Davor Malus

Neural networks in economic analyses of wastewater systems

During selection of optimum sewerage and wastewater treatment systems in rural settlements, often a large number of potential technical solutions is generated. The economic criterion is most frequently assigned the greatest importance which finally results that the solutions involving the lowest total costs are preferred. The conventional approach considerably complicates the choice of the optimum solution, because it requires great efforts in determining the size of considered solutions and preparing of corresponding cost estimates. The paper analyzes the possibility of using of neural networks in economic analyses of wastewater systems. The neural network NENECOS (NEural Network for approximate Estimation of COsts of wastewater Systems) has been created, which allows simple, fast and adequately accurate estimation of total or unit costs of construction, operation and maintenance of sewerage systems, without the need for prior sizing and preparing of cost estimates. This allows simple and more efficient economic comparison of a greater number of alternative solutions. The limitation of the neural network NENECOS is the possibility of approximate estimation only for smaller rural settlements up to 500 population equivalents.

Role of primary settling tanks on SBR plants for secondary treatment

SBR tehnologija predstavlja modifikaciju konvencionalnog postupka s aktivnim muljem na principu saržnog punjenja bioreaktora. Smanjenje ukupnog potrebnog volumena SBR reaktora s drugim stupnjem prociscavanja može se ostvariti uz prethodnu taložnicu kao prvi stupanj prociscavanja ispred SBR reaktora, sto je osobito izraženo kod uređaja za prociscavanje veceg kapaciteta kojima prethodi razdjelni sustav odvodnje. Uz izgradnju prethodnih taložnica ispred SBR reaktora, smanjuje se potrosnja kisika u odnosu na SBR uređaje bez prethodnih taložnica.