TA11 - Énergie et environnement / Energy and the Environment

Biomass plays a strategic role in low-carbon energy systems; however, its sustainability is primarily shaped by upstream feedstock and supply-side decisions, including feedstock choice, sourcing location, and transportation and logistics design. This study proposes a decision-oriented framework for assessing biomass supply chain sustainability, with a specific emphasis on upstream stages that remain insufficiently examined in the existing literature.
The analysis quantifies the availability of forestry biomass residues in Quebec by integrating regional production data. A set of transparent sustainability indicators is then applied to enable consistent comparisons across key supply-side decisions, particularly sourcing regions, capturing environmental performance, economic viability, social considerations, and supply risk. By linking resource availability with multi-dimensional sustainability metrics, the proposed framework overcomes a common limitation of biomass assessments that evaluate these dimensions in isolation.
The results provide decision-relevant insights for policymakers and supply chain actors, supporting strategic choices that enhance upstream sustainability while mitigating risks related to feedstock availability and sustainability.

Le Brésil est l'un des plus grands producteurs de biomasse du monde et l'un des pays les plus avancés en termes de bioénergie. Que ce soit par la production de biocharbon pour la métallurgie, la production de biométhane ou le bioraffinage de produits agroalimentaires et de carburants, ce pays est inspirant pour le Québec par de nombreux aspects. Le Québec, qui possède un important gisement de biomasse, déploie tranquillement son industrie de bioénergie, mais tarde comparativement au Brésil. La présentation illustrera le réseau d'acteurs et la chaine de valeur de la conversion énergétique de la biomasse au Québec comme au Brésil et mettra en lumière les conditions et stratégies implantées par le Brésil pour accélérer son déploiement dans le passé jusqu'à aujourd'hui.

This study introduces an innovative methodological framework that combines DEA, PCA and fuzzy J means clustering to develop a robust and adaptable Water Quality Index (WQI) for groundwater quality assessment. PCA is employed to cluster multi-dimensional physicochemical datasets and generates aggregation weights, enhancing information conservation and WQIs’ robustness. The DEA model’s surrogate inputs are produced through intrinsic aggregation of Optimistic Closeness Values (OCVs), which instills OCVs’ benchmarking impact into the water assessment process at early stages. The fuzzy J-means algorithm fosters objective data-driven water quality categories, avoiding arbitrary thresholds. Unlike expert-based approaches, which suffer eclipsing problems—where influential physicochemical parameters are overshadowed during aggregation—the proposed framework preserves variability and critical information inherent in original datasets.
Applied to 64 wells in Algeria's Hodna basin, the framework effectively identified 17.19% wells with superior water quality against 21.87% wells requiring intervention.

We investigate the discrete-time stochastic linear quadratic control problem for a population of cooperative agents under the hard equality constraint on total control inputs, motivated by demand response in renewable energy systems. We establish the optimal solution that respects hard equality constraints for systems with additive noise in the dynamics. The optimal control law is derived using dynamic programming and Karush-Kuhn-Tucker (KKT) conditions, and the resulting control solution depends on a discrete-time Riccati- like recursive equation. Application examples of coordinating the charging of a network of residential batteries to absorb excess solar power generation are demonstrated, and the proposed control is shown to achieve exact power tracking while considering individual State-of-Charge (SoC) objectives.