NEURAL NETWORK BASED ON-LINE RE-OPTIMISATION OF FED-BATCH PROCESSES USING ITERATIVE DYNAMIC PROGRAMMING FOR DISCRETE-TIME SYSTEMS

摘要

Optimisation of fed-batch processes can be described as a constrained nonlinear end-point dynamic optimisation problem. Although iterative dynamic programming (IDP) is feasible, it is usually very time-consuming and very difficult to apply to on-line optimisation because of solving the non-linear differential-algebraic equations of the process model in each iteration. The replacement of a rigorous mechanistic model by an equivalent neural network (NN) model takes the advantage of high speed processing, since simulation with a NN model involves only a few non-iterative algebraic calculations. To use IDP algorithm for NN model based on-line re-optimisation, a modified algorithm is proposed and is called as iterative dynamic programming for discrete-time system (IDPIDTS). The novel IDPIDTS algorithm can obtain a reduction of many times in computational time compared to the conventional IDP algorithm. In this paper, an effective optimisation and control scheme for on-line re-optimisation of fed-batch processes is proposed based on NN models and the novel IDP/DTS algorithm. The proposed scheme is illustrated using simulation studies of an ethanol fermentation process.