PubMed · 4989563
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C A Saravis, L Bonacker. 1970-10-03. Membrane-complement fixation test for hepatitis associated antigen.. https://doi.org/10.1038/228061a0
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The performance of a fluidized-bed reactor (FBR) based sulfate reducing bioprocess was predicted using artificial neural network (ANN). The FBR was operated at high (65 degrees C) temperature and it was fed with iron (40-90 mg/L) and sulfate (1,000-1,500 mg/L) containing acidic (pH = 3.5-6) synthetic wastewater. Ethanol was supplemented as carbon and electron source for sulfate reducing bacteria (SRB). The wastewater pH of 4.3-4.4 was neutralized by the alkalinity produced in acetate oxidation and the average effluent pH was 7.8 +/- 0.8. The oxidation of acetate is the rate-limiting step in the sulfidogenic ethanol oxidation by thermophilic SRB, which resulted in acetate accumulation. Sulfate reduction and acetate oxidation rates showed variation depending on the operational conditions with the maximum rates of 1 g/L/d (0.2 g/g volatile solids (VS)/d) and 0.3 g/L/d (0.06 g/g VS/d), respectively. This study presents an ANN model predicting the performance of the reactor and determining the optimal architecture of this model; such as best back-propagation (BP) algorithm and neuron numbers. The Levenberg-Marquardt algorithm was selected as the best of 12 BP algorithms and optimal neuron number was determined as 20. The developed ANN model predicted acetate (R=0.91), sulfate (R=0.95), sulfide (R=0.97), and alkalinity (R=0.94) in the FBR effluent. Hence, the ANN based model can be used to predict the FBR performance, to control the operational conditions for improved process performance.
Three laboratory-scale peat biofilters were operated at 90 s empty bed residence time (EBRT) for over a year. Biodegradation of ethyl acetate, toluene, or a 1:1 mixture were investigated. In first stage, inlet concentration was progressively increased from 0.4 to 4.5 g/m(3). The maximum elimination capacity (EC) found for ethyl acetate was 190 gC/m(3).h, and it was not affected by toluene. The maximum EC found for toluene as a sole contaminant was 150 gC/m(3).h, but the presence of ethyl acetate decreased the toluene maximum EC to 80 gC/m(3).h. From respirometry monitoring, values of 3.19 g CO(2)/gC and 3.06 g CO(2)/gC for pure ethyl acetate and pure toluene, respectively, were found, with overall yield coefficients of 0.13 g dry biomass produced per gram ethyl acetate consumed and 0.28 g dry biomass produced per gram toluene consumed. CO(2) production in the 1:1 mixture was successfully simulated. Dynamics of living and dead cells were monitored in four sections of the biofilters. Concentrations ranged between 2.6 x 10(9) and 3.0 x 10(10) cells per gram-dry peat for total bacteria, and 2.4 x 10(9)-1.9 x 10(10) cells per gram-dry peat for living bacteria. At high loads loss of bacterial density in the inlet zones, and increase in the dead cells percentages up to 60% was observed. In second stage, long-term performance at an inlet concentration of 1.5 g/m(3) was evaluated to show the process feasibility. Good agreement with previous data was obtained in terms of EC and CO(2) production. Restoration of living cells proportion was also observed.
Aerobic growth of Shewanella oneidensis MR-1 in minimal lactate medium was studied in batch cultivation. Acetate production was observed in the middle of the exponential growth phase and was enhanced when the dissolved oxygen (DO) concentration was low. Once the lactate was nearly exhausted, S. oneidensis MR-1 used the acetate produced during growth on lactate with a similar biomass yield as lactate. A two-substrate Monod model, with competitive and uncompetitive substrate inhibition, was devised to describe the dependence of biomass growth on lactate, acetate, and oxygen and the acetate growth inhibition across a broad range of concentrations. The parameters estimated for this model indicate interesting growth kinetics: lactate is converted to acetate stoichiometrically regardless of the DO concentration; cells grow well even at low DO levels, presumably due to a very low K(m) for oxygen; cells metabolize acetate (maximum specific growth rate, micro(max,A) of 0.28 h(-1)) as a single carbon source slower than they metabolize lactate (micro(max,L) of 0.47 h(-1)); and growth on acetate is self-inhibiting at a concentration greater than 10 mM. After estimating model parameters to describe growth and metabolism under six different nutrient conditions, the model was able to successfully estimate growth, oxygen and lactate consumption, and acetate production and consumption under entirely different growth conditions.