Casuarina seed-packed biofilter for the treatment of gaseous mixture: Performance, optimization, synergistic effects, neural model analysis, and kinetics

Assessing the interactions and biodegradation of multicomponent gas streams is crucial for understanding industrial operations. Thus, the lab-scale casuarina seed-packed biofilter was performed with industrial settings i.e. different inlet concentrations of benzene, toluene, and xylene (BTX) in a mixture for their synchronized removal. Comparative computational models were studied using response surface methodology (RSM) and artificial neural network (ANN) to predict the individual pollutant performance in terms of removal efficiency (RE) and elimination capacity (EC). The synergistic effects of the individual pollutants load on the RE and EC were studied effectively. The response surface plots indicated that the developed biofilter could potentially eliminate multicomponent in the order of benzene>xylene>toluene with the decreasing order of toluene>xylene>benzene inlet concentrations. The RSM model prediction was cross verified by investigating the causal index (CI) analysis, applied to govern the effect of one variable on another beyond mere connection. The CI analysis confirmed that the toluene and xylene had a negative effect on their own RE and had positive effects on other pollutant performance; however, toluene vigorously supported the benzene and xylene biodegradation. The computational models show significant fluctuation in predicting responses due to substrate competitive inhibition, accumulation of toxic intermediates, and increasing multipollutant loads. Finally, the mathematical model was employed to predict the theoretical EC and average biofilm thickness at different regimes of biofiltration. The identified significant interaction information is more beneficial in designing and operating the multicomponent biofilter rationally. • Casuarina seed-packed biofilter is effectively applied to the treatment of benzene, toluene, and xylene mixture. • ANN causal index analysis confirmed that the toluene and xylene have (-) effects on their RE. • The pollutant inlet loads were interchangeably dependent on each pollutant removal. • Corresponding to the prediction of performance indicators, ANN achieved better results than RSM. • The modified Ottengraf model successfully predicted the biofilm thickness in a range from 299 to 357 μm.