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Abstract

Industrial Wastewater Treatment Plants (WWTPs) are essential infrastructures for managing industrial waste, maintaining environmental quality, and protecting public health. However, high concentrations of organic and chemical contaminants increase energy demand, particularly for pumping and treatment processes, contributing to indirect greenhouse gas emissions. This study aims to develop a simulation model to assess operational and financial risks in industrial WWTPs. The framework focuses on the linear relationship between technical and financial variables under uncertain conditions, analyzed using Monte Carlo simulations. Variability in wastewater discharge and quality from multiple industrial tenants introduces technical risks that affect process efficiency, operational costs, and compliance with environmental standards. Key input variables, such as discharge volume, BOD/COD concentrations, and energy and chemical prices, are modeled as probability distributions. The simulation generates probabilistic outputs of operational costs and potential penalties. This approach enables systematic analysis of uncertainty and supports more effective, data-driven decision-making in WWTP management.

Keywords

Risk Simulation Model Financial System Wastewater Treatment Industrial Park

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Tambunan, T., Tawee, S., & Jin, O. F. (2026). Operational Risk Simulation Model and Financial System for Wastewater Treatment Plants (WWTP) in Industrial Parks. Golden Ratio of Mapping Idea and Literature Format, 6(2), 1800–1812. https://doi.org/10.52970/grmilf.v6i2.2187
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References

  1. References
  2. Adebayo, O.S., Hashemi, H., & Salami, M. (2021). Energy-efficient operation of industrial wastewater treatment systems. Sustainable Water Resources Management, 7(2), 203-214. https://doi.org/10.1007/s40899-021-00520-x
  3. Aghdam, E., Mohandes, S.R., Manu, P., Cheung, C., Kaltungo, A.Y., & Zayed, T. (2023). Predicting wastewater treatment plant quality parameters using artificial intelligence techniques.
  4. Ahmad, W., Malik, N., Edson, J., Bibby, K., Bivins, A., O'Brien, J.W., ... & Mueller, J.F. (2020). First confirmed detection of SARS-CoV-2 in untreated wastewater in Australia.
  5. Bixio, D., Parmentier, G., Rousseau, D., Verdonck, F., Meirlaen, J., Vanrolleghem, P.A., & Thoeye, C. (2022). Quantitative risk analysis tool for the design/simulation of urban wastewater treatment plants (WWTP).
  6. Chen, Y., & Zhang, M. (2021). Modeling financial uncertainty in wastewater treatment plant development: Monte Carlo approach. Resources, Conservation and Recycling, 170, 110-124. https://doi.org/10.1016/j.resconrec.2021.105602
  7. Chen, YH, Yan, C., Yang, YF, & Ma, JX (2020). Quantitative microbial risk assessment and sensitivity analysis for workers exposed to pathogenic bacterial bioaerosols in Multiple aeration modes at two wastewater treatment plants.
  8. Chang, S., Zhao, L., Liu, H., Li, J., Zhou, X., Liu, Y., Qiao, Z., Zhao, Y., & Yang
  9. Y. (2018). Monte Carlo-based integrated model for optimizing cost and pollution reduction in wastewater treatment processes in comprehensive industrial park in China.
  10. Djukic, A., Spanjers, H., & van Lier, J. (2016). Integrated risk-based planning for wastewater treatment plant systems in industrial areas. Water Science and Technology, 74(8), 1923-1931.https://doi.org/10.2166/wst.2016.348
  11. Demirdelen, T., Aksu, R.O., Yilmaz, K., Koç, D.D., Arikan, M., & Sener, A. (2023). Carbon Footprint Investigation of Textile Industry: PES and PP Based Products with Monte Carlo Uncertainty Analysis.
  12. Ferns of Castillo, A., Verduzco Garibay, M., Torres, O.D., Anda, J.D., Yebra Montes, C., Senator, C., & Gradilla-Hernández, M.S. (2020). Mathematical Modeling of Domestic Wastewater Treatment System with Combination of Septic Tank, an Upflow Anaerobic Filter and Constructed Wetland.
  13. Ganthavee, V., Fernando, MMR, & Trzcinski, AP (2024). Monte Carlo Simulation, Artificial Intelligence and Machine Learning-Based Modeling and Optimization of Three-Dimensional Electrochemical Treatment of Xenobiotic Textile Dye Wastewater.
  14. Golzar, F., Nilsson, D., & Martin, V. (2020). Wastewater Temperature Forecasting Based on Artificial Neural Network (ANN) Technique and Domestic Monte Carlo Sensitivity Analysis (Buildings).
  15. Kumar, R., & Patel, S. (2020). Application of Crystal Ball software in infrastructure risk modeling. Civil Infrastructure Simulation, 52(1), 34-46.https://doi.org/10.1016/j.cisinf.2020.01.005
  16. Kyung, D., Lee, S., & Bae, J. (2015). Greenhouse Gas Mitigation in Wastewater Treatment Plants: Effectiveness Analysis Cost. Journal Production Clean, 102, 149-158.https://doi.org/10.1016/j.jclepro.2015.04.045
  17. Kumar, A., Thanki, A., Padhiyar, H., Singh, N.K., Pandey, S., Yadav, M. & Yu, Z.G. 2021Controlling greenhouse gas emissions in WWTS through potential operational strategies: critical review. Chemosphere273,129694.
  18. Lee, H., & Wang, T. (2022). Monte Carlo simulation for wastewater management in industrial areas. Journal of Environmental Engineering, 148(4), 312-328. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001976
  19. Makhtar, MMZ, Tajarudin, HA, Samsudin, MDM, Vadivelu, VM, Shoparwe, NF, & Zainuddin, NI (2021). Membraneless microbial fuel cell: Monte-Saccharin simulation
  20. Carlo and sensitivity analysis for COD removal in dewatering sludge from municipal wastewater treatment plants.
  21. Pelin Yapıcıoğlu, H., et al. (2022). Energy intensity and emissions of industrial wastewater treatment processes. Environmental Technology & Innovation, 28, 102671. https://doi.org/10.1016/j.eti.2022.102671
  22. Parravicini, V., Torretta, V., & Raboni, M. (2016). Assessment of direct and indirect GHG emissions in wastewater treatment plants. Sustainability, 8(10), 1034.https://doi.org/10.3390/su8101034
  23. PRISMA. (2020). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: Statement PRISMA 2020. BMJ, 372, n71.https://doi.org/10.1136/bmj.n71
  24. Rabbani, M., Heidari, R., & Yazdanparast, R. (2018). Stochastic Multi-Period Industrial Hazardous Waste Location and Routing Problem: Integration of NSGA-II and Monte Carlo Simulation.
  25. Rumki, J., Tang, WZ, & Sillanpaa, M. (2020). Statistical Analysis of Anode Efficiency in Electrochemical Treatment of Industrial, Urban and Agricultural Waste and Sludge.
  26. Sin, G., & Espuña, A. (2020). Implementation of Monte Carlo methods in Chemical, Biochemical and Environmental Engineering.
  27. Smith, J., & Johnson, L. (2023). Financial risk assessment in industrial wastewater treatment projects using Monte Carlo simulation. Journal of Environmental Risk Management, 45(3), 245-260. https://doi.org/10.1016/j.jerm.2023.03.004
  28. Verhuelsdonk, M., Kaca, K., & Parlar, H. (2020). Economic evaluation of brewery wastewater reuse. Evaluation of Brewery Wastewater Reuse against Monte Carlo Simulation of Freshwater Scarcity.
  29. Yapıcıoğlu, P. (2020). Assessment of greywater footprint for dye industry wastewater treatment plant using Monte Carlo simulation: influence of reuse on GWF minimization.
  30. Yapıcıoğlu, P., & Yesi̇lnacar, MI (2022). Assessment of economic performance index of industrial wastewater treatment plants. Based on European Green Deal: significant impact of greenhouse gas emissions.
  31. Wang, KJ, Wang, PS, & Nguyen, PH (2021). Data-driven optimization model for coagulant dosage determination in industrial wastewater treatment.
  32. Wang, S., Li, Y., & Xiao, K. (2022). Matrix excitation-emission fluorescence as novel indicator of assimilable organic carbon in wastewater: Implications from coal chemical wastewater research.
  33. Wang, H., Yang, Y., Keller, A.A., Li, X., Feng, S., Dong, Y., Li, F., (2016). Comparative analysis of energy intensity and carbon emissions in wastewater treatment in USA, Germany, China and South Africa. Appl. Energy 184, 873-881. https://doi.org/10.1016/j.apenergy.2016.07.061.
  34. Wang, J.-H., Zhao, X.-L., Guo, Z.-W., Yan, P., Gao, X., Shen, Y., Chen, Y.-P.,
  35. (2022). comprehensive review of artificial neural network-based display management methods for energy and material saving in wastewater treatment plants. Environ. Res. 211, 113054. Wang, R.,
  36. Yapıcıoğlu, P., & Demir, Ö. (2021). Minimizing greenhouse gas emissions from industrial wastewater treatment plants in context of water-energy nexus.
  37. Zhian, T., Hashemi Monfared, S.A., Rashki, M., & Azizyan, G. (2024). Enhanced Decision Fusion for Wastewater Treatment System Selection Using Monte Carlo Simulation and Gray Analytical Hierarchy Process