Optimization of whole milk powder processing variables with neural networks and genetic algorithms

Spray-dried whole milk powder, one potential ingredient of milk chocolate, was exposed to high shear and elevated temperatures to increase the free fat content and to crystallize the lactose using a twin-screw continuous mixer/processor. Optimal process conditions were determined using neural networks and genetic algorithm optimization. Response surfaces methodology was used to design the experiments to collect data for the neural network modelling. A general regression neural network model was developed to predict the responses of lactose crystallinity and free fat content from the processor screw speed, process temperature, milk powder feed rate and lecithin addition rate. A genetic algorithm was used to search for a combination of the process variables for maximum free fat content and maximum crystallinity. The combinations of the process variables during genetic algorithm optimization were evaluated using the neural network model. The common optimum process conditions to maximize the free fat content and lactose crystallinity were determined to be 20 kg h^-1 feed rate, 284 rpm screw speed, 71.1°C process temperature and 0.01 kg h^-1 lecithin addition rate.