Efficient cobalt oxalate synthesis process optimization via second‐order modifier adaptation with transfer learning

Abstract

This paper proposes a second‐order modifier adaptation optimization based on the transfer model, which aims to improve the optimization efficiency. Although the traditional modifier adaptation strategy adds a bias to the model to meet the necessary condition of optimality (NCO), approximating only to the first‐order may neglect some key higher‐order information. Making use of as much higher‐order information as possible is an effective way to improve the efficiency of optimization. For this issue, by introducing the second‐order information into the modifier adaptation method to compensate the mismatch of higher‐order information during the optimization, the compensation of the mismatch between model and plant can get accelerated, thus improving the optimization efficiency. To overcome the difficulty of insufficient data for new process modelling, a process transfer model is also used to fit the relationship between manipulated variables and final product quality. The simulation study of the cobalt oxalate synthesis process shows that this strategy has better optimization efficiency.