Pub Date : 2023-06-26DOI: 10.1109/MED59994.2023.10185835
Shahab Heshmati-alamdari, G. Karras, M. Sharifi, G. Fourlas
This paper presents a novel control strategy for image-based visual servoing (IBVS) of underwater vehicle manipulator systems (UVMS) using control barrier functions (CBFs) to handle field of view (FoV) constraints and system’s operational limitations such as manipulator joint limits and vehicle velocity performances. The proposed approach combines the advantages of IBVS, which provides visual feedback for control, with CBFs, which can formally enforce visibility and safety constraints on the UVMS’s motion. A CBF-based control law is derived and integrated with the IBVS algorithm, which guarantees the satisfaction of FoV and system’s operational constraints and ensure stability of the closed-loop system. To deal with FoV constraints, the proposed method uses a FoV index to estimate the degree of visibility of the scene, which is used to adjust the control inputs accordingly. The effectiveness of the proposed strategy is demonstrated through realistic simulation results, showing improved performance and safety of the UVMS under FoV and operational constraints compared to traditional IBVS methods. The results indicate that the proposed approach can handle the challenging underwater environment, UVMS dynamics and the operational constraints effectively, making it a valuable control strategy for practical applications of UVMS.
{"title":"Control Barrier Function Based Visual Servoing for Underwater Vehicle Manipulator Systems under Operational Constraints","authors":"Shahab Heshmati-alamdari, G. Karras, M. Sharifi, G. Fourlas","doi":"10.1109/MED59994.2023.10185835","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185835","url":null,"abstract":"This paper presents a novel control strategy for image-based visual servoing (IBVS) of underwater vehicle manipulator systems (UVMS) using control barrier functions (CBFs) to handle field of view (FoV) constraints and system’s operational limitations such as manipulator joint limits and vehicle velocity performances. The proposed approach combines the advantages of IBVS, which provides visual feedback for control, with CBFs, which can formally enforce visibility and safety constraints on the UVMS’s motion. A CBF-based control law is derived and integrated with the IBVS algorithm, which guarantees the satisfaction of FoV and system’s operational constraints and ensure stability of the closed-loop system. To deal with FoV constraints, the proposed method uses a FoV index to estimate the degree of visibility of the scene, which is used to adjust the control inputs accordingly. The effectiveness of the proposed strategy is demonstrated through realistic simulation results, showing improved performance and safety of the UVMS under FoV and operational constraints compared to traditional IBVS methods. The results indicate that the proposed approach can handle the challenging underwater environment, UVMS dynamics and the operational constraints effectively, making it a valuable control strategy for practical applications of UVMS.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115326945","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 : 2023-06-26DOI: 10.1109/MED59994.2023.10185815
A. Baciu, C. Lazar
Data-driven control (DDC) algorithms have been developed in the last decades, whose design is based only on the data collected from the controlled plant, without using a process model. These techniques that do not use an explicit model of the system have become very attractive for the control of complex processes with high nonlinearities. This paper presents two DDC algorithms, one model-free adaptive control (MFAC), and the other model-free intelligent P(iP), whose performances are experimentally evaluated using the AERO 2 platform, a highly nonlinear aerospace system made by Quanser. The similarities and differences between the two DDC are succinctly presented and based on the results obtained through real-time experiments, the performances are compared.
{"title":"Experimental Comparison of Two Data-Driven Algorithms for Pitch Control of an Aerospace System","authors":"A. Baciu, C. Lazar","doi":"10.1109/MED59994.2023.10185815","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185815","url":null,"abstract":"Data-driven control (DDC) algorithms have been developed in the last decades, whose design is based only on the data collected from the controlled plant, without using a process model. These techniques that do not use an explicit model of the system have become very attractive for the control of complex processes with high nonlinearities. This paper presents two DDC algorithms, one model-free adaptive control (MFAC), and the other model-free intelligent P(iP), whose performances are experimentally evaluated using the AERO 2 platform, a highly nonlinear aerospace system made by Quanser. The similarities and differences between the two DDC are succinctly presented and based on the results obtained through real-time experiments, the performances are compared.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114738217","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 : 2023-06-26DOI: 10.1109/MED59994.2023.10185814
Mohamed N. Zareer, R. Selmic
This paper studies the spread of opinions in social media networks through the lens of opinion dynamics. As more human interactions and public discourse move online, understanding opinion formation and evolution in social media is crucial for issues such as virtual marketing, information dissemination, and social security. We introduce a novel approach using recurrent neural networks (RNN) to monitor and predict interactions in these networks. Our method uses two configurations of RNN algorithms to predict the opinions of agents in an online social network, with results showing its effectiveness in predicting diverse opinions. The first configuration uses a sigmoid activation function to predict the binary opinions output (agree, disagree), while the second configuration uses the softmax function to predict more detailed opinions. For the simulation results, we considered a group of five agents interacting in the Twitter network on the subject of COVID-19. The social interaction for a 30-day period was captured and opinion dynamics prediction using the RNN was verified.
{"title":"Predicting Opinions in Social Networks Using Recurrent Neural Networks","authors":"Mohamed N. Zareer, R. Selmic","doi":"10.1109/MED59994.2023.10185814","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185814","url":null,"abstract":"This paper studies the spread of opinions in social media networks through the lens of opinion dynamics. As more human interactions and public discourse move online, understanding opinion formation and evolution in social media is crucial for issues such as virtual marketing, information dissemination, and social security. We introduce a novel approach using recurrent neural networks (RNN) to monitor and predict interactions in these networks. Our method uses two configurations of RNN algorithms to predict the opinions of agents in an online social network, with results showing its effectiveness in predicting diverse opinions. The first configuration uses a sigmoid activation function to predict the binary opinions output (agree, disagree), while the second configuration uses the softmax function to predict more detailed opinions. For the simulation results, we considered a group of five agents interacting in the Twitter network on the subject of COVID-19. The social interaction for a 30-day period was captured and opinion dynamics prediction using the RNN was verified.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"274 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124237442","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 : 2023-06-26DOI: 10.1109/MED59994.2023.10185682
Khelil Sidi Brahim, A. Hajjaji, N. Terki, D. L. Alabazares
This paper deals with the constrained position and angle tracking control design for quadrotor under unknown upper bound disturbances. An adaptive integral sliding mode control (AISMC) is proposed to perform the position and angle tracking for the quadrotor subject the severe disturbances and input saturation constraints. The proposed approach that does not require a priori knowledge of disturbance boundaries, allows through an adaptation dynamic law to reduce the computing effort, to obtain a good tracking, and to avoid an overestimation of the gain of the mode sliding that will automatically handle input saturation constraints. Stability and convergence in finite time are proved by Lyapunov theory. The efficiency of the proposed method is shown by simulation
{"title":"Adaptive Integral Sliding Mode Control for Constrained Quadrotor Trajectory Tracking","authors":"Khelil Sidi Brahim, A. Hajjaji, N. Terki, D. L. Alabazares","doi":"10.1109/MED59994.2023.10185682","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185682","url":null,"abstract":"This paper deals with the constrained position and angle tracking control design for quadrotor under unknown upper bound disturbances. An adaptive integral sliding mode control (AISMC) is proposed to perform the position and angle tracking for the quadrotor subject the severe disturbances and input saturation constraints. The proposed approach that does not require a priori knowledge of disturbance boundaries, allows through an adaptation dynamic law to reduce the computing effort, to obtain a good tracking, and to avoid an overestimation of the gain of the mode sliding that will automatically handle input saturation constraints. Stability and convergence in finite time are proved by Lyapunov theory. The efficiency of the proposed method is shown by simulation","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129819748","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 : 2023-06-26DOI: 10.1109/MED59994.2023.10185829
S. Galve, V. Puig, Xavier Vilajosana
Practical implementation of data analytics in industrial environments has always been a problematic area because of data availability and quality. In this paper, a Gaussian sampling methodology is proposed to address the problem of imbalanced telemetry datasets that is one of the root causes that make modelling less reliable. By generating subsets that achieve homogeneous density distributions this problem is addressed. By comparing the impact of this method with the baseline case of random sampling, this paper aims to address this problem and propose a practical solution. A case study based on an industrial cooling device is used to assess and illustrate the proposed approach.
{"title":"Gaussian Sampling Approach to deal with Imbalanced Telemetry Datasets in Industrial Applications*","authors":"S. Galve, V. Puig, Xavier Vilajosana","doi":"10.1109/MED59994.2023.10185829","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185829","url":null,"abstract":"Practical implementation of data analytics in industrial environments has always been a problematic area because of data availability and quality. In this paper, a Gaussian sampling methodology is proposed to address the problem of imbalanced telemetry datasets that is one of the root causes that make modelling less reliable. By generating subsets that achieve homogeneous density distributions this problem is addressed. By comparing the impact of this method with the baseline case of random sampling, this paper aims to address this problem and propose a practical solution. A case study based on an industrial cooling device is used to assess and illustrate the proposed approach.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127500214","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 : 2023-06-26DOI: 10.1109/MED59994.2023.10185824
A. Giuseppi, Leonardo Pio Lo Porto, Andrea Wrona, Danilo Menegatti
Landslides are critical natural hazards whose frequency and severity are increasing due to climate change and human activities. The consequences of landslides are severe and can lead to the destruction of homes, infrastructures and the contamination of water supplies, with severe impact also on the local ecosystems and the disruption of natural habitats. This article examines the application of an ad-hoc neural network-based intelligent system to evaluate the landslide susceptibility of the terrain on the basis of satellite data. The proposed system is validated on data from Lombardia and Abruzzo, two Italian regions that have been particularly subject to the landslide phenomenon. Results indicate that the CNN model is able to correctly identify landslide occurrences with high accuracy, demonstrating that CNNs are capable of providing accurate susceptibility mapping at a local scale and surpassing the performance of existing solutions available in the literature.
{"title":"Landslide Susceptibility Prediction from Satellite Data through an Intelligent System based on Deep Learning","authors":"A. Giuseppi, Leonardo Pio Lo Porto, Andrea Wrona, Danilo Menegatti","doi":"10.1109/MED59994.2023.10185824","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185824","url":null,"abstract":"Landslides are critical natural hazards whose frequency and severity are increasing due to climate change and human activities. The consequences of landslides are severe and can lead to the destruction of homes, infrastructures and the contamination of water supplies, with severe impact also on the local ecosystems and the disruption of natural habitats. This article examines the application of an ad-hoc neural network-based intelligent system to evaluate the landslide susceptibility of the terrain on the basis of satellite data. The proposed system is validated on data from Lombardia and Abruzzo, two Italian regions that have been particularly subject to the landslide phenomenon. Results indicate that the CNN model is able to correctly identify landslide occurrences with high accuracy, demonstrating that CNNs are capable of providing accurate susceptibility mapping at a local scale and surpassing the performance of existing solutions available in the literature.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126568592","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 : 2023-06-26DOI: 10.1109/MED59994.2023.10185801
Fotis Panetsos, G. Karras, K. Kyriakopoulos, Odysseas Oikonomides, P. Kolios, Demetrios G. Eliades, C. Panayiotou
In this work, a nonlinear Model Predictive Control (NMPC) strategy is presented for stabilizing an Unmanned Aerial Vehicle (UAV) with a cable-suspended liquid collection device during water sampling from aquatic environments. Building upon our previous work, an NMPC scheme is developed which incorporates the disturbances acting on the multirotor and attains the accurate hovering of the vehicle while simultaneously state and input constraints are satisfied. Once the UAV is stabilized above the water surface, a custom electromechanical mechanism is activated to collect water samples. The performance of the proposed controller and the reliability of the sampling device are demonstrated through real-world experiments in a river with high water flow.
{"title":"A Nonlinear Model Predictive Control Strategy for Water Sampling Using a UAV with a Slung Mechanism","authors":"Fotis Panetsos, G. Karras, K. Kyriakopoulos, Odysseas Oikonomides, P. Kolios, Demetrios G. Eliades, C. Panayiotou","doi":"10.1109/MED59994.2023.10185801","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185801","url":null,"abstract":"In this work, a nonlinear Model Predictive Control (NMPC) strategy is presented for stabilizing an Unmanned Aerial Vehicle (UAV) with a cable-suspended liquid collection device during water sampling from aquatic environments. Building upon our previous work, an NMPC scheme is developed which incorporates the disturbances acting on the multirotor and attains the accurate hovering of the vehicle while simultaneously state and input constraints are satisfied. Once the UAV is stabilized above the water surface, a custom electromechanical mechanism is activated to collect water samples. The performance of the proposed controller and the reliability of the sampling device are demonstrated through real-world experiments in a river with high water flow.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132485007","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 : 2023-06-26DOI: 10.1109/MED59994.2023.10185843
D. Bazylev, A. Pyrkin, D. Dobriborsci
This paper is addressed to a problem of state observation for permanent magnet synchronous motor (PMSM) and its design parameter tuning via evolutionary algorithm. Recently proposed flux, position and speed observer that is based on nonlinear parameterization of motor model and dynamic regressor extension and mixing (DREM) technique is considered. Though global asymptotic convergence of this observer was guaranteed for all positive real values of several design parameters the choice of their values for a particular motor was not well considered. To overcome this drawback a genetic algorithm is used to perform automatic tuning of required coefficients minimizing cost function that is associated with estimation errors. Simulation results supplemented by verification demonstrate the efficiency of the proposed approach resulting in a set of easy-to-implement-in-practice values of design parameters.
{"title":"Nonlinear state observer for PMSM with evolutionary algorithm","authors":"D. Bazylev, A. Pyrkin, D. Dobriborsci","doi":"10.1109/MED59994.2023.10185843","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185843","url":null,"abstract":"This paper is addressed to a problem of state observation for permanent magnet synchronous motor (PMSM) and its design parameter tuning via evolutionary algorithm. Recently proposed flux, position and speed observer that is based on nonlinear parameterization of motor model and dynamic regressor extension and mixing (DREM) technique is considered. Though global asymptotic convergence of this observer was guaranteed for all positive real values of several design parameters the choice of their values for a particular motor was not well considered. To overcome this drawback a genetic algorithm is used to perform automatic tuning of required coefficients minimizing cost function that is associated with estimation errors. Simulation results supplemented by verification demonstrate the efficiency of the proposed approach resulting in a set of easy-to-implement-in-practice values of design parameters.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"5 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114014929","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 : 2023-06-26DOI: 10.1109/MED59994.2023.10185691
P. Papageorgiou, M. K. Bourdoulis, A. Alexandridis
The most common techniques employed for the control of doubly-fed induction generator (DFIG) wind turbine systems are restricted to either the well-known field-orientation control (FOC) or the direct-power control (DPC), with each one of them, however, suffering in one way or another from distinctive drawbacks. Instead of these standard methods, in this paper, a novel and nonlinear model-based control approach is adopted, which is developed in view of the entire system structure and characteristics. The key novelties introduced by the proposed design are due to an innovative technique, defined as 3s-FOC, which is formulated to enable the implementation of a simple cascade-mode PI-based control scheme that i) achieves stator field orientation without the need for estimating the actual flux, ii) guarantees system stability while simultaneously provide a relaxation on the transient response, iii) improves the closed-loop system dynamic behavior by employing extra damping terms in the inner-loop current regulators. The stability and state convergence properties of the complete system is firmly ensured as it is verified by a rigorous analysis based on advanced Lyapunov-based methods and input-to-state stability (ISS) techniques. Finally, a thorough simulation is conducted, which firmly verifies the theoretical results and the superior controlled system dynamic performance.
{"title":"DFIG Wind Turbine Novel Cascade Control guaranteeing Sensorless Field Orientation and Stability","authors":"P. Papageorgiou, M. K. Bourdoulis, A. Alexandridis","doi":"10.1109/MED59994.2023.10185691","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185691","url":null,"abstract":"The most common techniques employed for the control of doubly-fed induction generator (DFIG) wind turbine systems are restricted to either the well-known field-orientation control (FOC) or the direct-power control (DPC), with each one of them, however, suffering in one way or another from distinctive drawbacks. Instead of these standard methods, in this paper, a novel and nonlinear model-based control approach is adopted, which is developed in view of the entire system structure and characteristics. The key novelties introduced by the proposed design are due to an innovative technique, defined as 3s-FOC, which is formulated to enable the implementation of a simple cascade-mode PI-based control scheme that i) achieves stator field orientation without the need for estimating the actual flux, ii) guarantees system stability while simultaneously provide a relaxation on the transient response, iii) improves the closed-loop system dynamic behavior by employing extra damping terms in the inner-loop current regulators. The stability and state convergence properties of the complete system is firmly ensured as it is verified by a rigorous analysis based on advanced Lyapunov-based methods and input-to-state stability (ISS) techniques. Finally, a thorough simulation is conducted, which firmly verifies the theoretical results and the superior controlled system dynamic performance.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121433300","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 : 2023-06-26DOI: 10.1109/MED59994.2023.10185790
A. Kychkin, Georgios C. Chasparis, S. Ellero
The Parallel Flow Regenerative (PFR) lime kiln process is challenging with respect to the energy efficiency, product quality and production stops, due to the inability of the human operators to accurately predict the evolution of the process. Monitoring and controlling of such processes encounter several issues, related to the high mass and heat inertia of the process, data quality, production stops, operator’s experience, as well as unknown exogenous factors (e.g., quality of the fuel, and raw material properties). Hence, an automated control/optimization mechanism for properly configuring the process is not straightforward. In this paper, we present a selection of mechanisms for data preprocessing together with domain specific feature analysis that allow for capturing the short-term changes of the critical parameters of the process. Through these mechanisms, automated predictive modeling can be performed that can be used by the kiln operator or a predictive-based controller to modify fuel feed strategies to meet energy efficiency and product quality requirements. We validate the proposed data-based preprocessing and modeling approaches through experiments in real-world data sources.
{"title":"Automated Cross Channel Temperature Predictions for the PFR Lime Kiln Operating Support","authors":"A. Kychkin, Georgios C. Chasparis, S. Ellero","doi":"10.1109/MED59994.2023.10185790","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185790","url":null,"abstract":"The Parallel Flow Regenerative (PFR) lime kiln process is challenging with respect to the energy efficiency, product quality and production stops, due to the inability of the human operators to accurately predict the evolution of the process. Monitoring and controlling of such processes encounter several issues, related to the high mass and heat inertia of the process, data quality, production stops, operator’s experience, as well as unknown exogenous factors (e.g., quality of the fuel, and raw material properties). Hence, an automated control/optimization mechanism for properly configuring the process is not straightforward. In this paper, we present a selection of mechanisms for data preprocessing together with domain specific feature analysis that allow for capturing the short-term changes of the critical parameters of the process. Through these mechanisms, automated predictive modeling can be performed that can be used by the kiln operator or a predictive-based controller to modify fuel feed strategies to meet energy efficiency and product quality requirements. We validate the proposed data-based preprocessing and modeling approaches through experiments in real-world data sources.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116691312","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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