Over the last few months, Maji has been working closely with a 26 MGD plant, reported at approximately 120,000 m³/day, operating several biological reactors. The work includes weekly biomass analysis for each reactor separately, allowing the team to follow the biological condition of each process unit and not only the plant as a whole.
The focus here was one specific reactor: 2.4 MGD, reported at approximately 11,000 m³/day.
What made this project interesting was that the reactor was not in crisis. The process had been stable for a long period, under the supervision of the same lead process engineer for more than 25 years. That stability created an opportunity to move from troubleshooting to optimization.
When a reactor is already performing well, operational changes need to be made carefully. Reducing energy use is valuable, but not if it creates instability in DO, biomass health, settling, or treatment performance. The biomass baseline built through Maji gave the team a reference point: what stable biomass looked like in this specific reactor, and whether it remained stable during each adjustment.
Together with the plant team, the focus was placed on electricity consumption. Over approximately 2 months, diffuser depth was adjusted gradually, step by step. Each change influenced oxygen transfer, DO behavior, and reactor energy demand.
electricity consumption gradually decreased over time, showing how each operational adjustment contributed to the final 18% reduction.
The result was a measured 18% reduction in electricity consumption in this reactor, equal to approximately $4,000 per month in direct savings for the plant.
The important point is that the reduction was not achieved by simply lowering aeration and hoping for the best. Each adjustment was made together with the operators and lab team, while biomass health and treatment performance were monitored regularly. The reactor remained stable throughout the process, and in some aspects, performance even improved.
The practical value was clear:
18% lower electricity consumption in one 2.4 MGD reactor
Approximately $4,000/month in direct savings
Stable biomass and treatment performance during the optimization process
And this was only one reactor. The same approach is now being evaluated for the plant’s additional reactors.
The broader lesson is that biomass monitoring is not only useful when something goes wrong. When the process is stable, a clear biomass baseline can also support optimization. It gives experienced process engineers more confidence to test careful adjustments, verify the biological response, and reduce costs without compromising process stability.
For a large plant operating multiple reactors, this type of improvement can become meaningful very quickly. In this example, a controlled optimization process in one reactor delivered a measurable energy reduction, direct monthly savings, and a practical model for evaluating similar changes across the rest of the facility.