Optimization of operational parameters for ethanol production from Korean food waste leachate

Recently, research on the production of ethanol from waste has been accelerating for both ecological and economical reasons, primarily for its use as an alternative to petroleum based fuels. In this study, response surface methodology based 2[3] -full factorial central composite design was employed to optimize the parameters of ethanol production from Korean food waste leachate. The reducing sugar concentration of the food waste leachate determined by the dinitrosalicylic acid method was 75 g/L. A second order polynomial model was developed to evaluate the quantitative effects of temperature, pH and reducing sugar concentration in order to find an optimum condition for the ethanol production from food waste leachate. From the experimental result, maximum ethanol concentration of 24.17 g/L was obtained at the optimum condition of temperature [38°C], pH [5.45] and reducing sugar concentration [75 g/L]. The experimental value [24.17 g/L] agreed very well with the predicted one [23.66 g/L], indicating the suitability of the model employed and the success of response surface methodology in optimizing the conditions of ethanol production from food waste leachate. Canonical analysis indicated that the stationary point was a saddle point for the ethanol yield. Despite being a waste, an ethanol yield of 0.32 g ethanol/g reducing sugar demonstrated the potential of food waste leachate as a promising biomass resource for the production of ethanol

intelligent neural network model for evaluating performance of immobilized cell biofilter treating hydrogen sulphide vapors

Biofiltration has shown to be a promising technique for handling malodours arising from process industries. The present investigation pertains to the removal of hydrogen sulphide in a lab scale biofilter packed with biomedia, encapsulated by sodium alginate and poly vinyl alcohol. The experimental data obtained under both steady state and shock loaded conditions were modelled using the basic principles of artificial neural networks. Artificial neural networks are powerful data driven modelling tools which has the potential to approximate and interpret complex input/ output relationships based on the given sets of data matrix. A predictive computerised approach has been proposed to predict the performance parameters namely, removal efficiency and elimination capacity using inlet concentration, loading rate, flow rate and pressure drop as the input parameters to the artificial neural network model. Earlier, experiments from continuous operation in the biofilter showed removal efficiencies from 50 to 100% at inlet loading rates varying up to 13 g H2S/m[3]h. The internal network parameter of the artificial neural network model during simulation was selected using the 2[k] factorial design and the best network topology for the model was thus estimated. The results showed that a multilayer network [4-4-2] with a back propagation algorithm was able to predict biotilter performance effectively with R[2] values of 0.9157 and 0.9965 for removal efficiency and elimination capacity in the test data. The proposed artificial neural network model for biofilter operation could be used as a potential alternative for knowledge based models through proper training and testing of the state variables

Influence of dissolved oxygen concentration and aeration time on nitrite accumulation in partial nitrification process

This study investigated the influence of dissolved oxygen concentration and aeration time on nitrification and nitrite accumulation in an attempt to optimize the recently developed biological-partial-nitritation process for the treatment of strong nitrogen wastewaters. Investigation of dissolved oxygen concentration on ammonium and nitrite oxidation was carried out in a batch reactor. The dissolved oxygen concentration of 0.5 mg O2/L inhibited both ammonium as well as nitrite oxidation, while increase of dissolved oxygen concentration to tilde1 mg O2/L increased the ammonium oxidation rate and was comparable to that at higher dissolved oxygen concentrations. Experiments were carried out in a sequencing batch reactor for more than 100 days to investigate the influence of aeration time on nitrite accumulation. The dissolved oxygen concentration was controlled at tilde1.0 mg O2/L [in the range of 0.8-1.5 mg/L] during the aeration stage, and volatile suspended solid was maintained at 2.0 g/L while temperature and pH were 30 +/- 1§C and 8.3 +/- 0.1, respectively. In a typical cycle, complete nitrification occurred at aeration time longer than 6 h. When the aeration time was reduced to 4 h., tilde80% of partial nitritation was achieved. With a further reduction in aeration time to 3 h., nearly 1:1 nitrite/ammonium ratio was yielded. This result revealed that for the reactor design, aeration time determined by feasibility experiments must be considered based on the nitrogen strength in wastewater and biomass concentration in the reactor with dissolved oxygen concentration of tilde1.0 mg O2/L for satisfactory partial nitrification with subsequent processes such as anaerobic ammonium oxidation

Statistical analysis of main and interaction effects during the removal of BTEX mixtures in batch conditions using wastewater treatment plant sludge microbes

Biodegradation has proved to be a versatile technique to remediate benzene, toluene, ethyl benzene and xylene [BTEX] mixtures in contaminated soil and groundwater. In this study, a mixed microbial culture obtained from a wastewater treatment plant was used to degrade liquid phase BTEX, at initial concentrations varying between 15 to 75 mg/l. Experiments were conducted according to the 2k-1 fractional factorial design to identify the main and interaction effects of parameters and their influence on biodegradation of individual BTEX compounds in mixtures. The removal efficiencies of these compounds varied between 2 to 90% depending on the concentration of other compounds and also on their interaction effects. A statistical interpretation of the results was done based on the Fishers variance ratio [F] and probability [P] values. Though all the main effects were found significant [P < 0.05] at the 5% confidence level, the interactions between benzene and toluene and benzene and xylene concentrations were also found to be statistically significant and play a major role in affecting the total BTEX removal