Implementation of online optimization and control of complex processes near impossible in given time frame owing to large computational time of the models. Fast and efficient surrogate models such as ANN offer a credible solution to this problem. Optimization of the complex model can proceed with a data based surrogate model, thereby making it much faster than the conventional run. However, the process of surrogate model construction involves certain number of parameters that are usually fixed based on some heuristic, thereby constructing an inefficient model. This paper aims at presenting a novel parameter free surrogate building algorithm specifically focusing on artificial neural networks (ANN) for emulating the physics based models. The proposed algorithm intelligently designs the configuration of the network along with simultaneous determination of sample size required for training it to predict results with maximum accuracy. The novel sample size determination algorithm is based on a potential concept of hypercube sampling technique, which assures convergence to results in very less computational time. The multi-objective optimization framework ensures parsimonious behavior of the network. The proposed algorithm is utilized to build optimal ANN models for highly nonlinear polyvinyl acetate polymer reaction network model. Optimization of this complicated reaction network with the ANN surrogate in place is achieved nearly 12 times faster than the first principle based model. All this being possible with a surrogate building algorithm, which just takes the simulation model as input. © 2016 IEEE.