Sensorless Adaptive Control Systems to Boost Biogas Production in Anaerobic Digesters

Anele Sibanda^*
*Correspondence: Anele Sibanda, Department of Chemical Engineering, University of Cape Town, Cape Town, South Africa, Email:

Introduction

Biogas production through anaerobic digestion is a critical component of renewable energy systems, offering an efficient method for converting organic waste into methane-rich fuel. However, the efficiency of anaerobic digesters is highly sensitive to internal conditions, particularly temperature, which directly influences microbial activity and gas yield. Traditional temperature regulation methods depend on sensor-based feedback systems, which may be prone to faults, delays and cost inefficiencies, especially in large-scale or rural applications. To address these challenges, sensorless adaptive control systems particularly those powered by Artificial Neural Networks (ANNs) have emerged as innovative tools for optimizing methane output. These systems offer dynamic, real-time control without the need for physical sensors, reducing operational complexity and enhancing biogas production. By predicting internal states based on external parameters and historical patterns, sensorless adaptive control introduces a smarter, more sustainable approach to managing bioreactor environments [1].

Description

Sensorless adaptive control systems function by leveraging computational models that learn and replicate the behavior of traditional sensor-based systems. In anaerobic digesters, these systems utilize parameters such as ambient temperature, feedstock characteristics, retention time and microbial activity patterns to estimate internal temperatures and adjust heating mechanisms accordingly. Artificial Neural Networks are particularly well-suited for this task due to their ability to model nonlinear relationships and adapt to changing process dynamics. Once trained, these networks can forecast temperature requirements and deliver control signals with high precision, even in the absence of direct sensory input. This eliminates the risk of sensor degradation and improves fault tolerance, making the system more robust over long-term operation. Moreover, the use of ANN-based models allows for real-time response to environmental fluctuations, enabling consistent methane yield across varying external conditions.

From a practical perspective, deploying sensorless control systems in anaerobic digesters results in several operational benefits. Firstly, the reduction in hardware dependency lowers maintenance costs and eliminates failure points associated with traditional thermocouples or RTD sensors. Secondly, improved temperature stability contributes to a more favorable environment for methanogenic bacteria, enhancing the digestion rate and overall biogas production. Case studies and simulations have demonstrated that adaptive control systems can increase methane yield by optimizing thermal inputs and minimizing energy waste. Additionally, sensorless designs are ideal for decentralized or resource-limited settings, such as rural biogas plants, where technical support and replacement parts for sensors may be scarce. The integration of predictive analytics also allows for early fault detection and process optimization, ensuring continuous operation and maximizing energy recovery from waste [2].

Conclusion

The integration of sensorless adaptive control systems represents a transformative advancement in anaerobic digestion technology. By replacing vulnerable physical sensors with intelligent, ANN-based controllers, these systems offer a cost-effective and resilient solution for maintaining optimal conditions within biogas reactors. The result is an increase in methane yield, reduced energy consumption and improved system reliability all crucial for scaling up biogas as a clean energy source. As the global demand for sustainable energy grows, the adoption of smart control systems will be pivotal in unlocking the full potential of bioenergy solutions, particularly in settings where traditional sensor-based systems are impractical or unsustainable.

Acknowledgement

None.

Conflict of Interest

None.

References

1. Safavi, Saeid, Mohammad Amin Safavi, Hossein Hamid and Saber Fallah. "Multi-sensor fault detection, identification, isolation and health forecasting for autonomous vehicles." Sensors 21 (2021): 2547.

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2. Anand, Kundan, Alok Prakash Mittal and Bhavnesh Kumar. "ANN-based sensorless adaptive temperature control system to improve methane yield in an anaerobic digester." Biomass Convers Biorefin 13 (2023): 7265-7285.

Google Scholar Cross Ref Indexed at