Biomedical subjects

P Holubar

Publications and source records attributed to P Holubar.

4 recordsLinked to original sources

PubMed2003

Optimized biogas-fermentation by neural network control.

In this work several feed-forward back-propagation neural networks (FFBP) were trained in order to model, and subsequently control, methane production in anaerobic digesters. To produce data for the training of the neural nets, four anaerobic continuous stirred tank reactors (CSTR) were operated in steady-state conditions at organic loading rates (Br) of about 2 kg x m(-3) x d(-1) chemical oxygen demand (COD), and disturbed by pulse-like increase of the organic loading rate. For the pulses additional carbon sources were added to the basic feed (surplus- and primary sludge) to simulate cofermentation and to increase the COD. Measured parameters were: gas composition, methane production rate, volatile fatty acid concentration, pH, redox potential, volatile suspended solids and COD of feed and effluent. A hierarchical system of neural nets was developed and embedded in a Decision Support System (DSS). A 3-3-1 FFBP simulated the pH with a regression coefficient of 0.82. A 9-3-3 FFBP simulated the volatile fatty acid concentration in the sludge with a regression coefficient of 0.86. And a 9-3-2 FFBP simulated the gas production and gas composition with a regression coefficient of 0.90 and 0.80 respectively. A lab-scale anaerobic CSTR controlled by this tool was able to maintain a methane concentration of about 60% at a rather high gas production rate of between 5 to 5.6 m3 x m(-3) x d(-1).

Calibration↗

PubMed2003

Modelling of the production of gaseous by-products in anaerobic digestion.

Goal of the EU-Project AMONCO (Advanced Prediction, Monitoring and Controlling of Anaerobic Digestion Processes Behaviour towards Biogas Usage in Fuel Cells) is demonstration of the practical use of biogas in fuel cells. The right precondition is a biogas quality which fits into the fuel cells tolerances. Therefore the mission of the workgroup Environmental biotechnology is to control anaerobic digestion in a way that production of potential harmful by-products for fuel cells is reduced. A good understanding of the production of these by products is essential for an applicable decision support tool. This poster presents the modelling of hydrogen sulfide by means of hierarchical neural networks and a classical mathematical method.

Anaerobiosis↗

PubMed2003

A fuzzy logic approach to control anaerobic digestion.

One of the goals of the EU-Project AMONCO (Advanced Prediction, Monitoring and Controlling of Anaerobic Digestion Process Behaviour towards Biogas Usage in Fuel Cells) is to create a control tool for the anaerobic digestion process, which predicts the volumetric organic loading rate (Bv) for the next day, to obtain a high biogas quality and production. The biogas should contain a high methane concentration (over 50%) and a low concentration of components toxic for fuel cells, e.g. hydrogen sulphide, siloxanes, ammonia and mercaptanes. For producing data to test the control tool, four 20 l anaerobic Continuously Stirred Tank Reactors (CSTR) are operated. For controlling two systems were investigated: a pure fuzzy logic system and a hybrid-system which contains a fuzzy based reactor condition calculation and a hierachial neural net in a cascade of optimisation algorithms.

Anaerobiosis↗

PubMed1999

Effects of nitrogen limitation on biofilm formation in a hydrocarbon-degrading trickle-bed filter.

The effect of nitrogen limitation on young and mature steady-state biofilm in a trickle-bed filter was studied. Toluene and n-heptane were the sole carbon source. Biomass concentration, respiration, substrate-induced respiration, metabolic quotient, and total hydrocarbon degradation efficiency were measured. The aim of the experiment was to control excess biomass production in the trickle-bed filter by limiting the mineral nutrients and to achieve increased mineralization of the carbon source. Biofilm growth responded strongly to the amount of available nitrogen, whereas hydrocarbon degradation efficiency reached a maximum of 60% and could not be increased even by further addition of nitrogen. The experiments showed that 95% of the adsorbed carbon was mineralized completely and only 5% was used for biofilm formation. This complete mineralization can also be concluded from the metabolic quotient. The value of the latter was about 6-10 mg CO2-C g-1 Cmic h-1, indicating an expanded energy demand due to stress effects in the presence of nutrient deficiency. It was postulated that determination of the metabolic quotient could be an simple instrument to measure the rate of mineralization of carbon sources and also the rate of biomass formation in trickle-bed filters or biofilters.

Biofilms↗