Water Journal : Water Journal August 2012
refereed paper source management water AUGUST 2012 79 • BTO are well versed in Oxley Creek processes due to prior site engagement; • Ready availability of input data (trade waste officers and embedded process engineers are located at the STP). The input data described below was provided to the modellers as the basis of the model: • Pump station locations and the average dry weather flow (ADWF) for each pump station; • Plant design parameters; • Output trade waste data from the QUU trade waste customer database (known as LIWIS); • Environmental licence conditions; • Biosolids flows and quality specifications; • Effluent monitoring data; • Influent monitoring data; • STP plant return stream settings; • Input water quality specifications for the WCRWS. Key database inputs include the trade waste connections inputs (described in the paragraphs below); pump station network inputs; and STP capacity model inputs (these are assumptions associated with treatment processes such as inlet works, bioreactors, disinfection and sludge handling). Most of the variable data inputs are sourced from the known or aggregated discharge characteristics recorded within the QUU trade waste database (LIWIS), and will require regular updating; whereas the fixed data inputs sourced from management plans, licence conditions and plant design specifications are likely to require only infrequent review and update. To understand the nature of the variable data inputs, it is first necessary to understand the categorisation of trade waste customers within the QUU trade waste management system. Trade waste approvals are split into four categories: Cat A, Cat B, Cat C and Cat D. These categories are described in Table 1, along with the assumptions that have been made to enable manageable input of data to the mass load model. Where input data was unavailable, but may later become available (as for incidental removal rates and some design parameters or discharge conditions), hypothetical entries were made to support the development of the model. Model Overview The following sections describe the capability of the database model and show several of the input and output screens accessible from the entry screen (Figure 1). This is intended as an overview of the model's capability and is not exhaustive or detailed. Network pump stations page (Figure 2) The model provides a schematic of the major pump stations in the relevant network, and also the interconnections between the pump stations and the major flow path to the STP. The flows and loads calculated for the trade waste contributors are displayed Table 1. QUU trade waste categorisation. Category A Descriptor Discharge less than 275kL/annum with assumed domestic strength. Database Assumptions Input total number of Cat A connections An average flow from each connection Assumed domestic strength Category B Descriptor Discharge greater than 275kL/annum with assumed domestic strength. Database Assumptions Input total number of Cat B connections An average flow from each connection Assumed domestic strength Category C Descriptor Discharge greater than 275kL/annum with assumed less than domestic strength. Database Assumptions Input total number of Cat C connections An average flow from each connection Assumed contaminant concentrations of BOD=100mg/L, TSS=200mg/L, TN=13mg/L, TP=10mg/L Category C (sub group -- metal handler) Descriptor Typically Category C traders where QUU obtains regular flow and sampling data for metals contaminants Database Assumptions Input industry and monitoring data relevant to specific Cat C (metal handler) on individual basis Category D Descriptor Large trader with high-volume or high-strength trade waste (typically >20kg/day BOD or at QUU discretion) Database Assumptions Input industry and monitoring data relevant to specific Cat D customer on individual basis Figure 1. Front screen of mass load model.
Water Journal September 2012-1
Water Journal July 2012