Estimating biofilm reaction kinetics using hybrid mechanistic-neural network rate function model

Area of Science:

• Environmental Engineering
• Biotechnology
• Chemical Kinetics

Background:

• Biofilm processes are crucial in wastewater treatment but their complex kinetics are difficult to model.
• Traditional methods often rely on simplified or empirical model equations for rate estimation.
• Accurate kinetic estimation is vital for optimizing reactor performance and treatment efficiency.

Purpose of the Study:

• To develop an alternative method for estimating biofilm reaction rates without relying on predefined model equations.
• To integrate first principles modeling with artificial neural networks for a hybrid approach.
• To validate the proposed model using experimental data from an anaerobic fixed bed biofilm reactor.

Main Methods:

• Development of a hybrid mechanistic-neural network rate function model (HMNNRFM).
• Integration of a first principles model of the biofilm process with artificial neural networks.
• Validation of the model's steady-state solution and performance evaluation using experimental data.
• Statistical analysis using coefficient of determination (R2) and model efficiency (ME).

Main Results:

• The HMNNRFM effectively estimates the complex kinetics of biofilm processes.
• The model demonstrated strong performance when validated against experimental data from an anaerobic fixed bed biofilm reactor.
• Statistical analysis confirmed the significance and reliability of the proposed method.

Conclusions:

• The HMNNRFM offers an effective and robust approach for estimating biofilm reaction kinetics.
• This method provides a valuable tool for understanding and optimizing biofilm processes in industrial wastewater treatment.
• The hybrid modeling strategy overcomes limitations of traditional model-based approaches.