Pub Date : 2023-10-16DOI: 10.3390/inventions8050128
Ilya V. Myachin, Leonid O. Kononov
Flow chemistry offers several advantages for performing chemical reactions and has become an important area of research. It may seem that sufficient knowledge has already been acquired on this topic to understand how to choose the design of microreactor/micromixer and flow rate in order to achieve the desired outcome of a reaction. However, some experimental data are difficult to explain based on commonly accepted concepts of chemical reactivity and performance of microfluidic systems. In this mini review, we attempt to identify such data and offer a rational explanation of unusual results based on the supramer approach. We demonstrate that variation in flow regime (determined by mixer design and flow rate) can either improve or worsen the reactivity and lead to completely different products, including stereoisomers. It is not necessary to mix the reagents with maximum efficiency. The real challenge is to mix reagents the right way since at a too high or too low flow rate (in the particular mixer), the molecules of reagents are incorrectly presented on the surface of supramers, leading to altered stereoselectivity, or form tight supramers, in which most of the molecules are located inside the supramer core and are inaccessible for attack, leading to low yields.
{"title":"Mixer Design and Flow Rate as Critical Variables in Flow Chemistry Affecting the Outcome of a Chemical Reaction: A Review","authors":"Ilya V. Myachin, Leonid O. Kononov","doi":"10.3390/inventions8050128","DOIUrl":"https://doi.org/10.3390/inventions8050128","url":null,"abstract":"Flow chemistry offers several advantages for performing chemical reactions and has become an important area of research. It may seem that sufficient knowledge has already been acquired on this topic to understand how to choose the design of microreactor/micromixer and flow rate in order to achieve the desired outcome of a reaction. However, some experimental data are difficult to explain based on commonly accepted concepts of chemical reactivity and performance of microfluidic systems. In this mini review, we attempt to identify such data and offer a rational explanation of unusual results based on the supramer approach. We demonstrate that variation in flow regime (determined by mixer design and flow rate) can either improve or worsen the reactivity and lead to completely different products, including stereoisomers. It is not necessary to mix the reagents with maximum efficiency. The real challenge is to mix reagents the right way since at a too high or too low flow rate (in the particular mixer), the molecules of reagents are incorrectly presented on the surface of supramers, leading to altered stereoselectivity, or form tight supramers, in which most of the molecules are located inside the supramer core and are inaccessible for attack, leading to low yields.","PeriodicalId":14564,"journal":{"name":"Inventions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136112678","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}
Insular networks constitute ideal fields for investment in renewables and storage due to their excellent wind and solar potential, as well the high generation cost of thermal generators in such networks. Nevertheless, in order to ensure the stability of insular networks, network operators impose strict restrictions on the expansion of renewables. Storage systems render ideal solutions for overcoming the aforementioned restrictions, unlocking additional renewable capacity. Among storage technologies, hybrid battery-hydrogen demonstrates beneficial characteristics thanks to the complementary features that battery and hydrogen exhibit regarding efficiency, self-discharge, cost, etc. This paper investigates the economic feasibility of a private investment in renewables and hybrid hydrogen-battery storage, realized on the interconnected island of Crete, Greece. Specifically, an optimization formulation is proposed to optimize the capacity of renewables and hybrid battery-hydrogen storage in order to maximize the profit of investment, while simultaneously reaching a minimum renewable penetration of 80%, in accordance with Greek decarbonization goals. The numerical results presented in this study demonstrate that hybrid hydrogen-battery storage can significantly reduce electricity production costs in Crete, potentially reaching as low as 64 EUR/MWh. From an investor’s perspective, even with moderate compensation tariffs, the energy transition remains profitable due to Crete’s abundant wind and solar resources. For instance, with a 40% subsidy and an 80 EUR/MWh compensation tariff, the net present value can reach EUR 400 million. Furthermore, the projected cost reductions for electrolyzers and fuel cells by 2030 are expected to enhance the profitability of hybrid renewable-battery-hydrogen projects. In summary, this research underscores the sustainable and economically favorable prospects of hybrid hydrogen-battery storage systems in facilitating Crete’s energy transition, with promising implications for investors and the wider renewable energy sector.
{"title":"Sustainable Power Generation Expansion in Island Systems with Extensive RES and Energy Storage","authors":"Emmanuel Karapidakis, Christos Kalogerakis, Evangelos Pompodakis","doi":"10.3390/inventions8050127","DOIUrl":"https://doi.org/10.3390/inventions8050127","url":null,"abstract":"Insular networks constitute ideal fields for investment in renewables and storage due to their excellent wind and solar potential, as well the high generation cost of thermal generators in such networks. Nevertheless, in order to ensure the stability of insular networks, network operators impose strict restrictions on the expansion of renewables. Storage systems render ideal solutions for overcoming the aforementioned restrictions, unlocking additional renewable capacity. Among storage technologies, hybrid battery-hydrogen demonstrates beneficial characteristics thanks to the complementary features that battery and hydrogen exhibit regarding efficiency, self-discharge, cost, etc. This paper investigates the economic feasibility of a private investment in renewables and hybrid hydrogen-battery storage, realized on the interconnected island of Crete, Greece. Specifically, an optimization formulation is proposed to optimize the capacity of renewables and hybrid battery-hydrogen storage in order to maximize the profit of investment, while simultaneously reaching a minimum renewable penetration of 80%, in accordance with Greek decarbonization goals. The numerical results presented in this study demonstrate that hybrid hydrogen-battery storage can significantly reduce electricity production costs in Crete, potentially reaching as low as 64 EUR/MWh. From an investor’s perspective, even with moderate compensation tariffs, the energy transition remains profitable due to Crete’s abundant wind and solar resources. For instance, with a 40% subsidy and an 80 EUR/MWh compensation tariff, the net present value can reach EUR 400 million. Furthermore, the projected cost reductions for electrolyzers and fuel cells by 2030 are expected to enhance the profitability of hybrid renewable-battery-hydrogen projects. In summary, this research underscores the sustainable and economically favorable prospects of hybrid hydrogen-battery storage systems in facilitating Crete’s energy transition, with promising implications for investors and the wider renewable energy sector.","PeriodicalId":14564,"journal":{"name":"Inventions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136212652","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}
Pub Date : 2023-10-11DOI: 10.3390/inventions8050126
Matheus Paula, Wallace Casaca, Marilaine Colnago, José R. da Silva, Kleber Oliveira, Mauricio A. Dias, Rogério Negri
Wind energy has become a trend in Brazil, particularly in the northeastern region of the country. Despite its advantages, wind power generation has been hindered by the high volatility of exogenous factors, such as weather, temperature, and air humidity, making long-term forecasting a highly challenging task. Another issue is the need for reliable solutions, especially for large-scale wind farms, as this involves integrating specific optimization tools and restricted-access datasets collected locally at the power plants. Therefore, in this paper, the problem of forecasting the energy generated at the Praia Formosa wind farm, an eco-friendly park located in the state of Ceará, Brazil, which produces around 7% of the state’s electricity, was addressed. To proceed with our data-driven analysis, publicly available data were collected from multiple Brazilian official sources, combining them into a unified database to perform exploratory data analysis and predictive modeling. Specifically, three machine-learning-based approaches were applied: Extreme Gradient Boosting, Random Forest, and Long Short-Term Memory Network, as well as feature-engineering strategies to enhance the precision of the machine intelligence models, including creating artificial features and tuning the hyperparameters. Our findings revealed that all implemented models successfully captured the energy-generation trends, patterns, and seasonality from the complex wind data. However, it was found that the LSTM-based model consistently outperformed the others, achieving a promising global MAPE of 4.55%, highlighting its accuracy in long-term wind energy forecasting. Temperature, relative humidity, and wind speed were identified as the key factors influencing electricity production, with peak generation typically occurring from August to November.
{"title":"Predicting Energy Generation in Large Wind Farms: A Data-Driven Study with Open Data and Machine Learning","authors":"Matheus Paula, Wallace Casaca, Marilaine Colnago, José R. da Silva, Kleber Oliveira, Mauricio A. Dias, Rogério Negri","doi":"10.3390/inventions8050126","DOIUrl":"https://doi.org/10.3390/inventions8050126","url":null,"abstract":"Wind energy has become a trend in Brazil, particularly in the northeastern region of the country. Despite its advantages, wind power generation has been hindered by the high volatility of exogenous factors, such as weather, temperature, and air humidity, making long-term forecasting a highly challenging task. Another issue is the need for reliable solutions, especially for large-scale wind farms, as this involves integrating specific optimization tools and restricted-access datasets collected locally at the power plants. Therefore, in this paper, the problem of forecasting the energy generated at the Praia Formosa wind farm, an eco-friendly park located in the state of Ceará, Brazil, which produces around 7% of the state’s electricity, was addressed. To proceed with our data-driven analysis, publicly available data were collected from multiple Brazilian official sources, combining them into a unified database to perform exploratory data analysis and predictive modeling. Specifically, three machine-learning-based approaches were applied: Extreme Gradient Boosting, Random Forest, and Long Short-Term Memory Network, as well as feature-engineering strategies to enhance the precision of the machine intelligence models, including creating artificial features and tuning the hyperparameters. Our findings revealed that all implemented models successfully captured the energy-generation trends, patterns, and seasonality from the complex wind data. However, it was found that the LSTM-based model consistently outperformed the others, achieving a promising global MAPE of 4.55%, highlighting its accuracy in long-term wind energy forecasting. Temperature, relative humidity, and wind speed were identified as the key factors influencing electricity production, with peak generation typically occurring from August to November.","PeriodicalId":14564,"journal":{"name":"Inventions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136098328","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}
Pub Date : 2023-10-10DOI: 10.3390/inventions8050125
Sorin Gabriel Tomescu, Ion Mălăel, Rareș Conțiu, Sebastian Voicu
The oil and gas sector is important to the global economy because it covers the exploration, production, processing, transportation, and distribution of oil and natural gas resources. Despite constant innovation and development of technologies to improve efficiency, reduce environmental impact, and optimize operations in the gas and oil industry over the last few decades, there is still room to increase the efficiency of the industry’s equipment in order to reduce its carbon footprint. The separation of gas from oil is a critical stage in the technological production chain, and it is carried out using high-performance multi-phase separators to limit greenhouse gas emissions and have a low impact on the environment. In this study, an improved gas–oil separator configuration was established utilizing CFD techniques. Two separator geometry characteristics were studied. Both cases have the same number of subdomains, two porous media, and four fluid zones, but with a difference in the pitch of the cyclone from the inlet subdomain. The streamlines in a cross-plan of the separator and the distribution of the oil volume fraction from the intake to the outlet were two of the numerical results that were shown as numeric outcomes. The validation of these results was performed using an experimental testing campaign that had the purpose of determining the amount of lubricating oil that is discharged together with the compressed gas at the separator outlet.
{"title":"Experimental Validation of the Numerical Model for Oil–Gas Separation","authors":"Sorin Gabriel Tomescu, Ion Mălăel, Rareș Conțiu, Sebastian Voicu","doi":"10.3390/inventions8050125","DOIUrl":"https://doi.org/10.3390/inventions8050125","url":null,"abstract":"The oil and gas sector is important to the global economy because it covers the exploration, production, processing, transportation, and distribution of oil and natural gas resources. Despite constant innovation and development of technologies to improve efficiency, reduce environmental impact, and optimize operations in the gas and oil industry over the last few decades, there is still room to increase the efficiency of the industry’s equipment in order to reduce its carbon footprint. The separation of gas from oil is a critical stage in the technological production chain, and it is carried out using high-performance multi-phase separators to limit greenhouse gas emissions and have a low impact on the environment. In this study, an improved gas–oil separator configuration was established utilizing CFD techniques. Two separator geometry characteristics were studied. Both cases have the same number of subdomains, two porous media, and four fluid zones, but with a difference in the pitch of the cyclone from the inlet subdomain. The streamlines in a cross-plan of the separator and the distribution of the oil volume fraction from the intake to the outlet were two of the numerical results that were shown as numeric outcomes. The validation of these results was performed using an experimental testing campaign that had the purpose of determining the amount of lubricating oil that is discharged together with the compressed gas at the separator outlet.","PeriodicalId":14564,"journal":{"name":"Inventions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136293718","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}
Pub Date : 2023-10-09DOI: 10.3390/inventions8050124
Ilya Starodumov, Sergey Sokolov, Ksenia Makhaeva, Pavel Mikushin, Olga Dinislamova, Felix Blyakhman
Micrometer-sized particles are widely introduced as fluid flow markers in experimental studies of convective flows. The tracks of such particles demonstrate a high contrast in the optical range and well illustrate the direction of fluid flow at local vortices. This study addresses the theoretical justification on the use of large particles for obtaining vortex phenomena and its characterization in stenotic arteries by the Echo Particle Velocimetry method. Calcite particles with an average diameter of 0.15 mm were chosen as a marker of streamlines using a medical ultrasound device. The Euler–Euler model of particle motion was applied to simulate the mechanical behavior of calcite particles and 20 µm aluminum particles. The accuracy of flow measurement at vortex regions was evaluated by computational fluid dynamics methods. The simulation results of vortex zone formation obtained by Azuma and Fukushima (1976) for aluminum particles with the use of the optical velocimetry method and calcite particles were compared. An error in determining the size of the vortex zone behind of stenosis does not exceed 5%. We concluded that the application of large-size particles for the needs of in vitro studies of local hemodynamics is possible.
{"title":"Obtaining Vortex Formation in Blood Flow by Particle Tracking: Echo-PV Methods and Computer Simulation","authors":"Ilya Starodumov, Sergey Sokolov, Ksenia Makhaeva, Pavel Mikushin, Olga Dinislamova, Felix Blyakhman","doi":"10.3390/inventions8050124","DOIUrl":"https://doi.org/10.3390/inventions8050124","url":null,"abstract":"Micrometer-sized particles are widely introduced as fluid flow markers in experimental studies of convective flows. The tracks of such particles demonstrate a high contrast in the optical range and well illustrate the direction of fluid flow at local vortices. This study addresses the theoretical justification on the use of large particles for obtaining vortex phenomena and its characterization in stenotic arteries by the Echo Particle Velocimetry method. Calcite particles with an average diameter of 0.15 mm were chosen as a marker of streamlines using a medical ultrasound device. The Euler–Euler model of particle motion was applied to simulate the mechanical behavior of calcite particles and 20 µm aluminum particles. The accuracy of flow measurement at vortex regions was evaluated by computational fluid dynamics methods. The simulation results of vortex zone formation obtained by Azuma and Fukushima (1976) for aluminum particles with the use of the optical velocimetry method and calcite particles were compared. An error in determining the size of the vortex zone behind of stenosis does not exceed 5%. We concluded that the application of large-size particles for the needs of in vitro studies of local hemodynamics is possible.","PeriodicalId":14564,"journal":{"name":"Inventions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135095093","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}
Pub Date : 2023-09-27DOI: 10.3390/inventions8050123
Aleksandr Kulikov, Pavel Ilyushin, Anton Loskutov, Sergey Filippov
The use of modern methods for determining the fault location (FL) on overhead power lines (OHPLs), which have high accuracy and speed, contributes to the reliable operation of power systems. Various physical principles are used in FL devices for OHPLs, as well as various algorithms for calculating the distance to the FL. Some algorithms for FL on OHPLs use emergency mode parameters (EMP); other algorithms use measurement results based on wave methods. Many random factors that determine the magnitude of the error in calculating the distance to the FL affect the operation of FL devices by EMP. Methods based on deterministic procedures used in well-known FL devices for OHPLs do not take into account the influence of random factors, which significantly increases the time to search for the fault. The authors have developed a method of FL on OHPLs based on a multi-hypothetical sequential analysis using the Armitage algorithm. The task of recognizing a faulted section of an OHPL is formulated as a statistical problem. To do this, the inspection area of the OHPL is divided into many sections, followed by the implementation of the procedure for FL. The developed method makes it possible to adapt the distortions of currents and voltages on the emergency mode oscillograms to the conditions for estimating their parameters. The results of the calculations proved that the implementation of the developed method has practically no effect on the speed of the FL algorithm for the OHPL by EMP. This ensures the uniqueness of determining the faulted section of the OHPL under the influence of random factors, which leads to a significant reduction in the inspection area of the OHPL. The application of the developed method in FL devices for OHPLs will ensure the required reliability of power supply to consumers and reduce losses from power outages by minimizing the time to search for a fault.
{"title":"Fault Location Method for Overhead Power Line Based on a Multi-Hypothetical Sequential Analysis Using the Armitage Algorithm","authors":"Aleksandr Kulikov, Pavel Ilyushin, Anton Loskutov, Sergey Filippov","doi":"10.3390/inventions8050123","DOIUrl":"https://doi.org/10.3390/inventions8050123","url":null,"abstract":"The use of modern methods for determining the fault location (FL) on overhead power lines (OHPLs), which have high accuracy and speed, contributes to the reliable operation of power systems. Various physical principles are used in FL devices for OHPLs, as well as various algorithms for calculating the distance to the FL. Some algorithms for FL on OHPLs use emergency mode parameters (EMP); other algorithms use measurement results based on wave methods. Many random factors that determine the magnitude of the error in calculating the distance to the FL affect the operation of FL devices by EMP. Methods based on deterministic procedures used in well-known FL devices for OHPLs do not take into account the influence of random factors, which significantly increases the time to search for the fault. The authors have developed a method of FL on OHPLs based on a multi-hypothetical sequential analysis using the Armitage algorithm. The task of recognizing a faulted section of an OHPL is formulated as a statistical problem. To do this, the inspection area of the OHPL is divided into many sections, followed by the implementation of the procedure for FL. The developed method makes it possible to adapt the distortions of currents and voltages on the emergency mode oscillograms to the conditions for estimating their parameters. The results of the calculations proved that the implementation of the developed method has practically no effect on the speed of the FL algorithm for the OHPL by EMP. This ensures the uniqueness of determining the faulted section of the OHPL under the influence of random factors, which leads to a significant reduction in the inspection area of the OHPL. The application of the developed method in FL devices for OHPLs will ensure the required reliability of power supply to consumers and reduce losses from power outages by minimizing the time to search for a fault.","PeriodicalId":14564,"journal":{"name":"Inventions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135579954","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}
Pub Date : 2023-09-26DOI: 10.3390/inventions8050122
Umar Farooq Shafi, Imran Sarwar Bajwa, Waheed Anwar, Hina Sattar, Shabana Ramzan, Aqsa Mahmood
The combustion of agricultural storage represents a big hazard to the safety and quality preservation of crops during lengthy storage times. Cotton storage is considered more prone to combustion for many reasons, i.e., heat by microbial growth, exothermic and endothermic reactions in storage areas, and extreme weather conditions in storage areas. Combustion not only increases the chances of a big fire outbreak in the long run, but it may also affect cotton’s quality factors like its color, staple length, seed quality, etc. The cotton’s quality attributes may divert from their normal range in the presence of combustion. It is difficult to detect, monitor, and control combustion. The Internet of Things (IoT) offers efficient and reliable solutions for numerous research problems in agriculture, healthcare, business analytics, and industrial manufacturing. In the agricultural domain, the IoT provides various applications for crop monitoring, warehouse protection, the prevention of crop diseases, and crop yield maximization. We also used the IoT for the smart and real-time sensing of spontaneous combustion inside storage areas in order to maintain cotton quality during lengthy storage. In the current research, we investigate spontaneous combustion inside storage and identify the primary reasons for it. Then, we proposed an efficient IoT and machine learning (ML)-based solution for the early sensing of combustion in storage in order to maintain cotton quality during long storage times. The proposed system provides real-time sensing of combustion-causing factors with the help of the IoT-based circuit and prediction of combustion using an efficient artificial neural network (ANN) model. The proposed smart sensing of combustion is verified by a different set of experiments. The proposed ANN model showed a 99.8% accuracy rate with 95–98% correctness and 97–99% completeness. The proposed solution is very efficient in detecting combustion and enables storage owners to become aware of combustion hazards in a timely manner; hence, they can improve the storage conditions for the preservation of cotton quality in the long run. The whole article consists of five sections.
{"title":"Sensing Spontaneous Combustion in Agricultural Storage Using IoT and ML","authors":"Umar Farooq Shafi, Imran Sarwar Bajwa, Waheed Anwar, Hina Sattar, Shabana Ramzan, Aqsa Mahmood","doi":"10.3390/inventions8050122","DOIUrl":"https://doi.org/10.3390/inventions8050122","url":null,"abstract":"The combustion of agricultural storage represents a big hazard to the safety and quality preservation of crops during lengthy storage times. Cotton storage is considered more prone to combustion for many reasons, i.e., heat by microbial growth, exothermic and endothermic reactions in storage areas, and extreme weather conditions in storage areas. Combustion not only increases the chances of a big fire outbreak in the long run, but it may also affect cotton’s quality factors like its color, staple length, seed quality, etc. The cotton’s quality attributes may divert from their normal range in the presence of combustion. It is difficult to detect, monitor, and control combustion. The Internet of Things (IoT) offers efficient and reliable solutions for numerous research problems in agriculture, healthcare, business analytics, and industrial manufacturing. In the agricultural domain, the IoT provides various applications for crop monitoring, warehouse protection, the prevention of crop diseases, and crop yield maximization. We also used the IoT for the smart and real-time sensing of spontaneous combustion inside storage areas in order to maintain cotton quality during lengthy storage. In the current research, we investigate spontaneous combustion inside storage and identify the primary reasons for it. Then, we proposed an efficient IoT and machine learning (ML)-based solution for the early sensing of combustion in storage in order to maintain cotton quality during long storage times. The proposed system provides real-time sensing of combustion-causing factors with the help of the IoT-based circuit and prediction of combustion using an efficient artificial neural network (ANN) model. The proposed smart sensing of combustion is verified by a different set of experiments. The proposed ANN model showed a 99.8% accuracy rate with 95–98% correctness and 97–99% completeness. The proposed solution is very efficient in detecting combustion and enables storage owners to become aware of combustion hazards in a timely manner; hence, they can improve the storage conditions for the preservation of cotton quality in the long run. The whole article consists of five sections.","PeriodicalId":14564,"journal":{"name":"Inventions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134958607","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}
During the past few years, there has been a notable surge of interest in the field of smart structures. An intelligent structure is one that automatically responds to mechanical disturbances by minimizing oscillations after intelligently detecting them. In this study, a smart design that contains integrated actuators and sensors that can dampen oscillations is shown. A finite element analysis is used in conjunction with the application of dynamic loads such as wind force. The dynamic-loading-induced vibration of the intelligent piezoelectric structure is aimed to be mitigated using a μ-controller. The controller’s robustness against uncertainties in the parameters to address vibration-related concerns is showcased. This article offers a thorough depiction of the benefits stemming from μ-analysis and active vibration control in the behavior of intelligent structures. The gradual surmounting of these challenges is attributed to the increasing affordability and enhanced capability of electronic components used for control implementation. The advancement of μ-analysis and robust control for vibration reduction in intelligent structures is amply demonstrated in this study.
{"title":"Developments in the Use of Hinfinity Control and μ-Analysis for Reducing Vibration in Intelligent Structures","authors":"Amalia Moutsopoulou, Georgios E. Stavroulakis, Markos Petousis, Anastasios Pouliezos, Nectarios Vidakis","doi":"10.3390/inventions8050119","DOIUrl":"https://doi.org/10.3390/inventions8050119","url":null,"abstract":"During the past few years, there has been a notable surge of interest in the field of smart structures. An intelligent structure is one that automatically responds to mechanical disturbances by minimizing oscillations after intelligently detecting them. In this study, a smart design that contains integrated actuators and sensors that can dampen oscillations is shown. A finite element analysis is used in conjunction with the application of dynamic loads such as wind force. The dynamic-loading-induced vibration of the intelligent piezoelectric structure is aimed to be mitigated using a μ-controller. The controller’s robustness against uncertainties in the parameters to address vibration-related concerns is showcased. This article offers a thorough depiction of the benefits stemming from μ-analysis and active vibration control in the behavior of intelligent structures. The gradual surmounting of these challenges is attributed to the increasing affordability and enhanced capability of electronic components used for control implementation. The advancement of μ-analysis and robust control for vibration reduction in intelligent structures is amply demonstrated in this study.","PeriodicalId":14564,"journal":{"name":"Inventions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135817376","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}
This article discusses the construction of a dynamic model for controlling the position of the blades of a vertical-axis wind generator using an automatic approach; a method is presented that relates the rotation of the motor to the position of the blades, which allows the optimization of the operation of the control system. In the research process, an automatic approach is used, which makes it possible to carry out numerical calculations that predict the behavior of the system at various values of motor rotation. The model allows us to analyze the dependence of the position of the blades on the rotation of the motor and determine the optimal parameters of the mathematical control model. The main goal of our study is to develop a mathematical model of the mechanism for further adjustment of the wind turbine blade position control system depending on the wind speed.
{"title":"A Dynamic Control Model of the Blades Position for the Vertical-Axis Wind Generator by a Program Method","authors":"Ivaylo Stoyanov, Teodor Iliev, Alina Fazylova, Gulsara Yestemessova","doi":"10.3390/inventions8050120","DOIUrl":"https://doi.org/10.3390/inventions8050120","url":null,"abstract":"This article discusses the construction of a dynamic model for controlling the position of the blades of a vertical-axis wind generator using an automatic approach; a method is presented that relates the rotation of the motor to the position of the blades, which allows the optimization of the operation of the control system. In the research process, an automatic approach is used, which makes it possible to carry out numerical calculations that predict the behavior of the system at various values of motor rotation. The model allows us to analyze the dependence of the position of the blades on the rotation of the motor and determine the optimal parameters of the mathematical control model. The main goal of our study is to develop a mathematical model of the mechanism for further adjustment of the wind turbine blade position control system depending on the wind speed.","PeriodicalId":14564,"journal":{"name":"Inventions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863879","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}
Pub Date : 2023-09-25DOI: 10.3390/inventions8050121
Yang Luo, Xuesong Lu, Yi Chen, John Andresen, Mercedes Maroto-Valer
This paper investigates the heat transfer properties of liquefied natural gas (LNG) in a corrugated plate heat exchanger and explores its application in cold energy recovery for enhanced energy efficiency. The study aims to integrate this technology into a 500 MW gas-fired power plant and a district cooling system to contribute to sustainable city development. Using computational fluid dynamics simulations and experimental validation, the heat transfer behaviour of LNG in the corrugated plate heat exchanger is examined, emphasising the significance of the gas film on the channel wall for efficient heat transfer between LNG and water/ethylene glycol. The study analyses heat exchange characteristics below and above the critical point of LNG. Below the critical point, the LNG behaves as an incompressible fluid, whereas above the critical point, the compressible supercritical state enables a substantial energy recovery and temperature rise at the outlet, highlighting the potential for cold energy recovery. The results demonstrate the effectiveness of cold energy recovery above the critical point, leading to significant energy savings and improved efficiency compared to conventional systems. Optimal operational parameters, such as the number of channels and flow rate ratios, are identified for successful cold energy recovery. This research provides valuable insights for sustainable city planning and the transition towards low-carbon energy systems, contributing to the overall goal of creating environmentally friendly and resilient urban environments.
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