Login / Signup

Comprehensive review on ideas, designs and current techniques in solar dryer for food applications.

Rasaiah NaveenkumarManickam RavichandranRavikumar HarishJegan Joywin RuskinNagarajan PozhingiyarasanAnnadurai Kolanjinathan
Published in: Environmental science and pollution research international (2023)
Due to the expansion of residents, the consumption of non-renewable energy increased enormously, thus indirectly increasing pollution and affecting the surroundings. To reduce pollutions in the surroundings, it is recommended to choose non-conventional energy sources. By satisfying this, we can probably decrease the non-renewable sources of energy, by consuming the solar power in day-to-day life in the application of food drying process. In this review article, we have discussed the classification of solar dryer and the impact of design modifications performed in the components of solar dryer and assessed the various types of solar dryer performance, cost estimations and designs performed in solar dryer of food applications which were not discussed in the earlier research. The primary and critical task in designing the solar dryer is to achieve higher efficiency at minimum cost. Hence, proper analysis of drying application, selection of suitable components and suitable design must be carried out to attain efficient dryer. Considering these characteristics, this paper primarily focuses on the effective design parameters incorporated with various efficiency enhancement processes of the solar dryer in the applications of food drying techniques. Thus, this review paper delivers the various classifications, design parameters, performance enhancement methods, properties and valuable assets of solar dryer, which helps to develop the sustainable green eco-friendly environment most primarily, in the application of food drying process. This review article concreted the way for upcoming considerations and provided the techniques for the studies to convey the work for promoting method enhancements.
Keyphrases
  • human health
  • risk assessment
  • drinking water
  • machine learning
  • deep learning
  • heavy metals
  • low cost