Data-driven identification and model predictive control of biomass gasification process for maximum energy production

Furkan Elmaz and Özgün Yücel

Energy, 2020, vol. 195, issue C

Abstract: Biomass gasification is an environment-friendly energy conversion process that utilizes bio-waste materials to produce combustible gases. In recent literature, machine learning-based techniques are used to model biomass gasification process. Even though these methods are reported for being viable, developed models’ time-independent structure fundamentally limited their prediction capabilities. Furthermore, control of biomass gasification is not studied in the literature despite its importance for industrial applications. We conducted this study in two parts. Firstly, we developed a time-dependent identification model to describe and predict outcomes of biomass gasification using non-linear autoregressive with exogenous neural networks (NARXNN) and experimentally collected data set. The developed model showed exceptional success by reaching R2> 0.98 for all output variables. Secondly, we designed a model predictive controller (MPC) in order to control a certain output variable at the desired state. For this purpose, we created polynomial regression models and online optimization routines. Moreover, the designed controller is challenged in practical scenarios such as maximum hydrogen production to test its usability in practical applications. MPC showed satisfactory performance for all scenarios and also showed high compliance with the experimental data which further strengthened its practical usability potential.

Keywords: biomass; Gasification; Nonlinear autoregressive neural networks; Model predictive control; Polynomial regression; Hydrogen production (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:195:y:2020:i:c:s0360544220301444

DOI: 10.1016/j.energy.2020.117037

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