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Biofuel production using tire waste in fast pyrolysis: A life cycle assessment study

Cogent Environmental Science 2025年第1期11卷

作者： H. Afash B. Ozarisoy H. Altan a Department of Industrial Engineering and Business Information Systems University of Twente Enschede The Netherlandb Sustainable Environment and Energy Systems (SEES) Graduate Program Middle East Technical University Northern Cyprus Campus Guzelyurt Turkey c College of Technology and Environment London Southbank University London UK d Department of Architecture College of Architecture and Design Prince Mohammad Bin Fahd University Dhahran Saudi Arabia

ABSTRACTDespite exceeding 2 billion units annually, the management of waste tires remains a global challenge. Pyrolysis, a thermochemical conversion process, offers a potential waste-to-energy solution with valuable byproducts. However, concerns regarding its environmental sustainability, particularly emissions associated with sulfur content, persist. The aim of this study is to evaluate the environmental impacts of a large-scale pyrolysis reactor processing waste tires using life cycle assessment (LCA) as an environmental impact tool. In this study, using heating fuel oil (HFO) and non-condensable gas (NCG) as heating methods, two pyrolysis scenarios were investigated in terms of their environmental performance. The LCA identified abiotic depletion and acidification as key environmental concerns. The HFO scenario has shown a 15% greater impact on abiotic depletion and a 78% greater impact on acidification compared to the NCG scenario. This difference has been attributed to the higher sulfur content in HFO. Nevertheless, both pyrolysis scenarios have shown positive environmental benefits regarding marine and freshwater ecotoxicity compared to conventional alternatives. These findings support the potential of pyrolysis for sustainable waste tire management while highlighting the need for further optimization to minimize sulfur-related emissions. The findings of this study can be used as a roadmap towards adapting clean technologies such as pyrolysis as a waste management strategy for policymakers by applying holistic system boundaries and regulated pyrolysis production.

关键词： Life cycle assessment pyrolysis tire waste waste management

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Consumer behavior in clothing industry and its relationship with open innovation dynamics during the covid-19 pandemic

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Journal of Open Innovation: Technology, Market, and Complexity 2021年第4期7卷 211页

作者： Ong, Ardvin Kester S. Cleofas, Maria Arielle Prasetyo, Yogi Tri Chuenyindee, Thanatorn Young, Michael Nayat Diaz, John Francis T. Nadlifatin, Reny Redi, Anak Agung Ngurah Perwira School of Industrial Engineering and Engineering Management Mapúa University 658 Muralla St. Manila 1002 Philippines School of Graduate Studies Mapúa University 658 Muralla St. Intramuros Manila 1002 Philippines Department of Industrial Engineering Faculty of Engineering University of Santo Tomas Manila 1015 Philippines Navaminda Kasatriyadhiraj Royal Thai Air Force Academy Bangkok 10220 Thailand Department of Finance and Accounting Asian Institute of Management Manila 1229 Philippines Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia Industrial Engineering Department BINUS Graduate Program—Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia

The fashion industry has been continuously growing over the years, yet it is an industry that was greatly affected by the COVID-19 pandemic. The purpose of the study was to determine the factors affecting buying behavior of Filipinos towards clothing apparel during the COVID-19 pandemic. There were 457 respondents who voluntarily participated and answered an online questionnaire. Structural equation modeling (SEM) indicated that marketing mix was found to have significant effects on attitude, subjective norm, and perceived behavioral control, which subsequently led to purchase intention. In addition, the COVID-19 latent variable was found to have significant effects on self-perceived severity and self-efficacy, which subsequently led to attitude and purchase intention. Interestingly, marketing mix was found to have the highest effect on actual purchase, which indicated that innovation dynamics are the keys for the buying behavior. The SEM construct can be applied to determine the clothing apparel buying behaviors of consumers in other countries, particularly during the COVID-19 pandemic. Finally, advertisements, promotions, sales, and health safety should be considered as innovation dynamics during the COVID-19 pandemic. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Consumer behavior COVID-19 pandemic Fashion industry Innovation dynamic Open innovation Protection motivation theory Theory of planned behavior

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Ontology and Rules for Characterization of Sensors and Actuators Devices in AADL Models

Ontology and Rules for Characterization of Sensors and Actua...

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Brazilian Symposium on Computing System engineering (SBESC)

作者： Diego Camara Sales Cristian Koliver Leandro Buss Becker UFSC Florianopolis SC Brazil Federal Institute of Amazon - IFAM Campus Manaus Distrito Industrial AM Brazil Graduate Program on Automation Systems Engineering (PGEAS) UFSC Florianopolis SC Brazil

ISBN: (数字)9781728182865

ISBN: (纸本)9781728182872

This paper presents a set of ontologies to organize concepts, categories, properties, and relationships that are typically used by designers for characterizing sensing and actuation devices that compose a given system architecture (SA). From the characterization of such devices, modeling can be done using the Architecture Analysis and Design Language (AADL). AADL supports modeling and analysis of functional and nonfunctional properties to evaluate quality attributes from ontology models. The paper also proposes an AADL property set that can be applied to sensors and actuators (S&A), based on previous researches with ontologies applied to sensor networks. Specific characteristics are provided to describe systems' quality attributes. This aims to be used for analyzing the designed architecture, and also its probable future evolution.

关键词： Ontologies Actuators Software Sensor phenomena and characterization Computer architecture Standards ISO Standards

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Mine-to-client planning with Markov Decision Process

Mine-to-client planning with Markov Decision Process

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European Control Conference (ECC)

作者： João Marcelo Leal Gomes Leite Edilson F. Arruda Laura Bahiense Lino G. Marujo Industrial Engineering Program Alberto Luiz Coimbra Institute – Graduate School and Research in Engineering Federal University of Rio de Janeiro Rio de Janeiro Brazil Systems Engineer and Computer Science Program Alberto Luiz Coimbra Institute – Graduate School and Research in Engineering Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9783907144022

ISBN: (纸本)9781728188133

This works presents an innovative application of Markov Decision Process (MDP) to a medium-term mining logistics planning problem considering the mine-to-client supply chain. We implemented three distinct algorithms based on state-of-the-art approaches to solve large-scale problems, and compared their results. Furthermore, we combined all three variants in a single novel algorithm that attained fast convergence and may be an alternative to circumvent the curse of dimensionality underlying large scale problems.

关键词： Supply chains Planning Uncertainty Markov processes Computational modeling

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Short-term forecasting of Amazon rainforest fires based on ensemble decomposition model

arXiv

引用

arXiv 2020年

作者： da Silva, Ramon Gomes Ribeiro, Matheus Henrique Dal Molin Mariani, Viviana Cocco dos Santos Coelho, Leandro Pontifical Catholic University of Parana Industrial & Systems Engineering Graduate Program Curitiba Brazil Federal Technological University of Parana Department of Mathematics Pato Branco Brazil Federal University of Parana Dept. of Electrical Eng. Curitiba Brazil Pontifical Catholic University of Parana Mechanical Engineering Graduate Program Curitiba Brazil

Accurate forecasting is important for decision-makers. Recently, the Amazon rainforest is reaching record levels of the number of fires, a situation that concerns both climate and public health problems. Obtaining the desired forecasting accuracy becomes difficult and challenging. In this paper were developed a novel heterogeneous decomposition-ensemble model by using Seasonal and Trend decomposition based on Loess in combination with algorithms for short-term load forecasting multimonth-ahead, to explore temporal patterns of Amazon rainforest fires in Brazil. The results demonstrate the proposed decomposition-ensemble models can provide more accurate forecasting evaluated by performance measures. Diebold-Mariano statistical test showed the proposed models are better than other compared models, but it is statistically equal to one of them. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision making

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Radar Occupancy Prediction with Lidar Supervision while Preserving Long-Range Sensing and Penetrating Capabilities

arXiv

引用

arXiv 2021年

作者： Kung, Pou-Chun Wang, Chieh-Chih Lin, Wen-Chieh The Graduate Degree Program of Robotics National Yang Ming Chiao Tung University Hsinchu Taiwan The Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan The Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

Radar shows great potential for autonomous driving by accomplishing long-range sensing under diverse weather conditions. But radar is also a particularly challenging sensing modality due to the radar noises. Recent works have made enormous progress in classifying free and occupied spaces in radar images by leveraging lidar label supervision. However, there are still several unsolved issues. Firstly, the sensing distance of the results is limited by the sensing range of lidar. Secondly, the performance of the results is degenerated by lidar due to the physical sensing discrepancies between the two sensors. For example, some objects visible to lidar are invisible to radar, and some objects occluded in lidar scans are visible in radar images because of the radar's penetrating capability. These sensing differences cause false positive and penetrating capability degeneration, respectively. In this paper, we propose training data preprocessing and polar sliding window inference to solve the issues. The data preprocessing aims to reduce the effect caused by radar-invisible measurements in lidar scans. The polar sliding window inference aims to solve the limited sensing range issue by applying a near-range trained network to the long-range region. Instead of using common Cartesian representation, we propose to use polar representation to reduce the shape dissimilarity between long-range and near-range data. We find that extending a near-range trained network to long-range region inference in the polar space has 4.2 times better IoU than in Cartesian space. Besides, the polar sliding window inference can preserve the radar penetrating capability by changing the viewpoint of the inference region, which makes some occluded measurements seem non-occluded for a pretrained network. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

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A weblab control experiment using the ball and beam system and multiobjective optimization 5

A weblab control experiment using the ball and beam system a...

引用

5th Experiment at International Conference, exp.at 2019

作者： Kagami, Ricardo Massao Kovalski Da Costa, Guinther Mendes, Luciano Antonio Freire, Roberto Zanetti Industrial and Systems Engineering Graduate Program-PPGEPS Pontifícia Universidade Católica Do Paraná-PUCPR Curitiba Brazil Department of Control and Automation Engineering Pontifícia Universidade Católica Do Paraná-PUCPR Curitiba Brazil

ISBN: (纸本)9781728136370

y associating active learning methodologies and digital media as a form to improve educational quality, the diversity of experimentations in engineering education has been motivating researches in many areas with possibilities of increasing both students' knowledge and skills. Control engineering is an area where educational laboratories are usually costly, thus the use of virtual laboratories for remote experimentation became an interesting approach. In this work, it is presented a remote experimentation procedure for the ball and beam system, which is a nonlinear and unstable open loop process. Considering the advanced control topic, multiobjective optimization was introduced in the experiment in order to tune Proportional-Integral-Derivative (PID) controllers. Through the description of the system model, the multiobjective optimization technique, and the experiment configuration, students can simulate and test their controllers remotely in a real plant. © 2019 IEEE.

关键词： Multiobjective optimization

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Multi-step ahead Bitcoin Price Forecasting Based on VMD and Ensemble Learning Methods

Multi-step ahead Bitcoin Price Forecasting Based on VMD and ...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Ramon Gomes da Silva Matheus Henrique Dal Molin Ribeiro 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 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

Bitcoin is the leading currency in the cryptocurrency market capturing attention worldwide. Forecasting the Bitcoin price as accurate as possible is essential, but due to its high volatility this task is challenging. Many researchers try, through the years, to develop efficient models for predicting the Bitcoin price using several different data-driven approaches. The objective of this paper is to develop a novel decomposition-ensemble learning model that combines Variational Mode Decomposition (VMD) and Stacking-ensemble learning (STACK) with machine learning algorithms to forecast the Bitcoin price multi-step ahead. The algorithms are k-Nearest Neighbors, Support Vector Regression with Linear kernel, Feed-forward Artificial Neural Network with single-layer perceptron, Generalized Linear Model, and Cubist. Correlation matrix (CORR), principal component analysis (PCA), and Box-Cox transformation (BOXCOX) were used as data preprocessing techniques. Estimating the performance of the proposed models (namely VMD-STACK-CORR, VMD-STACK-PCA, and VMD-STACK-BOXCOX) using relative root mean square error, symmetric mean absolute percentage error, and absolute percentage error measures, defined that for one-day-ahead forecast VMD-STAK-BOXCOX model presented the better performance, and for two and three-days-ahead forecast VMD-STACK-CORR model was chosen, compared to VMD, STACK, and machine learning algorithms models' performance. Diebold-Mariano statistical test was conducted to evaluate a reduction in forecasting errors. Therefore, the proposed models (VMD-STACK-CORR, VMD-STACK-PCA, and VMD-STACK-BOXCOX) indeed forecast accurately Bitcoin price and outperformed the compared models (VMD, STACK, and machine learning models).

关键词： Predictive models Biological system modeling Data models Bitcoin Training Machine learning algorithms

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Analyzing the Implications of New Technologies to the Management of Operations – Protocol Proposal and Application Illustration

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Procedia Manufacturing 2019年 39卷 904-912页

作者： Paulo Henrique Brunheroto Cassiano Souza Beller Fernando Deschamps Manufacturing Engineering Graduate Program Department of Mechanical Engineering Federal University of Parana Curitiba PR Brazil Industrial and Systems Engineering Graduate Program Polytechnic School Pontifícia Universidade Católica do Paraná Curitiba PR Brazil

The increasing adoption of new automation and information technologies are transforming production systems into cyber-physical systems (CPS). Although consulting companies have developed management frameworks to better characterize the implications of these technologies to how operations are managed, mainly focusing on new business models, efficiency, and effectiveness, the real impacts on decision areas (e.g., quality management) and performance objectives (e.g., flexibility) are still unclear. How are management systems being transformed? Are new managerial approaches emerging that address the particularities of new technologies? What are the critical success factors for conducting this transformation? To address these questions, a more systematic approach needs to be undertaken. This paper contributes to this by presenting a protocol to be used in systematic studies of the implications of the adoption of new technologies to the management of operations, and by illustrating its application to the analysis of the adoption of a cognitive vision system in an automotive manufacturer primarily deployed for improving product quality. The analysis protocol is based on Pettigrew’s dimensions of context, content, and process for change initiatives and is further subdivided into information requirements, variables to be analyzed and its potential sources of data. The case shows that the application of the protocol was able to reveal that besides expected effects on quality and productivity, new forms of interaction among workers and between workers and supervisors emerged that need further investigation. From the results of the case, future applications of the protocol are discussed.

关键词： management of operations transformation quality management cognitive vision system

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Macroscopic Fundamental Diagrams According to the Different Flood Depths on Probe Vehicle Data in Urban Bangkok

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Journal of Traffic and Transportation engineering 2020年第1期8卷 20-28页

作者： Piyapong Suwanno Prasan Jitpat Rattanaporn Kasemsri Bethala Aditya Atsushi Fukuda Program of Transportation Systems Engineering Graduate School of Science and TechnologyNihon UniversityChiba 274-8501Japan Research Unit of Technology and Innovation on Civil Engineering College of Industrial Technology and ManagementRajamangala University of Technology SrivijayaNakhon Si Thammrat 80210Thailand School of Civil Engineering Suranaree University of TechnologyNakhon Ratchasima 30000Thailand Department of Transportation Systems Engineering College of Science and TechnologyNihon UniversityChiba 274-8501Japan

Impact of flood depth on traffic volume in two different zones of the Bangkok road network was investigated using traffic data obtained from probe vehicle trajectories.A Macroscopic Fundamental Diagram(MFD)was utilized to compare traffic flow rates across two road network zones in the city for a variety of flood depths,namely 0-5 cm,5-10 cm,10-15 cm,15-30 cm,and more than 30 *** of empirical analysis over an observation period of one year as 2019 showed that flood depths had a strong correlation with the MFD parameters of free-flow speed,maximum flow,and traffic jam *** particular,road floods greatly reduced average maximum flow across the inner city road network in *** floods had a significant impact on traffic characteristics of urban road networks.

关键词： Road flood probe vehicle MFD

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