Tag Archives: Environment

Protected: SWATER Mix 5.0: MultiFunctional Testing & Upgrading Software Tool for Urban and Industrial WWTP

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[EN] Water Footprint

WaterFootprint

Global Water Footprint Standard

The Global Water Footprint Standard – developed through a joint effort of the Water Footprint Network, its partners, and scientists of the University of Twente in the Netherlands – has garnered international support from major companies, policymakers, NGOs and scientists as an important step toward solving the world’s ever increasing water problems. The standard is contained in the Water Footprint Assessment Manual.

[EN] Water Quality Sensor Technology

EPA – Commonly Used Water Quality Sensors Can Detect Intentional Drinking Water Contamination

Free chlorine and total organic carbon sensors most successful for detecting contamination in tests using selected biological and chemical contaminants.

EPA has released Distribution System Water Quality Monitoring: Sensor Technology Evaluation Methodology and Results – A Guide for Sensor Manufacturers and Water Utilities, which summarizes the results of tests with various online (i.e., real-time) water quality sensors to see if they could provide dual use for early warning of intentional contamination, as well as monitoring general water
quality. Only sensors most commonly used by water utilities were tested.
Free chlorine and total organic carbon (TOC) sensors were the most successful in detecting a number of chemical and biological contaminants.

  • Free chlorine levels noticeably dropped in the presence of various contaminants
  • TOC sensors were successful in detecting carbon containing contaminants or carrier liquids

EPA- Distribution System Water Quality Monitoring: Sensor Technology Evaluation Methodology and Results

This report, titled “Distribution System Water Quality Monitoring: Sensor Technology Evaluation Methodology and Results – A Guide for Sensor Manufacturers and Water Utilities,” provides an overview of the U.S. Environmental Protection Agency’s (EPA’s) research results from investigating water quality monitoring sensor technologies that might be used to serve as a real-time contamination warning system (CWS) when a contaminant is introduced into a drinking water distribution system. EPA’s concept of CWS for protecting water distribution systems is discussed in Chapter 1.0. A principal component of such a system is online water quality monitoring.
Based on a review of available online water quality monitoring sensor technologies, an early determination was made that it was not technically feasible to accurately identify and quantify the many different types of contaminants that could potentially be introduced into the drinking water supply/distribution system. Furthermore, because online sensor technologies need to be economically suitable for mass deployment within a distribution system, EPA focused its research on identifying sensor technologies that could be used to detect anomalous changes in water quality due to contamination event(s). Once a water quality anomaly is detected, the water utility operator is alerted, and further actions (e.g., sampling and analysis) could be undertaken by the operator to identify and quantify the contaminant if necessary. This report focuses on EPA’s research on pilot-scale evaluations of available online water quality monitoring sensor instrumentation.

EPA -Water Quality Standards Academy

 

[EN] Water Quality EarlyWarning: 2WQI on-line index

WQIwaterWQI2

WQI- Unfailing Water Quality

Why it is so important to measure and evaluate in real time the quality of water with respect to its conformity with the target?

It is sufficient to think about the drinkng water vulnerability, with reference both to events of accidental pollution, that intentional, but the concept also applies to the secondary water management.

Having a pre-alarm signal (Early Warning) and a timely measurement of water quality, it can preserve phenomena induced by even very dangerous for health and for the management of the same water compared with the legal requirements.

Here is showed a “knowledge model based” software sensor (2WQI), that by using the class of origin and destination of water, it is able to interpret and extrapolate the on-line Quality Index of water (on a standardized scale 0-100), and the Quality /Compliance rate (2WQI). This is a special “Fuzzy” algorithm software application, able to interpret a measure of a “cluster” of on-line low-cost sensors as:  pH / ORP, Conductivity, Turbidity, ISE, etc..

The basic concept of 2WQI comes from the most well-known (but assessable only “off-line”) WQI quality index, developed in the early ’70s by the National Sanitation Foundation (NSF) to compare the quality of different water bodies and monitor the variations in time of the quality of a water body.

This algorithm is available as code to implement the common monitoring systems (PLC / SCADA).

At a glance:

  • EarlyWarning approach by use of real-time Trend Analysis. Thanks to Knowledge embedded Software Sensors, it is possible to make a Fuzzy Fusion among values from water quality on-line parameters, water process knowledge base and trend analysis forecasting, referring to required compliance of each industrial water re-use.
  •  A Bi-dimensional Water Quality Index (2WQI) will be developed as a real-time measurement of water management performance in terms of specific water quality (pollution level) and compliance (rate of respect of limits required in industrial process).

In fact, one of the technological limitations regarding the guarantee continuity and quality of the service integrated water, depends primarily on the ability to detect in real time, alarm events (early warning), the trend function and symptoms that frequently precede the occurrence of events critical process and /or operating / maintenance.

The ability to generate real-time information with the model 2WQI is an important advantage especially in the application of:

  • risk monitoring and security;
  • energy savings and process optimization;
  • monitoring of maintenance (predictive) according condition;
  • control and regulation of complex systems.

See also:  ASWR-Fuzzy-Logic-Water-Quality-Index-and-Importance-of-Water-Quality-Parame.pdf_2189

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[EN] WWTP/check: Checking Procedure for Biological Nutrient Removal Processes with evaluation of possible Energy/Cost Saving in pre-Denitrification/Nitrification scheme

WWTPWWTPcheck – Math Model 

Nitrogen Removal and Control Strategy in Continuous-Flow-Aeration 
DenitroNitroCheck-EN

WWTPcheck Model is based on typical Mathematical Models referring in the field (ASM 1/2/3, WPRC), but it supplemented with Performance Indicators (KPIs), which also provide information on the Residual Depurative Capacity. The predenitrification process was first developed and proposed by Ludzack and Ettinger (1962) and later modified by Barnard (1973), who completely separated the anoxic and aerobic reactors, recycling the settler underflow to the anoxic reactor, and providing an additional recycle from the aerobic to the anoxic reactor, see Figure below:

den-nit

WWTPcheck Flow scheme:  Modified Ludzack-Ettinger process (Denitro/Nitro)

Main Functionalities of Evaluation of WWTPcheck  procedure:

  • Percentage of Performance of Biological Nutrient Removal Process.
  • Min/Max values of Dissolved Oxygen required to avoid process problems (bulking, etc.).
  • Characterization of Functional Parameters of the Aeration System.
  • Percentage of possible Energy Saving in the Aeration System (variable DO setpoint calculation on the base of min/max biological need…).
  • Energy required for Aeration System.
  • Percentage of Electrical Energy Cost Saving.
  • Percentage of Sludge/Waste produced and their Cost.
  • Customized Input/Output Reporting.

The WWTP/check procedure is  available in MS-Excel file (.xls or .xlsx – MS Office-Excel 2007 or compatibles) to be better used as a checking tool and test.

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