A semi-empirical model for de-watering and cooling of leafy vegetables. (25th May 2022)

• Record Type: Journal Article
• Authors: Bianco, Nicola; Mauro, Alfonso William; Mauro, Gerardo Maria; Pantaleo, Antonio Marco; Viscito, Luca
• Is Part Of: Applied thermal engineering. Volume 208(2022)
• Publication Date: 2022-05-25
• Subjects: De-watering -- Fresh leafy vegetables -- Food science -- Food processing industry -- Agri-food -- Digital models; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2022.118227
• Languages: English
• ISSNs: 1359-4311

Abstract

Graphical abstract: Highlights: Integrated and energy efficient leafy vegetables processing is proposed. A semi-empirical approach is presented to model the fresh vegetables de-watering and cooling processes. Mass and heat transfer processes are calibrated with experimental data. Model validation vs real experimental data is satisfactory given the uncertainty of input data. Operational maps are provided to define optimal process conditions. Abstract: This paper presents a semi-empirical model for mass and heat transfer applied to de-watering and cooling of fresh leafy vegetables. This process aims at optimizing vegetables' moisture content and temperature through the interaction with conditioned airflows to ensure proper storage and preservation. It is implemented in different modules – i . e ., a first set of hot modules with hot air, a second set of cold modules with cold air – allowing to remove water from the vegetables and to achieve the desired temperature. A dedicated transfer model is developed to follow the evolution of the liquid droplets on the leaves during the process. It is based on water mass and energy balances on product and air sides, where the bed of leaves is treated as a porous medium. The mass and heat transfer coefficients are calibrated by comparison with experimental data. The model is validated with real data from the field, and a parametric analysis is implemented to show its potential application to optimize the process. The calibrated model presents satisfactory reliability – less than ±1.0 °C as average error for output temperature – according to the uncertainty of the approaches available in literature, thereby ensuring a robust performance assessment. This can support the process application in several fields of the agri-food industry with significant quality and productivity improvements. Finally, the model can be used to develop digital twins to foster the ongoing digitalization of the agri-food sector with a view to sustainability.