Optimize Wastewater Treatment with AI-based Guidance System

Optimize Wastewater Treatment with AI-based Guidance System

Table of Contents

1. Introduction
2. Background of the Work
3. Use of AI in Wastewater Treatment
4. Operational Guidance System
5. Random Forest Algorithm
6. Methodologies Used
7. Data Collection and Model Creation
8. Results and Evaluation
9. Limitations and Future Improvements
10. Operators' Acceptance of AI Technology

Introduction

🌍 Transforming Wastewater Treatment: The Role of AI-based Guidance Systems

In the realm of wastewater treatment, operational parameters play a crucial role in ensuring optimal performance and efficiency. As skilled engineers retire and a shortage of expertise looms, the need for operator support systems becomes paramount. In this article, we delve into the world of AI-based guidance systems for municipal wastewater treatment, exploring their development, evaluation, and potential for practical use. Join us as we unravel the breakthroughs of the V-DASH project – a collaborative endeavor by Japan Showed Works Agency, Yaskawa Electric Corporation, and Miso Industries, supported by the National Institute for Land and Infrastructure Management.

Background of the Work

🏭 Addressing the Challenges of Aging Engineers and Shrinking Populations

The field of municipal wastewater treatment heavily relies on biological treatment processes, particularly activated sludge processes. However, the operation of these processes demands expertise and experience, causing concern as aging engineers retire at a faster rate than young replacements can be found. This shortage of skilled engineers poses a significant social issue, especially in countries like Japan that face declining populations. To bridge this gap, the concept of an operator support system driven by artificial intelligence gains relevance, offering potential solutions to the evolving challenges in the industry.

Use of AI in Wastewater Treatment

🤖 Leveraging AI Technology for Enhanced Wastewater Treatment

Artificial intelligence has emerged as a powerful tool in various fields, and wastewater treatment is no exception. Several projects in Japan have explored the use of AI in different aspects of wastewater treatment, including equipment diagnosis, treatment performance assessment, and operational guidance. Among these projects, this article focuses on the operational guidance system – a promising endeavor that seeks to provide real-time recommendations for operating parameters using AI-based prediction models. Join us as we unravel the intricacies and possibilities of this innovative technology.

Operational Guidance System

🚀 Empowering Operators with Real-Time Recommendations

In municipal wastewater treatment plants, an operational guidance system driven by AI offers newfound potential for optimizing processes. By utilizing AI algorithms, such a system can accurately predict the values of crucial operating parameters. The system then provides operators with real-time recommendations for these parameters, allowing them to make informed decisions and fine-tune their plant's performance. Through the development of a user-friendly interface, operators gain access to personalized guidance, ensuring efficient operation and overcoming the challenges posed by the shortage of skilled engineers.

Random Forest Algorithm

🌳 Unraveling the Power of Random Forest

The operational guidance system relies on the random forest algorithm, a versatile machine learning technique widely used in regression tasks. Random forest comprises an ensemble of decision trees that independently predict the target parameter from a set of Relevant variables. By averaging the outputs of all decision trees, the algorithm generates robust and accurate predictions. Moreover, random forest provides insights into the influence of each variable on the prediction, allowing operators to gain a deeper understanding of their wastewater treatment process.

Methodologies Used

📚 Unveiling the Journey to Practical AI-based Guidance

To evaluate the predictive power of the guidance system, extensive methodologies were employed. Real wastewater treatment plant data served as the foundation for model creation and evaluation. Two critical operating parameters, namely air flow rate and waste activated sludge rate, were selected as targets for prediction due to their substantial impact on treatment plant performance. Through a meticulous process of data collection, model development, and validation, the system's efficacy and potential for practical use were assessed.

Data Collection and Model Creation

📊 Harnessing Real Wastewater Treatment Plant Data for Accuracy

In the pursuit of accurate predictions, the operational guidance system was meticulously trained using real-world data. A municipal wastewater treatment plant located in the southeastern part of Japan became the focal point of data collection. With 44 mega liters per day capacity, this conventional activated sludge plant provided an ideal setting for assessing the guidance system's capabilities. Through the extraction of extensive online process data stored in the SCADA system, a robust foundation for model creation was established.

Results and Evaluation

📈 Evaluating the Efficacy of the Operational Guidance System

The predictions generated by the operational guidance system underwent rigorous evaluation to gauge their accuracy and practical applicability. Mean Absolute Percentage Error (MAPE) served as the primary indicator for assessing prediction quality. The results revealed an impressive performance, with an annual average error of 4.2% for air flow rate prediction and 8.1% for waste activated sludge rate prediction. While falling within an acceptable range, the system exhibited challenges when faced with unforeseen events and extreme conditions. The article explores the implications of these findings and proposes potential avenues for improvement.

Limitations and Future Improvements

⚡ Moving Forward: Enhancements and Further Applications

While the operational guidance system showed promising results, it is not without limitations. The article discusses the necessity of periodic model updates to accommodate long-term changes in treatment conditions, particularly when confronted with unexperienced events. Additionally, the predictive capabilities of the system can be further enhanced by incorporating additional data from scratch processing facilities. By adopting a flexible and adaptive approach, the operational guidance system can evolve to meet the dynamic demands of the wastewater treatment industry.

Operators' Acceptance of AI Technology

🔮 Gauging the Willingness to Embrace AI Innovations

As technology advances, it is crucial to assess stakeholders' acceptance of AI-driven solutions in practical settings. A questionnaire survey conducted among plant operators in Japan shed light on their interest in the operational guidance system. An overwhelming 64% of respondents expressed positivity towards the system, highlighting its potential as a valuable tool in wastewater treatment operations. Furthermore, an astonishing 83% of respondents showed favorable inclinations towards the possibility of automatic operation using AI technology in the future. These results signify a shift towards greater receptiveness and highlight the growing importance of incorporating AI innovations.

Resources:

• Japan Showed Works Agency
• Yaskawa Electric Corporation
• Miso Industries
• National Institute for Land and Infrastructure Management