Abstract
Condensing boilers achieve higher efficiency than traditional boilers by using waste heat in flue gases to preheat cold return water entering the boiler. Water vapor produced during combustion is condensed into liquid form, thus recovering its latent heat of vaporization, leading to around 10–12% increased efficiency. Many countries have encouraged the use of condensing boilers with financial incentives. It is thus important to develop software tools to assess the correct functioning of the boiler and eventually detect problems. Current monitoring tools are based on boiler static maps and on large sets of historical data, and are unable to assess timely loss of performance due to degradation of the efficiency curve or water leakages. This work develops a set of fault detection and diagnosis tools for dynamic energy efficiency monitoring and assessment in condensing boilers, i.e. performance degradation and faults can be detected using real-time measurements: this real-time feature is particularly relevant because of the limited amount of data that can be stored by state-of-the-art building energy management systems. The monitoring tools are organized as follows: a bimodal parameter estimator to detect deviations of the efficiency of the boiler from nominal values in both condensing and noncondensing mode; a virtual sensor for the estimation of the water mass flow rate; filters to detect actuator and sensor faults, possibly due to control and sensing problems. Most importantly, structural properties for detection and isolation of actuators and sensing faults are given: these properties are crucial to understand which faults can be diagnosed given the available measurements. The effectiveness of these tools is verified via extensive simulations.
Highlights
Building energy use, mainly driven by heating, ventilating and air conditioning (HVAC) equipment, is responsible for over a third of Europe and US global energy consumption and CO2 emissions which are supposed to heavily contribute to climate change [1]
While most of the techniques used in fault detection and diagnosis for building automation purposes are based on steady-state reasoning, the purpose of this work is to explore the possibility of including dynamical models for fault detection and diagnosis purposes
It has to be underlined that current state-of-the-art monitoring tools are based of static maps of the equipment, and in order to assess loss of performance it is necessary to have large sets of historical data to be used to benchmark performance of different period in the life time of the equipment
Summary
Mainly driven by heating, ventilating and air conditioning (HVAC) equipment, is responsible for over a third of Europe and US global energy consumption and CO2 emissions which are supposed to heavily contribute to climate change [1]. In order to achieve higher energy efficiency levels, a range of technical solutions must help building professionals in selecting and installing the most suitable heating systems, and constantly monitor them, using fault detection and diagnosis tools [2]. It is of practical importance to develop tools that can use data in real-life and possibly detect performance changes timely: this can potentially be achieved by developing monitoring tools based on dynamic models of the HVAC equipment.
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