As society transitions away from fossil fuels toward renewable energy sources, finding alternatives that are reliable becomes imperative. Waste-to-energy bioprocesses are promising options due to their ability to oper...
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As society transitions away from fossil fuels toward renewable energy sources, finding alternatives that are reliable becomes imperative. Waste-to-energy bioprocesses are promising options due to their ability to operate independently of weather conditions or time of day, making them sustainable and potentially lucrative solutions. This paper proposes an updated bioeconomic model, based on previous research (Cherkaoui Dekkaki et al. in Math Methods Appl Sci 45(1):468-482, 2022;Cherkaoui Dekkaki and Djema in American controlconference pp. 2135-2140, 2023), to analyze investment in waste-to-energy technology and its associated valorization of waste treatment. This conceptual model represents a generic framework for studying waste-to-energy processes. By taking technological constraints into account, the updated model aims to optimize energy production processes and establish a sustainable business model. Indeed, using dynamic modeling, investment and valorization strategies will be evaluated through a maximization criterion over a finite time horizon, which is stated as an optimal control problem. The effective control strategies are then determined using the Pontryagin's maximum principle. Furthermore, direct optimization methods are applied to derive and validate the effectiveness of the obtained optimal strategy. This approach allows for a thorough evaluation of the economic and environmental impacts in waste-to-energy technologies, identifying optimal investment and valorization strategies to promote sustainable waste management practices. In addition, a sensitivity analysis is conducted to evaluate the robustness of the studied model, and provide insights into biotechnological limitations. Finally, an extensive numerical exploration of the turnpike-like features that characterize the optimal long-term behavior of the investment problem is widely discussed.
The proceedings contain 22 papers. The special focus in this conference is on Aerospace System Science and Engineering. The topics include: A Review of Some Key Issues in CFD-Based Throughflow Simulation;influence of ...
ISBN:
(纸本)9789819705498
The proceedings contain 22 papers. The special focus in this conference is on Aerospace System Science and Engineering. The topics include: A Review of Some Key Issues in CFD-Based Throughflow Simulation;influence of Multiple Parameters on the Efficiency of a Single Nozzle in a Heavy Gas Turbine Combustor;analysis of the Influence of Total Pressure Error on Air data Calibration;analysis of Impact Properties of Nanocomposites at Micron Scale;simulation Research on Air Distribution Optimization of Civil Aircraft Cabin Ventilation System;investigation of the 2-D Distribution Form of Bearing Stress in a Single-Bolt Single-Shear Metal-Composite Hybrid Joint;the Impact of Bleeding Slot Angles on the Performance of a Compressor;investigation into Provisions and Validation for Cockpit Smoke Evacuation on Civil Aircraft;numerical Investigation of Diffuser Curvilinear Meridional Shape on Centrifugal Compressor Stage Performance;modeling Method of Specimen Repair Techniques from Polymer Composite Material;process Approach as a Tool of the Knowledge-Intensive Industry Organization Management System;SysML-Based Approach for Functional Quantitative modeling of Civil Aircraft Systems;research on MagicGrid-Based Requirements Development process of Flight control System;airport Collaborative Decision-Making in Single Pilot Operations of Commercial Aircraft;Research on Capability Catalog Generation of UAV Intelligent System Based on DoDAF;research on the Contribution Rate of Shipboard Manned/Unmanned Aerial Vehicle Cooperative Operation Based on Wargame data Mining;cooperative Organization and Application Mechanism Based on Intention Environment Target for Maritime Ship-Aircraft Cooperation;LPV Robust Filter Based Fault Diagnosis Method for Aeroengine control System.
1,3-propanediol (1,3-PDO) is a significant product of fermentation, with glycerol serving as the primary substrate in most cases. Bioprocesscontrol based on real-time information of feedstock and main products is cru...
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1,3-propanediol (1,3-PDO) is a significant product of fermentation, with glycerol serving as the primary substrate in most cases. Bioprocesscontrol based on real-time information of feedstock and main products is crucial for reducing the cost of production. However, rapid quantification of 1,3-PDO and glycerol remains challenging due to their highly similar molecular structures. In this study, the feasibility of near-infrared (NIR) spectroscopy to monitor 1,3-PDO, glycerol, acetate, and butyrate concentrations in the fermentation process using strain Clostridium pasteurianum was evaluated. NIR spectra were acquired through at-line measurement involving sampling and ex-situ analysis or on-line measurement with a fiber optic probe immersed in fermentation broth, integrated with Partial Least Squares (PLS) regression to establish calibration models on a laboratory-scale and pilot-scale. The best PLS regressions of 1,3-PDO, glycerol, acetate, and butyrate with two measurement approaches provided excellent performance, with the root-mean-squared errors of prediction (RMSEP) of 1.656 g/L, 1.502 g/L, 0.746 g/L, and 0.557 g/L in at-line measurement and 1.113 g/L, 1.581 g/L, 0.415 g/L, and 0.526 g/L in on-line measurement. The cross-scale application performance of at-line measurement was evaluated by an external fermentation trial and an acceptable result was achieved. At-line measurement technique represents a superior choice for the optimization of fermentation process since the robustness across varying fermentation scales and its applicability in multiple bioreactors. Thus, a calibration model developed for one bioreactor is likely to be used in other bioreactors, which enables the reduction of modeling costs. On-line measurement technique, owing to its automated operation and frequent data acquisition, enables real-time monitoring and precise control of the fermentation process, thereby reducing cost and improving production efficiency.
To ensure the privacy preservation and transparent use of regulated medical big data at decentralized and distributed medical institutions, this paper proposes a blockchain-based collaborative dataanalysis framework ...
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To ensure the privacy preservation and transparent use of regulated medical big data at decentralized and distributed medical institutions, this paper proposes a blockchain-based collaborative dataanalysis framework to realize multiparty secure data sharing and cooperative medical knowledge extraction through a transparent and regulatory machine learning approach. A smart contract is employed on the blockchain as the underlying technique to realize autonomous control and transparent regulation of closed-loop data acquisition and analysis. Considering the execution complexity of smart contracts for analysis collaboration, Petri net is adopted to formulize the workflows of smart contracts, and it acts as the underlying on-chain learning (OcL) approach. Finally, an experimental case study is conducted using real-life medical data to verify and evaluate the effectiveness and efficiency of our framework. A prototype system is established to demonstrate the real-life distributed knowledge extraction demand of our cooperating company. Four groups of experiments are designed and conducted to determine the effectiveness and efficiency of the learning process. The results show that the proposed framework significantly outperforms federated learning (FL) in terms of accuracy on small datasets, where the framework achieves an accuracy of 55.050% compared to FL. Meanwhile, the framework exhibits superior convergence in loss compared to FL, with a difference of 76.663%. In the case of big datasets, the framework achieves a faster completion of model training by 58.883%, with lower CPU utilization by 44.023% and lower memory utilization by 16.227% compared to FL.
Accurate volumetric efficiency modeling is crucial for enhancing engine performance regarding fuel consumption and emissions, but it is challenging due to the variability of the intake process and valve strategies. Th...
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Accurate volumetric efficiency modeling is crucial for enhancing engine performance regarding fuel consumption and emissions, but it is challenging due to the variability of the intake process and valve strategies. Therefore, this paper proposes a physics-informed data-driven volumetric efficiency modeling method (PDM). Firstly, this paper constructs a model based on the simplified first law of physics to capture the main trends of volumetric efficiency changes. To improve the accuracy of the estimation, a PDM is proposed. This method includes a physical loss term and a data loss term. These loss terms are fused into a single fusion loss to train the neural network parameters, effectively merging the physical model with the neural network. The high correlation coefficient (R-2 = 0.958) between the PDM's volumetric efficiency estimates and the measured data demonstrates the robustness of the method. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)
Ensuring pedestrian safety at high-risk, high-volume locations, such as hospitals, schools, stadiums, etc., requires consideration of the observed and possible future trends at access points of the transportation netw...
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ISBN:
(纸本)9798350373981;9798350373974
Ensuring pedestrian safety at high-risk, high-volume locations, such as hospitals, schools, stadiums, etc., requires consideration of the observed and possible future trends at access points of the transportation network. Existing performance metrics are often spatially and temporally aggregated, limiting their usefulness in assessing safety risks for time periods and locations of interest. Latest connected-vehicle (CV) technologies have improved both the volumes and resolutions of vehicle-movement data, with telemetry reported every few seconds. This study utilizes CV data in a multi- criteria analysis (MCA) to prioritize infrastructure improvements that improve pedestrian safety in school zones. The methods are demonstrated to update priorities for thirty-two candidate improvements at a single school vulnerable to evolving traffic and other conditions. Six performance criteria are addressed, including four criteria informed by CV event observations. The results highlight scenarios, articulated by the day of week and hour of the day, that are most disruptive to the priorities for improvements. The approach has interest across domains of systems engineering where trends and critical incidents of environment, markets, regulations, wear and tear, demographics, obsolescence, workforce etc. should influence systems evaluation and requirements.
Business process similarity measures are of vital importance for process repository management applications, such as process query, process recommendation, and process clustering. Most existing approaches measure proc...
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Business process similarity measures are of vital importance for process repository management applications, such as process query, process recommendation, and process clustering. Most existing approaches measure process similarity by relying on control-flow structures only. This article investigates the role of data in process similarity measure. To incorporate data-flow information into business processcontrol flow, it proposes a data-aware workflow net (DWF-net) by extending the classical workflow net with data reading and writing semantics. Then, we introduce three types of similarity measures, i.e., data item set-based similarity, data operation set-based similarity, and data-aware behavior-based similarity, to quantify the similarity of data-aware business processes from different perspectives. Next, a methodology is introduced to help process analysts apply these three measures in a systematical way. Finally, we evaluate the effectiveness and applicability of the proposed similarity measures by a group of comparative experiments.
The proceedings contain 17 papers. The special focus in this conference is on Practice of Enterprise modeling. The topics include: Fostering Digital Progression of Society: Exploratory Case Studies of Third Place for ...
ISBN:
(纸本)9783031779077
The proceedings contain 17 papers. The special focus in this conference is on Practice of Enterprise modeling. The topics include: Fostering Digital Progression of Society: Exploratory Case Studies of Third Place for Services;using Enterprise modeling for Dealing with Complexity of Elderly Care in Sweden;evaluation of Categorization Patterns for Conceptual modeling of IoT Applications;SmartCML: A Visual modeling Language to Enhance the Comprehensibility of Smart Contract Implementations;assessing Model Quality Using Large Language Models;grass-Root Enterprise Modelling: How Large Language Models Can Help;investigating the Effectiveness of Feedback-Driven Exercises on Deadlock Detection Skills in Conceptual Modelling;knowledge Graphs as a Scholarly data Fabric: A data Silo Transformation Pipeline with Visualization Semantics;enriching Business process Event Logs with Multimodal Evidence;towards Timeline-Based Layout for process Mining;Conceptualisation and (Meta)modelling of Problem-Solution Chains in Early Business-IT Alignment and System Design;SymboleoAC: An Access control Model for Legal Contracts;Functional Security in Automation: The FAST Approach;Configuration of Software Product Lines Driven by the Softgoals: The TEAEM Approach;the Dual Nature of Organizational Policies.
Hospitals currently face numerous challenges in managing their pharmacy operations efficiently. While Business process Reengineering (BPR) has been proposed as a solution, its implementation in healthcare is more comp...
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Musicians spend more time practicing than performing live, but the process of rehearsal has been understudied. This paper introduces a dataset for using AI and machine learning to address this gap. The project observe...
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ISBN:
(数字)9783031564352
ISBN:
(纸本)9783031564345;9783031564352
Musicians spend more time practicing than performing live, but the process of rehearsal has been understudied. This paper introduces a dataset for using AI and machine learning to address this gap. The project observes the progression of pianists learning new repertoire over long periods of time by recording their rehearsals, generating a comprehensive multimodal dataset, the Rach3 dataset, with video, audio, and MIDI for computational analysis. This dataset will help investigating the way in which advanced students and professional classical musicians, particularly pianists, learn new music and develop their own expressive interpretations of a piece.
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