Biopolygeneration Diagnostic Index (BDI): An Exergy-Based Framework for Quantifying Maximum Utilization and Thermodynamic Performance in Biomass-Based Bioenergy Plants

The energy recovery of biomass is frequently implemented through single-output systems or passive management schemes, resulting in underutilization of its thermodynamic potential and losses in economic value, climate benefits, and useful co-products. This study formalizes the concept of biopolygeneration as a diagnostic principle aimed at maximizing biomass utilization through the simultaneous production of multiple energy services and the valorization of secondary streams. A dimensionless metric, the Biopolygeneration Diagnostic Index (BDI), is proposed to quantify this concept. The index is bounded within [0,1] and integrates five sub-indices: energy efficiency (IE), thermal integration (IT), energy self-sufficiency (IA), exergetic quality of outputs (IQ), and co-product valorization (IV). Weights were determined using the Analytic Hierarchy Process (w1=0.40, w2=0.24, w3=w4=0.14, w5=0.08; CR=0.007). The BDI was evaluated using six cases, including five operating plants and one validated computational model representing five biomass conversion technologies in four countries. Results ranged from 0.453 for an engine without combined heat and power (CHP) to 0.733 for a cascade trigeneration system. Under identical feed conditions, the incorporation of CHP (C1→C2) increased the BDI from 0.453 to 0.715, representing a 57.7% improvement attributable solely to heat recovery. Current limitations include the small validation sample (n=6) and the reconstruction of IA and IV from technological characteristics due to the absence of standardized reporting in the literature. Although these sub-indices account for only 22% of the total weighting (wIA+wIV=0.22), the present results should be considered a proof of concept rather than a fully empirical validation. The BDI provides a thermodynamically consistent framework for comparing bioenergy systems across technologies and supports technical, regulatory, and investment decision making. Broader validation using larger measurement-based datasets is required before claims of universality can be established.