4.7 Article

Monitoring energy efficiency of condensing boilers via hybrid first-principle modelling and estimation

期刊

ENERGY
卷 142, 期 -, 页码 121-129

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2017.09.124

关键词

Hybrid modelling; State-dependent switching; Dynamic monitoring; Condensing boiler; Multiple-model estimation

资金

  1. 'Advanced Methods for Building-Diagnostics and Maintenance' (AMBI)

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The operating principle of condensing boilers is based on exploiting heat from flue gases to pre-heat cold water at the inlet of the boiler: by condensing into liquid form, flue gases recover their latent heat of vaporization, leading to 10-12% increased efficiency with respect to traditional boilers. However, monitoring the energy efficiency of condensing boilers is complex due to their nonlinear dynamics: currently, (static) nonlinear efficiency curves of condensing boilers are calculated at quasi-stationary regime and 'a posteriori', i.e. from data collected during chamber tests: therefore, with this static approach, it is possible to monitor the energy efficiency only at steady-state regime. In this work we propose a novel model-based monitoring approach for condensing boilers that extends the operating regime for which monitoring is possible: the approach is based on a hybrid dynamic model of the condensing boiler, where state-dependent switching accounts for dynamically changing condensing/non condensing proportions. Monitoring the energy efficiency over the boiler's complete dynamic regime is possible via switching estimators designed for the different condensing/non condensing modes. By using real-world boiler efficiency data we show that the proposed approach results in a (dynamic) nonlinear efficiency curve which gives a more complete description of the condensing boilers operation than static nonlinear efficiency curves: in addition, the dynamic curve can be derived 'a priori', i.e. from first principles, or from data collected during normal boiler operation (without requiring special chamber tests). (C) 2017 The Authors. Published by Elsevier Ltd.

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