of accuracy is the same as the Haversine calculation. © 2020 IEEE.

关键词： Students

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Design and energy assessment of a new hybrid solar drying dome - Enabling Low-Cost, Independent and Smart Solar Dryer for Indonesia Agriculture 4.0

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IOP Conference Series: Earth and Environmental Science 2022年第1期998卷

作者： F E Gunawan A S Budiman B Pardamean E Djuana S Romeli N Hananda C Harito D P B Aji D N N Putri Stevanus Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia PT. Impack Pratama Industri Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia. Mechanical Engineering Department. Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia

Solar dryer is typically used for agricultural purposes in Indonesia. There are many economically important crops requiring storage or drying under particular environmental conditions such as temperature and humidity. High temperatures inside solar dryer prevents the growth of microorganism, and quickly reduce moisture content from the substance. A hybrid solar dryer is generally considered to provide the most optimum solution, however solar panels may be expensive and they still only provide heat or energy in the daytime. Hence, we propose here a new kind of hybrid solar dryer for 24/7 optimum conditions for crops - enabled by recent advances in energy technologies as well as Industry 4.0. This study aims to create an efficient, affordable and a self-sufficient intelligent energy system that will be applied to agriculture for storage or drying purposes by measuring the energy needs for the optimal drying system. Therefore, it is crucial to estimate and assess the critical energy needs for such new systems in order to optimize and design such smart solar dryer (SSD) system especially for Indonesia's agricultural needs. We use design experience of our industry partner (PT Impack Pratama Industri, Indonesia) who has been working extensively on such solar dryer dome (SDD) based on polycarbonate material (only solar irradiation, no other technologies) and theoretical framework based on first principles in thermodynamics to estimate and assess critical energy needs for such dome with all the smart technologies. The calculation was performed based on Mollier diagram and the result still a rough estimation of energy required.

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Protein Folding as a Jamming Transition

PRX Life

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PRX Life 2025年第1期3卷 013018-013018页

作者： Alex T. Grigas Zhuoyi Liu Jack A. Logan Mark D. Shattuck Corey S. O'Hern Graduate Program in Computational Biology and Biomedical Informatics Yale University New Haven Connecticut 06520 USA Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven Connecticut 06520 USA Department of Mechanical Engineering Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of New York New York New York 10031 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA

Proteins fold to a specific functional conformation with a densely packed core that controls their stability. Despite their importance, we lack a quantitative explanation for why all protein cores, regardless of their overall fold, possess the same average packing fraction 〈ϕ〉≈0.55. However, important developments in the physics of jamming in particulate systems can shed light on the packing of protein cores. Here, we extend the framework of jamming to describe core packing in collapsed polymers, as well as in all-atom models of folded proteins. First, we show in a spherical bead-spring polymer model (with and without bond-angle constraints) that as the hydrophobic interactions increase relative to thermal fluctuations, a jamming-like transition occurs when the core packing fraction exceeds ϕc with the same power-law scaling behavior for the potential energy Vr, excess contact number ΔN, and characteristic frequency of the vibrational density of states ω* versus Δϕ=ϕ−ϕc as that for jammed particulate systems. Then, we develop an all-atom model for proteins and find that, above ϕc∼0.55, protein cores undergo a jamming-like transition, but with anomalous power-law scaling for Vr, ΔN, and ω* versus Δϕ. The all-atom protein model remains close to the native protein structure during jamming and accurately refolds from partially unfolded states.

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