Conference Presentation | Other Publications |
Operational optimization and error detection in biomass boilers by model based monitoring: methods and practice
Zemann C, Niederwieser H, Gölles M.
Published 2023
Citation: Zemann C, Niederwieser H, Gölles M. Operational optimization and error detection in biomass boilers by model based monitoring: methods and practice. 7. Mitteleuropäische Biomassekonferenz: CEBC 2023. 20. Jan 2023. Oral presentation.
Abstract
One of the main tasks for operators of medium- and large-scale biomass boilers is the continuous operational monitoring of these plants in order to assess their performance, detect errors and identify possibilities for operational optimization. However, due to the high complexity of this task, errors are frequently detected too late or not at all, which can lead to even more costly secondary errors. In addition, possibilities for optimization remain unused in many existing plants, resulting in unnecessary pollutant emissions and low efficiencies.
To assist operators in performing this task and to achieve a high level of automation, methods for the automated, model-based monitoring of such plants have been focus of recent research activities. In this contribution, we will discuss the numerous possibilities provided by the application of such methods in a practical context. For this purpose, we present selected results from previous activities, demonstrating how methods for model-based monitoring were applied at combustion plants and used to enable automated error detection and support operational optimization.
Exemplary result 1: We developed a soft-sensor which accurately estimates the non-measurable internal state of heat exchangers and implemented it at a large-scale combustion plant with a nominal capacity of 38.2 MW. This soft-sensor uses a dynamic mathematical model of the heat exchanger in combination with measured data to determine a new estimate for the heat exchanger’s internal state every second. Based on this estimate, the soft-sensor accurately detects fouling and determines the non-measurable flue gas mass flow in real time. The estimated flue gas mass flow was used in a model-based control strategy which resulted in significant improvements of the combustion plant’s operational behaviour and load modulation capabilities. These results are discussed in this contribution.
Exemplary result 2: We developed a method for the real-time estimation of non-measurable fuel properties, i.e. chemical composition, bulk density, lower heating value, in biomass boilers. These estimates were subsequently used in a model-based control strategy and enabled the improvement of the biomass boiler’s fuel flexibility. Results of this estimator achieved for different biomass fuels, e.g. poplar wood chips, corncob grits and standard wood pellets, are discussed in this contribution.
On the basis of these selected results, it will be examined which possibilities arise from the use of methods for model-based monitoring in biomass boilers and also how these results can be extended to other technologies such as biomass gasifiers.