Appraisal of Industrial Pollutants in Sewage and Biogas Production Using Multivariate Analysis and Unsupervised Machine Learning Clustering
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Object
2.1.1. General Information
- Population equivalent (p.e.) of the WWTP: 180,000
- Maximum design hydraulic capacity of the WWTP: 42,200 m3/d
- Length of the combined sewage system: 76 km
- Length of the sanitary sewage system: 477.2 km
- Length of the rainwater system: 72.8 km
- Population equivalent (p.e.) of residents using sewage network: 93,708
- Population equivalent (p.e.) of industry using sewage network: 73,805
- Number of residents using septic tanks: 8821
- Number of residents using domestic treatment plants: 71
2.1.2. Energy Management
2.2. Analysed Parameters
2.3. Research Data Handling and Statistical Analysis
2.4. Data Preparation for Machine Learning
2.5. Unsupervised k-Means Method for Pollution Analysis in Sewage
2.6. The Assessment of Addition of Polyaluminium Chloride and Commercial Products for Struvite Reduction in Processing
3. Results
3.1. Concentrations of Pollution in Sewage at the Subsequent Treatment Stages
3.2. Selecting Optimal Cluster Number for Sewage Pollution Analysis
3.3. Technological Process Improvement Through Polyaluminium Chloride Addition and Struvite Reduction
3.4. The Analysis of Biogas Production at the WWTP
4. Discussion
4.1. Removal of the Ether Extract, Phenols, Chlorides and Sulfates from Sewage in Municipal WWTPs
4.2. Biogas Production and Coagulant Assessment to Enhance Treatment Processes
4.3. Segregation of Industrial Pollutants Using a Machine Learning Clustering Algorithm
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Share of Own Energy in Demand | Covering the Demand with Own Energy | Energy Sale | Energy Purchase | Energy Production | Energy Used | Year |
---|---|---|---|---|---|---|
(%) | (MWh/Month) | |||||
20 | 20 | 12.68 | 215.16 | 34.27 | 249.43 | A |
66 | 66 | 40.32 | 119.75 | 115.17 | 234.91 | B |
51 | 51 | 4.06 | 120.64 | 119.44 | 240.08 | C |
61 | 61 | 1.90 | 86.29 | 135.97 | 221.20 | D |
73 | 70 | 7.35 | 64.27 | 160.17 | 217.08 | E |
88 | 85 | 6.67 | 32.03 | 198.11 | 223.49 | F |
94 | 90 | 9.31 | 22.13 | 209.15 | 222.80 | G |
89 | 85 | 8.26 | 35.21 | 209.63 | 236.58 | H |
75 | 74 | 2.11 | 71.64 | 201.75 | 271.29 | I |
69 | 67 | 10.30 | 85.24 | 153.74 | 235.21 | Avg |
0.32 | 0.30 | 1.08 | 0.67 | 0.36 | 0.07 | CV |
Discharged Sewage Sludge | Composting | Biogas Production | Biomass | Year |
---|---|---|---|---|
(Thous. Dry Matter/Month) | (mg/Month) | (Thous. m3/Month) | (mg/L/Month) | |
279.35 | 834.5 | 98.62 | 5483 | A |
274.30 | 674.9 | 109.22 | 4668 | B |
234.85 | 652.5 | 86.25 | 4977 | C |
203.33 | 508.4 | 101.71 | 4275 | D |
244.97 | 555.1 | 120.60 | 4638 | E |
244.07 | 561.9 | 129.79 | 4977 | F |
256.35 | 581.9 | 127.02 | 5124 | G |
260.00 | 617.4 | 103.05 | 4846 | H |
267.32 | 721.6 | 131.97 | 5325 | I |
251.62 | 634.2 | 112.03 | 4924 | Avg |
0.09 | 0.15 | 0.13 | 0.07 | CV |
Appendix B
Polyaluminium Chloride Consumption in Relation to EE Load | EE Load in Raw Sewage | Monthly Polyaluminium Chloride Consumption | Year |
---|---|---|---|
(kg of Product/kg of EE Load) | (kg/Day) | (kg of Product/Month) | |
2.185 | 4096 | 8950 | H |
2.745 | 3475 | 9540 | I |
Polystabil KWS Consumption in Relation to the Volume of Discharged Sewage Sludge | Discharged Sewage Sludge | Monthly Polystabil KWS Consumption | Year |
---|---|---|---|
(L of Product/m3 of Discharged Sludge) | (m3/Month) | (L of Product/Month) | |
0.0125 | 15,050 | 188 | H |
0.0088 | 17,482 | 154 | I |
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Pollutants tested in raw sewage, mechanically treated sewage and totally treated sewage:
|
Coagulant tested for suspended particles removal:
|
Coagulant tested for struvite reduction:
|
Parameters tested for biogas production:
|
Sk | Kurt | CV | STD | Max | Avg | Min | Polluting Substance |
---|---|---|---|---|---|---|---|
(−) | (mg/L) | ||||||
Raw sewage | |||||||
0.45 | 3.68 | 0.09 | 15.33 | 229.00 | 169.94 | 127.00 | Ether extract |
0.76 | 1.06 | 0.38 | 0.009 | 0.051 | 0.022 | 0.009 | Phenols |
3.68 | 15.63 | 0.40 | 60.50 | 473.00 | 150.76 | 89.00 | Chlorides |
−0.04 | −0.76 | 0.21 | 20.23 | 141.25 | 96.02 | 53.50 | Sulfates |
Mechanically treated sewage | |||||||
−0.17 | −0.40 | 0.10 | 12.78 | 149.50 | 122.21 | 89.50 | Ether extract |
1.09 | 2.60 | 0.47 | 0.008 | 0.049 | 0.018 | 0.001 | Phenols |
3.68 | 17.23 | 0.39 | 55.25 | 447.00 | 143.44 | 51.00 | Chlorides |
1.05 | 4.41 | 0.25 | 24.10 | 203.25 | 96.81 | 38.25 | Sulfates |
Totally treated sewage | |||||||
−1.15 | 1.72 | 0.06 | 0.87 | 16.30 | 14.69 | 11.80 | Ether extract |
2.85 | 10.69 | 0.60 | 0.001 | 0.007 | 0.002 | 0.001 | Phenols |
2.25 | 6.91 | 0.29 | 40.90 | 310.00 | 138.68 | 78.00 | Chlorides |
1.76 | 8.77 | 0.29 | 22.63 | 194.50 | 79.37 | 33.50 | Sulfates |
Sulfates | Chlorides | Phenols | Ether Extract | Treatment Stage | ||||
---|---|---|---|---|---|---|---|---|
ηSTD | ηAvg | ηSTD | ηAvg | ηSTD | ηAvg | ηSTD | ηAvg | |
(%) | ||||||||
N/A | N/A | N/A | N/A | 12.87 | 23.97 | 5.64 | 27.98 | Mechanically treated sewage |
11.90 | 22.10 | 11.36 | 13.02 | 3.83 | 91.81 | 0.73 | 91.31 | Totally treated sewage |
a | ||||||
p | df2 | df1 | F | Wilks’ Lambda | Independent Variables | |
<0.001 | 256 | 1 | 2119 | 0.10 | Mechanically treated sewage | |
<0.001 | 256 | 1 | 117.4 | 0.68 | Totally treated sewage | |
b | ||||||
p | R2 | t | Standard Error | Coefficient | Independent Variables | Dependent Variable |
0.051 | − | 1.956 | 2.30 | 4.51 | Constant | Raw sewage |
<0.001 | 0.88 | 46.03 | 0.028 | 1.30 | Mechanically treated sewage | |
<0.001 | 0.24 | −10.83 | 0.029 | −0.318 | Totally treated sewage |
Polystabil KWS Consumption | Ether Extract Load | Discharged Sewage Sludge | Polyaluminium Chloride Consumption | Parameter of Correlation | |
---|---|---|---|---|---|
— | — | — | — | Polyaluminium chloride consumption | Spearman’s Rho |
— | — | — | — | p-value | |
— | — | — | — | Effect size (Fisher’s z) | |
— | — | — | — | SE Effect size | |
— | — | — | 0.488 | Discharged sewage | Spearman’s Rho |
— | — | — | 0.026 | p-value | |
— | — | — | 0.534 | Effect size (Fisher’s z) | |
— | — | — | 0.238 | SE Effect size | |
— | — | −0.614 | −0.443 | Ether extract load | Spearman’s Rho |
— | — | 0.004 | 0.046 | p-value | |
— | — | −0.716 | −0.476 | Effect size (Fisher’s z) | |
— | — | 0.241 | 0.237 | SE Effect size | |
— | 0.260 | −0.569 | −0.388 | Polystabil KWS consumption | Spearman’s Rho |
— | 0.254 | 0.008 | 0.083 | p-value | |
— | 0.266 | −0.646 | −0.410 | Effect size (Fisher’s z) | |
— | 0.234 | 0.240 | 0.236 | SE Effect size |
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Halecki, W.; Młyńska, A.; Chmielowski, K. Appraisal of Industrial Pollutants in Sewage and Biogas Production Using Multivariate Analysis and Unsupervised Machine Learning Clustering. Appl. Sci. 2025, 15, 6222. https://doi.org/10.3390/app15116222
Halecki W, Młyńska A, Chmielowski K. Appraisal of Industrial Pollutants in Sewage and Biogas Production Using Multivariate Analysis and Unsupervised Machine Learning Clustering. Applied Sciences. 2025; 15(11):6222. https://doi.org/10.3390/app15116222
Chicago/Turabian StyleHalecki, Wiktor, Anna Młyńska, and Krzysztof Chmielowski. 2025. "Appraisal of Industrial Pollutants in Sewage and Biogas Production Using Multivariate Analysis and Unsupervised Machine Learning Clustering" Applied Sciences 15, no. 11: 6222. https://doi.org/10.3390/app15116222
APA StyleHalecki, W., Młyńska, A., & Chmielowski, K. (2025). Appraisal of Industrial Pollutants in Sewage and Biogas Production Using Multivariate Analysis and Unsupervised Machine Learning Clustering. Applied Sciences, 15(11), 6222. https://doi.org/10.3390/app15116222