F. Shan, Xuzhen He, Haoding Xu, D. J. Armaghani, Daichao Sheng
Tunnel Boring Machines (TBMs) have become prevalent in tunnel construction due to their high efficiency and reliability. The proliferation of data obtained from site investigations and data acquisition systems provides an opportunity for the application of machine learning (ML) techniques. ML algorithms have been successfully applied in TBM tunnelling because they are particularly effective in capturing complex, non-linear relationships. This study focuses on commonly used ML techniques for TBM tunnelling, with a particular emphasis on data processing, algorithms, optimisation techniques, and evaluation metrics. The primary concerns in TBM applications are discussed, including predicting TBM performance, predicting surface settlement, and time series forecasting. This study reviews the current progress, identifies the challenges, and suggests future developments in the field of intelligent TBM tunnelling construction. This aims to contribute to the ongoing efforts in research and industry toward improving the safety, sustainability, and cost-effectiveness of underground excavation projects.
{"title":"Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review","authors":"F. Shan, Xuzhen He, Haoding Xu, D. J. Armaghani, Daichao Sheng","doi":"10.3390/eng4020087","DOIUrl":"https://doi.org/10.3390/eng4020087","url":null,"abstract":"Tunnel Boring Machines (TBMs) have become prevalent in tunnel construction due to their high efficiency and reliability. The proliferation of data obtained from site investigations and data acquisition systems provides an opportunity for the application of machine learning (ML) techniques. ML algorithms have been successfully applied in TBM tunnelling because they are particularly effective in capturing complex, non-linear relationships. This study focuses on commonly used ML techniques for TBM tunnelling, with a particular emphasis on data processing, algorithms, optimisation techniques, and evaluation metrics. The primary concerns in TBM applications are discussed, including predicting TBM performance, predicting surface settlement, and time series forecasting. This study reviews the current progress, identifies the challenges, and suggests future developments in the field of intelligent TBM tunnelling construction. This aims to contribute to the ongoing efforts in research and industry toward improving the safety, sustainability, and cost-effectiveness of underground excavation projects.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"120 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77559785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julles Mitoura dos Santos Junior, Ícaro Augusto Maccari Zelioli, A. Mariano
Process monitoring and forecasting are essential to ensure the efficiency of industrial processes. Although it is possible to model processes using phenomenological approaches, these are not always easy to apply and generalize due to the complexity of the processes and the high number of unknown parameters. This work aims to present a hybrid modeling architecture that combines a phenomenological model with machine learning models. The proposal is to enable the use of simplified phenomenological models to explain the basic principles behind a phenomenon. Next, the data-oriented model corrects deviations from the simplified model predictions. The research hypothesis consists of showing the benefits of integrating prior knowledge of chemical engineering in simplifying data-based models, enhancing their generalization and improving their interpretability. The gasification process of lignin biomass with supercritical water was used as a case study for this methodology and the variable to be observed was the production of hydrogen. The real experimental data of this process were augmented using Gibbs energy minimization with the Peng–Robinson equation of state, thus generating a more voluminous database that was considered as real process data. The ideal gas model was used as a simplified model, producing significant deviations in predictions (relative deviations greater than 20%). Deviations (∆H2 = H2real−H2predict) were used as the target variable for the machine learning model. Linear regression models (LASSO and simple linear regression) were used to predict ∆H2 and this variable was added to the simplified forecast model. This consisted of the hybrid prediction of the resulting hydrogen formation (H2predict). Among the verified models, the simple linear regression adjusted better to the values of ∆H2 (R2 = 0.985) and MAE smaller than 0.1. Thus, the proposed hybrid architecture allowed for the prediction of the formation of hydrogen during the gasification process of lignin biomass, despite the thermodynamic limitations of the ideal gas model. Hybridization proved to be robust as a process monitoring tool, providing the abstraction of non-idealities of industrial processes through simple, data-oriented models, without losing predictive power. The objective of the work was fulfilled, presenting a new possibility for the monitoring of real industrial processes.
{"title":"Hybrid Modeling of Machine Learning and Phenomenological Model for Predicting the Biomass Gasification Process in Supercritical Water for Hydrogen Production","authors":"Julles Mitoura dos Santos Junior, Ícaro Augusto Maccari Zelioli, A. Mariano","doi":"10.3390/eng4020086","DOIUrl":"https://doi.org/10.3390/eng4020086","url":null,"abstract":"Process monitoring and forecasting are essential to ensure the efficiency of industrial processes. Although it is possible to model processes using phenomenological approaches, these are not always easy to apply and generalize due to the complexity of the processes and the high number of unknown parameters. This work aims to present a hybrid modeling architecture that combines a phenomenological model with machine learning models. The proposal is to enable the use of simplified phenomenological models to explain the basic principles behind a phenomenon. Next, the data-oriented model corrects deviations from the simplified model predictions. The research hypothesis consists of showing the benefits of integrating prior knowledge of chemical engineering in simplifying data-based models, enhancing their generalization and improving their interpretability. The gasification process of lignin biomass with supercritical water was used as a case study for this methodology and the variable to be observed was the production of hydrogen. The real experimental data of this process were augmented using Gibbs energy minimization with the Peng–Robinson equation of state, thus generating a more voluminous database that was considered as real process data. The ideal gas model was used as a simplified model, producing significant deviations in predictions (relative deviations greater than 20%). Deviations (∆H2 = H2real−H2predict) were used as the target variable for the machine learning model. Linear regression models (LASSO and simple linear regression) were used to predict ∆H2 and this variable was added to the simplified forecast model. This consisted of the hybrid prediction of the resulting hydrogen formation (H2predict). Among the verified models, the simple linear regression adjusted better to the values of ∆H2 (R2 = 0.985) and MAE smaller than 0.1. Thus, the proposed hybrid architecture allowed for the prediction of the formation of hydrogen during the gasification process of lignin biomass, despite the thermodynamic limitations of the ideal gas model. Hybridization proved to be robust as a process monitoring tool, providing the abstraction of non-idealities of industrial processes through simple, data-oriented models, without losing predictive power. The objective of the work was fulfilled, presenting a new possibility for the monitoring of real industrial processes.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87216283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Rathnayake, Madduma Wellalage Pasan Maduranga, Valmik Tilwari, M. B. Dissanayake
The rapid expansion of the Internet of Things (IoT) and Machine Learning (ML) has significantly increased the demand for Location-Based Services (LBS) in today’s world. Among these services, indoor positioning and navigation have emerged as crucial components, driving the growth of indoor localization systems. However, using GPS in indoor environments is impractical, leading to a surge in interest in Received Signal Strength Indicator (RSSI) and machine learning-based algorithms for in-building localization and navigation in recent years. This paper aims to provide a comprehensive review of the technologies, applications, and future research directions of ML-based indoor localization for smart cities. Additionally, it examines the potential of ML algorithms in improving localization accuracy and performance in indoor environments.
{"title":"RSSI and Machine Learning-Based Indoor Localization Systems for Smart Cities","authors":"R. Rathnayake, Madduma Wellalage Pasan Maduranga, Valmik Tilwari, M. B. Dissanayake","doi":"10.3390/eng4020085","DOIUrl":"https://doi.org/10.3390/eng4020085","url":null,"abstract":"The rapid expansion of the Internet of Things (IoT) and Machine Learning (ML) has significantly increased the demand for Location-Based Services (LBS) in today’s world. Among these services, indoor positioning and navigation have emerged as crucial components, driving the growth of indoor localization systems. However, using GPS in indoor environments is impractical, leading to a surge in interest in Received Signal Strength Indicator (RSSI) and machine learning-based algorithms for in-building localization and navigation in recent years. This paper aims to provide a comprehensive review of the technologies, applications, and future research directions of ML-based indoor localization for smart cities. Additionally, it examines the potential of ML algorithms in improving localization accuracy and performance in indoor environments.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81248641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Juan Carlos León Gómez, Susana Estefany de León Aldaco, Jesus Aguayo Aguayo Alquicira
This paper aims to perform a literature review and statistical analysis based on data extracted from 38 articles published between 2018 and 2023 that address hybrid renewable energy systems. The main objective of this review has been to create a bibliographic database that organizes the content of the articles in different categories, such as system architecture, energy storage systems, auxiliary generation components used, and software employed, in addition to showing the algorithms and economic and reliability criteria for the optimization of these systems. In total, 38 articles have been analyzed, compared, and classified to provide an overview of the current status of simulation and optimization projects for hybrid renewable energy systems, highlighting clearly and appropriately the relevant trends and conclusions. A list of review articles has also been provided, which cover the aspects required for understanding HRESs.
{"title":"A Review of Hybrid Renewable Energy Systems: Architectures, Battery Systems, and Optimization Techniques","authors":"Juan Carlos León Gómez, Susana Estefany de León Aldaco, Jesus Aguayo Aguayo Alquicira","doi":"10.3390/eng4020084","DOIUrl":"https://doi.org/10.3390/eng4020084","url":null,"abstract":"This paper aims to perform a literature review and statistical analysis based on data extracted from 38 articles published between 2018 and 2023 that address hybrid renewable energy systems. The main objective of this review has been to create a bibliographic database that organizes the content of the articles in different categories, such as system architecture, energy storage systems, auxiliary generation components used, and software employed, in addition to showing the algorithms and economic and reliability criteria for the optimization of these systems. In total, 38 articles have been analyzed, compared, and classified to provide an overview of the current status of simulation and optimization projects for hybrid renewable energy systems, highlighting clearly and appropriately the relevant trends and conclusions. A list of review articles has also been provided, which cover the aspects required for understanding HRESs.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84390819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Paul, S. Islam, A. Mamun, Naymul Islam, A. Babafemi, S. Kong, Md. Jihad Miah
Waste tire rubber is one of the most concerning environmental pollution issues. With the increasing demand for automobile production, the rate of waste tire generation has also increased. However, these tires often end up stockpiled and not properly disposed of. This non-biodegradable waste poses severe fire, environmental, and health risks. Due to the progressively severe environmental problems caused by the disposal of waste tires, the feasibility of using such elastic waste materials as an alternative to natural aggregates has become a research topic. The main objective of this research is to investigate the changes in the mechanical and durability properties of concrete with the inclusion of waste tire rubber at specific contents. A total of 80 cylinders measuring 100 mm × 200 mm were cast with waste tire aggregate as a partial replacement for natural coarse aggregate (5% and 10% by weight of natural coarse aggregate). A surface treatment of tire aggregate using a cement coating was performed to study its effect on concrete properties. This research indicates a noticeable reduction in the compressive and split tensile strength of concrete containing untreated waste tire rubber compared to normal concrete made with natural aggregates. However, an improvement was observed when the surface of tire aggregates was coated with cement grout. Additionally, it was noted that the slump value, water absorption, and porosity increased as the percentage of rubber increased. Nevertheless, unlike normal concrete, the failure pattern in tire-mixed concrete occurs gently and uniformly, indicating ductile behavior.
废旧轮胎橡胶是最受关注的环境污染问题之一。随着汽车生产需求的增加，废旧轮胎的产生率也在增加。然而，这些轮胎往往最终被储存起来，而不是妥善处理。这种不可生物降解的废物会造成严重的火灾、环境和健康风险。由于废旧轮胎的处理所引起的环境问题日益严重，利用这种弹性废料替代天然骨料的可行性已成为一个研究课题。本研究的主要目的是探讨废轮胎橡胶在特定掺量下对混凝土力学性能和耐久性的影响。用废轮胎骨料铸造了80个尺寸为100 mm × 200 mm的钢瓶，作为天然粗骨料的部分替代(天然粗骨料重量的5%和10%)。采用水泥涂层对轮胎骨料进行表面处理，研究其对混凝土性能的影响。这项研究表明，与天然骨料制成的普通混凝土相比，含有未经处理的废轮胎橡胶的混凝土的抗压强度和劈裂抗拉强度明显降低。然而，当轮胎骨料表面涂上水泥浆液时，观察到改善。此外，随着橡胶含量的增加，坍落度值、吸水率和孔隙率也随之增加。然而，与普通混凝土不同，轮胎混合混凝土的破坏模式发生缓慢而均匀，表明延性行为。
{"title":"Treated Waste Tire Using Cement Coating as Coarse Aggregate in the Production of Sustainable Green Concrete","authors":"S. Paul, S. Islam, A. Mamun, Naymul Islam, A. Babafemi, S. Kong, Md. Jihad Miah","doi":"10.3390/eng4020083","DOIUrl":"https://doi.org/10.3390/eng4020083","url":null,"abstract":"Waste tire rubber is one of the most concerning environmental pollution issues. With the increasing demand for automobile production, the rate of waste tire generation has also increased. However, these tires often end up stockpiled and not properly disposed of. This non-biodegradable waste poses severe fire, environmental, and health risks. Due to the progressively severe environmental problems caused by the disposal of waste tires, the feasibility of using such elastic waste materials as an alternative to natural aggregates has become a research topic. The main objective of this research is to investigate the changes in the mechanical and durability properties of concrete with the inclusion of waste tire rubber at specific contents. A total of 80 cylinders measuring 100 mm × 200 mm were cast with waste tire aggregate as a partial replacement for natural coarse aggregate (5% and 10% by weight of natural coarse aggregate). A surface treatment of tire aggregate using a cement coating was performed to study its effect on concrete properties. This research indicates a noticeable reduction in the compressive and split tensile strength of concrete containing untreated waste tire rubber compared to normal concrete made with natural aggregates. However, an improvement was observed when the surface of tire aggregates was coated with cement grout. Additionally, it was noted that the slump value, water absorption, and porosity increased as the percentage of rubber increased. Nevertheless, unlike normal concrete, the failure pattern in tire-mixed concrete occurs gently and uniformly, indicating ductile behavior.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88876455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Hegde, Marta Mroczkowska, J. Mohan, A. C. Neves, Y. Kavanagh, B. Duffy, E. Tobin
Sol–gel coatings can provide anti-fouling and erosion resistance while being safe to use in the marine environment. MAPTMS/ZPO multilayer coatings deposited on the AA2024-T3 aluminium surface using the dip-coating method at three different thicknesses (2, 4, and 6 µm) are investigated in this work. The coatings are characterised in terms of physical and mechanical properties, and these properties are investigated in comparison to previously obtained cavitation erosion resistance levels of the coatings. Additionally, the efficiency of the coatings against biofouling was assessed using Phaeodactylum tricornutum, a marine diatom. The influence of the formation of organic–inorganic hybrid materials (OIHMs) from the prepared sols on the physical and mechanical properties of the coatings were analysed. A variety of techniques, including attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR), water contact angle (WCA) measurements, pencil hardness testing, cross-cut adhesion testing, a roughness profilometer, and nano-indentation, were performed on the bare and coated substrates. The results indicated that the thickness, hydrophobicity, and adherence of the coatings are strongly affected by the roughness. The elastic strain failure (H/E) and resistance to plastic deformation (H3/E2) coefficients were higher than those of the bare substrate before and after the cavitation erosion test, indicating that the coating had a higher ability to withstand deformation in comparison to the substrate alone. Furthermore, the microscopic analysis of a marine diatom, Phaeodactylum tricornutum, revealed that coated surfaces exhibited a decreased rate of bacterial adhesion and biofilm formation. The data show that sol–gel formed coatings outperform uncoated AA2024-T3 in terms of hardness, elastic strain, plastic deformation, and biofouling resistance. These characteristics are attributed to the coatings’ mechanical and adhesive capabilities, as well as their tribological behaviour.
{"title":"Influence of Physical and Mechanical Parameters on Cavitation Erosion and Antifouling Behaviour of Multilayer Silica-Based Hybrid Sol–Gel Coatings on Aluminium Alloys","authors":"M. Hegde, Marta Mroczkowska, J. Mohan, A. C. Neves, Y. Kavanagh, B. Duffy, E. Tobin","doi":"10.3390/eng4020081","DOIUrl":"https://doi.org/10.3390/eng4020081","url":null,"abstract":"Sol–gel coatings can provide anti-fouling and erosion resistance while being safe to use in the marine environment. MAPTMS/ZPO multilayer coatings deposited on the AA2024-T3 aluminium surface using the dip-coating method at three different thicknesses (2, 4, and 6 µm) are investigated in this work. The coatings are characterised in terms of physical and mechanical properties, and these properties are investigated in comparison to previously obtained cavitation erosion resistance levels of the coatings. Additionally, the efficiency of the coatings against biofouling was assessed using Phaeodactylum tricornutum, a marine diatom. The influence of the formation of organic–inorganic hybrid materials (OIHMs) from the prepared sols on the physical and mechanical properties of the coatings were analysed. A variety of techniques, including attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR), water contact angle (WCA) measurements, pencil hardness testing, cross-cut adhesion testing, a roughness profilometer, and nano-indentation, were performed on the bare and coated substrates. The results indicated that the thickness, hydrophobicity, and adherence of the coatings are strongly affected by the roughness. The elastic strain failure (H/E) and resistance to plastic deformation (H3/E2) coefficients were higher than those of the bare substrate before and after the cavitation erosion test, indicating that the coating had a higher ability to withstand deformation in comparison to the substrate alone. Furthermore, the microscopic analysis of a marine diatom, Phaeodactylum tricornutum, revealed that coated surfaces exhibited a decreased rate of bacterial adhesion and biofilm formation. The data show that sol–gel formed coatings outperform uncoated AA2024-T3 in terms of hardness, elastic strain, plastic deformation, and biofouling resistance. These characteristics are attributed to the coatings’ mechanical and adhesive capabilities, as well as their tribological behaviour.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79061708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Ascencio-Hurtado, R. C. Ambrosio Lázaro, J. Estrada-López, A. Torres Jacome
Energy harvesters are autonomous systems capable of capturing, processing, storing, and utilizing small amounts of free energy from the surrounding environment. Such energy harvesters typically involve three fundamental stages: a micro-generator or energy transducer, a voltage booster or power converter, and an energy storage component. In the case of harvesting mechanical vibrations from the environment, piezoelectric materials have been used as a transducer. For instance, PZT (lead zirconate titanate) is a widely used piezoelectric ceramic due to its high electromechanical coupling factor. However, the integration of PZT into silicon poses certain limitations, not only in the harvesting stage but also in embedding a power management electronics circuit. On the other hand, in thermoelectric (TE) energy harvesting, a recent approach involves using abundant, eco-friendly, and low-cost materials that are compatible with CMOS technology, such as silicon-based compound nanostructures for TE thin film devices. Thus, this review aims to present the current advancements in the fabrication and integration of Si-based thin-film devices for TE energy harvesting applications. Moreover, this paper also highlights some recent developments in electronic architectures that aim to enhance the overall efficiency of the complete energy harvesting system.
{"title":"Review of Si-Based Thin Films and Materials for Thermoelectric Energy Harvesting and Their Integration into Electronic Devices for Energy Management Systems","authors":"C. Ascencio-Hurtado, R. C. Ambrosio Lázaro, J. Estrada-López, A. Torres Jacome","doi":"10.3390/eng4020082","DOIUrl":"https://doi.org/10.3390/eng4020082","url":null,"abstract":"Energy harvesters are autonomous systems capable of capturing, processing, storing, and utilizing small amounts of free energy from the surrounding environment. Such energy harvesters typically involve three fundamental stages: a micro-generator or energy transducer, a voltage booster or power converter, and an energy storage component. In the case of harvesting mechanical vibrations from the environment, piezoelectric materials have been used as a transducer. For instance, PZT (lead zirconate titanate) is a widely used piezoelectric ceramic due to its high electromechanical coupling factor. However, the integration of PZT into silicon poses certain limitations, not only in the harvesting stage but also in embedding a power management electronics circuit. On the other hand, in thermoelectric (TE) energy harvesting, a recent approach involves using abundant, eco-friendly, and low-cost materials that are compatible with CMOS technology, such as silicon-based compound nanostructures for TE thin film devices. Thus, this review aims to present the current advancements in the fabrication and integration of Si-based thin-film devices for TE energy harvesting applications. Moreover, this paper also highlights some recent developments in electronic architectures that aim to enhance the overall efficiency of the complete energy harvesting system.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75907786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Roberto Carlos Alvarado-Maldonado, M. Ponce-Silva, Gregorio Saúl Olivar-Castellanos
The main contribution of this paper is the assets of the current and voltage ripples in a buck converter with an LED load. The results indicate that the ripples are different and that it is possible to reduce the passive filter concerning the model of the LED as a simple resistance. The paper presents the design and simulation of a buck converter as a power supply for an LED lamp. Modeling the LED as a resistor and a voltage source (SVRM), the equations to calculate the components of the circuit using the SVRM model are presented, where the Fourier series and phasors are used to calculate the output filter. The equations are validated with SPICE simulations. The results indicate that the SVRM model for the LED load affects the calculation of the output filter of the buck converter as well as the voltage and current ripples, making it a more precise design alternative to the proposed development.
{"title":"Assessment of the Current and Voltage Ripples of a Buck Converter as a Driver for LEDs Using a Non-Resistive Model","authors":"Roberto Carlos Alvarado-Maldonado, M. Ponce-Silva, Gregorio Saúl Olivar-Castellanos","doi":"10.3390/eng4020080","DOIUrl":"https://doi.org/10.3390/eng4020080","url":null,"abstract":"The main contribution of this paper is the assets of the current and voltage ripples in a buck converter with an LED load. The results indicate that the ripples are different and that it is possible to reduce the passive filter concerning the model of the LED as a simple resistance. The paper presents the design and simulation of a buck converter as a power supply for an LED lamp. Modeling the LED as a resistor and a voltage source (SVRM), the equations to calculate the components of the circuit using the SVRM model are presented, where the Fourier series and phasors are used to calculate the output filter. The equations are validated with SPICE simulations. The results indicate that the SVRM model for the LED load affects the calculation of the output filter of the buck converter as well as the voltage and current ripples, making it a more precise design alternative to the proposed development.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87685211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Liberos, R. González-Medina, I. Patrao, Enric Torán, G. Garcerá, E. Figueres
When converters are connected in parallel, a system with some benefits, including modularity and redundancy, is obtained. However, in these circumstances, circulating currents can appear that produce some adverse effects. In this work, a study of the low-frequency circulating currents that appear in three-phase inverters connected in parallel is performed. The study is focused on the effects produced by the parameter mismatch, namely inductance mismatches, power imbalance, and the use of different pulse with modulation (PWM) techniques. The nature of the circulating current produced by each of these factors were analyzed separately. Both simulation and experimental results are shown, which were obtained using a three-phase 10-kW prototype composed of two 5-kW inverters connected in parallel.
{"title":"Experimental Study of the Parameter Mismatch Effects on the Low Frequency Circulating Currents of Parallel Three Phase Inverters","authors":"M. Liberos, R. González-Medina, I. Patrao, Enric Torán, G. Garcerá, E. Figueres","doi":"10.3390/eng4020079","DOIUrl":"https://doi.org/10.3390/eng4020079","url":null,"abstract":"When converters are connected in parallel, a system with some benefits, including modularity and redundancy, is obtained. However, in these circumstances, circulating currents can appear that produce some adverse effects. In this work, a study of the low-frequency circulating currents that appear in three-phase inverters connected in parallel is performed. The study is focused on the effects produced by the parameter mismatch, namely inductance mismatches, power imbalance, and the use of different pulse with modulation (PWM) techniques. The nature of the circulating current produced by each of these factors were analyzed separately. Both simulation and experimental results are shown, which were obtained using a three-phase 10-kW prototype composed of two 5-kW inverters connected in parallel.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76744754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vectorcardiography (VCG) is a valuable diagnostic tool that complements the standard 12-lead ECG by offering additional spatiotemporal information to clinicians. However, due to the need for additional measurement hardware and too many electrodes in a clinical scenario if performed along with a standard 12-lead, there is a need to find methods to derive the VCG from the ECG. We have evaluated the use of Long Short-term Memory (LSTM) neural networks to learn the transformation from 12-lead ECG to VCG that is applicable across subjects and for each subject. We refer to these networks as generalized and personalized, respectively. We calculated the Root Mean Square Error (RMSE), R2, and Pearson correlation coefficient to compare waveforms of derived and actual VCG. We also extracted and compared diagnostic parameters from VCG, namely the QRS-loop magnitude, T-loop magnitude, and QRS-T spatial angle, from actual and derived VCGs using the Pearson correlation coefficient and Bland Altman limits of agreement. The personalized models performed better than generalized models in waveform comparisons and in the error of extracted diagnostic parameters from VCG waveforms. The use of personalized transformations for the derivation of VCG from standard 12-lead has the potential to improve and augment the diagnostic yield and accuracy of a standard 12-lead interpretation.
{"title":"A Comparison of Personalized and Generalized LSTM Neural Networks for Deriving VCG from 12-Lead ECG","authors":"Prashanth Shyam Kumar, Mouli Ramasamy, V. Varadan","doi":"10.3390/eng4020078","DOIUrl":"https://doi.org/10.3390/eng4020078","url":null,"abstract":"Vectorcardiography (VCG) is a valuable diagnostic tool that complements the standard 12-lead ECG by offering additional spatiotemporal information to clinicians. However, due to the need for additional measurement hardware and too many electrodes in a clinical scenario if performed along with a standard 12-lead, there is a need to find methods to derive the VCG from the ECG. We have evaluated the use of Long Short-term Memory (LSTM) neural networks to learn the transformation from 12-lead ECG to VCG that is applicable across subjects and for each subject. We refer to these networks as generalized and personalized, respectively. We calculated the Root Mean Square Error (RMSE), R2, and Pearson correlation coefficient to compare waveforms of derived and actual VCG. We also extracted and compared diagnostic parameters from VCG, namely the QRS-loop magnitude, T-loop magnitude, and QRS-T spatial angle, from actual and derived VCGs using the Pearson correlation coefficient and Bland Altman limits of agreement. The personalized models performed better than generalized models in waveform comparisons and in the error of extracted diagnostic parameters from VCG waveforms. The use of personalized transformations for the derivation of VCG from standard 12-lead has the potential to improve and augment the diagnostic yield and accuracy of a standard 12-lead interpretation.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78541576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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