An algebraic approach to modeling distributed multiphysics problems: The case of a DRI reactor

© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.This paper deals with the problem of modelling a chemical reactor for the Direct Reduction of Iron ore (DRI). Such a process is being increasingly promoted as a more viable alternative to the classic Blast Furnace for the production of iron from raw minerals. Due to the inherent complexity of the process and the reactor itself, its effective monitoring and control requires advanced mathematical models containing distributed-parameter components. While classical approaches such as Finite Element or Finite Differences are still reasonable options, for accuracy and computational efficiency reasons, an algebraic approach is proposed. A full multi-physical, albeit one-dimensional model is addressed and its accuracy is analysed

Plug-and-Play Fault Detection and control-reconfiguration for a class of nonlinear large-scale constrained systems

This paper deals with a novel Plug-and-Play (PnP) architecture for the control and monitoring of Large-Scale Systems (LSSs). The proposed approach integrates a distributed Model Predictive Control (MPC) strategy with a distributed Fault Detection (FD) architecture and methodology in a PnP framework. The basic concept is to use the FD scheme as an autonomous decision support system: once a fault is detected, the faulty subsystem can be unplugged to avoid the propagation of the fault in the interconnected LSS. Analogously, once the issue has been solved, the disconnected subsystem can be re-plugged-in. PnP design of local controllers and detectors allow these operations to be performed safely, i.e. without spoiling stability and constraint satisfaction for the whole LSS. The PnP distributed MPC is derived for a class of nonlinear LSSs and an integrated PnP distributed FD architecture is proposed. Simulation results in two paradigmatic examples show the effectiveness and the potential of the general methodology

A distributed scenario-based stochastic MPC for fault-tolerant microgrid energy management

This paper proposes a fault-tolerant energy management algorithm for microgrid systems composed of several agents. The method stems from the necessity to design an algorithm that takes explicitly into account the possibility of faults and their consequences to avoid solutions which are excessively conservative. A tree of possible fault scenarios is built in a completely distributed way by all the agents of the network; then the resulting optimization problem is solved through a distributed algorithm which not only does not require a high computational power for each agent, but keeps also private all local data and decision variables. The effectiveness of the proposed method is proved through simulation results

A distributed networked approach for fault detection of large-scale systems

Networked systems present some key new challenges in the development of fault diagnosis architectures. This paper proposes a novel distributed networked fault detection methodology for large-scale interconnected systems. The proposed formulation incorporates a synchronization methodology with a filtering approach in order to reduce the effect of measurement noise and time delays on the fault detection performance. The proposed approach allows the monitoring of multi-rate systems, where asynchronous and delayed measurements are available. This is achieved through the development of a virtual sensor scheme with a model-based re-synchronization algorithm and a delay compensation strategy for distributed fault diagnostic units. The monitoring architecture exploits an adaptive approximator with learning capabilities for handling uncertainties in the interconnection dynamics. A consensus-based estimator with timevarying weights is introduced, for improving fault detectability in the case of variables shared among more than one subsystem. Furthermore, time-varying threshold functions are designed to prevent false-positive alarms. Analytical fault detectability sufficient conditions are derived and extensive simulation results are presented to illustrate the effectiveness of the distributed fault detection technique

Model-Based Fault Detection and Estimation for Linear Time Invariant and Piecewise Affine Systems by Using Quadratic Boundedness

Quadratic boundedness is a notion of stability that is adopted to investigate the design of observers for dynamic systems subject to bounded disturbances. We will show how to exploit such observers for the purpose of fault detection. Toward this end, first of all we present the naive application of quadratic boundedness to construct state observers for linear time-invariant systems with state augmentation, i.e., where additional variables may be introduced to account for the occurrence of a fault. Then a Luenberger observer is designed to estimate the augmented state variable of the system in such a way to detect the fault by using a convenient threshold selection. Finally, such an approach is extended to piecewise affine systems by presenting a hybrid Luenberger observer and its related design based on quadratic boundedness. The design of all the observers for both linear time-invariant and piecewise affine systems can be done by using linear matrix inequalities. Simulation results are provided to show the effectiveness of the proposed approach

Fast-convergent fault detection and isolation in a class of nonlinear uncertain systems

The present work proposes a fast-convergent fault detection and isolation (FDI) scheme for linear systems affected by model uncertainties, such as unknown inputs or unbounded nonlinearities. The finite-time convergence is attained by transforming the I/O signals through Volterra operators with suitably designed kernel functions. A novel feature of the proposed approach is the exploitation of a system decomposition that allows removing the effect of intractable uncertainties while recasting the system dynamics in a form applicable for Volterra operators to achieve non-asymptotic estimation. Remarkably, the proposed approach can reconstruct the state variables of the system in an arbitrarily short time and the fault can be diagnosed efficiently by imposing detection and isolation thresholds on transformed signals. The detectability and isolability of the fault are also characterized. The proposed FDI scheme is applied in simulation to a web process system to diagnose the presence of actuator faults. Simulation results confirm the effectiveness of the proposed scheme in two scenarios with nonlinear uncertainties. Furthermore, comparisons are made between the proposed method and a Sliding Mode Control (SMC) method in terms of estimation performance and computational complexity

Partition-based Pareto-optimal state prediction method for interconnected systems using sensor networks

In this paper a novel partition-based state prediction method is proposed for interconnected stochastic systems using sensor networks. Each sensor locally computes a prediction of the state of the monitored subsystem based on the knowledge of the local model and the communication with neighboring nodes of the sensor network. The prediction is performed in a distributed way, not requiring a centralized coordination or the knowledge of the global model. Weights and parameters of the state prediction are locally optimized in order to minimise at each time-step bias and variance of the prediction error by means of a multi-objective Pareto optimization framework. Individual correlations between the state, the measurements, and the noise components are considered, thus assuming to have in general unequal weights and parameters for each different state component. No probability distribution knowledge is required for the noise variables. Simulation results show the effectiveness of the proposed method

Distributed cyber-attack isolation for large-scale interconnected systems

This work addresses the problem of cyber-attack isolation within a distributed diagnosis architecture for large-scale interconnected systems. Considering a distributed control architecture, malicious agents are capable of compromising the data exchanged between distributed controllers. Building on a distributed detection strategy existent in literature, in this paper we propose a distributed isolation algorithm to identify the attacked communication link. After presenting the isolation algorithm, we give a necessary and a sufficient condition for isolation to occur, relating to the structure of the physical interconnection matrices. We demonstrate the effectiveness of the proposed technique through numerical simulations

A MATTER OF STYLE.HOW MAP THINKING AND BIO-ONTOLOGIES SHAPE CONTEMPORARY MOLECULAR RESEARCH

ABSTRACT The aim of this thesis is to provide an epistemic analysis of the transformations occurring in contemporary biological research by considering the relation between molecular biology and computational biology. In particular, I will focus on bio-ontologies, as the tool which incarnates at best the new face of biomedical research. Such a choice is not arbitrary. By appealing to the notion of style of reasoning and way of knowing, I will show that bio-ontologies exemplify the rise and success of map thinking as the signature of a new way of doing molecular biology, while the theoretical tenets, established more than 30 years ago, still maintain their epistemic prominence. This is neither to say that experimentalism will disappear from science, nor that the experiments power will be diminished but rather that experiments will have a new role in the architecture of scientific efforts, precisely because of the increasing importance of classificatory approaches. Therefore, such a transition within biomedical research is indeed radical and profound but it does not involve paradigm shifts but rather a change in the practice. In this sense, it is a matter of style

Graph-Based Learning for Leak Detection and Localisation in Water Distribution Networks∗

We propose the application of geometric deep learning techniques to the challenging leak detection and isolation problem in water distribution networks (WDNs). Specifically, we train two Chebyshev polynomial kernel Graph Convolutional Networks for the task of prediction, and reconstruction of nodal pressures in a WDN. Comparing the two network outputs (a predicted healthy model state with a reconstructed observation) a residual signal is obtained and analysed to detect leakages. By exploiting topological properties in the proposed approach, leakage isolation is also performed. We benchmark our method on the BattLeDIM 2020 dataset