Novel non-uniform adaptive grid refinement control parameterization approach for biochemical processes optimization

Abstract Dynamic optimization is a very important way to increase the productivity or profitability of biochemical processes. As an efficient approach for solving these biochemical dynamic optimization problems, control vector parameterization encounters the difficulty of selecting an optimal discretization level which balances the computational cost with the desired solution quality to obtain high accuracy solution. To tackle this issue, a new slope analysis is proposed to analyze the control variables and discretization time grid, results find that low slope time grid nodes have less effect on the improvement of performance index and can be regarded as unnecessary nodes, while high ones are important time points. Based on this, a novel non-uniform adaptive grid refinement control parameterization approach is therefore presented, where the slope analysis is applied to refine or to coarsen the time grid so as to obtain a suitable discretization level with a small number of control intervals. By application in three well-known biochemical optimization problems, results show that the proposed method is able to achieve similar or even better performance indexes with small numbers of control intervals and lower computational costs.

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