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 become an indispensable tool in the food industry, addressing critical challenges related to efficiency, sustainability, and product quality. Traditional approaches, such as one-factor-at-a-time analysis, have been supplanted by more advanced methodologies like response surface methodology (RSM), which models interactions between variables, identifies optimal operating conditions, and significantly reduces experimental requirements. However, the increasing complexity of modern food production systems has necessitated the adoption of multi-objective optimization techniques capable of balancing competing goals, such as minimizing production costs while maximizing energy efficiency and product quality. Advanced methods, including evolutionary algorithms and comprehensive modeling frameworks, enable the simultaneous optimization of multiple variables, offering robust solutions to complex challenges. In addition, artificial neural networks (ANNs) have transformed optimization practices by effectively modeling non-linear relationships within complex datasets and enhancing prediction accuracy and system adaptability. The integration of ANNs with Industry 4.0 technologies—such as the Internet of Things (IoT), big data analytics, and digital twins—has enabled real-time monitoring and optimization, further aligning production processes with sustainability and innovation goals. This paper provides a comprehensive review of the evolution of optimization methodologies in the food industry, tracing the transition from traditional univariate approaches to advanced, multi-objective techniques integrated with emerging technologies, and examining current challenges and future perspectives.