DOI: https://doi.org/10.14710/jil.22.1.28-42

Model Biokinetika Sistem Kontak Stabilisasi Lumpur Air Lindi Berdasarkan Pengaruh Fosfor dan Variasi Waktu Detensi Unit Kontak

1Departemen Teknik Sipil dan Lingkungan, Fakultas Teknologi Pertanian, Institut Pertanian Bogor., Indonesia

2Program Studi Teknik Lingkungan, Fakultas Arsitektur Lanskap dan Teknologi Lingkungan, Universitas Trisakti, Indonesia

Received: 4 Nov 2022; Revised: 23 Jul 2023; Accepted: 5 Oct 2023; Available online: 15 Nov 2023; Published: 10 Dec 2023.
Editor(s): Budi Warsito

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Abstract

ABSTRAK
Tempat pemrosesan akhir sampah menghasilkan air lindi dalam kuantitas besar sehingga membutuhkan alternatif unit pengolahan biologis, seperti kontak stabilisasi untuk mereduksi kontaminan. Penelitian ini bertujuan untuk mengevaluasi kinerja unit kontak berdasarkan variasi waktu detensi (HRT) pada kondisi aliran tidak tunak serta menentukan estimasi parameter biokinetika di dalam persamaan model pertumbuhan mikroorganisme. Penelitian diawali dengan mengkonfigurasi unit kontak stabilisasi, menganalisis parameter kualitas air limbah, dan memprediksi parameter biokinetika untuk memperkirakan kualitas air limbah. Validasi nilai biokinetika dilakukan dengan menggunakan metode matematis dan jaringan saraf tiruan (JST). Fungsi penggunaan JST dalam estimasi chemical oxygen demand (COD) efluen tangki kontak dapat dilakukan dengan cepat, memperkecil kesalahan perhitungan estimasi dengan metode matematis, serta bebas digunakan pada kondisi apapun. Berdasarkan hasil penelitian, persentase efisiensi COD pada masing-masing HRT unit kontak 2, 3, dan 4 jam berturut-turut sebesar 26,9%; 35,1%; dan 46,5%. Pengaruh biokinetika terhadap unit pengolahan unit kontak tidak hanya untuk memprediksi pengolahan air limbah, tetapi juga untuk merancang, mengoperasikan, dan mengontrol sistem pengolahan. Berdasarkan metode statistika, model estimasi konsentrasi COD efluen tangki kontak terbaik adalah model dari model JST. Akan tetapi, model ini hanya mengestimasi konsentrasi efluen tanpa memperhitungkan nilai biokinetika. Model Jerusalimski menjadi pilihan terbaik dibandingkan Model Ming untuk mengestimasi nilai biokinetika Ke, Y, μmax, dan Ks berturut-turut sebesar 0,025 hari-1; 28,25 mgMLVSS/mg COD; 3,4 hari-1; dan 21,46 mg/L berdasarkan pengaruh fossfor dan waktu detensi pada kondisi tidak tunak. Peningkatan konsentrasi fosfor di dalam proses pengolahan akan memengaruhi dekomposisi mikroorganisme di dalam biomassa untuk mengambil oksigen terlarut dalam jumlah besar sehingga pertumbuhan mikroorganisme terhambat.

Kata kunci: biokinetika, fosfor, jaringan saraf tiruan, kontak stabilisasi, lindi, waktu detensi.


ABSTRACT
Landfills produce large quantities of leachate that pollutes the water system water pollution, requiring a contact stabilization treatment unit to reduce the high contaminant in the leachate. This study aims to evaluate the performance of the contact unit based on the variation of hydraulic retention time (HRT) under an unsteady state and to estimate the biokinetic parameters by using the microorganism growth model equation. The research was started by configurating the contact stabilization tank, analyzing the wastewater quality parameters, and predicting biokinetics parameters to estimate water quality effluent and design engineering. Biokinetic values were validated using mathematical methods and artificial neural networks (ANN). The function of using ANN in estimating of contact tank effluent chemical oxygen demand (COD) can be done quickly, minimizes estimation errors using mathematical methods and can be used in any condition. Based on research results, the percent removal of COD efficiency in each HRT contact tank of 2, 3, and 4 hours, were 26.9%; 35.1%; and 46.5%, respectively. The effect of biokinetics on contact tank treatment units is not only to predict wastewater treatment, but also to design, operate and control treatment systems. Based on the statistical analysis, the ANN model was the best model for estimating the contact tank effluent concentration. However, the ANN model provided the estimated effluent concentration without considering biokinetic values in detail. The Jerusalimski model is the best fit model compared to the Ming model to estimate the biokinetic values of Ke, Y, μmax, and Ks of 0.025 day-1, 28.25 mgMLVSS/mg COD, 3.4 day-1, and 21.46 mg/L; respectively, based on the effect of phosphorus and detention time in unsteady state. Increasing the phosphorus concentration in the treatment processing will affect the decomposition of microorganisms in the biomass to take up large amounts of dissolved oxygen until the growth of microorganisms is inhibited.

Keywords: artificial neural network, biokinetic, contact stabilization, hydraulic retention time, leachate, phosporus

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Keywords: biokinetika; fosfor; jaringan saraf tiruan; kontak stabilisasi; lindi; waktu detensi

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  1. Abunde, N. F., Asiedu, N., & Addo, A. (2017). Dynamics of inhibition patterns during fermentation processes-Zea Mays and Sorghum Bicolor case study. International Journal of Industrial Chemistry, 8(1), 91–99. https://doi.org/10.1007/s40090-016-0105-9
  2. Aho, K., Derryberry, D., & Peterson, T. (2014). Model selection for ecologists : the worldviews of AIC and BIC Author ( s ): Ken Aho , DeWayne Derryberry and Teri Peterson Stable URL : http://www.jstor.org/stable/43495189 Model selection for ecologi. Ecology, 95(3), 631–636.
  3. Al-Hadi, A. M., Lestari, D. A., & David, J. P. (2019). Comparison Study of BOD & COD of Leachate Quality (Case Study in Air Dingin Landfill and Jatibarang Landfill. Journal of Environmental Engineering and Waste Management, 4(1), 37. https://doi.org/10.33021/jenv.v4i1.692
  4. Ali, H. I., Abd El-Azim, M. M., Abd El-Rahman, M. S., Lotfy, A. O., & Mostafa, M. M. (2015). The effects of modification for contact stabilization activated sludge on EBPR. HBRC Journal, 11(1), 143–149. https://doi.org/10.1016/j.hbrcj.2014.02.004
  5. Allı, B., İnsel, G., Sözen, S., & Orhon, D. (2018). A novel modeling approach for evaluating microbial mechanism and design of contact stabilization process. Journal of Chemical Technology and Biotechnology, 93(4), 1121–1136. https://doi.org/10.1002/jctb.5471
  6. Anggraeni, D., Sutanhaji, A. T., & Bambang, R. W. (2014). Pengaruh Volume Lumpur Aktif dengan Proses Kontak Stabilisasi pada Efektivitas Pengolahan Air Limbah Industri Pengolahan Ikan. Jurnal Sumberdaya Alam Dan Lingkungan, 1(3), 6–12
  7. Arbib, Z., Ruiz, J., Álvarez-Díaz, P., Garrido-Pérez, C., Barragan, J., & Perales, J. A. (2013). Photobiotreatment: Influence of Nitrogen and Phosphorus Ratio in Wastewater on Growth Kinetics of Scenedesmus Obliquus. International Journal of Phytoremediation, 15(8), 774–788. https://doi.org/10.1080/15226514.2012.735291
  8. Arif, C. (2021). Aplikasi Kecerdasan Buatan dalam Bidang Pengelolaan Air dan Lingkungan (1st ed.). IPB Press
  9. Arunbabu, V., Indu, K. S., & Ramasamy, E. V. (2017). Leachate pollution index as an effective tool in determining the phytotoxicity of municipal solid waste leachate. Waste Management, 68, 329–336. https://doi.org/10.1016/j.wasman.2017.07.012
  10. Azimi, A. A., & Horan, N. J. (1991). The influence of reactor mixing characteristics on the rate of nitrification in the activated sludge process. Water Research, 25(4), 419–423. https://doi.org/10.1016/0043-1354(91)90078-5
  11. Bui, D. T., Pradhan, B., Nampak, H., Bui, Q. T., Tran, Q. A., & Nguyen, Q. P. (2016). Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS. Journal of Hydrology, 540, 317–330. https://doi.org/10.1016/j.jhydrol.2016.06.027
  12. Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014
  13. Chen, J., Zhao, B., An, Q., Wang, X., & Zhang, Y. X. (2016). Kinetic characteristics and modelling of growth and substrate removal by Alcaligenes faecalis strain NR. Bioprocess and Biosystems Engineering, 39(4), 593–601. https://doi.org/10.1007/s00449-016-1541-9
  14. Conklin, A., Stensel, H. D., & Ferguson, J. (2006). Growth Kinetics and Competition Between Methanosarcina and Methanosaeta in Mesophilic Anaerobic Digestion. Water Environment Research, 78(5), 486–496. https://doi.org/10.2175/106143006x95393
  15. Díaz, Á. L. S., Álvarez, V. M., Vasconcelos, D. de los C., Mier, M. V.-, & Peláez, M. L. S. (2021). Performance evaluation and kinetic modeling of an upflow anaerobic sludge blanket septic tank for domestic wastewater treatment. Environmental Science and Pollution Research, 28(47), 67414–67428. https://doi.org/10.1007/s11356-021-15141-5
  16. Dragoi, E. N., & Vasseghian, Y. (2021). Modeling of mass transfer in vacuum membrane distillation process for radioactive wastewater treatment using artificial neural networks. Toxin Reviews, 40(4), 1526–1535. https://doi.org/10.1080/15569543.2020.1744659
  17. Fathurahman, M. (2009). Pemilihan Model Regresi Terbaik Menggunakan Metode Akaike’s Information Criterion dan Schwarz Information Criterion. Jurnal Informatika Mulawarman, 4(3)
  18. Gonzalez, K. V. (2016). Example of Lactic Acid Production in Industrial Fermenter
  19. Ibrahim, B., & Erungan, A. C. (2009). Nilai parameter biokinetika proses denitrifikasi limbah cair industri perikanan pada rasio COD/TKN yang berbeda. Jurnal Hasil Perikanan Indonesia, 12(1), 31–45
  20. Jerusalimski, N. D., & Engamberdiev, N. B. (1969). Continuous Cultivation of Microorganisms. Academic Press
  21. Kamaruddin, M. A., Yusoff, M. S., Aziz, H. A., & Hung, Y.-T. (2014). Sustainable treatment of landfill leachate. Applied Water Science, 5(2), 113–126. https://doi.org/10.1007/s13201-014-0177-7
  22. Kumar, K., Singh, G. K., Dastidar, M. G., & Sreekrishnan, T. R. (2014). Effect of mixed liquor volatile suspended solids (MLVSS) and hydraulic retention time (HRT) on the performance of activated sludge process during the biotreatment of real textile wastewater. Water Resources and Industry, 5, 1–8. https://doi.org/10.1016/j.wri.2014.01.001
  23. Kurniawan, A., Wirasembada, Y. C., Park, K. Y., Kim, Y. M., Hur, J., & Cho, J. (2018). Estimation of biokinetic parameters in the acid fermentation of primary sludge using an anaerobic baffled reactor. Environmental Science: Water Research and Technology, 4(12), 1997–2011. https://doi.org/10.1039/c8ew00566d
  24. Li, Z., Tian, C., & Sheng, Y. (2022). Fluxes of chemical oxygen demand and nutrients in coastal rivers and their influence on water quality evolution in the Bohai Sea. Regional Studies in Marine Science, 52, 102322. https://doi.org/10.1016/j.rsma.2022.102322
  25. Luo, H., Zeng, Y., Cheng, Y., He, D., & Pan, X. (2020). Recent advances in municipal landfill leachate: A review focusing on its characteristics, treatment, and toxicity assessment. Science of the Total Environment, 703, 135468. https://doi.org/10.1016/j.scitotenv.2019.135468
  26. Matheri, A. N., Ntuli, F., Ngila, J. C., Seodigeng, T., & Zvinowanda, C. (2021). Performance prediction of trace metals and cod in wastewater treatment using artificial neural network. Computers and Chemical Engineering, 149. https://doi.org/10.1016/j.compchemeng.2021.107308
  27. Metcalf & Eddy Inc. (2013). Wastewater Engineering - Treatment and Resource Recovery (5th ed.). McGraw-Hill
  28. Ming, F., Howell, J. A., & Diaz, M. C. (1989). Mathematical simulation of anaerobic stratified biofilm processes. Computer Applications in Fermentation Technology: Modelling and Control of Biotechnological Processes, 69–77
  29. Perendeci, A., Arslan, S., Çelebi, S. S., & Tanyolaç, A. (2008). Prediction of effluent quality of an anaerobic treatment plant under unsteady state through ANFIS modeling with on-line input variables. Chemical Engineering Journal, 145(1), 78–85. https://doi.org/10.1016/j.cej.2008.03.008
  30. Semblante, G. U., Hai, F. I., Ngo, H. H., Guo, W., You, S. J., Price, W. E., & Nghiem, L. D. (2014). Sludge cycling between aerobic, anoxic and anaerobic regimes to reduce sludge production during wastewater treatment: Performance, mechanisms, and implications. Bioresource Technology, 155, 395–409. https://doi.org/10.1016/j.biortech.2014.01.029
  31. Sepehri, A., & Sarrafzadeh, M. H. (2019). Activity enhancement of ammonia‑oxidizing bacteria and nitrite‑oxidizing bacteria in activated sludge process: metabolite reduction and CO2 mitigation intensification process. Applied Water Science, 9(5), 1–12
  32. Stoychev, J. T., Dimitrova Lekova, S., & Petrov Terziyski, G. (2020). Studying and Modeling of the Process Acetification. International Journal of Scientific Research and Engineering Development, 3, 421–430
  33. Vikrant, K., Kim, K., Sik, Y., Tsang, D. C. W., Fai, Y., Shekhar, B., & Sharan, R. (2017). Science of the Total Environment Engineered / designer biochar for the removal of phosphate in water and wastewater. Science of the Total Environment, 616, 1242–1260. https://doi.org/10.1016/j.scitotenv.2017.10.193
  34. Xu, S., Yao, J., Ainiwaer, M., Hong, Y., & Zhang, Y. (2018). Advances and Challenges at the Waste-to-Bioenergy/Biorefinery Nexus. BioMed Research International, 2018. https://doi.org/10.1155/2018/3642363
  35. Zhang, Q. Q., Tian, B. H., Zhang, X., Ghulam, A., Fang, C. R., & He, R. (2013). Investigation on characteristics of leachate and concentrated leachate in three landfill leachate treatment plants. Waste Management, 33(11), 2277–2286. https://doi.org/10.1016/j.wasman.2013.07.021

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