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  • 1
    Electronic Resource
    Electronic Resource
    Weinheim : Wiley-Blackwell
    Chemical Engineering & Technology - CET 12 (1989), S. 96-102 
    ISSN: 0930-7516
    Keywords: Chemistry ; Industrial Chemistry
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology , Process Engineering, Biotechnology, Nutrition Technology
    Notes: The effects of alkali treatment, nitrogen supplement and hydraulic retention time on methane production rate from semi-continuous anaerobic digestion of 5% wheat straw-water mixtures were investigated. The experiments were carried out in laboratory scale fermenters, fed with 1 1 of basic, alkali treated and nitrogen supplemented 5% wheat straw-water mixtures, respectively, and maintained at 55 °C. Digestion experiments were performed for hydraulic retention times of 8, 10 and 15 days. The amount and composition of produced gas were measured until steady state was attained in each run. The steady-state methane production rates were found to increase with hydraulic retention time and with the type of slurry in the following order; basic, nitrogen supplemented and alkali treated slurry. Data obtained from the experiments were employed to determine the kinetics of methane production from anaerobic digestion of wheat straw, for the assessment of pretreatment effects on process kinetics. The predicted methane production rates were found to be in a reasonably good agreement with the measurements.
    Additional Material: 4 Ill.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Hydrobiologia 408-409 (1999), S. 139-144 
    ISSN: 1573-5117
    Keywords: eutrophication ; ecological modelling ; multiple regression
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract A research was made on the potential use of neural network based models in eutrophication modelling. As a result, an algorithm was developed to handle the practical aspects of designing, implementing and assessing the results of a neural network based model as a lake management tool. To illustrate the advantages and limitations of the neural network model, a case study was carried out to estimate the chlorophyll-aconcentration in Keban Dam Reservoir as a function of sampled water quality parameters (PO4phosphorus, NO3nitrogen, alkalinity, suspended solids concentration, pH, water temperature, electrical conductivity, dissolved oxygen concentration and Secchi depth) by a neural network based model. Alternatively, the same system was solved with a linear multiple regression model in order to compare the performances of the proposed neural network based model and the traditional linear multiple regression model. For both of the models, the linear correlation coefficients between the logarithms of observed and calculated chlorophyll-aconcentrations were calculated. The correlation coefficient R, the best linear fit between the observed and calculated values, was evaluated to assess the performances of the two models. R values of 0.74 and 0.71 were obtained for the neural network based model and the linear multiple regression model, respectively. The study showed that the neural network based model can be used to estimate chlorophyll-awith a performance similar to that of the traditional linear multiple regression method. However, for cases where the input and the output variables are not linearly correlated, neural network based models are expected to show a better performance.
    Type of Medium: Electronic Resource
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