Dynamic modelling of updraft gasifiers: Incidence of feedstock quality and operational variables in the transient model structure

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

  • Ricardo Narváez Cueva
  • R. Blanchard
  • G. Guerrón
  • Diego Chulde
  • Roger Dixon

Colleges, School and Institutes

External organisations

  • Energías Renovables (INER)
  • Loughborough University
  • Ecuador Instituto Nacional de Eficiencia Energética y Energías Renovables (INER)
  • Instituto Nacional de Eficiencia Energética y Energías Renovables

Abstract

This paper describes the definition of the transient model structure for an updraft gasifier and the input variables related to the process and the feedstock quality with the most significant influence on the dynamic models and the transient behaviour. For such purpose, a set of open-loop dynamics experiments were carried out in the gasifier. Moreover, the output variables performance was recorded together with the composition analysis of the municipal solid waste batch (MSW). The output and operational variables record was used as base information for performing regressions of transient models with the purpose of determining the model type choice that achieves the largest occurrence frequency of fitting percentage figures above 50%. In addition, the dataset of regression parameters is analysed through feature selection in order to establish the influence of feedstock quality parameters and independent dynamic operational variables in dynamic changes. The model structure selection determined that underdamped, second order with one zero transfer function (P2ZU) is the most accurate case for updraft gasifiers. Regarding the influence of feedstock-related information, feature selection results show that ultimate composition is the group of quality parameters with the most significant influence on transient behaviour. Results also show that recirculation flow rate is the operational variable whose effect in the output variables is the most likely to be predicted and potentially controlled. The results for this variable show that 64.3% of the performed regressions achieved a fitting percentage value above 50%.

Details

Original languageEnglish
Title of host publicationASME Proceedings: Modeling and Validation
Publication statusPublished - 11 Oct 2017
EventASME 2017 Dynamic Systems and Control Conference, DSCC 2017 - Tysons, United States
Duration: 11 Oct 201713 Oct 2017

Conference

ConferenceASME 2017 Dynamic Systems and Control Conference, DSCC 2017
CountryUnited States
CityTysons
Period11/10/1713/10/17