Change and motion characterize and persistently reshape the world around us, on scales from molecular to global. The subtle interplay between change (Reaction) and motion (Diffusion) gives rise to an astonishing intricate spatial or temporal pattern. These pattern formation in nature has been intellectually appealing for many scientists since antiquity. Periodic precipitation patterns, also known as Liesegang patterns (LP), are one of the stimulating examples of such self-assembling reaction-diffusion (RD) systems. LP formation has a great potential in micro and nano technology. So far, the research on LPs have been concentrated mostly around how these patterns are forming, to retrieve information to build a universal mathematical model for them. Researchers have developed various theoretical models to comprehensively construct the geometrical diversity of LPs. To the best of our knowledge, simulation studies of LPs assume an arbitrary value of RD parameters to explain experimental observation qualitatively. In this work, existing models were studied to understand the mechanism behind this phenomenon and challenges pertaining in models were understood and explained. These models are not computationally effective due to the presence of discontinuous precipitation rate in RD equations. To overcome the computational challenges smoothened Heaviside function have been introduced, which downsizes the computational time as well. Experiments were performed using conventional LP system (AgNO3-K2Cr2O7) to understand the effects of different gels and temperature on formed LPs. The model is extended for real parameter values to compare the simulated results with experimental data.