A Case Study on Optimization of Biomass Flow During Single-Screw Extrusion Cooking Using Genetic Algorithm (GA) and Response Surface Method (RSM)

In the present study, response surface method (RSM) and genetic algorithm (GA) were used to study the effects of process variables like screw speed, rpm (x1), L/D ratio (x2), barrel temperature (°C; x3), and feed mix moisture content (%; x4), on flow rate of biomass during single-screw extrusion cooking. A second-order regression equation was developed for flow rate in terms of the process variables. The significance of the process variables based on Pareto chart indicated that screw speed and feed mix moisture content had the most influence followed by L/D ratio and barrel temperature on the flow rate. RSM analysis indicated that a screw speed > 80 rpm, L/D ratio > 12, barrel temperature > 80 °C, and feed mix moisture content > 20% resulted in maximum flow rate. Increase in screw speed and L/D ratio increased the drag flow and also the path of traverse of the feed mix inside the extruder resulting in more shear. The presence of lipids of about 35% in the biomass feed mix might have induced a lubrication effect and has significantly influenced the flow rate. The second-order regression equations were further used as the objective function for optimization using genetic algorithm. A population of 100 and iterations of 100 have successfully led to convergence the optimum. The maximum and minimum flow rates obtained using GA were 13.19 × 10−7 m3/s (x1 = 139.08 rpm, x2 = 15.90, x3 = 99.56 °C, and x4 = 59.72%) and 0.53 × 10−7 m3/s (x1 = 59.65 rpm, x2 = 11.93, x3 = 68.98 °C, and x4 = 20.04%).