Browsing by Author "Sarah K. Liehr, Committee Member"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • No Thumbnail Available
    Evaluation of Bench-Scale Sequencing Batch Reactor Swine Waste Treatment Under Continuous and Cyclic Aeration
    (2007-05-03) Bennett, Todd Alan; Sarah K. Liehr, Committee Member; John J. Classen, Committee Chair; Michael R. Hyman, Committee Member
    The objectives of this project were to develop operating conditions for a bench-scale sequencing batch reactor to match the design of a full-scale sequencing batch reactor system for treating swine waste and to determine the effects of continuous, low oxygen versus cyclic aeration schemes on sequencing batch reactor system performance. The low aeration technique was intended to develop conditions for low oxygen nitrification and simultaneous nitrification and denitrification so that a comparison could be made to a typical cyclic aeration reactor for biological nitrogen and phosphorus removal. The performance of the two reactor configurations was measured by the settling efficiency, mass removal efficiency, and accumulation of chemical oxygen demand (COD), suspended solids (SS), total Kjeldahl nitrogen (TKN), and total phosphorus (TP). The performance of the reactors did not meet expectations due to excessive loading and source inconsistency. Operational changes to the solids wasting mechanism and to the cyclic aeration system were made during the experiment in an attempt to stimulate reactor performance, which provided insight into the responses of the two types of reactors to these changes. The performance of the continuous aeration reactors met or exceeded the performance of the cyclic aeration reactors, while receiving a 73% lower supply of oxygen. The results support the potential for equipment and energy savings by utilizing low-oxygen continuous aeration for the treatment of swine waste with sequencing batch reactors.
  • No Thumbnail Available
    Optimization of Wastewater Treatment Design under Uncertainty and Variability
    (2005-04-19) Doby, Troy A. Jr.; Daniel H. Loughlin, Committee Member; S. Ranjithan, Committee Member; E. Downey Brill, Committee Member; John W. Baugh, Jr., Committee Chair; Sarah K. Liehr, Committee Member; Francis L. de los Reyes, Committee Member
    Typical goals of domestic wastewater treatment are a low cost process that is reliable in terms of meeting effluent quality standards. Designers using traditional steady-state design with modeling of domestic wastewater treatment plants use scalar values as inputs. The inputs are typically of two types — (1) design loadings based upon historical data and (2) stoichiometric and kinetic parameters based upon literature values. There is typically not a way of knowing a priori the reliability of the design or whether the design is least cost using the traditional design approach. Designers using deterministic optimization with modeling of domestic wastewater treatment plants also use scalar values of the two types of inputs as with traditional design. While the designer may now know that the design is least cost given the inputs, there is no way of knowing a priori whether the design is the most reliable for the cost. It is possible to take an existing design — whether obtained by traditional design methods, deterministic optimization, or by any other design method — and determine the reliability of the design. To do so, however, requires characterization of both uncertainty and variability of the data. Uncertainty arises because of a lack of knowledge about an input value and its statistical distribution. Variability arises because of the heterogeneity of the processes determining the input value. In this particular case, the historical input loadings are known and thus the variability is characterized. The characterization of the load variability is then used for future predictions of behavior. The stoichiometric and kinetic parameter values for the particular case are not typically known and thus are uncertain. An approach to the uncertainty of these values is proposed. Different loading criteria (based on percentiles of historical flow and waste concentration data) were used in deterministic optimization. It was determined that the higher the flow percentile, the more expensive the design. However, a more reliable design could be found at a lower cost (and at a lower flow percentile). A different design procedure using stochastic programming is illustrated taking both cost and reliability into account during the design procedure. As a result, a reliability-cost tradeoff curve is generated. This curve is characterized by (1) a very steep portion where slight increases in cost lead to large improvements in reliability; and (2) a very flat portion where large increases in cost lead to small improvements in reliability. This design procedure also allows determination of the value of an experimental program characterizing the uncertainty and variability of the stoichiometric and kinetic parameters and their statistical distribution.