In the heart of Dhaka, Bangladesh, a groundbreaking solution is emerging to tackle one of the world’s most pressing environmental challenges: industrial wastewater management. Md. Jahid Hasan Mridha, a researcher from the Department of Computer Science and Engineering at BRAC University, has developed a real-time monitoring framework that could revolutionize how industries handle effluent treatment. This innovation, published in the IEEE Access journal, translates to ‘IEEE Open Access’, leverages the power of environmental IoT, colorimetry, and advanced learning theories to create a practical, low-cost solution for remote monitoring of effluent treatment plants (ETPs).
The textile and ready-made garment (RMG) industries are notorious for their significant contributions to industrial waste and effluent, leading to severe environmental pollution. ETPs are designed to treat and recycle this wastewater, but many factories worldwide either operate without them or fail to maintain them consistently due to high energy consumption. This results in the release of untreated waste, posing serious environmental risks. Mridha’s research aims to change this narrative by providing an automated monitoring solution that enhances industrial wastewater management.
At the core of Mridha’s system is a Convolutional Neural Network (CNN) that uses video-based binary classification to detect the operational status of ETPs. “The CNN model achieves an impressive accuracy of 98.3%,” Mridha explains, “making it a reliable tool for real-time monitoring of ETPs.” But the innovation doesn’t stop at detecting the On/Off state of ETPs. The system also employs K-Nearest Neighbor (KNN) to classify water quality based on sensor data, achieving an accuracy of 97%. Additionally, a Long Short-Term Memory (LSTM) network is used to forecast seasonal impacts on water quality, including inactive periods, with an accuracy of 94.9%.
One of the most intriguing aspects of this research is the use of colorimetry analysis. The system monitors watercolor variations as potential indicators of contamination. This approach adds another layer of data that can be used to assess water quality and detect anomalies in real-time. “Colorimetry provides a visual cue that complements the sensor data,” Mridha notes, “making the system more robust and reliable.”
The commercial impacts of this research are profound, particularly for the energy sector. By providing a low-cost, automated solution for ETP monitoring, industries can reduce their energy consumption and operational costs. Moreover, the system’s ability to forecast seasonal impacts on water quality can help industries plan their operations more efficiently, further reducing costs and environmental impact.
The potential applications of this research extend beyond the textile and RMG industries. The system’s accuracy and reliability make it a promising tool for broader environmental compliance. It could be used to monitor water quality in various industrial settings, ensuring that they meet regulatory standards and contribute to sustainability efforts. Furthermore, the system’s potential benefits for the agriculture and nutrition sectors are significant. By minimizing industrial pollution, the system can support the growth of sustainable agriculture and improve the quality of water used for irrigation and livestock.
Mridha’s research, published in IEEE Access, represents a significant step forward in the field of environmental IoT and deep learning. It demonstrates the potential of these technologies to address real-world challenges and create sustainable solutions. As industries worldwide grapple with the need to reduce their environmental impact, systems like Mridha’s offer a beacon of hope. They show that with the right tools and innovative thinking, we can create a future where industry and environment coexist harmoniously. The future of ETP monitoring is here, and it’s looking brighter than ever.