ABSTRACT
This study describes a model-based method for real-time optimization of the key filtration parameters in a submerged anaerobic membrane bioreactor (AnMBR) treating urban wastewater (UWW) and UWW mixed with domestic food waste (FW). The method consists of an initial screening to find out adequate filtration conditions and a real-time optimizer applied to a periodically calibrated filtration model for minimizing the operating costs. The initial screening consists of two statistical analyses: (1) Morris screening method to identify the key filtration parameters; (2) Monte Carlo method to establish suitable initial control inputs values. The operating filtration cost after implementing the control methodology was 0.047 per m3 (59.6% corresponding to energy costs) when treating UWW and 0.067 per m3 when adding FW due to higher fouling rates. However, FW increased the biogas productivities, reducing the total costs to 0.035 per m3. Average downtimes for reversible fouling removal of 0.4% and 1.6% were obtained, respectively. The results confirm the capability of the proposed control system for optimizing the AnMBR performance when treating both substrates.
