Determining Biomass Chemical Exergy Using A Novel Hybrid Intelligent Approach To Promote Biomass-Based Biorefineries

The issue of sustainability has become a strategic imperative for researchers attempting to address energy and environmental concerns using biorefinery approach. Exergy-based methods have shown significant promises in terms of their ability to reliably locate the hotspots of resource degradation in biorefineries. The key step in analyzing biorefineries exegetically is to calculate biomass chemical exergy which is a very computationally-intensive task. Interestingly, proximate and ultimate analysis methods show potential to reflect the chemical exergy content of biomass. Hence, the present study was devoted to introducing a novel hybrid intelligent approach to determine the chemical exergy content of biomass based on both the composition analysis methods. In the developed hybrid models, input score variables in each inner loop of partial least square (PLS) approach were correlated with its output score variables using hybrid adaptive neuro-fuzzy inference system and particle swarm optimization algorithm (ANFIS-PSO). Both the developed modeling systems showed acceptable accuracy in determining the chemical exergy values of biomass materials. The model derived from ultimate analysis was slightly more accurate than that from proximate analysis (mean absolute percentage error of 0.207 vs. 0.506, respectively). Nevertheless, simple and inexpensive character of proximate analysis can facilitate real-world applications of the respective model. Overall, the developed model can pave the way for developing sustainable biorefineries by computing the chemical exergy of biomass more accurately than complex thermodynamic models. (C) 2020 Elsevier Ltd. All rights reserved.