Multi-objective optimization of detergent pre-formulations using machine
learning techniques

Ketan Jangir, Abhisek Gour, Naveen Kumar Suniya, Santosh Kumari Meena, Kapil Parihar,

Journal of the Indian Chemical Society,
Volume 100, Issue 1, 2023, 100815, ISSN 0019-4522
(https://www.sciencedirect.com/science/article/pii/S0019452222004770)

Abstract: The right combination of surfactants and stabilizers in the detergent formulations plays a significant role in their cleaning performance. However, it becomes a complex optimization problem when the formulation is composed of multiple ingredients and the solution has to be optimized for competing performance metrics. In recent times, machine learning techniques have been used extensively to study such processes. In this research, a detergent pre-formulation has been designed using an aqueous solution of Tween-20, Ethanol and 1-Octanol. To determine the optimal values of the ingredients of the formulations, supervised machine learning models were developed and optimized for the Ross Miles Index 30 ml (RMI 30) and cleaning time (CT). A full factorial experimental design was performed and three regression models based on linear, 2FI and Quadratic designs were developed respectively for RMI30 and CT. ANOVA analysis of trained models reported an optimal p-value of 0.0018 for RMI 30 and less than 0.0001 for CT. The optimal values for RMI30 and CT obtained through regression models are 72.32 ml and 17.67 s. For multi-objective optimization, grey relational analysis was performed. Two pairs of optimal values corresponding to Rank 1 were recorded as 88.9 ml, 20 s (RMI30, CT); and 81.2 ml, 14 s (RMI30, CT) respectively. As a result, the optimal combination of Tween-20, Ethanol and 1-Octanol for maximizing the RMI30 and minimizing the CT are reported. The obtained optimal values were experimentally validated.

Keywords: Detergent; Pre-formulation; Machine learning; Regression analysis; Grey relational analysis

Download full research paper from the link below:

https://doi.org/10.1016/j.jics.2022.100815