Applied Chemical Engineering

The Law of mass action: Mathematical modelling and python implementation for chemical kinetics

Choon Kit chan

Faculty of Engineering and Quantity Surveying, INTI International University, Nilai, Negeri Sembilan, 71800, Malaysia

Pankaj Dumka

Department of Mechanical Engineering, Jaypee University of Engineering and Technology, A.B. Road, Raghogarh-473226, Guna, Madhya Pradesh, India

Rishika Chauhan

Department of Electronics and Communication Engineering, Jaypee University of Engineering and Technology, A.B. Road, Raghogarh-473226, Guna, Madhya Pradesh, India

Altafhussain G Momin

Department of Mechanical Engineering, L D College of Engineering, Ahmedabad, Gujarat, India

Rajashree Bhokare

Department of Electrical Engineering, Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, Maharashtra, 411018, India

Neelashetty K

Professor, EEE department, Guru Nanak Dev Engg College, Bidar, Karnataka, 585403, India

Subhav Singh

Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh-174103, India Division of research and development, Lovely Professional University, Phagwara, Punjab, India

Deekshant Varshaney

Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India Centre for Promotion of Research, Graphic Era (Deemed to be University), Uttarakhand, Dehradun, India

Feroz Shaik

Department of Mechanical Engineering, College of Engineering, Prince Mohammad Bin Fahd University, Al Khobar 31952, Saudi, Arabia

DOI: https://doi.org/10.59429/ace.v8i2.5644

Keywords: chemical kinetics; law of mass action; python programming; SciPy; NumPy; pandas; process innovation

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Abstract

This article explores the mathematical framework and computational implementation of the “Law of Mass Action” to model the kinetics of chemical reaction. The study begins with a detailed explanation of the governing equations, emphasizing the role of stoichiometry and reaction orders in dynamic systems. Using Python, a generalized computational framework was developed to solve systems of ordinary differential equations (ODEs) that describe concentration changes over time. The function solve_ivp has been used from the SciPy module to perform the task of solving ODEs. The solver is capable of handling complex reaction networks by incorporating a stoichiometric matrix, reaction rate constants, and reaction orders as inputs. The results are plotted and tabulated with the help of Matplotlib.pylab and Pandas modules. Two representative examples, including real-world chemical reactions, were solved to demonstrate the versatility and accuracy of the approach. Results show that this generalized methodology provides an efficient and adaptable tool for chemical reaction modelling. This work highlights the power of combining mathematics with modern programming to solve practical chemical engineering problems.

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