Computational fluid dynamic (CFD) simulations of chemical reacting flows demand excessive computational time in order to solve a large number of highly nonlinear chemical reaction source terms. The project aims to develop an algorithm for accelerated computations by developing high fidelity reduced-order data-based chemical kinetics solver using autoencoder and neural network algorithm. The basic framework has been developed and the new project will apply the algorithm for renewable fuel applications over a wide range of thermodynamic conditions and assess the fidelity and performance enhancement of the new algorithm. The student will gain understanding of various modern machine learning tools in engineering applications with hands-on experience of programming and implementation.