The problem of optimizing a batch process under model uncertainty using a batch-wise unfolded PLS (BW-PLS) model-based modifier-adaptation (MA) strategy lis described. The main idea behind the strategy is to use measurements and iteratively modify the model to compensate for the mismatch of the necessary condition of optimality (NCO) between the plant and the model-based optimization problem. It is proven that the popular data data-driven-model-based iterative learning control (ILC) strategy is equivalent to the proposed MA strategy using only zero order modifier. Inspired by the effectiveness of the ILC being enhanced by rebuilding the data-driven model, a more elaborate model updating scheme is proposed in this paper to improve the optimization performances. The heuristic rules for choosing filtering gain matrix are also presented to further accelerate the convergence rate and reduce the variation of the cost during the period of evolution. Finally, the efficacy of the proposed MA strategy is illustrated via a simulated typical batch reaction and a simulated cobalt oxalate synthesis process.