Although reservoir computing (RC) is an effective approach to designing and training recurrent neural networks, the optimization of neural network systems involves a number of manual, tweaking or brute-force searching parameters, such as network size, input scaling parameters, and spectral radius. To create an optimal echo state network (ESN), we propose a modified RC that combines sensitivity analysis input scaling regulation (SAISR) and redundant unit pruning algorithm (RUPA). SAISR is first employed to obtain the optimal input scaling parameters. In SAISR, an ESN without tuning is established, and then its input scaling parameters are tuned based on the Sobol' sensitivity analysis. Second, RUPA is employed to prune out the redundant readout connections. A fed-batch penicillin cultivation process is chosen to demonstrate the applicability of the modified RC. The results show that the input scaling parameter has a more important influence than other parameters in ESN, and SAISR-ESN outperforms ESN without tuning. The RUPA method improves the generalization performance and simplifies the size of ESN. The prediction performance of RUPA-SAISR-ESN is compared with those of the existing methods, and the results indicate the superiority of RUPA-SAISR-ESN in the fed-batch penicillin cultivation process.