Cultivation of freshwater microalgae in biodiesel wash water

Biodiesel wash water is a contaminating industrial effluent that must be treated prior to disposal. The use of this effluent as a low-cost alternative cultivation medium for microalgae could represent a viable supplementary treatment. We cultivated 11 microalgae species with potential use for biodiesel production to assess their growth capacities in biodiesel industrial washing waters. Only Monoraphidium contortum, Ankistrodesmus sp., Chlorococcum sp., and one unidentified Chlorophyceae species grew effectively in that effluent. M. contortum showed the highest growth capacity and had the second highest fatty acid content (267.9 mg g⁻¹ of DW), predominantly producing palmitic (20.9%), 7,10,13-hexadecatrienoic (14%), oleic (16.2%), linoleic (10.5%), and linolenic acids (23.2%). In the second phase of the experiment, the microalgae were cultivated in biodiesel wash water at 75% of its initial concentration as well as in WC (control) medium. After 21 days of cultivation, 25.8 and 7.2% of the effluent nitrate and phosphate were removed, respectively, and the chemical oxygen demand was diminished by 31.2%. These results suggest the possibility of cultivating biodiesel producing microalgae in industrial wash water effluents.

     

Physical and transport properties of aqueous amino alcohol solutions for CO2 capture from flue gas streams

Densities, viscosities and refractive indices of 4-diethylamino-2-butanol + water mixtures were measured over the entire concentration range of 0–1 mole fraction and temperature range from 298.15 to 343.15 K. Excess molar volumes, viscosity deviations, and molar refraction changes were calculated from the measurement results and correlated as a function of the mole fractions. Various models were used for correlation with the measured data. Out of these, the Redlich–Kister equation was the most suitable model that correlated best with experimental data. The percent absolute deviations obtained with this model were 0.03% for density, 0.88% for viscosity and 0.009% for refractive index.     

Toxicity evaluation of vinasse and biosolid samples in diplopod midgut: heat shock protein in situ localization

Environmental Science and Pollution Research - Large amounts of residues generated by agricultural, urban and industrial activities are dumped daily on the soil. This practice deserves special...     

Kinetics of sulfur dioxide- and oxygen-induced degradation of aqueous monoethanolamine solution during CO2 absorption from power plant flue gas streams

Studies of the kinetics of sulfur dioxide (SO₂)- and oxygen (O₂)-induced degradation of aqueous monoethanolamine (MEA) during the absorption of carbon dioxide (CO₂) from flue gases derived from coal- or natural gas-fired power plants were conducted as a function of temperature and the liquid phase concentrations of MEA, O₂, SO₂ and CO₂. The kinetic data were based on the initial rate which shows the propensity for amine degradation and obtained under a range of conditions typical of the CO₂ absorption process (3–7 kmol/m³ MEA, 6% O₂, 0–196 ppm SO₂, 0–0.55 CO₂ loading, and 328–393 K temperature). The results showed that an increase in temperature and the concentrations of MEA, O₂ and SO₂ resulted in a higher MEA degradation rate. An increase in CO₂ concentration gave the opposite effect. A semi-empirical model based on the initial rate, ?r_MEA = {6.74 × 10⁹ e^?(29,403/RT)[MEA]^0.02([O]^2.91 + [SO₂]^3.52)}/{1 + 1.18[CO₂]^0.18} was developed to fit the experimental data. With the higher order of reaction, SO₂ has a higher propensity to cause MEA to degrade than O₂. Unlike previous models, this model shows an improvement in that any of the parameters (i.e. O₂, SO₂, and CO₂) can be removed without affecting the usability of the model.     

Auf der Suche nach den Treibstoffen der Zukunft

The climatically-relevant emission of CO₂ which results from motor vehicle traffic offers a challenge for the automobile industry to produce highly efficient and economical motor vehicles. Furthermore, the production of fuels from regenerative energies may provide a more significant contribution over the long-term to make our mobility more compatible to the climate and to reduce our dependence on crude oil importation. Substantial reductions in emissions can be achieved through the application of regenerative fuels, especially in combination with more energy-efficient hybrid or fuel-cell vehicles, or through the addition of biogenic components to conventional fuels. Coordinated efforts between the automobile industry, the energy industries and the responsible politicians are mandatory in order to achieve ecologically-tolerable motor vehicle traffic.     

Soil identification and chemometrics for direct determination of nitrate in soils using FTIR-ATR mid-infrared spectroscopy

The use of mid-infrared attenuated total reflectance (ATR) spectroscopy enables direct measurement of nitrate concentration in soil pastes, but strong interfering absorbance bands due to water and soil constituents limit the accuracy of straightforward determination. Accurate subtraction of the water spectrum improves the correlation between nitrate concentration and its nu3 vibration band around 1350 cm(-1). However, this correlation is soil-dependent, due mostly to varying contents of carbonate, whose absorbance band overlaps the nitrate band. In the present work, a two-stage method is developed: First, the soil type is identified by comparing the "fingerprint" region of the spectrum (800-1200 cm(-1)) to a reference spectral library. In the second stage, nitrate concentration is estimated using the spectrum interval that includes the nitrate band, together with the soil type previously identified. Three methods are compared for estimating nitrate concentration: integration of the nitrate absorbance band, cross-correlation with a reference spectrum, and principal component analysis (PCA) followed by a neural network. When using simple band integration, the use of soil specific calibration curves leads to determination errors ranging from 5.5 to 24 mg[N]/kg[dry soil] for the mineral soils tested. The cross-correlation technique leads to similar results. The combination of soil identification with PCA and neural network modeling improves the predictions, especially for soils containing calcium carbonate. Typical prediction errors for light non-calcareous soils are about 4 mg[N]/kg[dry soil], whereas for soils containing calcium carbonate they range from 6 to 20 mg[N]/kg[dry soil], which is less than four percent of the concentration range investigated.