An energy perspective on modelling, supervision, and control of large-scale industrial systems: Survey and framework

Abstract

Energy is a universal concept that can be used across physical domains to describe complex large-scale industrial systems. This brief survey and framework gives a perspective on energy as a unifying domain for system modelling, supervision, and control. Traditionally, modelling and control problems have been approached by adopting a signal-processing paradigm. However, this approach becomes problematic when considering non-linear systems. A behavioural viewpoint, which incorporates energy as basis for modelling and control, is considered a viable solution. Since energy is seen as a unifying concept, its relationship to Euler-Lagrange equations, state space representation, and Lyapunov functions is discussed. The connection between control and process supervision using passivity theory coupled with a system energy balance is also established. To show that complex industrial systems comprising multiple energy domains can be modelled by means of a single electric circuit, its application to a large-scale thermo-hydraulic system is presented. Next, a simple non-linear transmission impedance electric circuit is used to illustrate how energy can be used to not only describe a system, but also serve as basis for system optimisation. An energy-based framework is proposed whereby energy is used as a unifying domain to work in, to analyse, and to optimise large-scale industrial systems.

An Energy Graph-Based Approach to Fault Diagnosis of a Transcritical CO2 Heat Pump

“…Table 1 lists the corresponding components found in the heat pump system with their respective functions. Gas flow meter Measures the mass flow rate of carbon dioxide through the gas loop 13 Evaporator centrifugal pump Circulates the water to be cooled through the evaporator 14 Gas cooler centrifugal pump Circulates the water to be heated through the gas cooler…”

“…Another successful technique often implemented is artificial neural network modeling due to its ability to predict complex system features [10].Despite the success of data-driven methods, future fault diagnosis methods will need to meet a variety of system needs and challenges such as robustness, uncertainty handling, and big data processing. According to Severson et al [11] this will call for new methods to draw from the strengths of both knowledge-and data-based approaches to form what is called hybrid techniques [12].The energy-based visualization concept was first introduced by van Schoor et al [13]. This concept aims to address condition monitoring, control and optimization of large-scale industrial plants in a generic way using energy as the main parameter.…”

“…The energy-based visualization concept was first introduced by van Schoor et al [13]. This concept aims to address condition monitoring, control and optimization of large-scale industrial plants in a generic way using energy as the main parameter.…”

A comparative study of energetic model-based fault detection using HBG and COG formalisms

“…There have been several recent publications that are concerned with using energy as a unified domain to model a process and then apply qualitative or quantitative features for supervision and control. 9 A behavioral viewpoint, which incorporates energy and information flow as basis for modeling, is mostly treated like a viable solution. The author 10 introduces an approach that discerns energy and power exchanges induced by the presence of faulty situations.…”

“…Using signal processing methods, the elements of this set are compared to the residual signal to determine the fault characteristics and magnitude. An energy perspective on modeling, supervision, and control of large‐scale industrial systems is given in Van Schoor et al The connection between the control and process supervision using passivity theory coupled with a system energy balance is likewise established. Despite many control strategies presented in the literature as stated above, there are a restricted number of results associated to the diagnosis of faults using the Hamiltonian formalism and the COG tool.…”