Application of Genetic Algorithms in Optimisation of Solar Dryers

The fish industry in Kenya, with a production of over 350,000 metric tonnes earns USD 105 million which accounts for about 5% of the national Gross Domestic Product, provides 3% of skilled and unskilled employment. However, this industry is threatened as the fish is harvested at high moisture content of about 5kg/kg, dry basis, and at this moisture content, if not preserved, it undergoes spontaneous spoilage in 24 hours. At the artisanal fishermen level, the most viable preservation option is solar drying, in which fish is enclosed in a solar dryer, shielding it from contamination, and destruction. Depending on the conditions in the dryer, fish can either be over-dried or under-dried, resulting in heavy losses at household and national level. That is why it is recommended that appropriate designs and optimization principles and models for solar dryers should always be developed and adopted as explained in this study. This would result in effective and efficient energy harnessing and quality enhancement of solar dried food material, with the possibility of reducing food losses, improving food security and raising the level of income at farm level.