Abstract: Distillery wastewater in the form of spent wash is a complex and strong industrial effluent, with high load of organic pollutants that may deplete dissolved oxygen on being discharged into aquatic systems and contaminate groundwater by leaching of pollutants, while untreated spent wash disposed on land acidifies the soil. Stringent legislative measures have therefore been framed in different countries for discharge standards of distillery effluent. Utilising the organic pollutants present in various types of wastes as food by mixed microbial populations is emerging as an eco-friendly approach in the recent years, in which complex organic matter is converted into simpler forms, and simultaneously useful gases are produced as renewable and clean energy sources. In the present study, wastewater from a rice bran based distillery has been used as the substrate in a dark fermenter, and native microbial consortium from the digester sludge has been used as the inoculum to treat the wastewater and produce hydrogen. After optimising the operational conditions in batch reactors, sequential batch mode and continuous flow stirred tank reactors were used to study the best operational conditions for enhanced and sustained hydrogen production and removal of pollutants. Since the rate of hydrogen production by the microbial consortium during dark fermentation is influenced by concentration of organic matter, pH and temperature, these operational conditions were optimised in batch mode studies. Maximum hydrogen production rate (347.87ml/L/d) was attained in 32h dark fermentation while a good proportion of COD also got removed from the wastewater. Slightly acidic initial pH seemed to favor biohydrogen production. In continuous stirred tank reactor, high H2 production from distillery wastewater was obtained from a relatively shorter substrate retention time (SRT) of 48h and a moderate organic loading rate (OLR) of 172 g/l/d COD.
Abstract: This review summarizes the potential of starch
agroindustrial residues as substrate for biohydrogen production.
Types of potential starch agroindustrial residues, recent developments
and bio-processing conditions for biohydrogen production will be
discussed. Biohydrogen is a clean energy source with great potential
to be an alternative fuel, because it releases energy explosively in
heat engines or generates electricity in fuel cells producing water as
only by-product. Anaerobic hydrogen fermentation or dark
fermentation seems to be more favorable, since hydrogen is yielded
at high rates and various organic waste enriched with carbohydrates
as substrate result in low cost for hydrogen production. Abundant
biomass from various industries could be source for biohydrogen
production where combination of waste treatment and energy
production would be an advantage. Carbohydrate-rich nitrogendeficient
solid wastes such as starch residues can be used for
hydrogen production by using suitable bioprocess technologies.
Alternatively, converting biomass into gaseous fuels, such as
biohydrogen is possibly the most efficient way to use these
agroindustrial residues.
Abstract: The objective of this study was to investigate hydrogen production from alcohol wastewater by anaerobic sequencing batch reactor (ASBR) under thermophillic operation. The ASBR unit used in this study had a liquid holding volume of 4 L and was operated at 6 cycles per day. The seed sludge taken from an upflow anaerobic sludge blanket unit treating the same wastewater was boiled at 95 °C for 15 min before being fed to the ASBR unit. The ASBR system was operated at different COD loading rates at a thermophillic temperature (55 °C), and controlled pH of 5.5. When the system was operated under optimum conditions (providing maximum hydrogen production performance) at a feed COD of 60 000 mg/l, and a COD loading rate of 68 kg/m3 d, the produced gas contained 43 % H2 content in the produced gas. Moreover, the hydrogen yield and the specific hydrogen production rate (SHPR) were 130 ml H2/g COD removed and 2100 ml H2/l d, respectively.
Abstract: A predictive clustering hybrid regression (pCHR)
approach was developed and evaluated using dataset from H2-
producing sucrose-based bioreactor operated for 15 months. The aim
was to model and predict the H2-production rate using information
available about envirome and metabolome of the bioprocess. Selforganizing
maps (SOM) and Sammon map were used to visualize the
dataset and to identify main metabolic patterns and clusters in
bioprocess data. Three metabolic clusters: acetate coupled with other
metabolites, butyrate only, and transition phases were detected. The
developed pCHR model combines principles of k-means clustering,
kNN classification and regression techniques. The model performed
well in modeling and predicting the H2-production rate with mean
square error values of 0.0014 and 0.0032, respectively.
Abstract: In this work, biohydrogen production via dark
fermentation from alcohol wastewater using upflow anaerobic sludge
blanket reactors (UASB) with a working volume of 4 L was
investigated to find the optimum conditions for a maximum hydrogen
yield. The system was operated at different COD loading rates (23,
31, 46 and 62 kg/m3d) at mesophilic temperature (37 ºC) and pH 5.5.
The seed sludge was pretreated before being fed to the UASB system
by boiling at 95 ºC for 15 min. When the system was operated under
the optimum COD loading rate of 46 kg/m3d, it provided the
hydrogen content of 27%, hydrogen yield of 125.1 ml H2/g COD
removed and 95.1 ml H2/g COD applied, hydrogen production rate of
18 l/d, specific hydrogen production rate of 1080 ml H2/g MLVSS d
and 1430 ml H2/ L d, and COD removal of 24%.
Abstract: A combination of photosynthetic bacteria along with
anaerobic acidogenic bacteria is an ideal option for efficient
hydrogen production. In the present study, the optimum
concentration of substrates for the growth of Rhodobacter
sphaeroides was found by response surface methodology. The
optimum combination of three individual fatty acids was determined
by Box Behnken design. Increase of volatile fatty acid concentration
decreased the growth. Combination of sodium acetate and sodium
propionate was most significant for the growth of the organism. The
results showed that a maximum biomass concentration of 0.916 g/l
was obtained when the concentrations of acetate, propionate and
butyrate were 0.73g/l,0.99g/l and 0.799g/l, respectively. The growth
was studied under an optimum concentration of volatile fatty acids
and at a light intensity of 3000 lux, initial pH of 7 and a temperature
of 35°C.The maximum biomass concentration of 0.92g/l was
obtained which verified the practicability of this optimization.
Abstract: To decompose organochlorides by bioremediation, co-culture biohydrogen producer and dehalogenation microorganisms is a useful method. In this study, we combined these two characteristics from a biohydrogen producer, Rhodopseudomonas palustris, and a dehalogenation microorganism, Pseudomonas putida, to enchance halorespiration in R. palustris. The genes encoding cytochrome P450cam system (camC, camA, and camB) from P. putida were expressed in R. palustris with designated expression plasmid. All tested strains were cultured to log phase then presented pentachloroethane (PCA) in media. The vector control strain could degrade PCA about 78% after 16 hours, however, the cytochrome P450cam system expressed strain, CGA-camCAB, could completely degrade PCA in 12 hours. While taking chlorinated aromatic, 3-chlorobenzoate, as sole carbon source or present benzoate as co-substrate, CGA-camCAB presented faster growth rate than vector control strain.
Abstract: The performance of a sucrose-based H2 production in
a completely stirred tank reactor (CSTR) was modeled by neural
network back-propagation (BP) algorithm. The H2 production was
monitored over a period of 450 days at 35±1 ºC. The proposed model
predicts H2 production rates based on hydraulic retention time
(HRT), recycle ratio, sucrose concentration and degradation, biomass
concentrations, pH, alkalinity, oxidation-reduction potential (ORP),
acids and alcohols concentrations. Artificial neural networks (ANNs)
have an ability to capture non-linear information very efficiently. In
this study, a predictive controller was proposed for management and
operation of large scale H2-fermenting systems. The relevant control
strategies can be activated by this method. BP based ANNs modeling
results was very successful and an excellent match was obtained
between the measured and the predicted rates. The efficient H2
production and system control can be provided by predictive control
method combined with the robust BP based ANN modeling tool.