Optimization Strategies for Hybrid Drying Systems: Enhancing Energy Efficiency, Cost, and Quality in Food Processing
- 1School of Mechanical, Medical and Process Engineering, Faculty of Engineering, Advanced Drying and Sustainable Energy Research (ADSER), Queensland University of Technology (QUT), 2 George Street, Brisbane, QLD, 4000, Australia
- 2Department of Mechanical Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh
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).
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BibTeX
@article{pereira2026optimization,
title = {{Optimization Strategies for Hybrid Drying Systems: Enhancing Energy Efficiency, Cost, and Quality in Food Processing}},
author = {Nileema Blanch Pereira and Mohammad U. H. Joardder and Azharul Karim},
journal = {Food Engineering Reviews},
year = {2026},
month = jun,
doi = {10.1007/s12393-026-09446-9},
url = {https://doi.org/10.1007/s12393-026-09446-9},
note = {CC BY},
}
RIS
TY - JOUR
TI - Optimization Strategies for Hybrid Drying Systems: Enhancing Energy Efficiency, Cost, and Quality in Food Processing
AU - Nileema Blanch Pereira
AU - Mohammad U. H. Joardder
AU - Azharul Karim
PY - 2026
DA - 2026/06/01
JO - Food Engineering Reviews
DO - 10.1007/s12393-026-09446-9
UR - https://doi.org/10.1007/s12393-026-09446-9
AB - 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).
SN - 1866-7910
SN - 1866-7929
ER -