With a view to achieve several goals in the smart grid (SG) such as making the production and delivery of electricity more cost-effective as well as providing consumers with available information which assists them in controlling their cost, the advanced metering infrastructure (AMI) system has been playing a major role to realize such goals. The AMI network, as an essential infrastructure, typically creates a two-way communication network between electricity consumers and the electric service provider for collecting of the big data generated from consumer’s smart meters (SM). Specifically, there is a crucial element called a data concentrator unit (DCU) employed to collect the boundless data from smart meters before disseminating to meter data management system (MDMS) in the AMI systems. Hence, the location of DCU has significantly impacted the quality of service (QoS) of AMI network, in particular the average throughput and delay. This work aims at developing an efficient algorithm in determining the minimum number of DCUs and computing their optimum locations in which smart meters can communicate through good quality wireless links in the AMI network by employing the IEEE 802.15.4g with unslotted CSMA/CA channel access mechanism. Firstly, the optimization algorithm computes the DCU location based on a minimum hop count metric. Nevertheless, it is possible that multiple positions achieving the minimum hop count may be found; therefore, the additional performance metric, i.e. the average throughput and delay, will be utilized to select the ultimately optimal location. In this paper, the maximum throughput with the acceptable averaged delay constraint is proposed by considering the behavior of the AMI meters, which is almost stationary in the AMI network. In our experiment, the algorithm is demonstrated in different scenarios with different densities of SM, including urban, suburban, and rural areas. The simulation results illustrate that the smart meter density and the environment have substantially impacted on a decision for DCU location, and the proposed methodology is significantly effective. Furthermore, the QoS in urban area, i.e. a highly populated area for SM, of the AMI network is better than those in the suburban and rural areas, where the SM density is quite sparse, because multiple available hops and routes created by neighboring meters in the dense area can help improve the average throughput and delay with the minimum hop count.
{"title":"An Efficient Algorithm in Computing Optimal Data Concentrator Unit Location in IEEE 802.15.4g AMI Networks","authors":"Songserm Tanakornpintong, C. Pirak","doi":"10.4186/ej.2021.25.8.87","DOIUrl":"https://doi.org/10.4186/ej.2021.25.8.87","url":null,"abstract":"With a view to achieve several goals in the smart grid (SG) such as making the production and delivery of electricity more cost-effective as well as providing consumers with available information which assists them in controlling their cost, the advanced metering infrastructure (AMI) system has been playing a major role to realize such goals. The AMI network, as an essential infrastructure, typically creates a two-way communication network between electricity consumers and the electric service provider for collecting of the big data generated from consumer’s smart meters (SM). Specifically, there is a crucial element called a data concentrator unit (DCU) employed to collect the boundless data from smart meters before disseminating to meter data management system (MDMS) in the AMI systems. Hence, the location of DCU has significantly impacted the quality of service (QoS) of AMI network, in particular the average throughput and delay. This work aims at developing an efficient algorithm in determining the minimum number of DCUs and computing their optimum locations in which smart meters can communicate through good quality wireless links in the AMI network by employing the IEEE 802.15.4g with unslotted CSMA/CA channel access mechanism. Firstly, the optimization algorithm computes the DCU location based on a minimum hop count metric. Nevertheless, it is possible that multiple positions achieving the minimum hop count may be found; therefore, the additional performance metric, i.e. the average throughput and delay, will be utilized to select the ultimately optimal location. In this paper, the maximum throughput with the acceptable averaged delay constraint is proposed by considering the behavior of the AMI meters, which is almost stationary in the AMI network. In our experiment, the algorithm is demonstrated in different scenarios with different densities of SM, including urban, suburban, and rural areas. The simulation results illustrate that the smart meter density and the environment have substantially impacted on a decision for DCU location, and the proposed methodology is significantly effective. Furthermore, the QoS in urban area, i.e. a highly populated area for SM, of the AMI network is better than those in the suburban and rural areas, where the SM density is quite sparse, because multiple available hops and routes created by neighboring meters in the dense area can help improve the average throughput and delay with the minimum hop count.","PeriodicalId":32885,"journal":{"name":"AlKhawarizmi Engineering Journal","volume":"76 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86784943","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}
Pattarasiri Fagkaew, Marina Phea, T. Poyai, Nattawin Chawaloesphonsiya, Pisut Painmanakul
{"title":"Removal of Hydrocarbons from Drill Cuttings Using Flotation Enhanced Stirred Tank (FEST)","authors":"Pattarasiri Fagkaew, Marina Phea, T. Poyai, Nattawin Chawaloesphonsiya, Pisut Painmanakul","doi":"10.4186/ej.2021.25.8.11","DOIUrl":"https://doi.org/10.4186/ej.2021.25.8.11","url":null,"abstract":"","PeriodicalId":32885,"journal":{"name":"AlKhawarizmi Engineering Journal","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82379105","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 : 2021-08-31DOI: 10.4186/ej.2021.25.8.173
A. Prayitno, I. Nilkhamhang
This paper proposes a distributed model reference adaptive controller (DMRAC) for vehicle platoons with constant spacing policy, subjected to uncertainty in control effectiveness and inertial time lag. It formulates the uncertain vehicle dynamics as a matched uncertainty, and is applicable for both directed and undirected topologies. The directed topology must contain at least one spanning tree with the leader as a root node, while the undirected topology must be static and connected with at least one follower receiving information from the leader. The proposed control structure consists of a reference model and a main control system. The reference model is a closed-loop system constructed from the nominal model of each follower vehicle and a reference control signal. The main control system consists of a nominal control signal based on cooperative state feedback and an adaptive term. The nominal control signal allows the followers cooperatively track the leader, while the adaptive term suppresses the effects of uncertainties. Stability analysis shows that global tracking errors with respect to the reference model and with respect to the leader are asymptotically stable. The states of all followers synchronize to both the reference and leader states. Moreover, with the existence of unknown external disturbances, the global tracking errors remain uniformly ultimately bounded. The performance of the controlled system is verified through the simulations and validates the efficacy of the proposed controller.
{"title":"Distributed Model Reference Adaptive Control for Vehicle Platoons with Uncertain Dynamics","authors":"A. Prayitno, I. Nilkhamhang","doi":"10.4186/ej.2021.25.8.173","DOIUrl":"https://doi.org/10.4186/ej.2021.25.8.173","url":null,"abstract":"This paper proposes a distributed model reference adaptive controller (DMRAC) for vehicle platoons with constant spacing policy, subjected to uncertainty in control effectiveness and inertial time lag. It formulates the uncertain vehicle dynamics as a matched uncertainty, and is applicable for both directed and undirected topologies. The directed topology must contain at least one spanning tree with the leader as a root node, while the undirected topology must be static and connected with at least one follower receiving information from the leader. The proposed control structure consists of a reference model and a main control system. The reference model is a closed-loop system constructed from the nominal model of each follower vehicle and a reference control signal. The main control system consists of a nominal control signal based on cooperative state feedback and an adaptive term. The nominal control signal allows the followers cooperatively track the leader, while the adaptive term suppresses the effects of uncertainties. Stability analysis shows that global tracking errors with respect to the reference model and with respect to the leader are asymptotically stable. The states of all followers synchronize to both the reference and leader states. Moreover, with the existence of unknown external disturbances, the global tracking errors remain uniformly ultimately bounded. The performance of the controlled system is verified through the simulations and validates the efficacy of the proposed controller.","PeriodicalId":32885,"journal":{"name":"AlKhawarizmi Engineering Journal","volume":"97 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78601027","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 : 2021-08-31DOI: 10.4186/ej.2021.25.8.127
A. Ibrahim, W. Majeed
{"title":"The Design of Bunch Shaker and the Date Fruit Detachment Force","authors":"A. Ibrahim, W. Majeed","doi":"10.4186/ej.2021.25.8.127","DOIUrl":"https://doi.org/10.4186/ej.2021.25.8.127","url":null,"abstract":"","PeriodicalId":32885,"journal":{"name":"AlKhawarizmi Engineering Journal","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73405492","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}
Pichit Thienthong, N. Teerasuttakorn, K. Nuanyai, S. Chantaraskul
The trend and human lifestyle have been changing, which lead to the tremendously increasing demand for data usage over wireless communication systems even on the go. Traffic offload has been used for LTE Heterogeneous Networks (HetNets) to optimize overall system capacity via load balancing mechanisms among network tiers. In this work, the two main techniques used for interference coordination in the multi-tier systems i.e. Almost Blank Subframe (ABS) and Cell Range Expansion (CRE) have been focused on. Resource scheduling is one of the major issues in LTE HetNets aimed at efficient radio resource allocation. Based on the implementation of ABS and CRE mechanisms, this work investigates the system performance while different scheduling schemes are implemented. Five scheduling schemes including Round Robin (RR), BestChannel Quality Identification (Best-CQI), Maximum Throughput (Max-TP), Proportional Fairness (PF), and Resource Fairness (RF) are considered here. The simulation studies include a comparison of the LTE HetNet system performance under different ABS and CRE configured parameters as well as employing different scheduling mechanisms. System performance is observed in terms of the average throughput, the peak throughput, the edge throughput, and the fairness index. The results provide recommendations on the system configurations as well as the choice of a scheduler that can be considered or suitable for different scenarios and network planning objectives. Coined from these results, the Best-CQI and the Max-TP mechanism offer the highest peak throughput and the high average throughput. The RR, PF, and RF provide the high cell edge throughput and fairness index, however, the peak throughput has been compromised.
{"title":"Comparative Study of Scheduling Algorithms in LTE HetNets with Almost Blank Subframe","authors":"Pichit Thienthong, N. Teerasuttakorn, K. Nuanyai, S. Chantaraskul","doi":"10.4186/ej.2021.25.8.39","DOIUrl":"https://doi.org/10.4186/ej.2021.25.8.39","url":null,"abstract":"The trend and human lifestyle have been changing, which lead to the tremendously increasing demand for data usage over wireless communication systems even on the go. Traffic offload has been used for LTE Heterogeneous Networks (HetNets) to optimize overall system capacity via load balancing mechanisms among network tiers. In this work, the two main techniques used for interference coordination in the multi-tier systems i.e. Almost Blank Subframe (ABS) and Cell Range Expansion (CRE) have been focused on. Resource scheduling is one of the major issues in LTE HetNets aimed at efficient radio resource allocation. Based on the implementation of ABS and CRE mechanisms, this work investigates the system performance while different scheduling schemes are implemented. Five scheduling schemes including Round Robin (RR), BestChannel Quality Identification (Best-CQI), Maximum Throughput (Max-TP), Proportional Fairness (PF), and Resource Fairness (RF) are considered here. The simulation studies include a comparison of the LTE HetNet system performance under different ABS and CRE configured parameters as well as employing different scheduling mechanisms. System performance is observed in terms of the average throughput, the peak throughput, the edge throughput, and the fairness index. The results provide recommendations on the system configurations as well as the choice of a scheduler that can be considered or suitable for different scenarios and network planning objectives. Coined from these results, the Best-CQI and the Max-TP mechanism offer the highest peak throughput and the high average throughput. The RR, PF, and RF provide the high cell edge throughput and fairness index, however, the peak throughput has been compromised.","PeriodicalId":32885,"journal":{"name":"AlKhawarizmi Engineering Journal","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80827759","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}
Warittorn Cheevachaipimol, Bhudharhita Teinwan, P. Chutima
The operational effectiveness of airports and airlines greatly relies on punctuality. Many conventional machine learning and deep learning algorithms are applied in the analysis of air traffic data. However, the hybrid deep learning (HDL) model demonstrates great success with superior results in many complex problems, e.g. image classification and behaviour detection based on video data. Interestingly, no previous attempts have been made to apply the concept of HDL in analysing structured air traffic data before. Hence, this research investigates the effectiveness of the HDL in the departure delays severity prediction (i.e. on-time, delay and extremely delay) for 10 major airports in the U.S. that experience high ground and air congestion. The proposed HDL model is a combination of a feed-forward artificial neural network model with three hidden layers and a conventional gradient boosted tree model (XGBoost). Utilising the passenger flight on-time performance data from the U.S. Department of Transportation, the proposed HDL model achieves a sharp rise of 22.95% in accuracy when compared to a pure neural network model. However, with current data used in this research, a pure machine learning model achieves the best prediction accuracy.
{"title":"Flight Delay Prediction Using a Hybrid Deep Learning Method","authors":"Warittorn Cheevachaipimol, Bhudharhita Teinwan, P. Chutima","doi":"10.4186/ej.2021.25.8.99","DOIUrl":"https://doi.org/10.4186/ej.2021.25.8.99","url":null,"abstract":"The operational effectiveness of airports and airlines greatly relies on punctuality. Many conventional machine learning and deep learning algorithms are applied in the analysis of air traffic data. However, the hybrid deep learning (HDL) model demonstrates great success with superior results in many complex problems, e.g. image classification and behaviour detection based on video data. Interestingly, no previous attempts have been made to apply the concept of HDL in analysing structured air traffic data before. Hence, this research investigates the effectiveness of the HDL in the departure delays severity prediction (i.e. on-time, delay and extremely delay) for 10 major airports in the U.S. that experience high ground and air congestion. The proposed HDL model is a combination of a feed-forward artificial neural network model with three hidden layers and a conventional gradient boosted tree model (XGBoost). Utilising the passenger flight on-time performance data from the U.S. Department of Transportation, the proposed HDL model achieves a sharp rise of 22.95% in accuracy when compared to a pure neural network model. However, with current data used in this research, a pure machine learning model achieves the best prediction accuracy.","PeriodicalId":32885,"journal":{"name":"AlKhawarizmi Engineering Journal","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85584246","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 : 2021-08-31DOI: 10.4186/ej.2021.25.8.163
Jitpinun Piriyataravet, W. Kumwilaisak, J. Chinrungrueng, Teerawat Piriyatharawet
Thispaperpresents an intelligentbus stopdetermination frombusGlobalPositioningSystem(GPS) trajectories. Amixture of deep neural networks and a time filtering algorithm is used in the proposed algorithm. A deep neural network uses the speed histogram and azimuth angle at each location as input features. A deep neural networks consists of the convolutional neural networks (CNN), fully connected networks, and bidirectional Long-Short Term Memory (LSTM) networks. It predicts the soft decisions of bus stops at all locations along the route. The time filtering technique was adopted to refine the results obtained from the LSTM network. The time histograms of all locations was built where the high potential timestamps are extracted. Then, a linear regression is used to produce an approximate reliable timestamp. Each time distribution can be derived using data updated at that time slot and compared to a reference distribution. Locations are predicted as bus stop locations when timestamp distributions close to the reference distributions. Our technique was tested on real bus service GPS data from National Science and Technology Development Agency (NATDA, Thailand). The proposed method can outperform other existing bus stop detection systems.
{"title":"Determining Bus Stop Locations using Deep Learning and Time Filtering","authors":"Jitpinun Piriyataravet, W. Kumwilaisak, J. Chinrungrueng, Teerawat Piriyatharawet","doi":"10.4186/ej.2021.25.8.163","DOIUrl":"https://doi.org/10.4186/ej.2021.25.8.163","url":null,"abstract":"Thispaperpresents an intelligentbus stopdetermination frombusGlobalPositioningSystem(GPS) trajectories. Amixture of deep neural networks and a time filtering algorithm is used in the proposed algorithm. A deep neural network uses the speed histogram and azimuth angle at each location as input features. A deep neural networks consists of the convolutional neural networks (CNN), fully connected networks, and bidirectional Long-Short Term Memory (LSTM) networks. It predicts the soft decisions of bus stops at all locations along the route. The time filtering technique was adopted to refine the results obtained from the LSTM network. The time histograms of all locations was built where the high potential timestamps are extracted. Then, a linear regression is used to produce an approximate reliable timestamp. Each time distribution can be derived using data updated at that time slot and compared to a reference distribution. Locations are predicted as bus stop locations when timestamp distributions close to the reference distributions. Our technique was tested on real bus service GPS data from National Science and Technology Development Agency (NATDA, Thailand). The proposed method can outperform other existing bus stop detection systems.","PeriodicalId":32885,"journal":{"name":"AlKhawarizmi Engineering Journal","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82612394","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}
Supplier selection has become one of the essential effects on the entire electronic supply chain network to gain competitiveness. In the upstream supply chain, companies are able to achieve a high quality and value of products to reduce the potential risks from both internal and external stakeholders by selecting the right suppliers. The case study company produces a nano sim-card connector in which four different types of raw materials are processed into different parts. Currently, the case study company selects each raw material supplier based on its appraisal record. Nevertheless, the appraisal record is measured by the department of procurement. When candidate suppliers are categorized at the same level, the cost becomes the priority criteria to select the supplier, which increases the potential risks of, for example, the components defect rate, a penalty from clients, and a reduction in orders. This paper proposed a Fuzzy analytic hierarchy process (FAHP) model for the selection of raw material suppliers by collecting data from two of the company’s departments (procurement and engineering) and the clients to address qualitative and quantitative elements, uncertainty, and linguistic vagueness based on the company’s scenario in two parts. First, the main and sub-criteria can be weighted using a decision-maker (DM) to identify the level of importance. Second, the FAHP model also dealt with personal preferences and judgement so that the right supplier(s) for each raw material could be selected by collecting and computing the data from the respondents. Then, the sensitivity analysis is applied to observe how the decisions change when the model parameters in the top five sub-criteria change. The proposed model can offer better information and solutions for the DM in the case study company to differentiate the crucial main and sub-criteria and select the suitable raw material suppliers effectively.
{"title":"Fuzzy Analytical Hierarchy Process for Supplier Selection: A Case Study in An Electronic Component Manufacturer","authors":"C. Tsai, N. Phumchusri","doi":"10.4186/ej.2021.25.8.73","DOIUrl":"https://doi.org/10.4186/ej.2021.25.8.73","url":null,"abstract":"Supplier selection has become one of the essential effects on the entire electronic supply chain network to gain competitiveness. In the upstream supply chain, companies are able to achieve a high quality and value of products to reduce the potential risks from both internal and external stakeholders by selecting the right suppliers. The case study company produces a nano sim-card connector in which four different types of raw materials are processed into different parts. Currently, the case study company selects each raw material supplier based on its appraisal record. Nevertheless, the appraisal record is measured by the department of procurement. When candidate suppliers are categorized at the same level, the cost becomes the priority criteria to select the supplier, which increases the potential risks of, for example, the components defect rate, a penalty from clients, and a reduction in orders. This paper proposed a Fuzzy analytic hierarchy process (FAHP) model for the selection of raw material suppliers by collecting data from two of the company’s departments (procurement and engineering) and the clients to address qualitative and quantitative elements, uncertainty, and linguistic vagueness based on the company’s scenario in two parts. First, the main and sub-criteria can be weighted using a decision-maker (DM) to identify the level of importance. Second, the FAHP model also dealt with personal preferences and judgement so that the right supplier(s) for each raw material could be selected by collecting and computing the data from the respondents. Then, the sensitivity analysis is applied to observe how the decisions change when the model parameters in the top five sub-criteria change. The proposed model can offer better information and solutions for the DM in the case study company to differentiate the crucial main and sub-criteria and select the suitable raw material suppliers effectively.","PeriodicalId":32885,"journal":{"name":"AlKhawarizmi Engineering Journal","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88113531","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}
“Flight time” of unmanned aerial vehicle (UAV) or drone flying robot is the key component for supporting industrial activities. In practice, most battery-powered drones can fly 20 30 minutes for a single charging cycle. When the battery depleted, the drone is forced to come back to the station to recharge, or swap in a charged battery. However, these tasks are manually done by human multiple times. Aside from the inconvenience, human error and inappropriate force application may damage the socket compartment or loosen the locking system between battery and socket, making higher risk of the battery accidentally fall off from the socket during the flight. This research presents a “Small power station” to automatically load and unload battery from the drone’s mainframe with a constant force. The station has two main functions: drone positioning, and six-slot-battery exchange mechanism. Product design and development (PDD) and Kano analysis method were applied to properly list necessary compartments of the designed station. Finite element analysis (FEA) and kinematic calculation were applied to virtually check whether or not the developed platform was designed in the safety boundary. “DJI Matrice 100” drone was applied as the case study to demonstrate the proposed approach.
{"title":"Conceptual Design and Analysis of Small Power Station for Supporting Unmanned Aerial Vehicle (UAV) Deployment","authors":"Surya Alhadi, Suchada Rianmora, M. Phlernjai","doi":"10.4186/ej.2021.25.8.51","DOIUrl":"https://doi.org/10.4186/ej.2021.25.8.51","url":null,"abstract":"“Flight time” of unmanned aerial vehicle (UAV) or drone flying robot is the key component for supporting industrial activities. In practice, most battery-powered drones can fly 20 30 minutes for a single charging cycle. When the battery depleted, the drone is forced to come back to the station to recharge, or swap in a charged battery. However, these tasks are manually done by human multiple times. Aside from the inconvenience, human error and inappropriate force application may damage the socket compartment or loosen the locking system between battery and socket, making higher risk of the battery accidentally fall off from the socket during the flight. This research presents a “Small power station” to automatically load and unload battery from the drone’s mainframe with a constant force. The station has two main functions: drone positioning, and six-slot-battery exchange mechanism. Product design and development (PDD) and Kano analysis method were applied to properly list necessary compartments of the designed station. Finite element analysis (FEA) and kinematic calculation were applied to virtually check whether or not the developed platform was designed in the safety boundary. “DJI Matrice 100” drone was applied as the case study to demonstrate the proposed approach.","PeriodicalId":32885,"journal":{"name":"AlKhawarizmi Engineering Journal","volume":"990 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77127734","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}
Watchara Uraisakul, B. Chalermsinsuwan, P. Piumsomboon
This study’s objective is to investigate the process structure and operating variables that affect the efficiency of the CLC combined with humid air turbine (HAT) unit to produce electricity. The investigation was carried out by using the Aspen Plus program with Peng-Robinson-Boston-Mathias (PR-BM) thermodynamics properties. In this study, the process structure and operating parameters were investigated. The process structure was related to process configuration, which reflected the number of compressor stages. The operating parameters were pressure, airflow rate, and compression methods. The four investigated responses consist of LHV efficiency, power production from the air reactor, work of air compressors, and air compressor discharge temperature. The 3k factorial experimental design was employed. After that, the result was analyzed by the analysis of variance (ANOVA). The result showed that the highest LHV efficiency was at 55.87 % when seven stages of compressors were used and the operating condition was at 15 atm of pressure in the air reactor, air compression using method 3, and 61,000 kmol/hr of airflow rate. The pressure and the method of compression highly affected LHV efficiency, as shown by their p-values. The pressure had the highest effect on LHV efficiency. The high pressure provided high power production. Method 3 provided the highest discharged temperature from the air compressor, which was the reason for the high power production in the air reactor. The compression ratio of the last compressor would be 65% of the pressure in the air reactor. Moreover, the efficiency could be improved to 57.67% by increasing the loading of Ni on the oxygen carrier from 25% to 40%. The benefit of the paper will be preliminary data for operation and investment decisions on a CLC power production because this result has not yet been demonstrated.
{"title":"Simulation of Process Structure and Operating Parameters on the Efficiency of the Chemical Looping Combustion Combined with Humid Air Turbine Cycle Using Statistical Experimental Design","authors":"Watchara Uraisakul, B. Chalermsinsuwan, P. Piumsomboon","doi":"10.4186/ej.2021.25.8.21","DOIUrl":"https://doi.org/10.4186/ej.2021.25.8.21","url":null,"abstract":"This study’s objective is to investigate the process structure and operating variables that affect the efficiency of the CLC combined with humid air turbine (HAT) unit to produce electricity. The investigation was carried out by using the Aspen Plus program with Peng-Robinson-Boston-Mathias (PR-BM) thermodynamics properties. In this study, the process structure and operating parameters were investigated. The process structure was related to process configuration, which reflected the number of compressor stages. The operating parameters were pressure, airflow rate, and compression methods. The four investigated responses consist of LHV efficiency, power production from the air reactor, work of air compressors, and air compressor discharge temperature. The 3k factorial experimental design was employed. After that, the result was analyzed by the analysis of variance (ANOVA). The result showed that the highest LHV efficiency was at 55.87 % when seven stages of compressors were used and the operating condition was at 15 atm of pressure in the air reactor, air compression using method 3, and 61,000 kmol/hr of airflow rate. The pressure and the method of compression highly affected LHV efficiency, as shown by their p-values. The pressure had the highest effect on LHV efficiency. The high pressure provided high power production. Method 3 provided the highest discharged temperature from the air compressor, which was the reason for the high power production in the air reactor. The compression ratio of the last compressor would be 65% of the pressure in the air reactor. Moreover, the efficiency could be improved to 57.67% by increasing the loading of Ni on the oxygen carrier from 25% to 40%. The benefit of the paper will be preliminary data for operation and investment decisions on a CLC power production because this result has not yet been demonstrated.","PeriodicalId":32885,"journal":{"name":"AlKhawarizmi Engineering Journal","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79388880","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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