Biomass is a key renewable resource for advancing sustainable and circular energy systems. In contrast to prior reviews that predominantly emphasized well-established biomass types and conventional conversion technologies, this work offers a comparative synthesis that underscores underutilized feedstocks and emerging valorization pathways, providing a strategic perspective for sustainable process development. This review critically examines the current state of high-value-added bioproducts derived from biomass, focusing on their relevance to climate mitigation and resource efficiency. It explores sustainable process design strategies that enhance the environmental and economic performance of biomass conversion. Particular attention is given to recent advances in process intensification, including novel reactor configurations and heat integration techniques. The integration of sustainability assessment tools and multi-objective optimization approaches is analyzed to support data-driven decision-making. Multi-product biorefineries are discussed as central platforms for valorizing diverse feedstocks, supported by emerging models for supply chain integration. Present limitations such as feedstock heterogeneity, infrastructure constraints, and energy coupling challenges are reviewed, along with new opportunities in digitalization, modularization, and policy support. The novelty of this work lies in its cross-sectional synthesis of technologies, methodologies, and system-level strategies, offering a unified framework to unlock the full potential of biomass as a strategic vector for sustainable process development.

Optimization has played a fundamental role in the evolution of chemical engineering, enabling systematic decision-making under technical, economic, and environmental constraints. This review presents a structured and comparative analysis of the historical development and current state of optimization methodologies applied to chemical engineering, covering the transition from early linear and nonlinear programming approaches to advanced data-driven and artificial intelligence-based frameworks. A systematic literature review was conducted following the PRISMA guidelines, through which a total of 101 articles were retained for analysis. The results indicate that mixed-integer programming and decomposition-based methods remain widely adopted for structured industrial problems, while metaheuristic and hybrid data-driven approaches have experienced significant growth in recent years. In particular, a clear trend toward the integration of machine learning and surrogate modeling techniques is observed, driven by the need to address large-scale, non-convex, and highly nonlinear systems. The analysis reveals a clear methodological shift from classical linear optimization frameworks toward hybrid optimization strategies capable of addressing large-scale, non-convex, and highly nonlinear problems. Finally, current challenges and future research directions are identified, emphasizing the need for robust hybrid approaches that combine mathematical programming and intelligent algorithms to effectively manage complexity in next-generation chemical systems.