An intelligent optimization model analysis and design of bio-filtration in raw water quality improvement

Currently clean water treatment technology is in-need to optimize available environmental-friendly bio-filtration technology as means for pre-treatment of raw water quality improvement with the intelligent computational model. The objectives of this research is to identify the component and process in systems analysis, to develop the design for an intelligent optimization model of bio-filtration in raw water quality improvement. In this paper, optimization model of bio-filtration was simulated by using Process Hierarchy Diagram (PHD) and Business Process Modeling and Notation (BPMN) model with numerical integration to assist researchers in clean water technology by using artificial neuron network (ANN). The intelligent system was designed in two processing variables as the model inputs, namely pollutant concentration and hydraulic retention time (HRT). By modifying and controlling value of these variables, researchers determined the influences on the three bio-filtration crucial biochemical properties, such as pollutant index (PI), mass balance (MB), and microbial growth (MG). Model verification result showed that the model succeeded conducting an optimization for all properties. Thus this proposed system is potentially to optimize model of bio-filtration in raw water improvement in a simpler and fully controllable model.