Neural Modelling in the Exploration of the Biomethane Potential from Cattle Manure: A Case Study on Herds Structure from Wielkopolskie, Podlaskie, and Mazowieckie Voivodeships in Poland
Abstract
:1. Introduction
2. Materials and Methods
2.1. Obtaining Data for Modelling
- Collection of data from the Agency for Restructuring and Modernization of Agriculture (raw data disclosed by the Agency at the request of A. Wawrzyniak). The data concerned livestock production in rural areas by municipality/province. The data obtained concerned cattle counts in the provinces of Poland selected for the study.
- Calculation of the amount of waste generated from keeping cattle. Calculations performed separately for manure and for slurry. Calculations were made on the basis of literature data [33].
- : amount of manure (tons)
- : amount of slurry (m3)
- x: cattle population (units)
- LSU: Livestock Unit. An index of animals per unit according to Regulation of the Council of Ministers of 12 February 2020 [34].
- O: average amount of manure per year per cattle unit (Mg/LSU·year)
- and are the conversional factorsbased on animal keeping systems in barns as listed in Table 1.
- -
- One ton of manure produces an average of 60 m3 of biogas
- -
- 1 m3 of slurry produces 28 m3 of biogas on average
- -
- The calorific value of biogas is between 19 and 23 (MJ/m3)
- : average amount of biogas containing 60% methane from a unit amount of animal feces (m3∙(tons or m3) −1)
- : estimated amount of slurry (m3)
- : estimated amount of manure (tons)
2.2. Simulation Studies
3. Results
3.1. Biomethane Potential in the Analysed Voivodeships
3.2. Results on ANN Simulation
4. Discussion
4.1. Biomethane Potential in the Analysed Voivodeships
4.2. ANN Simulation
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Animal Keeping System | ||
---|---|---|
Voivodeship | Stands with a Slatted Floor () | Stands with a Solid Floor () |
Mazowieckie | 0.149 | 0.851 |
Podlaskie | 0.770 | 0.230 |
Wielkopolskie | 0.343 | 0.657 |
Input Variables (Descriptors)-Number of Animals of a Certain Type in the Herd Structure |
1 Calves 2 Bulls 6–12 months 3 Bulls 12–24 months 4 Bulls > 24 months 5 Heifers 6–12 months 6 Heifers 12–18 months 7 Dairy Cows months |
Output (options) |
O-1 amount of biogas from slurry for examined voivodeship O-2 amount of biogas from manure for examined voivodeship |
1 | 2 | 3 | 4 | 5 | 6 | 7 | O-1 | O-2 | |
---|---|---|---|---|---|---|---|---|---|
No. | Calves DJP | Bulls 6–12 Months | Bulls 12–24 Months | Bulls >24 Months | Heifers 6–12 Months | Heifers 12–18 Months | Dairy Cows | Amount of Biogas from Manure (m3) | Amount of Biogas from Slurry (m3) |
1 | 410.25 | 243.3 | 1012.8 | 294 | 545.7 | 1361.6 | 13,527 | 12,653,669 | 1,956,062 |
2 | 396.75 | 186.9 | 737.6 | 238 | 547.5 | 1308 | 13,843 | 12,466,307 | 1,927,098 |
3 | 174.45 | 148.8 | 620 | 163.8 | 202.5 | 476 | 4511 | 4,641,118 | 717,445 |
4 | 69.6 | 66.9 | 257.6 | 67.2 | 100.8 | 195.2 | 1792 | 1,854,335 | 286,652 |
5 | 11.25 | 13.5 | 35.2 | 14 | 13.5 | 28.8 | 382 | 376,274 | 58,166 |
6 | 286.2 | 223.5 | 808 | 175 | 338.1 | 820 | 8115 | 7,835,354 | 1,211,225 |
7 | 96.3 | 87.9 | 391.2 | 81.2 | 106.8 | 195.2 | 2195 | 2,337,194 | 361,294 |
8 | 33 | 38.4 | 208 | 65.8 | 24.9 | 76.8 | 542 | 749,772 | 115,903 |
9 | 69 | 49.5 | 180.8 | 67.2 | 102 | 201.6 | 2395 | 2,394,325 | 370,126 |
10 | 67.35 | 54.3 | 172.8 | 72.8 | 82.5 | 165.6 | 2267 | 2,210,135 | 341,653 |
… | … | … | … | … | … | … | … | … | … |
108 | 476.25 | 257.1 | 1160 | 274.4 | 590.1 | 1567.2 | 13,928 | 13,158,840 | 2,034,153 |
Learning File | Validation File | Test File | Type of Neural Network | |
---|---|---|---|---|
Podlaskie voivodeship | ||||
slurry | ||||
S.D. ratio | 0.059180 | 0.03348 | 0.095860 | MLP: 7-2-1 |
Correlation | 0.998305 | 0.99954 | 0.995798 | |
manure | ||||
S.D. ratio | 0.94429 | 0.02336 | 0.0616 | MLP: 7-7-1 |
Correlation | 0.99902 | 0.999754 | 0.998841 | |
Mazowieckie voivodeship | ||||
slurry | ||||
S.D. ratio | 0.01703 | 0.01429 | 0.02317 | MLP: 7-7-1 |
Correlation | 0.999863 | 0.9999 | 0.999816 | |
manure | ||||
S.D. ratio | 0.010581 | 0.01127 | 0.01075 | MLP: 7-5-1 |
Correlation | 0.999944 | 0.999945 | 0.999942 | |
Wielkopolskie voivodeship | ||||
slurry | ||||
S.D. ratio | 0.02645 | 0.02817 | 0.02472 | MLP: 7-5-1 |
Correlation | 0.999653 | 0.999605 | 0.999703 | |
manure | ||||
S.D. ratio | 0.01502 | 0.008993 | 0.01077 | MLP: 7-5-1 |
Correlation | 0.999887 | 0.99996 | 0.999953 |
Calves | Bulls 6–12 Months | Bulls 12–24 Months | Bulls > 24 Months | Heifers 6–12 Months | Heifers 12–18 Months | Dairy Cows | |
---|---|---|---|---|---|---|---|
Podlaskie voivodeship | |||||||
Slurry-analysis for training set | |||||||
Rank | 2 | 5 | 7 | 4 | 3 | 6 | 1 |
Error | 782,535.2 | 331,844.3 | 234,644.5 | 402,790.21 | 456,897.9 | 315,716.2 | 2,383,259 |
Quotient | 3.557651 | 1.508668 | 1.066768 | 1.83121 | 2.077201 | 1.435345 | 10.83504 |
Slurry-analysis for validation set | |||||||
Rank | 2 | 5 | 7 | 4 | 3 | 6 | 1 |
Error | 641,876.1 | 244,916.7 | 131,120.5 | 288,690.6 | 350,421.5 | 168,073.5 | 2,344,526 |
Quotient | 5.280928 | 2.015012 | 1.078772 | 2.375153 | 2.883035 | 1.382797 | 19.28919 |
Manure-analysis for training set | |||||||
Rank | 5 | 6 | 4 | 7 | 3 | 2 | 1 |
Error | 346,955.1 | 174,304.3 | 400,851 | 173,594.9 | 537,229 | 557,505.1 | 2,107,914 |
Quotient | 2.17774 | 1.094059 | 2.516029 | 1.089607 | 3.372035 | 3.499303 | 13.23078 |
Manure-analysis for validation set | |||||||
Rank | 5 | 6 | 4 | 7 | 3 | 2 | 1 |
Error | 278,951.5 | 89,571.02 | 394,983.2 | 79,676.33 | 454,054.6 | 471,651.7 | 2,006,711 |
Quotient | 3.609639 | 1.159051 | 5.111093 | 1.031014 | 5.875479 | 6.103185 | 25.96689 |
Mazowieckie voivodeship | |||||||
Slurry-analysis for training set | |||||||
Rank | 6 | 7 | 4 | 5 | 2 | 3 | 1 |
Error | 103,841.8 | 69,071.22 | 199,142.9 | 164,292.1 | 212,249.7 | 205,649 | 1,830,063 |
Quotient | 2.397802 | 1.594917 | 4.598388 | 3.793654 | 4.901038 | 4.748621 | 42.25781 |
Slurry-analysis for validation set | |||||||
Rank | 6 | 7 | 4 | 5 | 3 | 2 | 1 |
Error | 96,951.83 | 66,217.75 | 174,988.8 | 156,794.4 | 192,573.9 | 204,318.1 | 1,696,270 |
Quotient | 2.835091 | 1.936357 | 5.117068 | 4.585022 | 5.631296 | 5.974724 | 49.60278 |
Manure-analysis for training set | |||||||
Rank | 4 | 6 | 2 | 5 | 3 | 7 | 1 |
Error | 110,416.7 | 67,805.55 | 327,842 | 106,089.1 | 200,687 | 58,909.33 | 2,127,190 |
Quotient | 3.866067 | 2.374104 | 11.47887 | 3.714542 | 7.026739 | 2.062617 | 74.48018 |
Manure-analysis for validation set | |||||||
Rank | 5 | 6 | 2 | 4 | 3 | 7 | 1 |
Error | 85,550.34 | 67,821.77 | 337,742.4 | 112,396.3 | 177,292.7 | 50,618.03 | 1,857,479 |
Quotient | 3.076455 | 2.438923 | 12.14547 | 4.041856 | 6.37558 | 1.820263 | 66.79635 |
Wielkopolskie voivodeship | |||||||
Slurry-analysis for training set | |||||||
Rank | 6 | 5 | 3 | 4 | 7 | 2 | 1 |
Error | 164,558.3 | 193,348.8 | 393,748.6 | 218,477.6 | 85,506.16 | 407,828.6 | 1,191,321 |
Quotient | 2.608864 | 3.065301 | 6.242385 | 3.463685 | 1.355592 | 6.465605 | 18.88689 |
Slurry-analysis for validation set | |||||||
Rank | 6 | 5 | 2 | 4 | 7 | 3 | 1 |
Error | 154,924.7 | 199,205.6 | 361,707.1 | 212,250.6 | 83,400.45 | 357,936.4 | 1,010,796 |
Quotient | 2.609011 | 3.354724 | 6.091332 | 3.574408 | 1.404506 | 6.027831 | 17.02231 |
Manure-analysis for training set | |||||||
Rank | 6 | 5 | 2 | 4 | 7 | 3 | 1 |
Error | 126,985.5 | 149,031.5 | 450,806.2 | 165,423.2 | 79,804.95 | 209,430.4 | 1,260,193 |
Quotient | 3.629729 | 4.259889 | 12.88576 | 4.728427 | 2.28113 | 5.98632 | 36.02112 |
Manure-analysis for validation set | |||||||
Rank | 6 | 5 | 2 | 4 | 7 | 3 | 1 |
Error | 121,494.7 | 158,065.2 | 514,812.5 | 184,429.8 | 78,210.54 | 261,297 | 1,466,372 |
Quotient | 4.614739 | 6.003798 | 19.55415 | 7.005207 | 2.970675 | 9.924857 | 55.69727 |
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Wawrzyniak, A.; Przybylak, A.; Sujak, A.; Boniecki, P. Neural Modelling in the Exploration of the Biomethane Potential from Cattle Manure: A Case Study on Herds Structure from Wielkopolskie, Podlaskie, and Mazowieckie Voivodeships in Poland. Sensors 2023, 23, 164. https://doi.org/10.3390/s23010164
Wawrzyniak A, Przybylak A, Sujak A, Boniecki P. Neural Modelling in the Exploration of the Biomethane Potential from Cattle Manure: A Case Study on Herds Structure from Wielkopolskie, Podlaskie, and Mazowieckie Voivodeships in Poland. Sensors. 2023; 23(1):164. https://doi.org/10.3390/s23010164
Chicago/Turabian StyleWawrzyniak, Agnieszka, Andrzej Przybylak, Agnieszka Sujak, and Piotr Boniecki. 2023. "Neural Modelling in the Exploration of the Biomethane Potential from Cattle Manure: A Case Study on Herds Structure from Wielkopolskie, Podlaskie, and Mazowieckie Voivodeships in Poland" Sensors 23, no. 1: 164. https://doi.org/10.3390/s23010164
APA StyleWawrzyniak, A., Przybylak, A., Sujak, A., & Boniecki, P. (2023). Neural Modelling in the Exploration of the Biomethane Potential from Cattle Manure: A Case Study on Herds Structure from Wielkopolskie, Podlaskie, and Mazowieckie Voivodeships in Poland. Sensors, 23(1), 164. https://doi.org/10.3390/s23010164