IEEE Transactions on Evolutionary Computation, IEEE Press, 8(5), 471–489.Ĭlark, S. An evolutionary approach to pattern-based time series segmentation. New York: Information Science Reference.Ĭhung, L., Fu, T. Wang (Ed.), Encyclopaedia of data warehousing and mining (pp. Official Journal of the European Community, L, 257, 26–40.Ĭhandola, V., Banerjee, A., & Kumar, V. Directive concerning integrated pollution prevention and control (96/61/EEC). Official Journal of the European Community, L135, 40–52.ĬEC.
Directive concerning urban wastewater treatment (91/271/EEC). Journal of Water Resources Planning and Management, 125, 3–13.ĬEC. Historical development of wet-weather flow management. Water Science and Technology, 31, 1–12.īurian, S. Flow and pollutant measurements in a combined sewer system to operate a wastewater treatment plant and its storage tank during storm events. Journal - Water Pollution Control Federation, 368–375.īertrand-Krajewski, J.-L., Lefebvre, M., Lefai, B., & Audic, J.-M. Evaluation of treatment plant performance: Causes, frequency, and duration of upsets. Agenzia regionale per la protezione ambientale. In KDD Workshop on Temporal Data Mining, p. Implementing the obtained results in dynamic process simulation models can improve the plant operational efficiency in managing the fluctuating loads.Īntunes, C. The results confirm that the method suggested within this study based on plant routinely collected data can be used for planning the emergency response and long-term preparedness for extreme climate conditions in a WWTP. Two significant weather-based influent scenarios are assessed by kernel density estimation. According to the TSDM results, a case-specific wet-weather definition is proposed for the Castiglione Torinese WWTP. A time series data mining (TSDM) method is implemented with MATLAB computing package for segmentation of time series by use of a sliding window algorithm (SWA) to partition the available records associated with wet and dry weather events. Relationships between P I and volumetric influent flow rate (Q in), chemical oxygen demand (COD), ammonium (N-NH 4), and total suspended solids (TSS) are investigated. 2009–2016) of historical data in addition to arithmetic mean daily precipitation rates ( P I) of the plant catchment area are elaborated. This study focuses on estimating the frequency and duration of wet-weather events and their impacts on influent flow and wastewater characteristics of the largest Italian wastewater treatment plant (WWTP) located in Castiglione Torinese.
Time-consuming and expensive local sampling and monitoring campaigns are usually carried out to estimate the characteristic flow and pollutant concentrations of CSO water. (1983) Changes in the normal maximal expiratory flow-volume curve with growth and aging.Since the introduction of environmental legislations and directives, the impact of combined sewer overflows (CSO) on receiving water bodies has become a priority concern in water and wastewater treatment industry. (1989) New regression equations for predicting peak expiratory flow in adults. But if he already has a measured peak flow of 568 L/min, the percentage is 88.76. His predicted peak flow value would be 639.948 L/min. ■ Or in the case of a male aged 27 with a height of 186 cm. Her estimated peak flow value would be 467.4 L/min. ■ Let’s take for instance the case of a female aged 32 with a height of 175cm. You can use the form as many times as you like varying the information or personal data. If you already have a peak flow determination you can input that as well to get an extra info in the result.īased on peak flow formulas, the calculator computes the predicted value in your case and extracts the percentage the measured figure is out of the prediction. The former can be put in either metric (cm) or English (inches) measurement. You need to select your gender and input your age and height. This is a quick health tool that determines the estimated or predicted peak flow based on your data.
0 kommentar(er)
