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Related Concept Videos

Bioreactor Controls-I01:28

Bioreactor Controls-I

Maintaining optimal conditions within fermenters is essential for maximizing microbial productivity and ensuring process efficiency. This lesson focuses on key parameters—temperature, foam, pH, carbon dioxide, oxygen, and pressure—and their precise measurement and control strategies in fermentation systems.Temperature ControlTemperature regulation is critical due to the exothermic nature of many fermentation processes. In small laboratory fermenters, temperature is commonly monitored using...
Bioreactor Design and Operational System01:29

Bioreactor Design and Operational System

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...
Bioreactor Controls-II01:18

Bioreactor Controls-II

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 fermentor via a sparger...
Bioreactor Controls-III01:22

Bioreactor Controls-III

Strain improvement is a foundational strategy in industrial microbiology aimed at maximizing microbial productivity, particularly because natural isolates typically yield commercially valuable products in very low concentrations. Although optimizing the culture medium and environmental conditions can improve yields, these adjustments are inherently limited by the organism’s genetic potential. As a result, the focus shifts toward genetic modifications to enhance biosynthetic capacity. The...
Open and closed-loop control systems01:17

Open and closed-loop control systems

Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal and...
Methods of Medium Optimization01:28

Methods of Medium Optimization

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...

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Updated: May 21, 2026

Operation of a 25 KWth Calcium Looping Pilot-plant with High Oxygen Concentrations in the Calciner
06:34

Operation of a 25 KWth Calcium Looping Pilot-plant with High Oxygen Concentrations in the Calciner

Published on: October 25, 2017

Collaborative Dynamic Optimization Control for Municipal Solid Waste Incineration Process.

Weimin Huang, Xi Meng, Junfei Qiao

    IEEE Transactions on Cybernetics
    |May 19, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel collaborative dynamic optimization control (CDOC) scheme to enhance municipal solid waste incineration (MSWI) performance. The CDOC scheme effectively manages waste property fluctuations for improved operational efficiency and pollution control.

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    Continuously-stirred Anaerobic Digester to Convert Organic Wastes into Biogas: System Setup and Basic Operation

    Published on: July 13, 2012

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    Last Updated: May 21, 2026

    Operation of a 25 KWth Calcium Looping Pilot-plant with High Oxygen Concentrations in the Calciner
    06:34

    Operation of a 25 KWth Calcium Looping Pilot-plant with High Oxygen Concentrations in the Calciner

    Published on: October 25, 2017

    Continuously-stirred Anaerobic Digester to Convert Organic Wastes into Biogas: System Setup and Basic Operation
    11:31

    Continuously-stirred Anaerobic Digester to Convert Organic Wastes into Biogas: System Setup and Basic Operation

    Published on: July 13, 2012

    Area of Science:

    • Environmental Engineering
    • Process Control
    • Artificial Intelligence

    Background:

    • Municipal solid waste incineration (MSWI) faces challenges in operational optimization due to variable waste properties and dynamic conditions.
    • Existing control schemes struggle with optimal set-point determination and effective tracking amidst process variations.
    • Growing demands for pollution control and renewable energy necessitate advanced control strategies for MSWI.

    Purpose of the Study:

    • To propose a collaborative dynamic optimization control (CDOC) scheme for enhancing MSWI operational performance.
    • To integrate optimization and control strategies within a multimodal optimization-based framework.
    • To address challenges in set-point tracking and process variations in MSWI.

    Main Methods:

    • A data-driven surrogate-assisted dynamic optimization scheme using a parallel cell coordinate-based multimodal multiobjective competitive swarm optimization algorithm.
    • A knowledge transfer-based dynamic response strategy to handle environmental changes.
    • An adaptive multivariable model predictive control strategy for optimal control law derivation.

    Main Results:

    • The proposed CDOC scheme demonstrates superb tracking control performance on industrial data.
    • The scheme achieves promising optimization performance, balancing multiple performance indices.
    • Effective response to irregular changes in the optimization environment was observed.

    Conclusions:

    • The CDOC scheme offers an effective solution for optimizing MSWI processes under dynamic conditions.
    • Integration of advanced optimization and adaptive control improves operational efficiency and reliability.
    • The approach shows significant potential for real-world industrial applications in waste-to-energy sectors.