Abstract The increasing global demand for sustainable food processing practices, coupled with the need to address global energy issues and environmental pollution, has underscored the importance of optimizing drying technologies. While conventional drying methods, such as hot air drying, remain prevalent, they are highly energy-intensive and often compromise product quality. In response, hybrid drying systems (HDS), particularly those combining microwave, heat pump, or dehumidifier and renewable energy sources, have emerged as promising alternatives. However, their broader adoption is constrained by complex interactions between multiple heat and mass transfer mechanisms, which complicate process control, limit scalability, and hinder systematic optimization. This review critically evaluates existing optimization strategies for HDS, highlighting key limitations in current approaches that rely heavily on empirical, univariate, or fragmented methodologies. Relevant studies were identified through a structured literature search using the Scopus database, limited to articles published between 2020 and 2025. The review explores a spectrum of optimization tools, including statistical methods, AI-driven approaches, nature-inspired algorithms, and physics-based models, assessing their applicability, scalability, and integration potential. Emphasis is placed on aligning optimization strategies with specific decision-making parameters such as drying efficiency, energy use, cost, and product quality. The review further proposes a structured framework for selecting hybrid dryer configurations and optimization techniques based on defined objectives. By addressing current research gaps, this work provides insights that support the development of clean, efficient, and resilient food drying systems, with potential relevance to broader sustainability objectives, including the United Nations Sustainable Development Goals (SDGs).
Pereira et al. (Mon,) studied this question.