Experimental analysis and modeling of the methane degradation in a three stage biofilter using composted sawdust as packing media.

Methane gas is a very effective greenhouse gas and the second-largest contributor to global warming. Biofiltration is an effective technology that uses microorganisms to degrade the pollutant by oxidizing it. In this work, the performance of a biofilter with supporting filter media, consisting of composted sawdust, is evaluated at three different sampling ports. Furthermore, a transient model is developed to predict methane concentration at various heights and times. The developed model is validated with the experimental data and shows good agreement with the experimental data. The highest removal efficiency and elimination capacity was found to be 72% and 0.108 g m-3 h-1 respectively. The effect of parameters such as specific surface area, the reaction rate constant, biofilm thickness and airflow rate were studied on the outlet methane concentration. Under similar conditions, the simulations showed that the removal efficiency of 95% might be achieved for the height of 2 m.

Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques.

The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box-Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.