BIO-REACT: A Deep Learning and Metaheuristic Framework for Intelligent Optimization of Biochemical Reactor Dynamic.

Abstract

Biochemical reactors form the backbone of numerous industrial processes, including wastewater treatment, pharmaceutical fermentation, and bio-based chemical production. However, managing the highly nonlinear, dynamic, and interdependent nature of these systems poses significant challenges for traditional control and modeling techniques. Addressing this problem, the present study introduces BIO-REACT, an intelligent framework that integrates deep learning with metaheuristic optimization for the predictive modeling and optimization of biochemical reactor. The major objective of proposed framework is to enhance real-time decision-making and control by accurately modeling reactor behavior and adaptively optimizing key process parameters such as pH, hydraulic retention time, substrate concentration, and temperature. The proposed framework employs the statistical feature analysis to identify the most influential parameters, followed by training a deep learning model to capture the nonlinear system dynamics. A metaheuristic optimization layer then fine-tunes the process conditions to maximize product yield and operational efficiency under dynamic scenarios. Extensive validation using synthetic and semi-real-time datasets demonstrated that BIO-REACT achieves over 95% predictive accuracy, reduces mean square error by 18.6%, and improves biochemical yield by 13.4% compared to conventional models. These results confirm the capability of hybrid intelligent framework to manage complex reactor behaviors with minimal manual intervention. The proposed BIO-REACT offers a scalable, adaptable solution for smart bio-processing, supporting the broader shift toward autonomous and sustainable industrial operations.