Modeling and optimization of sugarcane juice clarification process

Abstract

Sugarcane juice clarification is an important operation in the production process of sugar industry. At present, the two major production indicators (clear juice color value and gravity purity of the mixed juice) in sugarcane juice clarification process still cannot be measured online, and can only be obtained by offline test. In addition, when production condition changes, the setting and adjustment of the key operational variables are made by experienced workers. The delay caused by the offline test and the randomness of the key operational variables caused by manual adjustment will affect the quality of final product. To address these two issues, we firstly constructed a data-driven model based on the deep kernel extreme learning machine (DK-ELM) to predict the key production indicators for clarification process. The model which combined the kernel method and the multi-layer extreme learning machine (ML-ELM) proved successful in prediction of clear juice color value and gravity purity. Then, we proposed a strategy for optimization of operational variables, avoiding the randomness caused by manual setting. A multi -objective function including the purity and color value was constructed. The niche multi-objective particle swarm optimization (PSO) algorithm was used to solve the multi -objective function under typical working conditions in the production process to get optimal set of key operational variables. The results showed that the niche multi-objective particle swarm optimization (PSO) algorithm can provide optimum production indexes for sugarcane juice clarification.