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Identification of sampling points for the detection of SARS-CoV-2 in the sewage system
Identification of sampling points for the detection of SARS-CoV-2 in the sewage system
A suitable tool for monitoring the spread of SARS-CoV-2 is to identify potential sampling points in the wastewater collection system that can be used to monitor the distribution of COVID-19 disease affected clusters within a city. The applicability of the developed methodology is presented through the description of the 72,837 population equivalent wastewater collection system of the city of Nagykanizsa, Hungary and the results of the analytical and epidemiological measurements of the wastewater samples. The wastewater sampling was conducted during the 3rd wave of the COVID-19 epidemic. It was found that the overlap between the road system and the wastewater network is high, it is 82 %. It was showed that the proposed methodological approach, using the tools of network science, determines confidently the zones of the wastewater collection system and provides the ideal monitoring points in order to provide the best sampling resolution in urban areas. The strength of the presented approach is that it estimates the network based on publicly available information. It was concluded that the number of zones or sampling points can be chosen based on relevant epidemiological intervention and mitigation strategies. The algorithm allows for continuous effective monitoring of the population infected by SARS-CoV-2 in small-sized cities.
Network-based Observability and Controllability Analysis of Dynamical Systems: the NOCAD toolbox [version 2]
Network-based Observability and Controllability Analysis of Dynamical Systems: the NOCAD toolbox [version 2]
The network science-based determination of driver nodes and sensor placement has become increasingly popular in the field of dynamical systems over the last decade. In this paper, the applicability of the methodology in the field of life sciences is introduced through the analysis of the neural network of Caenorhabditis elegans. Simultaneously, an Octave and MATLAB-compatible NOCAD toolbox is proposed that provides a set of methods to automatically generate the relevant structural controllability and observability associated measures for linear or linearised systems and compare the different sensor placement methods.
Evaluation of the Complexity, Controllability and Observability of Heat Exchanger Networks Based on Structural Analysis of Network Representations
Evaluation of the Complexity, Controllability and Observability of Heat Exchanger Networks Based on Structural Analysis of Network Representations
The design and retrofit of Heat Exchanger Networks (HENs) can be based on several objectives and optimisation algorithms. As each method results in an individual network topology that has a significant effect on the operability of the system, control-relevant HEN design and analysis are becoming more and more essential tasks. This work proposes a network science-based analysis tool for the qualification of controllability and observability of HENs. With the proposed methodology, the main characteristics of HEN design methods are determined, the effect of structural properties of HENs on their dynamical behaviour revealed, and the potentials of the network-based HEN representations discussed. Our findings are based on the systematic analysis of almost 50 benchmark problems related to 20 different design methodologies.
Operating regime model based multi-objective sensor placement for data reconciliation
Operating regime model based multi-objective sensor placement for data reconciliation
Although the number of sensors in chemical production plants is increasing thanks to the IoT revolution, it is still a crucial problem what to measure and how to place the sensors as such the resulted sensor network be robust and cost-effectively provide the required information. This problem is especially relevant in flexible multi-purpose, multi-product production plants when there are significant differences among the operating regions. The present work aims the development of a sensor placement methodology that utilizes the advantages of local linear models. Realizing the often conflicting nature of the key objectives of sensor placement, the problem is formulated as a multi-objective optimization task taking into consideration the cost, estimation accuracy, observability and fault detection performance of the designed networks and simultaneously seeking for the optimal solutions under multiple operating regimes. The effectiveness of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II)-based solution of the defined problem is demonstrated through benchmark examples.
Network-based Observability and Controllability Analysis of Dynamical Systems: the NOCAD toolbox
Network-based Observability and Controllability Analysis of Dynamical Systems: the NOCAD toolbox
Network science has become increasingly important in life science over the last decade. The proposed Octave and MATLAB-compatible NOCAD toolbox provides a set of methods which enables the structural controllability and observability analysis of dynamical systems. In this paper, the functionality of the toolbox is presented, and the implemented functions demonstrated.
The new version of our toolbox:
Network distance-based simulated annealing and fuzzy clustering for sensor placement ensuring observability and minimal relative degree
Network distance-based simulated annealing and fuzzy clustering for sensor placement ensuring observability and minimal relative degree
Network science-based analysis of the observability of dynamical systems has been a focus of attention over the past five years. The maximum matching-based approach provides a simple tool to determine the minimum number of sensors and their positions. However, the resulting proportion of sensors is particularly small when compared to the size of the system, and although structural observability is ensured, the system demands additional sensors to provide the small relative order needed for fast and robust process monitoring and control. In this paper, two clustering and simulated annealing-based methodologies are proposed to assign additional sensors to the dynamical systems. The proposed methodologies simplify the observation of the system and decrease its relative order. The usefulness of the proposed method is justified in a sensor-placement problem of a heat exchanger network. The results show that the relative order of the observability is decreased significantly by an increase in the number of additional sensors.
Design-Oriented Structural Controllability and Observability Analysis of Heat Exchanger Networks
Design-Oriented Structural Controllability and Observability Analysis of Heat Exchanger Networks
The control-relevant design and analysis of Heat Exchanger Networks (HENs) is an essential issue in terms of the design and intensification of sustainable production systems. The structural controllability and observability of HENs should be studied based on their dynamical model. Recently, a maximum matching based algorithm was developed to determine the locations of the minimum number of actuators and sensors needed to ensure the controllability and observability of linear dynamical systems. In this paper, the ability of concentrated parameter state-space models of the heat exchanger units to serve as building blocks of the network of state variables in HENs, and the use of the resultant network of state variables to study the control of relevant topologies and properties of HENs is highlighted. Based on the results of the systematic analysis, the structural patterns that facilitate the control and observation of HENs with a relatively small number of actuators and sensors were determined. Two methods were proposed to define the sets of additional actuators and sensors which are required to improve the operability of the network by decreasing the order of the controlled system. The first method is based on the interpretation of the placement of the sensors and actuators as a set cover problem, while the second one uses two network science-based measures: closeness centrality and betweenness centrality. The proposed methodologies are demonstrated on a benchmark example that is well covered in the literature.
Network science and control theory
Network science and control theory
Controllability and observability in complex networks – the effect of connection types
Controllability and observability in complex networks – the effect of connection types
Network theory based controllability and observability analysis have become widely used techniques. We realized that most applications are not related to dynamical systems, and mainly the physical topologies of the systems are analysed without deeper considerations. Here, we draw attention to the importance of dynamics inside and between state variables by adding functional relationship defined edges to the original topology. The resulting networks differ from physical topologies of the systems and describe more accurately the dynamics of the conservation of mass, momentum and energy. We define the typical connection types and highlight how the reinterpreted topologies change the number of the necessary sensors and actuators in benchmark networks widely studied in the literature. Additionally, we offer a workflow for network science-based dynamical system analysis, and we also introduce a method for generating the minimum number of necessary actuator and sensor points in the system.
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