An algorithmic approach for system-specific modelling of activated sludge bulking in an SBR

摘要

Using a step-by-step approach, a system--specific non--mechanistic model based on statistical analyses of real-world response of process parameters to bulking, was formulated for an aerobic Sequencing Batch Reactor (SBR). The approach involved 2 phases - “Diagnosis” and “Analysis”. In “Diagnosis” phase, model parameters were identified via statistical rules and existing knowledge base on bulking, while in the “Analysis” phase, the parameters were modelled with the Sludge Volume Index (SVI) to formulate the non--mechanistic model. Validation results yielded satisfactory results for the modelling analysis. The statistical approach was executed using a multivariate data analysis package and resulted in a non--mechanistic model that was practically appealing to practitioners for its ease of use as a predictive tool for bulking. This approach, being dependent only on data analysis and statistical modelling, required no bench-scale experiments to be conducted for preliminary identification of the bulking problem. Furthermore, such an approach being algorithmic in nature, could potentially form the concept behind design of expert systems for very rapid, and economical diagnosis of bulking problems before a more in-depth and slow analytical approach involving costly laboratory procedures is adopted.