Dynamic Modelling for Control of High Rate Anaerobic Wastewater Treatment Processes - Jones, Richard M. (My University, 1992)
The overall goal of this research was to develop an improved understanding of the dynamic behavior necessary to define monitoring and control requirements of high rate anaerobic biological wastewater treatment processes. This required generation of an extensive non-steady state data set.
Data were collected during dynamic experiments on a 77 L pilot scale anaerobic fluidized bed, operated at the Wastewater Technology Centre in Burlington, Ontario. Experiments consisted of 12 and 36 hour pulse inputs of substrates specific to the distinct groups of microorganisms present in the process, and ranged from 7 to 21 days in length. The methane and carbon dioxide production rates, biogas hydrogen content, pH, effluent concentrations of volatile acid intermediates and chemical oxygen demand (COD) were measured using on-line instrumentation or laboratory analysis of discrete samples.
Experimental results demonstrated the relative importance of substrate and product inhibition on various reaction steps in the process, and indicated that he short term dynamic response can change significantly over time. The gas phase hydrogen concentration, previously proposed as an indicator of process stability, was found to have limited utility as a monitoring variable. The loss in potential methane production and the increased chemical requirements for pH control were shown to represent a significant cost during an upset. This suggests that in addition to the environmental incentive, an economic incentive exists to maintain stable operation of the process.
A mechanist, four -bacterial population dynamic model of the process was formulated. Due to lack of suitable mechanistic models for bacterial concentrations, the model was only able to predict the short term dynamic response. An extended Kalman filter was used to combine the four population model with stochastic models for the bacteria concentration states. A sensitivity analysis was required to select a subset of parameters and stochastic states for estimation.
The extended Kalman filter allowed the model to track the measured states, although in some cases this tracking could only be achieved through unrealistic adjustments of the bacterial concentration states. The time-variable behavior of the estimated stochastic states indicated a number of potential model improvements.
Keywords: Chemical Engineering; Chemical Engineering