Wastewater Oil Separation with Automatic pH Balancing System: A Review

Industrial wastewater generated from petroleum industries, automobile workshops, food processing plants, textile industries, and chemical manufacturing units contains large quantities of oil contaminants, grease particles, suspended solids, and acidic or alkaline substances. Improper discharge of untreated wastewater causes severe environmental pollution, affects aquatic ecosystems, contaminates groundwater resources, and creates major public health concerns. Conventional wastewater treatment systems often suffer from limitations such as low oil separation efficiency, inaccurate pH control, high manual intervention, and increased operational cost. Therefore, the development of automated wastewater treatment systems integrating efficient oil separation and automatic pH balancing mechanisms has become an important research area in environmental engineering.
This review paper presents a comprehensive study on wastewater oil separation technologies integrated with automatic pH balancing systems. Various oil-water separation methods such as gravity separation, dissolved air flotation, electrocoagulation, membrane filtration, and electromagnetic separation are critically reviewed. The paper also discusses the role of ultrasonic level sensors, pH sensors, solenoid valves, and intelligent control systems in achieving automated wastewater treatment. The proposed system continuously monitors wastewater level and pH conditions while automatically controlling oil separation and chemical dosing processes. Automated pH balancing ensures that treated wastewater remains within the acceptable environmental discharge range.
The review highlights the advantages of automation including improved treatment efficiency, reduced human intervention, accurate monitoring, reduced chemical wastage, enhanced environmental safety, and operational reliability. Challenges such as high installation cost, maintenance requirements, sensor calibration, and energy consumption are also discussed. Furthermore, future developments involving Artificial Intelligence (AI), Internet of Things (IoT), smart monitoring systems, and energy-efficient technologies are analyzed for sustainable wastewater management. The study concludes that integrated oil separation and automatic pH balancing systems provide an efficient, economical, and environmentally sustainable solution for industrial wastewater treatment applications.

Wastewater treatment, Oil-water separation

1. Ahmad A.L., Sumathi S., Hameed B.H. (2006). Coagulation of residue oil and suspended solid in palm oil mill effluent by chitosan. Chemical Engineering Journal, 118, 99–105. [Google Scholar] [Crossref]

2. Chen G. (2004). Electrochemical technologies in wastewater treatment. Separation and Purification Technology, 38, 11–41. [Google Scholar] [Crossref]

3. Ibrahim D.S. (2011). Oil-water separation in industrial wastewater treatment. Journal of Environmental Management, 92, 295–303. [Google Scholar] [Crossref]

4. Karthikeyan R., Kumar P., Sharma A. (2021). Smart environmental monitoring systems for industrial wastewater treatment applications. International Journal of Environmental Science and Technology, 18(4), 1123–1135. [Google Scholar] [Crossref]

5. Kumar S., Verma A. (2020). Automatic pH monitoring and control system for industrial wastewater treatment. International Journal of Engineering Research and Technology, 9(5), 210–216. [Google Scholar] [Crossref]

6. Li Y., Zhang H. (2022). Advanced oil-water separation techniques for industrial wastewater treatment. Environmental Technology Review, 11(2), 95–110. [Google Scholar] [Crossref]

7. Metcalf & Eddy (2014). Wastewater Engineering: Treatment and Resource Recovery. McGraw-Hill Education. [Google Scholar] [Crossref]

8. Patel H., Shah R. (2021). Smart wastewater management using IoT and automation. Journal of Water Process Engineering, 39, 101–112. [Google Scholar] [Crossref]

9. Rana S., Gupta M. (2019). Sensor-based automation in wastewater treatment systems. International Journal of Smart Engineering Systems, 7(3), 145–156. [Google Scholar] [Crossref]

10. Sharma P., Kulkarni V. (2018). Electrocoagulation technology for oily wastewater treatment from automobile industries. Journal of Environmental Chemical Engineering, 6(2), 2125–2134. [Google Scholar] [Crossref]

11. Singh R. (2016). Wastewater treatment using dissolved air flotation technology. International Journal of Environmental Science, 7(3), 145–151. [Google Scholar] [Crossref]

12. Tchobanoglous G., Burton F.L., Stensel H.D. (2013). Water and Wastewater Engineering. McGraw-Hill Publications. [Google Scholar] [Crossref]

13. Verma N., Singh P. (2020). PLC-based industrial wastewater treatment automation systems. International Journal of Industrial Automation and Control Engineering, 5(1), 55–64. [Google Scholar] [Crossref]

14. Zhao X., Liu J., Wang Y. (2014). Application of membrane technology in oily wastewater treatment. Desalination, 346, 120–128. [Google Scholar] [Crossref]

15. Rathod, N.J., Chopra, M.K., Chaurasiya, P.K. et al. Optimization on the Turning Process Parameters of SS 304 Using Taguchi and TOPSIS. Ann. Data. Sci. 10, 1405–1419 (2023). https://doi.org/10.1007/s40745-021-00369-2 [Google Scholar] [Crossref]

16. Rathod, N.J., Chopra, M.K., Shelke, S.N. et al. Investigations on hard turning using SS304 sheet metal component grey based Taguchi and regression methodology. Int J Interact Des Manuf 18, 2653–2664 (2024). https://doi.org/10.1007/s12008-023-01244-5 [Google Scholar] [Crossref]

17. N.J. Rathod, M.K. Chopra, U.S. Vidhate, N.B. Gurule, U.V. Saindane, Investigation on the turning process parameters for tool life and production time using Taguchi analysis, Materials Today: Proceedings, Volume 47, Part 17, 2021, Pages 5830-5835, https://doi.org/10.1016/j.matpr.2021.04.199. [Google Scholar] [Crossref]

18. Rathod, N.J., Chopra, M.K., Chaurasiya, P.K. et al. Design and optimization of process parameters for hard turning of AISI 304 stainless steel using Taguchi-GRA-PCA. Int J Interact Des Manuf 17, 2403–2414 (2023). https://doi.org/10.1007/s12008-022-01021-w [Google Scholar] [Crossref]

19. Rathod, N.J., Chopra, M.K., Chaurasiya, P.K. et al. Optimization of Tool Life, Surface Roughness and Production Time in CNC Turning Process Using Taguchi Method and ANOVA. Ann. Data. Sci. 10, 1179–1197 (2023). https://doi.org/10.1007/s40745-022-00423-7 [Google Scholar] [Crossref]

20. N.J. Rathod, M.K. Chopra, U.S. Vidhate, U.V. Saindane, Multi objective optimization in turning operation of SS304 sheet metal component, Materials Today: Proceedings, Volume 47, Part 17, 2021, Pages 5806-5811, https://doi.org/10.1016/j.matpr.2021.04.143. [Google Scholar] [Crossref]

21. P. K. Chaurasiya, N. J. Rathod, P. K. Jain, V. Pandey, Shashikant and K. Lala, "Material Selection for Optimal Design Using Multi-Criteria Decision Making," 2023 3rd International Conference on Advancement in Electronics & Communication Engineering (AECE), GHAZIABAD, India, 2023, pp. 206-210, doi: 10.1109/AECE59614.2023.10428303. [Google Scholar] [Crossref]

22. Mahesh T. Dhande, et al. 2023. HMCMA: Design of an Efficient Model with Hybrid Machine Learning in Cyber security for Enhanced Detection of Malicious Activities. International Journal on Recent and Innovation Trends in Computing and Communication. 11, 11s (Oct. 2023), 721–734. DOI:https://doi.org/10.17762/ijritcc.v11i11s.9729. [Google Scholar] [Crossref]

23. T. Dhande M, Tiwari S, Rathod N. Design of an efficient Malware Prediction Model using Auto Encoded & Attention-based Recurrent Graph Relationship Analysis. Int. Res. J. multidiscip. Technovation [Internet]. 2025 Jan. 22 [cited 2025 Dec. 13];7(1):71-87. Available from: https://asianrepo.org/index.php/irjmt/article/view/103 [Google Scholar] [Crossref]

24. Kalangi, C., Rathod, N.J., Madhuri, K.S. et al. Performance optimization of ethanol blends in diesel model using Taguchi and grey relational approach. Sci Rep 15, 36048 (2025). https://doi.org/10.1038/s41598-025-20009-6 [Google Scholar] [Crossref]

25. Rathod, N.J., Bonde, P. & Nehete, H.R. Parametric optimization of WEDM of SS 304 stainless steel for material removal rate and surface roughness using Taguchi and Response Surface Methodology. Interactions 246, 60 (2025). https://doi.org/10.1007/s10751-025-02273-0 [Google Scholar] [Crossref]

26. Nikhil Janardan Rathod , Praveen B. M. , Mayur Gitay, Sidhhant N. Patil, Mohan T. Patel, (2025) Optimization Of Multiple Objectives in The Machining Process of SS304 Sheet Metal Components.. Journal of Neonatal Surgery, 14 (14s), 801-809 [Google Scholar] [Crossref]

27. Rathod, N.J. et al. (2026). Wire Electrical Discharge Machining Process Parameter Optimization via the Taguchi Method. In: Al-Ramahi, N., Musleh Al-Sartawi, A.M.A., Kanan, M. (eds) Artificial Intelligence in the Digital Era. Studies in Systems, Decision and Control, vol 594. Springer, Cham. https://doi.org/10.1007/978-3-031-89771-9_6 [Google Scholar] [Crossref]

28. Rathod, N.J. et al. (2026). Aluminum Machining Process Parameter Optimization in WEDM with the GRA Approach. In: Al-Ramahi, N., Musleh Al-Sartawi, A.M.A., Kanan, M. (eds) Artificial Intelligence in the Digital Era. Studies in Systems, Decision and Control, vol 594. Springer, Cham. https://doi.org/10.1007/978-3-031-89771-9_16 [Google Scholar] [Crossref]

29. Rathod, N.J. et al. (2026). Optimizing Wire Electric Discharge Machining Process Parameters for AISI 304 Stainless Steel via Taguchi Design of Experiments. In: Al-Ramahi, N., Musleh Al-Sartawi, A.M.A., Kanan, M. (eds) Artificial Intelligence in the Digital Era. Studies in Systems, Decision and Control, vol 594. Springer, Cham. https://doi.org/10.1007/978-3-031-89771-9_8 [Google Scholar] [Crossref]

30. Rathod, N.J. et al. (2026). SS304 CNC Turning Process Mathematical Modeling and Machining Parameter Optimization Utilizing the Taguchi Technique. In: Al-Ramahi, N., Musleh Al-Sartawi, A.M.A., Kanan, M. (eds) Artificial Intelligence in the Digital Era. Studies in Systems, Decision and Control, vol 594. Springer, Cham. https://doi.org/10.1007/978-3-031-89771-9_2 [Google Scholar] [Crossref]

31. Nikhil Janardan Rathod, Praveen B. M., Mayur Gitay, Sidhhant N. Patil, Mohan T. Patel, (2025) Implementation of Machine Learning Approaches for the Modeling and Predictive Turning Maintenance Operations Incorporating Lubrication and Cooling in Systems of Manufacturing. Journal of Neonatal Surgery, 14 (15s), 1741-1748. [Google Scholar] [Crossref]

32. Nikhil J. Rathod, Nilesh Ingale, Mahesh T. Dhande, Santhosh H Pawar, A REVIEW ON AI ENABLED MULTI MODEL CARDIOVASCULAR MONITORING SYSTEM FOR PREDICTION OF PHYSIOLOGICAL STRESS, Department of Computer Science and Technology, 2025, 180724/IJORAR-1019, [Google Scholar] [Crossref]

33. Mayur Jayant Gitay, Dr. Nilesh Diwakar, Dr. M. K. Chopra, Dr. Nikhil J. Rathod, Analysis of the performance of a CI engine operating in dual fuel mode with biogas and biodiesel, Eur. Chem. Bull. 2023,12(3) 4462-4472 [Google Scholar] [Crossref]

34. Prashant S. Raut, Dr. Nilesh Diwakar, Sumit Raut, Dr. Nikhil J. Rathod, Performance Evaluation and Emission Characteristic of Biodiesel (Methyl Ester) CI Engine, Eur. Chem. Bull. 2023, 12 (S3), 2240 – 2245 [Google Scholar] [Crossref]

35. Sumit R. Raut, Dr. Nilesh Diwaka, Prashant S. Raut, Dr. N. J. Rathod, Additives Characterization for Enhanced Biodiesel Performance in a CI Engine, Eur. Chem. Bull. 2023, 12 (S3), 2234 – 2239 [Google Scholar] [Crossref]

36. Sumit R. Raut, Dr. Nilesh Diwakar, Prashant S. Raut, Dr. N. J. Rathod, The Technique Performance Evaluation and Emission Characteristics of diesel, cerium oxide, and ethanol, Eur. Chem. Bull. 2023, 12 (S3), 3172 – 3184. [Google Scholar] [Crossref]

37. Prashant S. Raut, Dr. Nilesh Diwakar, Sumit Raut, Dr. Nikhil J. Rathod, The Technique Performance Evaluation and Emission Characteristics of Biodiesel, Eur. Chem. Bull. 2023, 12 (S3), 3147 – 3155 [Google Scholar] [Crossref]

• An Adaptive Joint Filtering Approach to Wireless Relay Network for Transmission Rate Maximization
• IoT-Integrated Mercury Substance Detection System for Cosmetic Product Safety
• Design and Implementation of Solar PV-Based Railway Microgrid for Linke Hofmann Busch Coaches
• Cost Control Techniques on Civil Engineering Projects in Oyo State, Nigeria
• Strength and Predictive Modeling of Corn Cob Ash Blended Concrete Using Multi-Output Artificial Neural Network Approach