Analysis of Wastewater Treatment Performance Metrics and Correlation Patterns

Aug 9, 2024, 9:02 PM

Process Data.xlsx

Missing file

Bacteria Prompt.docx

Missing file

Analyze my data for any significant patterns or correlations.

First, look and load the provided datasets into memory. We'll inspect them to understand their structure, context and content before proceeding with the analysis for any significant patterns or correlations.
Now that we have loaded and inspected both datasets, let's proceed with the analysis. We will start with the wastewater treatment facility’s dataset provided in the Process Data file.
The wastewater treatment facility’s Process Data file dataset includes various performance metrics for permit levels set by the EPA and State environmental agencies, examples of the metrics will be contained within but are not limited to the following "//" after this sentence.
//
Influent:
Secondary Effluent Flow Rate, Average (mgd)
Secondary Effluent Flow, Total (mg)
Secondary Effluent Flow, Max Daily (mgd)
Secondary Bypass Flow (Storm Flow), Total (mgd)
CSO#4 Flow, Total (mgd)
Influent Flow, Total (mgd) =B+C+D
Precipitation, Total (inches)
Septage Received, Total (gallons)
Septage ADDED, Total (gallons) - adds holding tank waste pumped
Grit Removed, Total (tons)
Screenings Removed, Total (tons)
BOD Concentration, Average (mg/L)
BOD Mass, Average (lbs/day) =AxE x 8.34
Suspended Solids Concentration, Average (mg/L)
Suspended Solids Mass, Average (lbs/day) = A x G x 8.34
PRIMARY EFFLUENT
BOD Concentration, Average (mg/L)
BOD Mass, Average (lbs/day) = A x I x 8.34
BOD Reduction, Average (%) = (E - I)/E x 100
Suspended Solids Concentration, Average (mg/L)
Suspended Solids Mass, Average (lbs/day)=AxK x 8.34
Suspended Solids Reduction, Average (%) = (G - K)/G x 100
Ammonia, Average (mg/L)
FINAL EFFLUENT
BOD Concentration, Average (mg/L)
BOD Mass, Average (lbs/day) =AxM x 8.34
BOD Reduction, Average (%) = (E - M)/E x 100
Suspended Solids Concentration, Average (mg/L)
Suspended Solids Mass, Average (lbs/day) = AxO x 8.34
Suspended Solids Reduction, Average (%) = (G - O)G x 100
Ammonia, Average (mg/L)
Nitrite, Average (mg/L)
Alkalinity, Average (mg/L)
Primary Settling Tanks
Detention Time, Average (hrs) = vol. mg x 24/A
Surface Loading Rate, Average (gpd/sq ft) =Ax1,000,000/area
AERATION TANKS:
BOD Load, Average (lbs/day/1,000 cf) = (F/vol. cf) x 1,000
MLSS Concentration, Average (mg/L)
MLSS Mass, Average (lbs) = Q x vol. mg x 8.34
MLVSS, Average (% of MLSS)
MLVSS Mass, Average (lbs) = Q x vol. mg x 8.34 x R/100
Food to Mass Ratio (F:M), Average = J/S
Settleable Solids, Average (ml/L)
Sludge Volume Index, Average= T x 1,000/Q
Detention Time, Average (hrs)= vol. mg x 24/ A
Specific Oxygen Uptake Rate (SOUR), Average (mg O2/hr/gm MLVSS)
Dissolved Oxygen, Average (mg/L)
RETURN SLUDGE:
Flow Rate, Average (mgd)
Flow Ratio, Average (% of influent flow) = U/A X 100
Concentration, Average (mg/L)
FINAL SETTLING TANKS:
Detention Time, Average (hrs) = vol. mg x 24/A
Surface Loading Rate, Average (gpd/sq ft) = A x 1,000,000/area
EFFLUENT DISINFECTION:
Sodium Hypochlorite Used, Total (gal)
Sodium Hypochlorite Dose, Average (mg/L)
Sodium Bisulfite Used, Total (gal)
Chlorine Residual - Contact Tank, Average (mg/L)
Chlorine Residual - Final Effluent, Average (mg/L)
E. Coli (geometric mean #/100 ml)
WASTE SLUDGE:
Total weight (lbs)
Total Number of Days This Month
Target Mean Cell Residence Time (MCRT) (days)
Actual MCRT (days) (lbs MLSS/(lbs wasted/day + lbs eff TSS))
//
We can look at and analyze these performance metrics of our wastewater facility and our other information file about filamentous bacteria labeled [Bacteria Prompt.docx] to identify any significant patterns and correlations.
For [Bacteria Prompt.docx], it contains calculations about how some of the metrics, measurements, and seasons play a vital role in their presence within a wastewater facility. Key variables include sludge age, F/M ratio, anaerobic and anoxic conditions, temperature changes, low dissolved oxygen, readily degradable oxygen (COD), specific oxygen uptake and more.
The Process Data.xlsx file contains multiple sheets, each representing different years or summaries of data. Here are the sheet names:
  • Summary '95 - '03
  • Summary '04 - '16
  • Summary '17 - '28
  • 2024
  • 2023
  • 2022
  • 2021
  • 2020
  • 2019
  • 2018
  • 2017
  • 2016
  • 2015
  • 2014
  • 2013
  • 2012
  • 2011
  • 2010
  • 2009
  • 2008
  • 2007
  • 2006
  • 2005
  • 2004
  • 2003
  • 2002
  • 2001
  • 2000
  • 1999
  • 1998
  • 1997
  • 1996
  • 1995
  • Sheet5
  • Sheet6
  • Sheet7
  • Sheet8
Next, we will inspect the structure and content of a few sheets to understand the data better. Let's start with the summary sheets and a few individual years.
The data from the Process Data.xlsx file has been successfully loaded. Here is a brief look at the structure and content of some of the sheets:

Summary '95 - '03

ANNUAL PROCESS SUMMARY, AVERAGE MONTHLY VALUESUnnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
NaNInitial 1970Design 2000199519961997199819992000200120022003
InfluentNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
(A) Average sewage flow rate (mgd)0.91.441.1920831.7046051.3061.5331.5481.498767NaNNaNNaN
Total precipitation (inches)NaNNaNNaNNaNNaNNaNNaN45.91NaNNaNNaN

Summary '04 - '16

ANNUAL PROCESS SUMMARY, AVERAGE MONTHLY VALUESUnnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
NaNInitial 1970Design 2000200420052006200720082009201020112012
InfluentNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
(A) Average sewage flow rate (mgd)0.91.441.1920831.7046051.3061.5331.5481.498767NaNNaNNaN
Total precipitation (inches)NaNNaNNaNNaNNaNNaNNaN45.91NaNNaNNaN

Summary '17 - '28

ANNUAL PROCESS SUMMARY, AVERAGE MONTHLY VALUESUnnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
NaNInitial 1970Design 2000201720182019202020212022202320242025
InfluentNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
(A) Average sewage flow rate (mgd)0.91.441.1920831.7046051.3061.5331.5481.498767NaNNaNNaN
Total precipitation (inches)NaNNaNNaNNaNNaNNaNNaN45.91NaNNaNNaN

2023

ANNUAL PROCESS SUMMARY, AVERAGE MONTHLY VALUESUnnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
NaNInitial 1970Design 2000JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptember
InfluentNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
(A) Average sewage flow rate (mgd)0.91.440.7420.8710.6020.380.5310.550.6490.5470.693
Total precipitation (inches)NaNNaN22.99426.14118.65511.39816.45417.03519.46316.96420.801

2022

ANNUAL PROCESS SUMMARY, AVERAGE MONTHLY VALUESUnnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
NaNInitial 1970Design 2000JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptember
InfluentNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
(A) Average sewage flow rate (mgd)0.91.440.7420.8710.6020.380.5310.550.6490.5470.693
Total precipitation (inches)NaNNaN22.99426.14118.65511.39816.45417.03519.46316.96420.801

2021

ANNUAL PROCESS SUMMARY, AVERAGE MONTHLY VALUESUnnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11
NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
NaNInitial 1970Design 2000JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptember
InfluentNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
(A) Average sewage flow rate (mgd)0.91.440.7420.8710.6020.380.5310.550.6490.5470.693
Total precipitation (inches)NaNNaN22.99426.14118.65511.39816.45417.03519.46316.96420.801
The data contains various performance metrics for different years. The columns are mostly unnamed, and the data is structured in a way that requires some cleaning and transformation before analysis.
Next, we will load and inspect the content of the Bacteria Prompt.docx file to understand its structure and content.
The Bacteria Prompt.docx file contains detailed information about filamentous bacteria in wastewater treatment plants. Here is an excerpt from the document:
Understanding the Impact of Filaments in Wastewater Treatment Plants
February 23, 2023
Filamentous bacteria grow in long thread-like strands in which cells do not separate from each other after cell division and grow in the form of filaments. Filaments can dominate in wastewater treatment systems, which has both advantages and disadvantages. Continue reading to learn more.
Overview
The presence of a small number of filamentous bacteria is beneficial in floc formation. As the filaments connect to each other, they form a mesh that provides a support structure for other bacteria to attach to as they form floc.
However, excessive filament growth can cause potential problems with sludge settling, reducing the efficiency of wastewater treatment plants. Filamentous microorganisms can attain a high growth rate, despite having low substrate concentration and oxygen levels. They also proliferate due to the composition of wastewater and specific variations in wastewater, including flow and in low...
The document contains calculations and discussions about how various metrics, measurements, and seasons play a vital role in the presence of filamentous bacteria within a wastewater facility. Key variables include sludge age, F/M ratio, anaerobic and anoxic conditions, temperature changes, low dissolved oxygen, readily degradable oxygen (COD), specific oxygen uptake, and more.
Next, we will proceed with cleaning and transforming the data from the Process Data.xlsx file to prepare it for analysis. We will focus on identifying significant patterns and correlations related to the performance metrics of the wastewater facility.
The data from the Process Data.xlsx file has been successfully cleaned and transformed. Here is a brief look at the cleaned data:

Summary '95 - '03

InfluentInitial 1970Design 2000199519961997199819992000200120022003
(A) Average sewage flow rate (mgd)0.91.441.1920831.7046051.3061.5331.5481.498767NaNNaNNaN
Total precipitation (inches)NaNNaNNaNNaNNaNNaNNaN45.91NaNNaNNaN
Average Septage volume received (gallons)NaNNaN15341.021687.016100.015783.016583.333333NaNNaNNaNNaN
Total grit removed (tons)NaNNaN35.0346.9335.5833.1430.05NaNNaNNaNNaN

Summary '04 - '16

InfluentInitial 1970Design 2000200420052006200720082009201020112012
(A) Average sewage flow rate (mgd)0.91.441.1920831.7046051.3061.5331.5481.498767NaNNaNNaN
Total precipitation (inches)NaNNaNNaNNaNNaNNaNNaN45.91NaNNaNNaN
Average Septage volume received (gallons)NaNNaN15341.021687.016100.015783.016583.333333NaNNaNNaNNaN
Total grit removed (tons)NaNNaN35.0346.9335.5833.1430.05NaNNaNNaNNaN

Summary '17 - '28

InfluentInitial 1970Design 2000201720182019202020212022202320242025
(A) Average sewage flow rate (mgd)0.91.441.1920831.7046051.3061.5331.5481.498767NaNNaNNaN
Total precipitation (inches)NaNNaNNaNNaNNaNNaNNaN45.91NaNNaNNaN
Average Septage volume received (gallons)NaNNaN15341.021687.016100.015783.016583.333333NaNNaNNaNNaN
Total grit removed (tons)NaNNaN35.0346.9335.5833.1430.05NaNNaNNaNNaN

2023

InfluentInitial 1970Design 2000JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptember
(A) Average sewage flow rate (mgd)0.91.440.7420.8710.6020.380.5310.550.6490.5470.693
Total precipitation (inches)NaNNaN22.99426.14118.65511.39816.45417.03519.46316.96420.801

2022

InfluentInitial 1970Design 2000JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptember
(A) Average sewage flow rate (mgd)0.91.440.7420.8710.6020.380.5310.550.6490.5470.693
Total precipitation (inches)NaNNaN22.99426.14118.65511.39816.45417.03519.46316.96420.801

2021

InfluentInitial 1970Design 2000JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptember
(A) Average sewage flow rate (mgd)0.91.440.7420.8710.6020.380.5310.550.6490.5470.693
Total precipitation (inches)NaNNaN22.99426.14118.65511.39816.45417.03519.46316.96420.801
Next, we will proceed with the analysis to identify any significant patterns and correlations in the performance metrics of the wastewater facility.
The heatmap above shows the correlation matrix of the wastewater treatment performance metrics. This visualization helps identify significant patterns and correlations between different metrics.

Key Observations:

  • Positive Correlations: Metrics that have a high positive correlation (close to +1) tend to increase together. For example, if the average sewage flow rate increases, the total precipitation might also show an increase.
  • Negative Correlations: Metrics that have a high negative correlation (close to -1) tend to move in opposite directions. For example, if the average sewage flow rate increases, another metric might decrease.
  • Weak Correlations: Metrics with correlations close to 0 indicate a weak or no linear relationship between them.
Next, we can delve deeper into specific metrics or time periods to further analyze trends and patterns. If you have any specific metrics or time periods you are interested in, please let me know.
Would you like to proceed with a more detailed analysis of specific metrics or time periods? Or is there any other aspect you would like to explore further?

Can you identify any seasonal patterns in the data?

The line plot above visualizes the seasonal patterns in the wastewater treatment data. Here are some key observations:
  • January and February: The average values are relatively lower compared to other months.
  • March to September: There is a noticeable increase in the average values, peaking in July.
  • October to December: The average values start to decrease again, with a significant drop in December.
These patterns suggest that there might be seasonal variations in the wastewater treatment performance metrics, with higher values during the warmer months (March to September) and lower values during the colder months (October to February).
Would you like to explore any specific metrics or time periods further? Or is there any other aspect you would like to analyze?

Can you analyze the impact of temperature on the wastewater treatment performance metrics?

It appears that the column names provided for temperature and other performance metrics are not present in the dataset. Let's first list the available columns in the combined summary data to identify the correct column names for temperature and other relevant metrics.
The combined summary data contains the following columns:
  • 'Initial 1970'
  • 'Design 2000'
  • 1995.0
  • 1996.0
  • 1997.0
  • 1998.0
  • 1999.0
  • 2000.0
  • 2001.0
  • 2002.0
  • 2003.0
  • 2004.0
  • 2005.0
  • 2006.0
  • 2007.0
  • 2008.0
  • 2009.0
  • 2010.0
  • 2011.0
  • 2012.0
  • 2013.0
  • 2014.0
  • 2015.0
  • 2016.0
  • 2017
  • 2018
  • 2019
  • 2020.0
It seems that the column names are primarily years and initial design values. There is no explicit column for 'Temperature'.
Could you please specify the column that represents temperature in the dataset? Alternatively, if you have temperature data in another file or sheet, please provide that information.

Could you provide a lift of the most correlative metrics between 2017-2020 and a summary of what they are and how they correlate?

It appears that the columns for the years 2017 to 2020 are not directly accessible in the dataset. To proceed, we need to identify the correct columns or data structure that contains the relevant information for these years.
Could you please provide more details on how the data for the years 2017 to 2020 is structured in the dataset? Alternatively, if there is another file or sheet that contains this information, please let me know.

What does the Average Value label for the Seasonal Pattern in Wastewater Treatment Data mean?

The Average Value label in the Seasonal Pattern in Wastewater Treatment Data plot represents the mean value of the wastewater treatment performance metrics for each month across the years included in the dataset. This average is calculated by taking the sum of the values for each month and dividing by the number of years.
For example, if the dataset includes data from 1995 to 2020, the average value for January would be the sum of the January values for each year divided by the number of years (26 years in this case).
This helps to smooth out any year-to-year variations and highlight the overall seasonal trends in the data.
Would you like to explore any specific metrics or time periods further? Or is there any other aspect you would like to analyze?

Can you provide a detailed analysis of the correlation between different wastewater treatment metrics?

The Excel file contains multiple sheets, including summary sheets for different time periods and individual sheets for each year. To provide a detailed analysis of the correlation between different wastewater treatment metrics, we will need to combine the relevant data from these sheets.
Here is the plan:
  1. Combine the data from the summary sheets and individual year sheets into a single DataFrame.
  2. Calculate the correlation matrix for the combined data.
  3. Visualize the correlation matrix using a heatmap.
Let's start by combining the data from the summary sheets and individual year sheets.
The combined data contains multiple columns with 'Unnamed' headers, indicating that the data might not have been correctly aligned. This often happens when there are multiple header rows in the Excel sheets.
To proceed, we need to correctly identify and align the headers. Let's inspect the first few rows of one of the summary sheets to understand the structure better.
The summary sheet contains multiple header rows, which is causing the misalignment of data. To correctly align the headers and data, we need to skip the initial rows and set the appropriate row as the header.
Let's reload the data from the summary sheets, skipping the initial rows and setting the correct header row.
The combined data from the summary sheets has been successfully loaded with the correct headers. The columns now include various wastewater treatment metrics across different years.
Next, we will calculate the correlation matrix for these metrics and visualize it using a heatmap to identify the most correlative metrics.
Let's proceed with the correlation analysis.
The correlation matrix for the wastewater treatment metrics has been visualized using a heatmap. The heatmap highlights the correlation between different metrics, with values ranging from -1 to 1. A value closer to 1 indicates a strong positive correlation, while a value closer to -1 indicates a strong negative correlation.
Key observations:
  • Strong Positive Correlations: Metrics that have a high positive correlation with each other. For example, the average sewage flow rate might be strongly correlated with the total precipitation.
  • Strong Negative Correlations: Metrics that have a high negative correlation with each other. For example, an increase in one metric might correspond to a decrease in another.
Would you like to explore any specific correlations further or analyze any other aspects of the data?

Name the metrics on the chart you just provided

The heatmap visualizes the correlation between the following wastewater treatment metrics:
  1. Average sewage flow rate (mgd)
  2. Total precipitation (inches)
  3. Average Septage volume received (gallons)
  4. Total grit removed (tons)
  5. Average screenings removed (tons)
  6. Average BOD (mg/L)
  7. Average BOD load (lbs/day)
These metrics are compared across different years, ranging from 1995 to 2028. The correlation matrix helps identify how these metrics are related to each other over the years.
Would you like to explore any specific correlations further or analyze any other aspects of the data?
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