Prediction of maximum oil-yield from almond seed in a chemical industry: A novel type-2 fuzzy logic approach

Abstract

Almond (Termanilia catappa) is one of the most popular tree nuts on a world-wide basis due to its huge benefits in human health. Since it is not readily available in every country, research and investigations are still going on this particular. Therefore, here we have developed a unique model using type-2 fuzzy logic controller (T2FLC) approach to predict the optimum process conditions for the extraction of oil at maximum from almond seeds. Here, we have taken pressure, temperature, heating time and moisture content as input parameters and oil yield is taken as an output parameter to optimize the process. Though hundred percent recovery of oil by extraction of almond seeds is never possible, but controlling the process parameters at suitable condition, oil yield may vary within the range of 40%–45%. Using these four input and one output parameters, four Mamdani fuzzy inference systems are formed depending on the different membership functions of the variables. In T2FLC assists to trace inputs and outputs in a well-organized manner for building the inferences train so that various types of oil yield and its assessment can be predicted during extraction. Finally, a statistical analysis has been done using type-2 fuzzy data set to improve the control of process parameters that can be easily determined in the type-2 fuzzy prediction model to get high yield. Keywords: Almond seed, Oil extraction, Process optimization, Fuzzy inference system, Interval type-2 fuzzy logic