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

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

Abonyi: Visualization of Topology Representing Networks

Abstract. As data analysis tasks often have to face the analysis of huge and complex data sets there is a need for new algorithms that combine vector quantization and mapping methods to visualize the hidden data structure in a low-dimensional vector space. In this paper a new class of algorithms is defined. Topology representing networks are applied to quantify and disclose the data structure and different nonlinear mapping algorithms for the low-dimensional visualization are applied for the mapping of the quantized data. To evaluate the main properties of the resulted topology representing network based mapping methods a detailed analysis based on the wine benchmark example is given