Neural network and on-off control of bicarbonate alkalinity in a fluidised-bed anaerobic digester

A laboratory-scale fluidised-bed anaerobic digester with a sintered glass carrier, Siran®, was operated for 8 months on a simulated baker's yeast wastewater (12,000 mg soluble CODl^-1 at a loading rate of 27 kg COD m^-3 d^-1, giving 75% removal of soluble COD. Percentage CO₂, H₂ concentration, gas flow rare and pH were measured continuously. An on-line bicarbonate alkalinity (BA) monitor was used in experiments comparing two control strategies, adjusting digester buffering by addition of NaHCO₃ solution during organic overloads. The first, an on-off controller with a set point at the steady-state level (2700 mg CaCO₃ l^-1, maintained BA concentration but resulted in levels above the upper set point. Thus, to avoid consuming excess NaHCO₃ the rate of delivery and solution strength must be carefully adjusted. The second was a controller developed from a neural network trained on BA data from an anaerobic filter operating on ice-cream processing wastewater (alkalinity around 1400 mg CaCO₃ l^-1. Without re-training, despite the different steady-state BA levels and reactor type, the neural network based controller was capable of maintaining stable BA levels during overload without overshoot. Control of BA during overloads did not prevent changes in gaseous CO₂ and H₂ concentrations and gas flow rate.