MbrlCatalogueTitleDetail

Do you wish to reserve the book?
Data Mining Application in Assessment of Weather-Based Influent Scenarios for a WWTP: Getting the Most Out of Plant Historical Data
Data Mining Application in Assessment of Weather-Based Influent Scenarios for a WWTP: Getting the Most Out of Plant Historical Data
Hey, we have placed the reservation for you!
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Data Mining Application in Assessment of Weather-Based Influent Scenarios for a WWTP: Getting the Most Out of Plant Historical Data
Oops! Something went wrong.
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Title added to your shelf!
Title added to your shelf!
View what I already have on My Shelf.
Oops! Something went wrong.
Oops! Something went wrong.
While trying to add the title to your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Data Mining Application in Assessment of Weather-Based Influent Scenarios for a WWTP: Getting the Most Out of Plant Historical Data
Data Mining Application in Assessment of Weather-Based Influent Scenarios for a WWTP: Getting the Most Out of Plant Historical Data

Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
How would you like to get it?
We have requested the book for you! Sorry the robot delivery is not available at the moment
We have requested the book for you!
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Data Mining Application in Assessment of Weather-Based Influent Scenarios for a WWTP: Getting the Most Out of Plant Historical Data
Data Mining Application in Assessment of Weather-Based Influent Scenarios for a WWTP: Getting the Most Out of Plant Historical Data
Journal Article

Data Mining Application in Assessment of Weather-Based Influent Scenarios for a WWTP: Getting the Most Out of Plant Historical Data

2019
Request Book From Autostore and Choose the Collection Method
Overview
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. Time-consuming and expensive local sampling and monitoring campaigns are usually carried out to estimate the characteristic flow and pollutant concentrations of CSO water. 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. Eight years (viz. 2009–2016) of historical data in addition to arithmetic mean daily precipitation rates (PI) of the plant catchment area are elaborated. Relationships between PI and volumetric influent flow rate (Qin), chemical oxygen demand (COD), ammonium (N-NH4), and total suspended solids (TSS) are investigated. 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. According to the TSDM results, a case-specific wet-weather definition is proposed for the Castiglione Torinese WWTP. Two significant weather-based influent scenarios are assessed by kernel density estimation. 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. Implementing the obtained results in dynamic process simulation models can improve the plant operational efficiency in managing the fluctuating loads.