Elzaki Transform Based Accelerated Homotopy Perturbation Method for Multi-dimensional Smoluchowski's Coagulation and Coupled Coagulation-fragmentation Equations

TL;DR

This paper introduces an accelerated Elzaki transform combined with homotopy perturbation method to efficiently solve complex population balance equations without discretization, outperforming existing semi-analytical techniques.

Contribution

The novel scheme integrates Elzaki transform with HPM, eliminating the need for Adomian polynomials and convergence parameters, and extends to multiple population balance models.

Findings

The method achieves high accuracy in solving nonlinear coagulation equations.

Numerical examples demonstrate the scheme's convergence and efficiency.

Comparison with existing methods shows improved performance.

Abstract

This article aims to establish a semi-analytical approach based on the homotopy perturbation method (HPM) to find the closed form or approximated solutions for the population balance equations such as Smoluchowski's coagulation, fragmentation, coupled coagulation-fragmentation and bivariate coagulation equations. An accelerated form of the HPM is combined with the Elzaki transformation to improve the accuracy and efficiency of the method. One of the significant advantages of the technique lies over the classic numerical methods as it allows solving the linear and non-linear differential equations without discretization. Further, it has benefits over the existing semi-analytical techniques such as Adomian decomposition method (ADM), optimized decomposition method (ODM), and homotopy analysis method (HAM) in the sense that computation of Adomian polynomials and convergence parameters are not required. The novelty of the scheme is shown by comparing the numerical findings with the existing results obtained via ADM, HPM, HAM and ODM for non-linear coagulation equation. This motivates us to extend the scheme for solving the other models mentioned above. The supremacy of the proposed scheme is demonstrated by taking several numerical examples for each problem. The error between exact and series solutions provided in graphs and tables show the accuracy and applicability of the method. In addition to this, convergence of the series solution is also the key attraction of the work.