Related Experiment Video
Updated: Apr 25, 2026

15:19
Development of Sulfidogenic Sludge from Marine Sediments and Trichloroethylene Reduction in an Upflow Anaerobic Sludge Blanket Reactor
Published on: October 15, 2015
8.7K
Study on decaying characteristics of activated sludge from a circular plug-flow reactor using response surface
En Xie1, Aizhong Ding1, Junfeng Dou1
1College of Water Sciences, Beijing Normal University, Beijing 100875, China.
Bioresource Technology
|August 29, 2014
Summary
pH levels are the primary driver of activated sludge decay, impacting both cell activity and death. Temperature and dissolved oxygen also influence sludge activity, but to a lesser extent.
Area of Science:
- Environmental Science
- Microbiology
- Biotechnology
Background:
- Activated sludge is crucial for wastewater treatment.
- Understanding factors affecting sludge decay is vital for process optimization.
- Previous studies have explored various decay factors with varying methodologies.
Purpose of the Study:
- To investigate the decay characteristics of activated sludge.
- To determine the most significant factors influencing sludge decay: pH, temperature, and dissolved oxygen.
- To quantify the contribution of reduced cell activity versus cell death to overall sludge decay.
Main Methods:
- Utilized a laboratory-scale circular plug-flow reactor for activated sludge generation.
- Employed batch experiments with response surface methodology for experimental design.
- Combined LIVE/DEAD® Baclight technique with 2,3,5-triphenyl tetrazolium chloride - dehydrogenase activity assay.
Main Results:
- pH was identified as the most influential factor in activated sludge decay.
- Temperature and dissolved oxygen were found to be secondary influential factors.
- Sludge activity decay was attributed to 40.94-90.03% reduced cell activity, with the remainder due to cell death.
Conclusions:
- pH is the dominant factor controlling activated sludge decay.
- Wastewater treatment processes should prioritize pH control for sludge stability.
- Both reduced cellular activity and cell death contribute to sludge decay, with reduced activity being the major component.
Related Concept Videos
Response Surface Methodology
887
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
The process of RSM involves several key steps:
887
Bioreactor Design and Operational System
191
Bioreactors are engineered vessels designed to cultivate microorganisms under controlled conditions for industrial bioprocessing. They maintain sterility and allow precise regulation of pH, temperature, oxygen, and nutrient levels to optimize microbial growth and metabolite production. Bioreactors range from small laboratory units of 1 liter to industrial systems holding up to 500,000 liters, though only about 75% of their volume is actively used for fermentation. The remaining headspace...
191
Bioreactor Controls-II
73
In aerobic fermentations, oxygen is vital for microbial growth and metabolite production. Since air comprises only about 20% oxygen and the gas is poorly soluble in water—just 9 ppm at 20°C—supplying sufficient oxygen becomes a critical challenge, especially in high-demand processes like yeast growth or citric acid production. Even a fully saturated broth may offer only a few seconds of oxygen availability.To address this, sterile or scrubbed air is introduced into the...
73
Methods of Medium Optimization
69
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
69

