Model Optimization Using Artificial Intelligence Algorithms for Biological FoodWaste Degradation

Abstract

Food waste is categorized as the largest degradable component in the waste stream. Degradation of food waste that involved aerobic bacteria is the most suitable approach to dispose of this waste. The main objective of this research is to evaluate the optimum condition of aerobic bacteria growth for food waste degradation by comparing the implementation of response surface method (RSM) and genetic algorithm. Preliminary experiment is conducted to determine the best time for aerobic bacteria growth. Then, evaluation of five factors such as temperature, time, type of nutrient, agitation rate and inoculum size is done by conducting experiments according to the experimental table that is constructed by using design expert software. Growth of aerobic bacteria can be determined by measuring the optical density (OD) of the bacteria. Aerobic bacteria at the best growth condition are mixed with the food waste for degradation process. The ability of aerobic bacteria to degrade food waste is determined by monitoring the pH, moisture content and ratio of volatile solid to total solid (VS/TS) of food waste on the first and twentieth days of degradation. The result analysis using RSM showed that the optimum condition for aerobic bacteria growth is at 37_°C and 200_rpm in commercial nutritional supplement (CNS) medium with 10% (v/v) of inoculum size for 20_h. At this optimum condition, the OD value was 2.264 while optimization using genetic algorithm generated the OD value at 2.643 where this is 14% improvement from the RSM.