Optimal design and modelling of sustainable bio-catalytic enzyme for wastewater treatment using response surface methodology and artificial neural network

ABSTRACT

The accessibility of various wastewater treatment systems has become increasingly prominent in current times, coinciding with a rising concern regarding the substantial content of food waste that poses a significant challenge in waste management. In this context, ecoenzymes have emerged as a promising technique for water treatment due to their remarkable efficiency in pollutant removal and cost-effectiveness. Nevertheless, many experts encounter difficulties in determining the optimal combination of ecoenzyme variables to maximize pollutant removal. The objective of this study is to apply a comprehensive approach utilizing Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and Multi-objective Genetic Algorithm (MOGA) methods for the modeling and optimization of ecoenzyme-based water treatment processes. The research outcomes demonstrate a noteworthy alignment between the actual removal rates of Total Suspended Solids (TSS), Volatile Suspended Solids (VSS), and Total Ammonia Nitrogen (TAN) with the values predicted by both RSM and ANN models, validating the robustness of the regression analysis (R² >0.95). From the optimization results, it is elucidated that to attain the maximum removal of TSS, VSS, and TAN, the treatment process should be conducted over a period of 3.5 days, employing specific enzyme concentrations of 0.07 units/ml for protease, 0.027 units/ml for amylase, and 0.36 units/ml for lipase. This study employed machine learning approaches and MOGA optimization to predict the optimum ecoenzyme properties for maximum wastewater removal utilizing the concept of a low-cost, sustainable purification technology.

KEYWORDS:

• Ecoenzyme
• response surface methodology
• artificial neural network
• multi-objective optimization genetic algorithm
• water treatment

Acknowledgements

The authors acknowledged the funding support from Ministry of Education with Penelitian Terapan Unggulan Perguruan Tinggi (PTUPT) grant with contract number 132/E5/PG.02.00.PT/20226 and Universitas Pertamina for Upskilling Research Grant with contract number 30/UP-WRP.1/PJN/III/2023.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Kementerian Riset Teknologi Dan Pendidikan Tinggi Republik Indonesia [132/E5/PG.02.00.PT/20226].

Notes on contributors

Reda Rizal

Reda Rizal is an Associate Professor in the Department of Industrial Engineering at Universitas Pembangunan Nasional Veteran Jakarta. He obtained his doctoral degree from Universitas Indonesia in the area of environmental science. His research interests include environmental impact and industrial ecology.

Fitri Wahyuni

Fitri Wahyuni completed her Bachelor of Engineering from the State University of Jakarta and continued her master’s degree at Universitas Gadjah Mada. Her research interest is in energy conversion through CFD studies.

James Julian

James Julian is an Assistant Professor in the Department of Mechanical Engineering at Universitas Pembangunan Nasional Veteran Jakarta. He completed his doctoral degree at Universitas Indonesia in 2018. His research interests include drag reduction, aerodynamic performance, and energy conversion.

Nasruddin

Nasruddin is a Professor in the Department of Mechanical Engineering at Universitas Indonesia. He completed his PhD in 2005 at RWTH Aachen, Germany. His research interests are energy optimization, refrigeration, and adsorption for environmental applications.

Fayza Yulia

Fayza Yulia is an Assistant Professor in the Department of Mechanical Engineering at Universitas Pertamina. She completed her doctoral degree at Universitas Indonesia in 2021. Her research interests are in carbon capture, adsorption, material development, and energy optimization.