Design consideration and optimization of process parameters in fiber extraction unit via modelling studies

Demand for natural fibers as an alternative to synthetic fibers is on the rise. Constraints of current fiber extraction units are limited to single raw material and has low energy efficiency. Hence, this study was taken up for the design and development of a multiple fiber extractor, modelling and optimization of the machine operating parameters, and economic analysis of the developed prototype. The designed machine operates based on the raspador principle. Box Behnken design (BBD) and artificial neural network (ANN) were used to evaluate the performance and optimize the variables of the designed multiple fiber extractor. The input variables included roller speed (550, 735, and 920 rpm), gap/clearance of the roller (2, 3, and 4 mm) and the pitch/distance between the spline slots (0.5, 1.5, and 2.5 mm), while the output variables were yield (kg), capacity (kg/h), energy consumption (kJ), and efficiency (%).The prediction accuracy of quadratic models (R-2 = 0.962-0.985) developed in BBD were better than the ANN models (R-2 = 0.831-0.946). The optimized combination was found to be 714.23 rpm roller speed, 3.12 mm gap, and 1.49 mm pitch of the blade, which yielded (1.48 kg) a desirability value of 0.95. The XRD results indicated the highest crystallinity index was recorded for Pineapple leaf fiber compared to banana fiber. SEM results revealed that extracted BF having typical network structure (cellulose, hemicellulose, lignin, and waxes) compared to smooth PLF (lignin and hemicellulose). The fibers extracted using the developed machine was utilized for making biodegradable cutlery.