Article
Green & Sustainable Science & Technology
Xugang Zhang, Lu Xu, Hua Zhang, Zhigang Jiang, Wei Cai
Summary: Remanufacturing is a burgeoning technology with high economic benefits and low resource consumption. A novel emergy-based intelligent decision-making model is proposed to address the challenges in remanufacturing scheme evaluation and provide comprehensive evaluation indicators for economic and environmental performance. The feasibility and effectiveness of the model are demonstrated through a case analysis, offering valuable insights for policymakers and enterprises in formulating management strategies and development plans.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Biodiversity Conservation
M. Hamidah, I. Mohd Hasmadi, L. S. L. Chua, W. S. Y. Yong, K. H. Lau, I Faridah-Hanum, H. Z. Pakhriazad
Summary: This study proposes a methodology using MCDM-AHP for assessing the criterion weights of Malaysian IPA. The weights were determined through questionnaire surveys and the study found that threatened habitats, threatened species, endemism, and botanical richness are the most important criteria.
GLOBAL ECOLOGY AND CONSERVATION
(2022)
Article
Mathematics
Chin-Yi Chen, Jih-Jeng Huang
Summary: This paper presents an innovative method that integrates dynamic Bayesian networks (DBNs) with the analytic hierarchy process (AHP) to model dynamic interdependencies between criteria in multi-criteria decision-making (MCDM) problems. The proposed method extends the AHP to accommodate time-dependent issues and reduces to the conventional AHP when ignoring specific information, making it a more general AHP model.
Article
Operations Research & Management Science
Madjid Tavana, Mehdi Soltanifar, Francisco J. Santos-Arteaga
Summary: The Analytical Hierarchy Process (AHP) is a reliable method for multi-criteria decision-making, but faces challenges such as complexity in pairwise comparisons and consistency limitations. By introducing hybrid methods that combine AHP with popular weighting methods, efficiency and acceptability can be improved.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Engineering, Environmental
Fei Wang, Swee Pin Yeap
Summary: Uncontrolled release of dyes from industrial effluents has caused significant environmental pollution, highlighting the need for effective strategies to remove dye molecules. Fe3O4 nanoparticles and other magneto-adsorbents have shown promising dye adsorption efficiency, but systematic comparison is needed to select the most suitable adsorbent for industrial implementation.
JOURNAL OF WATER PROCESS ENGINEERING
(2021)
Article
Chemistry, Multidisciplinary
Yue Liu, Tao Shi, Ao Yang, Jingzheng Ren, Weifeng Shen, Chang He, Sara Toniolo
Summary: This paper proposes a decision-making framework based on process simulation and the fuzzy PROMETHEE II method to evaluate the performances of waste management alternatives. The framework can handle the problems of lacking data and uncertainty, and provides useful references and suggestions for decision-makers through establishing criteria system, conducting sustainability assessment, and performing sensitivity and uncertainty analysis.
ACS SUSTAINABLE CHEMISTRY & ENGINEERING
(2022)
Article
Environmental Sciences
Priyanka Yadav, Sudeep Yadav, Dhananjay Singh, Rimika Madan Kapoor, Balendu Shekher Giri
Summary: Recent developments in biogas upgradation have created new opportunities for its utilization, but the implementation of upgradation technologies is still not at the required scale. A multicriteria decision-making methodology, such as AHP, is necessary to select the most appropriate technology, considering the benefits and drawbacks of each option. This study applied AHP and found that water scrubbing and membrane separation technologies ranked first and second among the alternatives.
Article
Construction & Building Technology
Zhen Han, Xiaoqian Li, Jiaqi Sun, Mo Wang, Gang Liu
Summary: Building performance design is crucial for sustainable urban development, involving conflicting performance criteria such as energy consumption and daylighting. This study proposes a multi-criteria decision-making method based on sensitivity analysis and analytic hierarchy process, enabling real-time interactive optimization of building performance. Application of the method to a case study in China demonstrates improved performance and increased design efficiency for architects. This method enhances decision-making in building sustainable design and supports the improvement of building performance and urban sustainability.
ENERGY AND BUILDINGS
(2023)
Article
Health Care Sciences & Services
David Stein
Summary: In order to meet the dental service demands of German citizens, constant and thoughtful planning of supply and demand is crucial. A study analyzed the opinions of 375 dentists through an anonymous online survey, using the analytic hierarchy process (AHP) to rank 9 factors extracted from existing scientific literature. The study found that 5 local environmental factors have a significant impact on the decision-making process of dentistry founders in Germany.
INQUIRY-THE JOURNAL OF HEALTH CARE ORGANIZATION PROVISION AND FINANCING
(2023)
Article
Engineering, Multidisciplinary
Pratiksha Lohakare, Anand Bewoor, Ravinder Kumar, Nejla Mahjoub Said, Mohsen Sharifpur
Summary: This research aims to develop a methodology for selecting the material of a piston for a new design of engine using a multi-criteria decision-making method. The study considers critical parameters of a Caterpillar engine and uses Ansys forte FEA simulation tool to accurately predict these parameters. The results of the simulation are then used as input for the material selection benchmark using the AHP methodology.
ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS
(2022)
Article
Geosciences, Multidisciplinary
Matej Vojtek, Jana Vojtekova, Romulus Costache, Quoc Bao Pham, Sunmin Lee, Arfan Arshad, Satiprasad Sahoo, Nguyen Thi Thuy Linh, Duong Tran Anh
Summary: The study aimed to identify flood-prone areas in the Topa river basin, Slovakia, using two different approaches: MCDA-AHP and machine learning (BCT and BRT). Results showed that the machine learning models had higher accuracy compared to the MCDA-AHP model, providing valuable insights for more effective flood risk assessment according to EU Floods Directive.
GEOMATICS NATURAL HAZARDS & RISK
(2021)
Article
Green & Sustainable Science & Technology
Katerina Kabassi
Summary: Educators in Environmental Education often struggle to identify and select programs that can effectively utilize resources and achieve desired outcomes due to a lack of evaluation expertise. Currently, there is limited research on comparing multi-criteria decision-making models in evaluating environmental education programs.
Article
Engineering, Industrial
Wuyang Sun, Yifei Zhang, Ming Luo, Zhao Zhang, Dinghua Zhang
Summary: The selection of cutting parameters is crucial in machining aviation parts with high performance requirements. This paper proposes a novel multi-criteria decision-making system to determine the optimal cutting parameters from multiple alternatives. The proposed system combines the technique for order preference by similarity to an ideal solution (TOPSIS) and adversarial interpretive structural modeling (AISM) to make the decision.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Chetna Gupta, Jose Maria Fernandez-Crehuet, Varun Gupta
Summary: This article presents a novel multi-criteria decision-making process model to assist SME decision-makers in selecting the most suitable technology. By combining multiple key criteria, this approach provides practical ranking results for decision-makers.
PEERJ COMPUTER SCIENCE
(2022)
Article
Chemistry, Multidisciplinary
Aiman Mazhar Qureshi, Ahmed Rachid
Summary: Decision making is the process of choosing alternatives based on organized information and evaluation. MCDMs can enhance the reliability of decision making in complex engineering fields. This study applied various MCDMs to select urban heat mitigation measurements and evaluated their reliability using different normalization techniques. The results indicate that the weighted sum method and PROMETHEE provide consistent and reliable results across all normalization techniques.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Mechanical
Lin Li, Chaozhong Guo, Jihong Yan, Fu Zhao, John W. Sutherland
Summary: The environmental performance of machining processes is crucial for sustainable manufacturing, yet few studies have addressed energy and material flows using a common perspective. This paper proposes an environmental evaluation method for milling tool path strategies that considers energy and material flows, providing a quantitative calculation to characterize total exergy loss. The method aims to support intelligent manufacturing and assist manufacturers in making reliable decisions to reduce environmental impact during the machining stage in the industry 4.0 era.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
(2022)
Article
Green & Sustainable Science & Technology
Wo Jae Lee, Byung Gun Joung, John W. Sutherland
Summary: Different products have varying environmental impacts throughout their life cycle stages. Maintenance for product life extension is often seen as beneficial for the environment, but in some cases, early product failure can lead to a better performing and more environmentally friendly replacement. This paper develops a methodology using time-varying efficiency erosion models to quantify and compare the environmental and economic performance of different maintenance strategies. A case study on the maintenance of an electric motor driving a pump is presented, and various maintenance strategies are compared in terms of their environmental and economic performance. This method can help manufacturing plants select maintenance strategies that optimize both environmental and economic outcomes.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Chemistry, Analytical
Mustafa Abdallah, Byung-Gun Joung, Wo Jae Lee, Charilaos Mousoulis, Nithin Raghunathan, Ali Shakouri, John W. W. Sutherland, Saurabh Bagchi
Summary: Smart manufacturing systems are the future of manufacturing applications, aiming to detect faults quickly and reduce maintenance costs. This research analyzes four datasets from sensors in manufacturing testbeds, using deep learning techniques to identify sensor defects. The study also evaluates the performance of traditional and ML-based forecasting models for predicting sensor data. The findings demonstrate that careful selection of training data and transfer learning can improve predictive failure classification, enabling predictive maintenance.
Article
Engineering, Industrial
Bingbing Li, Tongzi Wu, Shijie Bian, John W. Sutherland
Summary: In order to provide affordable and energy-saving solutions for small and medium-sized manufacturers (SMMs), a unified framework is proposed to generate predictive models that can support real-time disaggregation of power consumption from combined inputs and identify machine states automatically. The framework converts raw power consumption into a time series with historical pattern detection capabilities, while a learning architecture uses stacked long short-term memory (LSTM) layers as encoders for embedding generation with sequential awareness. Experimental results show a minimum accuracy of 93.65% in the ideal case of real-time energy usage and machine state prediction.
CIRP ANNALS-MANUFACTURING TECHNOLOGY
(2023)
Article
Engineering, Industrial
Haiyue Wu, Matthew J. Triebe, John W. Sutherland
Summary: This paper proposes a Transformer-based classifier that can efficiently identify different known types and severity levels of fault conditions, as well as detect novel faults. The method utilizes time-frequency spectrograms transformed from raw vibration signals as input to the classifier for known fault classification. When detecting a novel fault condition, a simple yet effective technique based on Mahalanobis distance is adopted to determine whether the fault comes from a previously unseen condition, and the model is retrained using incremental learning. Experimental results demonstrate that the proposed method outperforms baseline models and a cutting-edge model in terms of fault diagnosis and novelty identification.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Environmental Sciences
Huaqing Li, Lin Li, Fengfu Yin, Fu Zhao, John W. Sutherland
Summary: This paper proposes a classification method to improve the accuracy of waste smartphone plastics classification by optimizing spectral data preprocessing and spectral feature extraction.
JOURNAL OF MATERIAL CYCLES AND WASTE MANAGEMENT
(2023)
Article
Engineering, Environmental
Sidi Deng, Zhongqing Xiao, Wencai Zhang, Aaron Noble, Subodh Das, Yuehwern Yih, John W. Sutherland
Summary: This study proposes and evaluates a novel process called gas-assisted microflow extraction (GAME) for efficiently recovering precious metals from waste printed circuit boards (WPCBs). An economic analysis is conducted to verify the feasibility of the GAME-based process at an industrial scale, and cost-effective production strategies are further investigated. This study may establish a paradigm for economically-informed decisions in sustainable technologies.
RESOURCES CONSERVATION AND RECYCLING
(2023)
Article
Engineering, Environmental
Thomas Maani, Nehika Mathur, Chuanbing Rong, John W. Sutherland
Summary: Due to the growing interest in decarbonization, clean energy technologies like electric vehicles and wind turbines are receiving increased attention. These technologies rely on powerful rare earth permanent magnets, specifically Neodymium-Iron-Boron magnets. Neodymium, a critical material subject to supply chain risks, can be mitigated through circular economy strategies. This study estimates Neodymium use in the US, predicts end-of-life flows of products containing these magnets, evaluates the potential for recovering Neodymium from these products, and assesses its significance in meeting future demand.
RESOURCES CONSERVATION AND RECYCLING
(2023)
Article
Engineering, Manufacturing
Anant Raj, Dongli Huang, Benjamin Stegman, Hany Abdel-Khalik, Xinghang Zhang, John W. Sutherland
Summary: The limited repeatability of part quality in additive manufacturing hinders its transition to large-scale manufacturing. Process fluctuations, both random and systemic, contribute to the variations in part properties. In-situ sensors are employed to measure these fluctuations and improve efficiency by estimating systemic variations in the signals.
JOURNAL OF MANUFACTURING PROCESSES
(2023)
Article
Thermodynamics
Junhua Zhao, Li Li, Lingling Li, Yunfeng Zhang, Jiang Lin, Wei Cai, John W. Sutherland
Summary: This study establishes a multi-dimension coupling model of energy consumption for machining process, which considers the specifications of machine tools, workpieces, and processes. The influence factors of energy consumption are systematically analyzed and the internal interact relationship among each dimensional parameter is illustrated. Experimental results show that the optimal machining configurations can effectively reduce energy consumption and improve the energy-efficiency of CNC machining.
Article
Chemistry, Multidisciplinary
Xiaoyu Zhou, Mariappan Parans Paranthaman, John W. Sutherland
Summary: There is a growing demand for clean energy technologies that rely on high-performance rare earth permanent magnets (REPMs) such as neodymium-iron-boron (NdFeB) magnets. However, the supply of these magnets is at risk due to China's dominance in rare earth element supply. This paper compares the economic feasibility of two processing methods, injection molding (IM) and big-area additive manufacturing (BAAM), for producing bonded magnets from recycled magnet materials. The results show that BAAM is more profitable and economically viable than IM.
ACS SUSTAINABLE CHEMISTRY & ENGINEERING
(2023)
Article
Engineering, Manufacturing
K. F. Ehmann, S. G. Kapoor, T. R. Kurfess, A. J. Shih, M. J. Triebe, J. W. Sutherland
Summary: In 1973, a group of distinguished manufacturing engineering researchers held the first North American Metalworking Research Conference, which later became the North American Manufacturing Research Conference. In 1982, the North American Manufacturing Research Institution (NAMRI) merged with SME. This article commemorates 50 years of accomplishments, milestones, and evolving themes while reflecting on current challenges and exploring ways to contribute and innovate in advanced manufacturing.
JOURNAL OF MANUFACTURING PROCESSES
(2023)
Article
Engineering, Manufacturing
Anant Raj, Charlie Owen, Benjamin Stegman, Hany Abdel-Khalik, Xinghang Zhang, John W. Sutherland
Summary: The critical issue of part quality repeatability in metal additive manufacturing techniques, particularly for sensitive applications like aerospace and nuclear, can be alleviated by in-situ monitoring and control. This study develops machine learning models based on in-situ monitoring to predict important properties of printed parts. The models accurately predict ductility-related properties and distinguish between high-density and low-density samples.
JOURNAL OF MANUFACTURING PROCESSES
(2023)
Article
Engineering, Manufacturing
K. F. Ehmann, S. G. Kapoor, T. R. Kurfess, A. J. Shih, M. J. Triebe, J. W. Sutherland
Summary: In 1973, a group of influential manufacturing engineering researchers organized the first NAMRC, which eventually evolved into NAMRI in 1982. As we celebrate 50 years of success, it is a time to remember our achievements, milestones, and evolving themes. We also reflect on the current challenges and explore how we can contribute and innovate in advanced manufacturing in the future.
JOURNAL OF MANUFACTURING PROCESSES
(2023)
Article
Energy & Fuels
Jesus R. Perez-Cardona, John W. Sutherland, Scott D. Sudhoff
Summary: Electric Vehicles (EVs) are considered clean energy technologies in the transportation sector, but the environmental footprint associated with their materials raises concerns about their cleanliness. This study aims to address the supply risk (SR) issues of EVs by developing a design model for electric traction motors using a genetic algorithm. The model prioritizes minimizing motor mass, energy consumption, and materials with high SR. The case study of a surface-mounted permanent magnet synchronous motor demonstrates the relationships between objectives and variables. Future work should consider minimizing environmental impact and cost.
IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY
(2023)
Article
Green & Sustainable Science & Technology
Lars odegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro, Paal Engelstad
Summary: This study investigates uncertainty modeling in wind power forecasting using different parametric and non-parametric methods. Johnson's SU distribution is found to outperform Gaussian distributions in predicting wind power. This research contributes to the literature by introducing Johnson's SU distribution as a candidate for probabilistic wind forecasting.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Xing Liu, Qiuchen Wang, Yunhao Wen, Long Li, Xinfang Zhang, Yi Wang
Summary: This study analyzes the characteristics of process parameters in three lean gas ethane recovery processes and establishes a prediction and multiobjective optimization model for ethane recovery and system energy consumption. A new method for comparing ethane recovery processes for lean gas is proposed, and the addition of extra coolers improves the ethane recovery. The support vector regression model based on grey wolf optimization demonstrates the highest prediction accuracy, and the multiobjective multiverse optimization algorithm shows the best optimization performance and diversity in the solutions.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Cairong Song, Haidong Yang, Xian-Bing Meng, Pan Yang, Jianyang Cai, Hao Bao, Kangkang Xu
Summary: The paper proposes a novel deep learning-based prediction framework, aTCN-LSTM, for accurate cooling load predictions. The framework utilizes a gate-controlled multi-head temporal convolutional network and a sparse probabilistic self-attention mechanism with a bidirectional long short-term memory network to capture both temporal and long-term dependencies in the cooling load sequences. Experimental results demonstrate the effectiveness and superiority of the proposed method, which can serve as an effective guide for HVAC chiller scheduling and demand management initiatives.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Zhe Chen, Xiaojing Li, Xianli Xia, Jizhou Zhang
Summary: This study uses survey data from the Loess Plateau in China to evaluate the impact of social interaction on the adoption of soil and water conservation (SWC) technology by farmers. The study finds that social interaction increases the likelihood of farmers adopting SWC, and internet use moderates this effect. The positive impact of social interaction on SWC adoption is more pronounced for farmers in larger villages and those who join cooperative societies.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Chenghua Zhang, Yunfei Yan, Kaiming Shen, Zongguo Xue, Jingxiang You, Yonghong Wu, Ziqiang He
Summary: This paper reports a novel method that significantly improves combustion performance, including heat transfer enhancement under steady-state conditions and adaptive stable flame regulation under velocity sudden increase.
JOURNAL OF CLEANER PRODUCTION
(2024)
