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arXiv 2023年

作者： Li, Sirui Dong, Roy Wu, Cathy The Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States The Industrial & Enterprise Engineering department The University of Illinois at Urbana-Champaign UrbanaIL61801 United States The Laboratory for Information & Decision Systems The Institute for Data Systems and Society The Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

Autonomous vehicles (AVs) enable more efficient and sustainable transportation systems. Ample studies have shown that controlling a small fraction of AVs can smooth traffic flow and mitigate traffic congestion. However, deploying AVs in real-world systems poses challenges due to safety and cost concerns. A viable alternative approach that can be implemented in the near future is coarse-grained guidance, where human drivers are guided by real-time instructions, updated every ∆ seconds, to stabilize the traffic. While previous theoretical studies consider stability analysis for continuous AV control, this article presents the first integrated theoretical analysis that directly relates the guidance provided to the human drivers to the traffic flow stability outcome. Casting the problem into the Lyapunov stability framework, this study derives sufficient conditions for coarse-grained guidance with hold length ∆ to stabilize the system, and provides extensions of the analysis to incorporate additional human driving behaviors such as magnitude error and reaction delay. Numerical simulations reveal that the theoretical analysis closely matches simulated results. The analysis further offers insights into the relationship between system parameter and stability criteria, and can be leveraged to design improved controllers with greater maximum hold length. Copyright © 2023, The Authors. All rights reserved.

关键词： Stability criteria

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Sensitivity of the distributed optical fiber sensor to measure soil movement for landslide monitoring application

Sensitivity of the distributed optical fiber sensor to measu...

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Student Conference on Research and Development (SCOReD)

作者： Abdulwahhab Essa Hamzah Tan Wui Kin Mohd Saiful Dzulkefly Zan Nor Shahidah Mohd Nazer Hisham Mohamad Yosuke Tanaka Mustafa Essa Hamzah Ahmad Ashrif A. Bakar Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Photonics Technology Laboratory Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia Department of Communications Engineering Iraq University College Basrah Iraq Department of Earth Sciences and Environment Faculty of Science and Technology Universiti Kebangsaan Malaysia (UKM) UKM Bangi Selangor Malaysia Civil & Environmental Engineering Department Universiti Teknologi PETRONAS seri Iskandar Perak Malaysia Division of Advanced Electrical and Electronic Engineering Institute of Engineering Tokyo University of Agriculture and Technology Japan

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

Landslides are one of the major global geohazards, contributing to significant economic and social losses to private and public properties. In this paper, we deployed a Brillouin optical time-domain analysis (BOTDA) interrogator to measure soil movement and identify the soil movement location. The method was analyzed experimentally, detecting the soil movement location at around 935 m to 955 m of fiber. In addition, the system’s sensitivity was analyzed for soil movement within the displacement range of 0 mm to $\mathbf{4 0 \mathrm { mm }}$, with increments of 5 mm. The experimental result shows that the frequency shift of 1.66 MHz corresponds to 1 mm, and the tensile strain of $31.4 \mu \varepsilon$ corresponds to 1 mm. Moreover, the high reliability of measurement accuracy, demonstrated by an average of nine measurements with an error of 2.76 MHz, further supports the effectiveness of the system for detecting and monitoring soil movement in real-world applications.

关键词： Optical fibers Optical fiber sensors Accuracy Sensitivity Soil measurements Scattering Soil Optical variables measurement Motion measurement Monitoring

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Machine-learning-assisted prediction and optimized kinetic modelling of residual chlorine decay for enhanced water quality management

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Chemosphere 2023年第1期341卷 140011页

作者： Jafari, Iman Luo, Rongmo Lim, Fang Yee Hui, Ng Szu Jiangyong, Hu Department of Civil & Environmental Engineering National University of Singapore 1 Engineering Drive 2 Singapore 117576 Singapore Research and Development Department Xylem Water Solutions Singapore Pte Ltd 3A International Business Park Tower B #10-10/18 ICON@IBP 609935 Singapore Department of Industrial Systems Engineering and Management National University of Singapore 1 Engineering Drive 2 Singapore 117576 Singapore

The quality of water changes from source to tap, presenting challenges in maintaining consistent water quality across the system. Predicting water quality in distribution systems, including disinfectant residual loss and by-product formation, has been the subject of research since the early 1990s. Although numerous models have been proposed to predict residual chlorine decay, disputes exist among researchers and experts over the superiority of certain models. Accordingly, this study modified the existing process-based bulk decay models by replacing the initial Total Residual Chlorine (TRC) concentration parameter with TRC demand, leading to an improvement in the models' performance. The modification resulted in a 38.03%, 28.02%, 23.11%, and 33.29% average improvement in Mean Squared Error (MSE) values for the First Order Model (FOM), Parallel First Order Model (PFOM), Second Order Model (SOM), and Parallel Second Order Model (PSOM), respectively. The study also introduced an online predictive method based on a Machine Learning (ML) algorithm that predicts the first-order TRC bulk decay rate by using water quality parameters as inputs. A Gaussian Process Regression (GPR) model was used to predict the kinetic parameters in FOM, which accurately predicted the test sets for most of the cases. In addition, a new methodology was proposed in this study for predicting TRC in water distribution systems that incorporates the variability of source natural organic matter, operational actions, and water demands. This method seeks to develop high-fidelity and robust water quality predictions that provide operational decision support for optimized distribution system management. In conclusion, this study emphasizes the importance of understanding water quality changes from source to tap and the challenges of maintaining consistent water quality across the system. The study suggests modifying existing models and introducing a novel methodology for predicting residual chlorine in wa

关键词： Water quality

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Geothermal Energy-Driven Electricity Generation Model for Singapore 18th

Geothermal Energy-Driven Electricity Generation Model for Si...

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18th Conference of the Associated Research Centers for the Urban Underground Space, ACUUS 2023

作者： Chidire, Anurag Chan, Jerry Veerasamy, Balaganesha Balasubramaniam Yang, Lizhong Tjiawi, Hendrik Poh, Jonathan Chuah, Cliff Khiok Eng Lim, Jian Wei Mark Wu, Wei Massier, Tobias Romagnoli, Alessandro Chair of Renewable and Sustainable Energy Systems Technical University of Munich Arcisstraße 21 Munich80333 Germany TUMCREATE 1 Create Way CREATE Tower #10-02 Singapore138602 Singapore Energy Research Institute Nanyang Technological University 1 Cleantech Loop Singapore637141 Singapore Surbana Jurong Campus 38 Cleantech Loop #01-31 Singapore636741 Singapore School of Civil and Environmental Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore School of Mechanical and Aerospace Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

ISBN: (纸本)9789819712564

Singapore is exploring potential geothermal reservoirs to reduce its reliance on natural gas and transition towards sustainable energy sources. With only 4% of registered power generation capacity coming from renewables, the city-state aims to achieve net-zero emissions by 2050. The study focuses on harnessing low- to medium-temperature geothermal resources for electricity generation using organic Rankine cycle (ORC), which is a heat-to-power conversion technology. The simulation results project that each Enhanced Geothermal System (EGS) well doublet has an electrical generation capacity of 0.93 MWe, equivalent to about 7 GWh annually. This EGS-ORC system can be scaled up to exploit the heat within Singapore’s granite. Furthermore, the study conducts comprehensive analyses, employing Life Cycle Assessment (LCA) and Life Cycle and Cost Analysis (LCCA) methodologies. LCA reveals that geothermal electricity's global warming potential is significantly lower than Singapore’s current grid emission factor. LCCA estimations show the net-present levelized cost of electricity is competitive with Singapore’s long-run marginal cost and aligns with the Uniform Singapore Energy Price. The rejected hot fluid from the geothermal system can further enhance its economic viability. The study offers valuable insights for urban planners, policymakers, and researchers in Singapore's transition towards resilient and eco-conscious energy systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Clean energy

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Model-free Learning of Corridor Clearance: A Near-term Deployment Perspective

arXiv

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arXiv 2023年

作者： Suo, Dajiang Jayawardana, Vindula Wu, Cathy The Polytechnic School Ira A. Fulton Schools of Engineering Arizona State University TempeAZ85281 United States The Department of Electrical Engineering and Computer Science Laboratory for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Laboratory for Information & Decision Systems Department of Civil and Environmental Engineering Institute of Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States

An emerging public health application of connected and automated vehicle (CAV) technologies is to reduce response times of emergency medical service (EMS) by indirectly coordinating traffic. Therefore, in this work we study the CAV-assisted corridor clearance for EMS vehicles from a short term deployment perspective. Existing research on this topic often overlooks the impact of EMS vehicle disruptions on regular traffic, assumes 100% CAV penetration, relies on real-time traffic signal timing data and queue lengths at intersections, and makes various assumptions about traffic settings when deriving optimal model-based CAV control strategies. However, these assumptions pose significant challenges for near-term deployment and limit the real-world applicability of such methods. To overcome these challenges and enhance real-world applicability in near-term, we propose a model-free approach employing deep reinforcement learning (DRL) for designing CAV control strategies, showing its reduced overhead in designing and greater scalability and performance compared to model-based methods. Our qualitative analysis highlights the complexities of designing scalable EMS corridor clearance controllers for diverse traffic settings in which DRL controller provides ease of design compared to the model-based methods. In numerical evaluations, the model-free DRL controller outperforms the model-based counterpart by improving traffic flow and even improving EMS travel times in scenarios when a single CAV is present. Across 19 considered settings, the learned DRL controller excels by 25% in reducing the travel time in six instances, achieving an average improvement of 9%. These findings underscore the potential and promise of model-free DRL strategies in advancing EMS response and traffic flow coordination, with a focus on practical near-term deployment. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Arsenic removal from water and soils using pristine and modified biochars

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Biochar 2022年第1期4卷 255-280页

作者： Wei Zhang Yoora Cho Meththika Vithanage Sabry M.Shaheen Jörg Rinklebe Daniel S.Alessi Chia-Hung Hou Yohey Hashimoto Piumi Amasha Withana Yong Sik Ok Korea Biochar Research Center APRU Sustainable Waste Management and Division of Environmental Science and Ecological EngineeringKorea UniversitySeoul 02841South Korea School of Environmental Science and Engineering Guangzhou UniversityGuangzhou 510006China Ecosphere Resilience Research Center Faculty of Applied SciencesUniversity of Sri JayewardenepuraNugegoda 10250Sri Lanka Sustainability Cluster University of Petroleum and Energy StudiesUttarkhandDehradunIndia The Institute of Agriculture The University of Western AustraliaCrawleyAustralia School of Architecture and Civil Engineering Institute of Foundation EngineeringWater-and Waste-ManagementLaboratory of Soil-and Groundwater-ManagementUniversity of WuppertalPauluskirchstraße742285 WuppertalGermany Department of Earth and Atmospheric Sciences University of AlbertaEdmontonAB T6G 2E3Canada Graduate Institute of Environmental Engineering National Taiwan UniversityTaipei 10617TaiwanChina Department of Bioapplications and Systems Engineering Tokyo University of Agriculture and TechnologyTokyoJapan Department of Arid Land Agriculture Faculty of MeteorologyEnvironmentand Arid Land AgricultureKing Abdulaziz University21589 JeddahSaudi Arabia Department of Soil and Water Sciences Faculty of AgricultureUniversity of Kafrelsheikh33516 Kafr El-SheikhEgypt

Arsenic(As)is recognized as a persistent and toxic contaminant in the environment that is harmful to ***,a porous carbonaceous material with tunable functionality,has been used widely as an adsorbent for remediating As-contaminated water and *** types of pristine and modified biochar are available,and significant efforts have been made toward modifying the surface of biochars to increase their adsorption capacity for *** capacity is influenced by multiple factors,including biomass pyrolysis temperature,pH,the presence of dissolved organic carbon,surface charge,and the presence of phosphate,silicate,sulfate,and microbial *** As adsorption in modified biochars is attributed to several mechanisms including surface complexation/precipitation,ion exchange,oxidation,reduction,electrostatic interactions,and surface functional groups that have a relatively higher affinity for *** biochars show promise for As adsorption;however,further research is required to improve the performance of these *** example,modified biochars must be eco-friendly,cost-effective,reliable,efficient,and sustainable to ensure their widespread application for immobilizing As in contaminated water and *** relevant research to address these issues relies on a thorough understanding of biochar modifications to *** study presents an in-depth review of pristine and modified biochars,including their production,physicochemical properties,and As adsorption ***,a comprehensive evaluation of biochar applications is provided in As-contaminated environments as a guide for selecting suitable biochars for As removal in the field.

关键词： Engineered biochar Removal mechanisms Arsenic pollution Water pollution Soil pollution

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Climate Change Impact on Sub-Tropical Lakes Ecosystem – Lake Kinneret as a Case Study

SSRN

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SSRN 2023年

作者： Regev, Shajar Carmel, Yohay Gal, Gideon Schlabing, Dirk Kinneret Limnological Laboratory Israel Oceanographic and Limnological Research Migdal14950000 Israel Faculty of Civil and Environmental Engineering The Technion-Israel Institute of Technology Haifa3200003 Israel University of Stuttgart Institute for Modelling Hydraulic and Environmental Systems Stuttgart70569 Germany

Climate change is anticipated to alter lake ecosystems by affecting water quality, potentially resulting in loss of ecosystem services. Subtropical lakes have high temperatures to begin with and are expected to exhibit higher temperatures all year round which might affect the thermal structure and ecological processes in a different manner than lakes in temperate zones. In this study we explore the ecosystem response of the sub-tropical Lake Kinneret to climate change with lake ecosystem models. We increase projection reliability by using weather generator and ensemble modeling, confronting uncertainty of both climate projections and lake models. The study included running two 1D hydrodynamic-biogeochemical models over one thousand realizations of two gradual temperature increase scenarios that span over 49 years. Our predictions show that an increase in air temperature would have subtle effects on stratification properties but may result in considerable changes to biogeochemical processes. Water temperature rise would cause a reduction in dissolved oxygen. Both of these changes would produce elevated phosphate and lowered ammonium concentrations. In turn, these changes are predicted to modify the phytoplankton community, expressed chiefly in increased cyanobacteria blooms at the expense of green phytoplankton and dinoflagellates;these changes may culminate in overall reduction of primary production. Identification of these trends would not be possible without the use of many realizations of climate scenarios. The use of ensemble modelling increased prediction reliability and highlighted elements of uncertainty. Though we use Lake Kinneret, the patterns identified most likely indicate processes that are expected in sub-tropical lakes in general. © 2023, The Authors. All rights reserved.

关键词： Lakes

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The First Deep Geothermal Exploratory Slimhole at Admiralty Lane, Singapore 18th

The First Deep Geothermal Exploratory Slimhole at Admiralty ...

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18th Conference of the Associated Research Centers for the Urban Underground Space, ACUUS 2023

作者： Tjiawi, Hendrik Romagnoli, Alessandro Massier, Tobias Wu, Wei Lim, Jian Wei Mark Chuah, Cliff Khiok Eng Poh, Jonathan Yang, Lizhong Lu, Dazhao Chidire, Anurag Veerasamy, Balaganesha Balasubramaniam Chan, Jerry Energy Research Institute Nanyang Technological University 1 Cleantech Loop Singapore637141 Singapore TUMCREATE 1 Create Way CREATE Tower #10-02 Singapore138602 Singapore Surbana Jurong Campus 38 Cleantech Loop #01-31 Singapore636741 Singapore School of Mechanical and Aerospace Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore School of Civil and Environmental Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Chair of Renewable and Sustainable Energy Systems Technical University of Munich Arcisstraße 21 Munich80333 Germany

ISBN: (纸本)9789819712564

Singapore has at least two known hot springs with surface water temperatures of 50–90 °C. Together with its estimated high heat flow, Singapore is thought to have a geothermal potential. To assess the potential, we focus our study on the northern part of Singapore, where elevated shallow subsurface temperatures have been identified. Two deep exploratory slimhole sites are selected within this region. One slimhole has been successfully drilled to a record-breaking depth of 1.1 km within Singapore’s granite bedrock at Admiralty Lane. Subsurface temperatures are measured, and rock core samples are collected from this slimhole. Data analysis reveals a high geothermal gradient at depths beyond 800 m, surpassing the global average continental geothermal gradient. If this temperature gradient remains constant beyond 1.1 km deep, the rock temperature at 4–5 km deep can be estimated to reach 200 °C. The temperatures are found to be hotter than many other non-volcanic regions around the world. Additionally, laboratory measurements on the rock core samples found that the granite at the Admiralty Lane site, also known as the Simpang granite, has an above-average radiogenic heat production, that if consistent to over 10 km deep, can contribute significantly to Singapore’s anomalously high heat flow. The study to date finds that Singapore’s Simpang granite has high geothermal gradient, heat production, and heat flow. These findings underscore Singapore’s promising geothermal potential, warranting further exploration to deeper depths, such as 5 km. This journey could unlock opportunities for sustainable energy initiatives. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Geothermal fields

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Teaching Basic Concepts in Machine Learning to engineering Students: A Hands-on Approach

Teaching Basic Concepts in Machine Learning to Engineering S...

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2024 ASEE Annual Conference and Exposition

作者： Obada, David O. Abolade, Simeon A. Akinpelu, Shittu B. Oyedeji, Ayodeji N. Okafor, Emmanuel Odili, Cynthia U. Ogenyi, Vanessa F. Ategbe, Victor S. Eberemu, Adrian O. Anafi, Fatai O. Ahmed, Abdulkarim S. Bako, Raymond B. Akande, Akinlolu Department of Mechanical Engineering Ahmadu Bello University Zaria810222 Nigeria Mathematical Modelling and Intelligent Systems for Health and Environment Research Group School of Science Atlantic Technological University SligoF91 YW50 Ireland Modelling and Computation for Health and Society Atlantic Technological University SligoF91 YW50 Ireland SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Department of Water Resources and Environmental Engineering Ahmadu Bello University Zaria810222 Nigeria Department of Polymer and Textile Engineering Ahmadu Bello University Zaria810222 Nigeria Department of Civil Engineering Ahmadu Bello University Zaria810222 Nigeria Department of Chemical Engineering Ahmadu Bello University Zaria810222 Nigeria Department of Educational Foundations and Curriculum Ahmadu Bello University Zaria810222 Nigeria Africa Centre of Excellence on New Pedagogies in Engineering Education Ahmadu Bello University Zaria810222 Nigeria Multifunctional Materials Laboratory Shell Office Complex Department of Mechanical Engineering Ahmadu Bello University Zaria810222 Nigeria

According to a recent survey conducted by the Corporate Member Council of the American Society of engineering Education (ASEE), there exists a notable disparity in the skill sets of engineering graduates about Artificial Intelligence (AI). To address this disparity from the African context, the Africa Centre of Excellence on New Pedagogies in engineering Education organized a machine learning (ML) workshop for engineering students from different disciplines. Seventy-three (73) students enrolled for the workshop and the modules covered during this workshop were: Introduction to ML Models, ML Frameworks, Additive Explanations in ML, Performance Metrics, and Introduction to Ensemble Learning Techniques. The hands-on session involved the use of categorical boosting (CatBoost), an ensemble learning technique, to predict the bulk modulus, a mechanical property, of 199 ABX3 perovskite materials which was used as a problem set. The input features influencing the CatBoost model decisions were subsequently established. Correlation analysis on the input feature space removed features with high collinearity. The SHapley Additive exPlanations (SHAP) was used to analyze the decision-making rationale of the model. Evaluation of the model performance based on the coefficient of determination R2 value (0.94) revealed that the model demonstrates good performance in predicting the bulk modulus of the perovskite materials used during the practical sections. The survey results after the teaching and practical sessions indicate that the learning modules are an effective introduction for novice engineering students in this domain and raise awareness of the importance of this important sub-section of AI. © American Society for engineering Education, 2024.

关键词： Students

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Systemic Risk Analysis of Agriculture with Climate and Other Stressors

Systemic Risk Analysis of Agriculture with Climate and Other...

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Annual IEEE systems Conference

作者： Rebecca A. Rebar Megan C. Marcellin Gigi Pavur Davis C. Loose John J. Cardenas Saddam Q. Waheed Benjamin D. Trump James H. Lambert Department of Systems and Information Engineering University of Virginia Charlottesville VA USA Department of Civil and Environmental Engineering University of Virginia Charlottesville VA USA United States Agency for International Development Libya Iraq Ministry of Water Resources Baghdad Iraq Engineer Research and Development Center US Army Corps of Engineers Concord MA USA

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

Climate and other stressors such as aging facilities, population shifts, political tensions, and technological advancements compound challenges affecting logistics systems, including availability and sourcing of food and water supplies. Ensuring availability of resources is critical to maintaining economic and social balances. For arid regions, particularly those with transboundary watersheds, identifying and addressing risks to the agriculture sector due to water scarcity is critical. The complexities surrounding resource allocation cross a multitude of high-level system inputs and outputs. This paper develops a method of evaluating the sensitivities of agriculture system order to water scarcity and other stressors, focusing on grain crop production in Iraq. The method incorporates various socioeconomic factors in addition to hydrology data to test the impact of scenarios on system order. The results include identifying scenarios that most and least matter, in support of policy, investment, and other decision-making. Tracking of these scenarios helps to ensure the food and water security of Iraq. The paper is relevant to the variety of systems engineering applications that are addressing hybrid threats in planning and operations for complex and interdependent systems.

关键词： Sensitivity Sociology Watersheds systems engineering and theory Agriculture Security Risk analysis

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