Process-Based Control Strategies for BOD Reduction in Industrial Wastewater Treatment

Abstract

Biochemical Oxygen Demand (BOD) is one of the most critical regulatory and performance parameters in industrial wastewater treatment, representing the biodegradable organic load present in effluent streams. Stable and effective BOD reduction is essential for regulatory compliance, environmental protection, and reliable biological treatment plant operation. While advanced automation and artificial intelligence tools are gaining attention, a significant portion of industrial wastewater treatment plants continue to rely on process-based control approaches grounded in fundamental engineering principles. This paper presents a comprehensive review of process-based BOD control strategies that do not involve artificial intelligence. Emphasis is placed on load equalization, aeration control, nutrient balance, sludge age management, and secondary clarification optimization. Practical monitoring techniques and operational best practices are discussed, demonstrating that consistent BOD removal efficiencies of 85–95% can be achieved through disciplined application of core process fundamentals.

Introduction

The text discusses process-based (non-AI) control strategies for reducing Biochemical Oxygen Demand (BOD) in industrial wastewater treatment plants. BOD is a key indicator of biodegradable organic pollution and a critical regulatory compliance parameter. Poor BOD control can lead to oxygen depletion, biomass stress, sludge bulking, foaming, and reduced treatment efficiency, making effective management essential for stable plant operation.

Industrial BOD originates from sources such as biodegradable organics, oils and grease, unreacted raw materials, process drains, and contaminated utility streams. Effective control relies on understanding these sources and maintaining optimal biological treatment conditions. Core factors influencing BOD removal include dissolved oxygen, sludge age, hydraulic retention time, nutrient balance, temperature, and pH.

The paper emphasizes practical process-driven control strategies, including influent equalization to manage load fluctuations, optimized primary treatment to reduce organic loading, controlled aeration to maintain adequate oxygen levels with minimal energy use, and proper nutrient supplementation. Additional measures include managing sludge age and return sludge flow, optimizing secondary clarifier performance, and using physico-chemical aids when necessary to support solids separation during disturbances.

Routine monitoring of parameters such as DO, MLSS, SVI, pH, temperature, and COD is highlighted as essential for timely operational adjustments. When applied systematically, these conventional strategies enable industrial wastewater treatment plants to achieve 85–95% BOD removal efficiency, demonstrating that disciplined process control remains highly effective even without advanced AI-based systems.

Conclusion

Effective BOD reduction in industrial wastewater treatment plants can be reliably achieved through robust process-based control strategies without reliance on artificial intelligence or advanced automation. Fundamental practices such as influent equalization, aeration optimization, nutrient balancing, sludge age control, and efficient clarification remain the cornerstone of biological treatment performance. Adherence to these principles ensures regulatory compliance, operational stability, and long-term plant sustainability.

References

[1] Metcalf & Eddy, Wastewater Engineering: Treatment and Resource Recovery, 5th Edition, McGraw-Hill, 2014. [2] Tchobanoglous, G., Burton, F. L., & Stensel, H. D., Wastewater Engineering, McGraw-Hill, 2003. [3] Water Environment Federation (WEF), Operation of Municipal Wastewater Treatment Plants, Manual of Practice No. 11, 2016. [4] Environmental Protection Agency (EPA), Activated Sludge Process Design and Control, EPA Manuals, USA. [5] Indian Central Pollution Control Board (CPCB), Guidelines for Industrial Effluent Treatment, Government of India.

Copyright

Copyright © 2026 Deepak Shankhdhar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.