Predictive Energy Requirement Models in Bio-fiber Comminution Process

Abstract

Comminution of bio-fibers is necessary for their effective application in development of reinforced composite and feedstock; however, comminution is an energy intensive process and minimization of specific energy required in the size reduction of bio-fibers is an optimization task in design of various particulate based materials. The objective of this study was to model specific energy requirement in bio-fiber comminution process. The banana and coir bio-fibers used in the study were respectively extracted from post-harvest banana stem and outer shell of a coconut. Characterization of treated bio-fibers was done using Fourier Transform Infrared Spectroscopy and comminution process was carried out in a laboratory-scale knife mill. Electrical energy was recorded using a plug-in power energy meter while Kick's, Rittinger's and Bond's energy laws provide insight into the relationship between particle size reduction and energy consumption during comminution process. The Rosin Rammler-Bennett (RRB) and Gaudin-Schuhmann (GS) models were fitted with MATLAB ® R2014a. The Artificial Neural Networks (ANN) and Response Surface Method (RSM) was utilised in predicting specific energy requirements as controlled by fiber treatment conditions, milling speed, initial fiber length and drying temperature. The predictive accuracy of the RSM and ANN models were adjudged adequate in terms of the process independent variables. A global optimal condition was found at 2% NaOH concentration, 29 mm fiber length, 1981 rpm mill speed and 41 °C drying temperature. At these conditions, specific energy was 1.430 kJ/kg for banana fiber and 1.109 kJ/ kg for coir fiber having desirability of unity.