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Life Cycle Assessment and Sustainability analysis of Anaerobic Digestion

Life Cycle Assessment and Sustainability Analysis of Anaerob...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Rahul Kumar Rajak Prashant Baredar Gaurav Dwivedi Raj Suman Energy Centre MANIT Bhopal Bhopal India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

In order to comprehend, maximize, and expand this complex biological process, mathematical modeling in anaerobic digestion is crucial. As a result of anaerobic digestion, dig estate and biogas are produced when microorganisms break down organic matter in the absence of oxygen. In order to depict and evaluate the dynamics of anaerobic digestion systems, mathematical models are essential. Dynamic models that monitor changes in substrates and products over time, as well as mass balance and kinetic models that characterize reaction rates, are among them Prominent models comprise Anaerobic Digestions Model No. 1, which incorporates different phases of the digestion process, and Biochemical Methane Potential models, which determine the potential for producing methane from substrates. These model plays a crucial role in process parameter optimization, scaling from laboratory to industrial scale, and improving real-time monitoring and control. They also aid in waste management plans and assess environmental effects by forecasting performance, efficiency, and sustainability.

关键词： Biological system modeling Computational modeling Predictive models Mathematical models data models Biogas Kinetic theory Sustainable development Substrates Optimization

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Rapid Detection of Anomalies in Battery Energy Storage System data For data Cleaning

Rapid Detection of Anomalies in Battery Energy Storage Syste...

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IEEE Electrical Energy Storage Application and Technologies conference (EESAT)

作者： Alasdair Crawford Vilayanur Viswanathan Joseph Thompson Steven Willard Di Wu Pacific Northwest National Laboratory Richland WA

data analytics is pivotal in assessing the techni-cal characteristics and performance of Battery Energy Storage Systems (BESS), underpinning BESS modeling, optimization, and control. PNNL has collected diverse and comprehensive real-world BESS operational datasets in collaboration with the Electric Power Research Institute and multiple Washington State utilities, allowing for BESS analytics and modeling. However, raw datasets frequently harbor anomalies from measurement errors and equipment malfunctions, impacting BESS reliability and analysis accuracy. To address the challenge, this paper presents a methodology for the rapid detection of anomalous charge or discharge cycles within BESS operational data, expediting the cleaning process while ensuring data integrity. Using case studies from real BESS operational datasets, we demonstrate that the proposed method detects anomalies and aids in their resolution, improving system performance characterization. It also reveals recurring data anomaly sources, offering insights for data cleaning. Practitioners can gain valuable insights from the identified anomalous cycles in the real-world datasets along with the investigative process for root cause analyses and essential data cleaning steps.

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The Exploration of a Multi-Level Fuzzy Comprehensive Evaluation Model

The Exploration of a Multi-Level Fuzzy Comprehensive Evaluat...

引用

control, Electronics and Computer Technology (ICCECT), IEEE International conference on

作者： Ziyao Wang Shaer Yu Zekai Zhu Leicester International Institute Dalian University of Technology Dalian China

ISBN: (数字)9798350380958

ISBN: (纸本)9798350380965

Based on the exploration of a multilevel fuzzy comprehensive evaluation, this paper investigates the application of the principles of fuzzy mathematics and combines the Analytic Hierarchy process (AHP) with fuzzy theory to conduct multilevel fuzzy comprehensive assessments. During the study, Various data processing tools, such as Excel, MATLAB, and SPSS, are employed. After initial preprocessing and selection, mathematical models are developed through multidimensional processing and statistical analysis. By integrating the Isolation Forest algorithm with the ARIMA model, abnormal values in the collected data are successfully eliminated. It also avoids the possibility of erroneously deleting normal data by the Isolation Forest model. Taking into account the time series effects among the potential variables, LSTM time series prediction model is ultimately chosen, mitigating the drawbacks of traditional RNN neural networks, such as gradient vanishing and exploding. By applying the model corresponding to the actual problem, the multilevel fuzzy comprehensive evaluation is discussed.

关键词： Adaptation models Uncertainty Computational modeling Atmospheric modeling Time series analysis Forestry Predictive models

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Comparative analysis for System modeling Using System Identification Techniques

Comparative Analysis for System Modeling Using System Identi...

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Electrical Engineering (ICEENG), 12th International conference on

作者： Mayada Annan Mohamed Hassan Mahmoud Ashry Dept. Military Production Ministry Science Technology Excellent STCE Cairo Egypt Dept. Automatic Control Egyptian Armed Forces Cairo Egypt Dept. Automatic Control Arab Academy for Science Technology AAST Cairo Egypt

ISBN: (数字)9798350343427

ISBN: (纸本)9798350343434

System modelling is an essential task for controller design. System identification is widely used for dynamic systems modelling and provides high performance practical dynamic systems modelling. In this paper, a least square approach and recursive least square based identification techniques are proposed for comparative analysis of dynamic modelling of a single -input single-output tank process. Chirp and multi-sine signals are utilized for system modelling as they provide the required data for capturing the system dynamics. A comparative analysis is carried out with nonlinear recursive least square techniques such as ARX model, ARMAX model, Box-Jenkins model, Output-Error model for evaluating the proposed approach effectiveness. The ARX method provided high performance for single-input single-output tank system identification in the presence of system noise.

关键词： Analytical models Chirp Estimation Mean square error methods Systems modeling Mathematical models data models

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INTEGRATION OF data SCIENCE INTO THERMAL-FLUIDS ENGINEERING EDUCATION

INTEGRATION OF DATA SCIENCE INTO THERMAL-FLUIDS ENGINEERING ...

引用

ASME International Mechanical Engineering Congress and Exposition (IMECE)

作者： Hu, Han Heo, Connor Univ Arkansas Fayetteville AR 72701 USA

ISBN: (纸本)9780791886694

To improve the integration of data science into thermal fluids education, a technical elective course is developed to introduce a wide range of machine learning and deep learning algorithms to engineering students, including principal component analysis, multiplayer perceptron, convolutional neural networks (CNN), long short-term memory (LSTM) networks, reinforcement learning (RL), generative algorithms (GA), and generative adversarial networks for mechanical engineering applications, including visualization-based physical quantity predictions, dynamic signal classification, and prediction, data-driven control of dynamical systems, surrogate modeling, dimensionality reduction, among others. The lectures cover the fundamental concepts and examples of developing machine learning models using Python and MATLAB. To facilitate students' practice of applying data science in solving mechanical engineering progress, this course has touchpoints in several key areas of mechanical engineering, including fluid mechanics, heat transfer, materials science, design, and dynamics/control. Twenty-five students, including seven undergraduate and eighteen graduate students, took the course and the outcomes are very fruitful and encouraging. A variety of data science algorithms have been leveraged to solve mechanical engineering research problems, including generative designs of air-cooled heat sinks, Gaussian process regression for battery lifetime prediction and femtosecond laser manufacturing parameters, GA for two-phase cooling heat exchanger design, coupled PCA and LSTM for microcapsule deformation prediction, CNN for boiling regime classification and laser-manufactured textures classification, bidirectional recurrent neural networks basecalling of DNA and RNA sequences, coupled PCA and MLP porous medium morphology classification, RL for the control of soft robotics. The course has so far led to 25 student-centered machine learning projects, two conference papers, and an Honors the

关键词： data science thermal fluids engineering education statistical modeling

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Combining diagnostics, modeling, and control systems for automated alignment of the TES beamline 14

Combining diagnostics, modeling, and control systems for aut...

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14th International conference on Synchrotron Radiation Instrumentation, SRI 2021

作者： Nash, B. Abell, D.T. Nagler, R. Moeller, P. Keilman, M. Pogorelov, I. Goldring, N. Rakitin, M. Lynch, J. Giles, A. Walter, A. Maldonado, J. Morris, T. Bak, S. Du, Y. RadiaSoft LLC 6640 Gunpark Dr Suite 200 BoulderCO80301 United States STATE33 1831 SE 7th Ave PortlandOR97214 United States Brookhaven National Laboratory UptonNY11973-5000 United States

X-ray beamlines are essential components of all synchrotron light sources. Practical operations involve frequent variation in beamline component positions and orientation, particularly when photon beam parameters shift due to experimental needs, or due to variations in the incoming photon beam. The alignment process can be time consuming and takes away from valuable beam time for experimental data collection. We describe progress in the automation of certain alignment tasks on the tender-energy X-ray spectroscopy (TES) beamline at the National Synchrotron Light Source ii (NSLS-ii). The beamline is controlled using the BlueSky software in which high level experimental plans guide the beamline components during an experiment. Numerous software packages exist for beamline modeling, and they may be tied to the beamline control system using a package we are continuing to develop called Sirepo-Bluesky. The photon beam distribution may be measured with fluorescent screens, and a relation between beam and machine state can be found by varying the mirror and aperture settings over a multi-dimensional range. We describe the results of such parameter varying measurements and how we are combining Sirepo-Bluesky with machine learning methods and reduced models to automate mirror alignment on the TES beamline. © Published under licence by IOP Publishing Ltd.

关键词： control systems

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XV International Scientific and Technical conference: Applied Mechanics and Systems Dynamics, AMSD 2021

XV International Scientific and Technical Conference: Applie...

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15th International Scientific and Technical conference: Applied Mechanics and Systems Dynamics, AMSD 2021

The proceedings contain 103 papers. The topics discussed include: modeling of geometric correspondences and transformations via geometry constructions language;application of quasi-rotation surface segments in architectural prototyping;method of analytical modeling and simulation of technological processes subject to the influence of external factors;the use of interpolation methods for modelling multifactor processes based on an experiment planning matrix;system for determining the state of the object surface based on fuzzy output rules;indicators for the preliminary assessment of magnetotelluric data sufficiency;features of an acousto-optical two-channel laser displacement interferometer with multi-frequency photodetectors with subpycometer resolution;analysis of modern measuring instruments for research diagnostic systems of print media equipment;and automation of the control process of a gas pipeline network with a complex topological structure.

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Research on the Construction and Application of the Digital Twin Platform for Loading Systems

Research on the Construction and Application of the Digital ...

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Industrial Automation, Robotics and control Engineering (IARCE), International conference on

作者： Yingchao Wu Qi He Huashi Yang Xiaoke Gao Northwest Institute of Mechanical and Electrical Engineering Xianyang China School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

This paper introduces a construction method of the digital twin platform for an artillery automatic loading system. Taking a real fully automatic loading system as the research object, a corresponding digital twin visualization platform is established. And through projectile loading tests, the application and accuracy verification of the established digital twin platform are carried out. Firstly, the concepts of automatic loading system and digital twin are elaborated, and the challenges of applying digital twin technology in the field of artillery are pointed out. Then, this paper describes the construction process of the digital twin platform. The platform construction process mainly includes four important steps: (1) obtaining training data for the surrogate model, (2) establishing a surrogate model, (3) real-time data acquisition and communication, (4) platform design and modeling. In this study, the training data for the surrogate model is mainly obtained through finite element analysis and real projectile loading tests. The RBF method is used to establish a surrogate model for real-time calculation of the structural mechanical properties in the actual loading process. A sensor network composed of laser sensors, rotary encoders, and fiber Bragg grating sensors is set up to collect data. The TCP communication protocol is established for data transmission between the actual loading system and the digital twin platform. Finally, the functions of the platform are verified. The platform can realize the interaction between virtual and real data, monitor the mechanical properties of the loading system in real time, and the error of the surrogate model is not greater than 15%.

关键词： Projectiles Loading Training data Robot sensing systems Real-time systems data models Digital twins Finite element analysis Load modeling Strain

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Soft-Sensing System Design Based on LabVIEW

Soft-Sensing System Design Based on LabVIEW

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Cyber-Energy Systems and Intelligent Energy (ICCSIE), International conference on

作者： Guoliang Cai Xiaonan Mu Guicheng Wang Changyu Wang Changliang Guan Wenju Yu Zijin Intelligent Control (Xiamen) Technology Co. Ltd. Xiamen China State grid Liaoning integrated energy service co. Ltd. Shenyang China School of Electrical and Electronics Engineering Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350373318

ISBN: (纸本)9798350373325

Expounding the implementation process of an online estimation system for glutamic acid bacterial concentration designed using LabVIEW with graphical programming language and BP neural network soft-sensing. By selecting parameters such as ventilation, sugar supplementation, $\mathbf{pH}$ , and temperature during the glutamic acid fermentation process as auxiliary variables for soft sensor modeling, the important biological parameter affecting the fermentation process and product quality, bacterial concentration, is determined as the target variable for softsensing modeling. The BP neural network in the machine learning toolkit provided by LabVIEW is called, and it is modeled by the BP neural network is trained and tested using sample data. Through simulation results analysis, it has been proven that soft-sensing system of LabVIEW can achieve online estimation of bacterial concentration in the glutamic acid fermentation process.

关键词： Training Temperature sensors Microorganisms Neural networks Estimation Ventilation data models Fermentation System analysis and design Testing

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In-line Sensor-based process control of the Calendering process for Lithium-Ion Batteries 55

In-line Sensor-based Process Control of the Calendering Proc...

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55th CIRP conference on Manufacturing Systems, CIRP CMS 2022

作者： Mayr, Andreas Schreiner, David Stumper, Benedikt Daub, Rüdiger Institute for Machine Tools and Industrial Management Technical University of Munich Boltzmannstr. 15 Garching85748 Germany

The calendering process is the final property-defining step in the manufacturing of electrodes for lithium-ion batteries. Calendering significantly affects mechanical properties by compacting the electrodes and increases the lithium-ion batteries' volumetric energy density. The choice of calendering parameters is primarily based on empirical data, considering the final battery cell properties. The drive for increased energy densities and the corresponding high compaction rates results in an increased electrode defect and scrap occurrence. This leads to challenges in process control which are addressed in this paper by developing an agile and predictive sensor-based process control. Hereby an efficient determination of process parameters is enabled, resulting in a reduction of scrap and manufacturing costs. Applying in-line data acquisition for electrode thickness and surface topography allows real-time feedback of measurement data at the machine-product interface back into the calendering system control. Reliable data acquisition is established by integrating two sensor systems for coating thickness measurement (confocal sensors) and defect detection (laser triangulation sensor) in a cascaded arrangement, enabling real-time evaluation of the product properties achieved as a function of the process parameters. The associated potentials and challenges regarding a predictive analysis of machine and product behavior are addressed in this context. The experimental results demonstrate that the combination of both sensor systems offers the potential to establish an agile and innovative process control for the calendering process of lithium-ion batteries. © 2022 The Authors. Published by Elsevier B.V.

关键词： calendering electrode production lithium-ion battery process control sensor integration

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