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Related Concept Videos

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Microbial growth control refers to various methods employed to inhibit, reduce, or eliminate microorganisms to ensure safety and hygiene across different settings. These methods are categorized based on the target environment and the level of microbial control required.Biocides are versatile agents designed to control microorganisms by either inhibiting their growth or outright killing them. These agents work through various physical, chemical, mechanical, or biological mechanisms. The...
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Radiation and filtration are essential tools for microbial control, targeting microorganisms through distinct mechanisms. Radiation eliminates microbes by damaging their DNA, either killing them or inhibiting their growth. Based on wavelength, radiation is classified into two types: nonionizing and ionizing radiation.Non-ionizing radiation, such as UV radiation (200–400 nm), is absorbed by DNA, causing defects that effectively disinfect surfaces, air, and water, including safety cabinets.
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Biological agents offer an effective means of controlling microbial growth by leveraging natural processes like predation, competition, and the secretion of antimicrobial substances.Predatory bacteria such as Bdellovibrio species target and kill pathogens like Salmonella and E. coli. They are widely used in poultry farms to control infections. Myxococcus species help combat plant-pathogenic fungi. These naturally occurring predators serve as eco-friendly alternatives to chemical pesticides and...
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Related Experiment Video

Updated: Oct 11, 2025

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An effective dynamic immune optimization control for the wastewater treatment process.

Fei Li1,2,3, Zhong Su4, Gongming Wang5

  • 1School of Automation, Beijing Information Science & Technology University, Beijing, 100192, People's Republic of China. lifei@bistu.edu.cn.

Environmental Science and Pollution Research International
|November 28, 2021
PubMed
Summary

This study introduces a dynamic multi-objective immune system optimization control (DMOIA-OC) for wastewater treatment plants. The novel approach effectively balances conflicting performance indicators like energy use and effluent quality.

Keywords:
Adaptive dynamic optimizationComplex optimization problemDynamic characteristics of WWTPsMultiple performance indicatorsSelf-organizing recurrent fuzzy neural network controlThe best Pareto solution

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Area of Science:

  • Environmental Engineering
  • Process Control
  • Computational Intelligence

Background:

  • Wastewater treatment processes (WWTPs) face challenges in optimizing multiple, often conflicting, performance indicators.
  • Existing control schemes struggle to adaptively manage complex environmental variables and achieve optimal performance.
  • Balancing energy consumption with effluent quality remains a critical issue in WWTP management.

Purpose of the Study:

  • To design an effective optimization control scheme for complex WWTPs.
  • To address the conflict between multiple performance indicators, specifically energy consumption and effluent quality.
  • To develop a method that adaptively optimizes WWTP performance in response to environmental changes.

Main Methods:

  • A dynamic multi-objective immune system optimization control (DMOIA-OC) scheme was developed.
  • The control process was structured into dynamic and tracking control layers.
  • Adaptive models for energy consumption and effluent quality were established.
  • An adaptive dynamic immune optimization algorithm was employed to handle conflicting indicators.
  • Pareto solutions were analyzed to select optimal dissolved oxygen and nitrate nitrogen levels.
  • The method was evaluated using the Benchmark Simulation Platform (BSM1).

Main Results:

  • The DMOIA-OC method successfully addressed the complex optimization problem in WWTPs.
  • The proposed scheme demonstrated a competitive advantage in its control effect.
  • The adaptive nature of the algorithm allowed for effective response to environmental dynamics.
  • Optimal values for dissolved oxygen and nitrate nitrogen were identified.

Conclusions:

  • The DMOIA-OC provides an effective solution for optimizing multiple performance indicators in wastewater treatment.
  • The method offers a competitive advantage in control performance for WWTPs.
  • This approach enhances the ability to manage complex and conflicting objectives in environmental engineering.