Machine Learning vs. Real-World Data: Assessing ANN Performance in COD Removal in Animal Feed Processing Wastewater

Beta Cahaya Pertiwi, Dean Tirkaamiana, Maudy Pratiwi Novia Matovanni, Ketut Sumada

Abstract

This research established an artificial neural network (ANN) aimed at optimizing ozonation for chemical oxygen demand (COD) reduction in animal feed plant wastewater. Experimental data (200-1000 mg/L COD, 100-180 min treatment) were used to train a 10-8 neuron artificial neural network, resulting in a predicted removal rate of 97.4% at 180 minutes for 1000 mg/L COD (MSE=15.9, R²=0.34).  Experiments indicated a marginally higher efficiency of 97.83% at 160 minutes; however, the ANN's conservative recommendation of 180 minutes is more appropriate for industrial scalability. The model successfully identified non-linear degradation patterns of recalcitrant organics, illustrating the potential of artificial neural networks for optimizing wastewater treatment.  This study connects laboratory research with industrial application via machine learning, establishing a framework that balances efficiency and operational practicality. Future improvements may integrate real-time process data to increase accuracy.

Full Text:

PDF

References

“Perusahaan Produsen Pakan Ternak di Lampura Diduga Lalai Mengelola Limbah, Pemkab Jangan Tutup Mata - Medinaslampungnews,” (n.d.). https://www.medinaslampungnews.co.id/2024/02/22/kembali-ditemukan-perusahaan-produsen-pakan-ternak-di-lampura-diduga-lalai-mengelola-limbah/ (accessed June 15, 2025).

“Tak Berizin dan Buang Limbah Sembarangan, PT Inti Daging Jaya di KBB Disegel – jabarekspres.com,” (n.d.). https://jabarekspres.com/berita/2024/11/13/tak-berizin-dan-buang-limbah-sembarangan-pt-inti-daging-jaya-di-kbb-disegel/ (accessed June 15, 2025).

S. Niju, V. Shruthi, K. Priyadharshini, “Comprehensive insights into biological and bio-electrochemical treatment of the sago industry wastewater: Challenges and future perspectives,” Sustainable Chemistry for the Environment. 10 100242 (2025). https://doi.org/10.1016/J.SCENV.2025.100242.

J.A. Silva, “Advanced Oxidation Process in the Sustainable Treatment of Refractory Wastewater: A Systematic Literature Review,” Sustainability (Switzerland). 17 3439 (2025). https://doi.org/10.3390/SU17083439/S1.

G. Gopalakrishnan, R.B. Jeyakumar, A. Somanathan, “Challenges and Emerging Trends in Advanced Oxidation Technologies and Integration of Advanced Oxidation Processes with Biological Processes for Wastewater Treatment,” Sustainability 2023, Vol. 15, Page 4235. 15 4235 (2023). https://doi.org/10.3390/SU15054235.

P. Kumari, A. Kumar, “ADVANCED OXIDATION PROCESS: A remediation technique for organic and non-biodegradable pollutant,” Results in Surfaces and Interfaces. 11 100122 (2023). https://doi.org/10.1016/J.RSURFI.2023.100122.

C. V. Rekhate, J.K. Srivastava, “Recent advances in ozone-based advanced oxidation processes for treatment of wastewater- A review,” Chemical Engineering Journal Advances. 3 100031 (2020). https://doi.org/10.1016/J.CEJA.2020.100031.

A. Abdelfattah, S.S. Ali, H. Ramadan, E.I. El-Aswar, R. Eltawab, S.H. Ho, T. Elsamahy, S. Li, M.M. El-Sheekh, M. Schagerl, M. Kornaros, J. Sun, “Microalgae-based wastewater treatment: Mechanisms, challenges, recent advances, and future prospects,” Environmental Science and Ecotechnology. 13 100205 (2023). https://doi.org/10.1016/J.ESE.2022.100205.

E. Chatfield, B. Abbassi, “Evaluation of Electrocatalytic Ozonation Process for Hydroxyl Radical Production,” Processes. 13 784 (2025). https://doi.org/10.3390/PR13030784/S1.

P. Kumari, A. Kumar, “ADVANCED OXIDATION PROCESS: A remediation technique for organic and non-biodegradable pollutant,” Results in Surfaces and Interfaces. 11 100122 (2023). https://doi.org/10.1016/J.RSURFI.2023.100122.

D. Lo, G.T. Harris, al -, W. Oktiawan, B. Prasetyo Samadikun, A. Sulton Ashari, E. Fathul Karamah, R. Rezeki Najeges, M. Zaki Zahirsyah, “The influence of ozone dosage, exposure time and contact temperature of ozone in controlling food quality (case study: tofu),” IOP Conf Ser Mater Sci Eng. 509 012117 (2019). https://doi.org/10.1088/1757-899X/509/1/012117.

Y. Zhou, Z. Yang, S. Chen, W. Sun, Y. Sun, “Ozonation Treatment of Simulated Wastewater Containing Characteristic Pollutants from the Petrochemical Industry,” Water (Switzerland). 17 605 (2025). https://doi.org/10.3390/W17040605/S1.

M. Kamali, L. Appels, X. Yu, T.M. Aminabhavi, R. Dewil, “Artificial intelligence as a sustainable tool in wastewater treatment using membrane bioreactors,” Chemical Engineering Journal. 417 128070 (2021). https://doi.org/10.1016/J.CEJ.2020.128070.

M. Ibrahim, A. Haider, J.W. Lim, B. Mainali, M. Aslam, M. Kumar, M.K. Shahid, “Artificial neural network modeling for the prediction, estimation, and treatment of diverse wastewaters: A comprehensive review and future perspective,” Chemosphere. 362 142860 (2024). https://doi.org/10.1016/J.CHEMOSPHERE.2024.142860.

A. Pintar, M. Besson, P. Gallezot, J. Gibert, D. Martin, “Toxicity to Daphnia magna and Vibrio fischeri of Kraft bleach plant effluents treated by catalytic wet-air oxidation,” Water Res. 38 289–300 (2004). https://doi.org/10.1016/J.WATRES.2003.09.027.

O. Lefebvre, X. Shi, J.G. Tein, H.Y. Ng, “Suitability of ozone pre-treatment for amoxicillin wastewater,” Water Science and Technology. 68 2492–2496 (2013). https://doi.org/10.2166/wst.2013.534.

A. Jierula, S. Wang, T.M. Oh, P. Wang, “Study on Accuracy Metrics for Evaluating the Predictions of Damage Locations in Deep Piles Using Artificial Neural Networks with Acoustic Emission Data,” Applied Sciences 2021, Vol. 11, Page 2314. 11 2314 (2021). https://doi.org/10.3390/APP11052314.

DOI: https://doi.org/10.20961/equilibrium.v9i1.104192

Refbacks

  • There are currently no refbacks.