Deep-learning based monitoring of FOG layer dynamics in wastewater pumping stations

Deep learning-based flocculation sensor for automatic control of flocculant dose in sludge dewatering processes during wastewater treatment

In sludge dewatering of most wastewater treatment plants (WWTPs), the dose of polymer flocculant is manually adjusted through direct visual inspection of the flocs without the aid of any instruments. Although there is a demand for the development of automatic control of flocculant dosing, this has been challenging owing to the lack of a reliable flocculation sensor. To address this issue, this study developed a novel image sensor for measuring the degree of flocculation (DF) based on deep learning. Two types of sludge samples were used in the laboratory-scale flocculation tests: excess sludge and mixtures of excess sludge and raw wastewater. To search for a deep learning regression model suitable for DF inference, ten models, including convolutional neural networks, vision transformers, and a multilayer perceptron MLP mixer, were comparatively analysed. The ConvNeXt and MLP mixer models showed the highest accuracies with coefficients of determination (R2) greater than 0.9. The region contributing to the DF inference in the flocculation images was visualised using a modulus-weighted, gradient-weighted regression activation map. A prototype of the flocculation sensor was constructed using an inexpensive EdgeAI device. This device comprises a single-board computer and an integrated graphical processing unit (GPU) and is equipped with a quantised ConvNeXt model. The maximum inference speed of the sensor was 12.8 frames per second (FPS). The flocculation control tests showed that the sensor could control the DF to the target value by adjusting the polymer flocculant dose. Therefore, the flocculation sensor can provide a data-driven approach to the management of the flocculation process, thereby facilitating the automation of WWTPs.

Research progress of the artificial intelligence application in wastewater treatment during 2012-2022: a bibliometric analysis

This study identified literatures from the Web of Science Core Collection on the application of artificial intelligence in wastewater treatment from 2011 to 2022, through bibliometrics, to summarize achievements and capture the scientific and technological progress. The number of papers published is on the rise, and especially, the number of papers issued after 2018 has increased sharply, with China contributing the most in this regard, followed by the US, Iran and India. The University of Tehran has the largest number of papers, WATER is the most published journal, and Nasr M has the largest number of articles. Collaborative network has been developed mainly through cooperation between European countries, China and the US. Remote sensing in developing countries needs to be further integrated with water quality monitoring programs. It is worth noting that artificial neural network is a research hotspot in recent years. Through keyword clustering analysis, 'machine learning' and 'deep learning' are hot keywords that have emerged since 2019. The use of neural networks for predicting the effectiveness of treatment of difficult to degrade wastewater is a future research trend. The rapid advancement of deep learning provides the opportunity to build automated pipeline defect detection systems through image recognition.

The role of restaurant wastewater for producing bioenergy towards a circular bioeconomy: A review on composition, environmental impacts, and sustainable integrated management

Population inflation has led to the unprecedented increase in urbanization, thus causing negative impacts on environmental sustainability. Recently, there is an upsurge in the number of restaurants due to the changing lifestyles of the people round the globe. For instance, there were 167,490 food and beverage establishments in 2015, representing an annual growth rate of 5.1% since 2010 in Malaysia. The rapid growth of restaurants has implicated a negative impact due to the generation of highly polluted restaurant wastewater (RWW). RWW is mainly generated during the cooking, washing, and cleaning operations. RWW typically contain fat, oil, and grease (FOG) resulting from residues of meat, deep-fried food, baked items and butter, and has caused serious blockages of sewer due to clogging and eventually sewage backup. This has increased the required frequency of cleaning and sanitary sewer overflows (SSOs). Results from the previous studies have shown that FOG can be treated using physical, chemical, and biological processes. Different technologies have been applied for the treatment of FOG and other pollutants (COD, BOD, SS and NH₄-N) present in RWW. Therefore, this review aims to provide an in-depth understanding of the characteristics of RWW, chemical and physical characteristics of FOG with the mechanism of its formation and utilization for biocomposites, biogas and biodiesel productions for circular bioeconomy. Besides, this review has discussed the potential treatment technologies comprehensively for RWW which is currently remain understudied. Integrated sustainable management of FOG with technoeconomic analysis of bioproducts, sustainable management with international initiatives and previous studies are also summarized. Hence, this review aims towards providing better alternatives in managing RWW at sources, including its treatment and potential of its biorefinery, therefore eventually contributing towards environmental sustainability.

The role of deep learning in urban water management: A critical review

Deep learning techniques and algorithms are emerging as a disruptive technology with the potential to transform global economies, environments and societies. They have been applied to planning and management problems of urban water systems in general, however, there is lack of a systematic review of the current state of deep learning applications and an examination of potential directions where deep learning can contribute to solving urban water challenges. Here we provide such a review, covering water demand forecasting, leakage and contamination detection, sewer defect assessment, wastewater system state prediction, asset monitoring and urban flooding. We find that the application of deep learning techniques is still at an early stage as most studies used benchmark networks, synthetic data, laboratory or pilot systems to test the performance of deep learning methods with no practical adoption reported. Leakage detection is perhaps at the forefront of receiving practical implementation into day-to-day operation and management of urban water systems, compared with other problems reviewed. Five research challenges, i.e., data privacy, algorithmic development, explainability and trustworthiness, multi-agent systems and digital twins, are identified as key areas to advance the application and implementation of deep learning in urban water management. Future research and application of deep learning systems are expected to drive urban water systems towards high intelligence and autonomy. We hope this review will inspire research and development that can harness the power of deep learning to help achieve sustainable water management and digitalise the water sector across the world.

Flow Measurements Derived from Camera Footage Using an Open-Source Ecosystem

Sensors used for wastewater flow measurements need to be robust and are, consequently, expensive pieces of hardware that must be maintained regularly to function correctly in the hazardous environment of sewers. Remote sensing can remedy these issues, as the lack of direct contact between sensor and sewage reduces the hardware demands and need for maintenance. This paper utilizes off-the-shelf cameras and machine learning algorithms to estimate the discharge in open sewer channels. We use convolutional neural networks to extract the water level and surface velocity from camera images directly, without the need for artificial markers in the sewage stream. Under optimal conditions, our method estimates the water level with an accuracy of ±2.48% and the surface velocity with an accuracy of ±2.08% in a laboratory setting—a performance comparable to other state-of-the-art solutions (e.g., in situ measurements).

Machine learning in natural and engineered water systems

Water resources of desired quality and quantity are the foundation for human survival and sustainable development. To better protect the water environment and conserve water resources, efficient water management, purification, and transportation are of critical importance. In recent years, machine learning (ML) has exhibited its practicability, reliability, and high efficiency in numerous applications; furthermore, it has solved conventional and emerging problems in both natural and engineered water systems. For example, ML can predict various water quality indicators in situ and real-time by considering the complex interactions among water-related variables. ML approaches can also solve emerging pollution problems with proven rules or universal mechanisms summarized from the related research. Moreover, by applying image recognition technology to analyze the relationships between image information and physicochemical properties of the research object, ML can effectively identify and characterize specific contaminants. In view of the bright prospects of ML, this review comprehensively summarizes the development of ML applications in natural and engineered water systems. First, the concept and modeling steps of ML are briefly introduced, including data preparation, algorithm selection and model evaluation. In addition, comprehensive applications of ML in recent studies, including predicting water quality, mapping groundwater contaminants, classifying water resources, tracing contaminant sources, and evaluating pollutant toxicity in natural water systems, as well as modeling treatment techniques, assisting characterization analysis, purifying and distributing drinking water, and collecting and treating sewage water in engineered water systems, are summarized. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of ML algorithms for different studies, as well as prospects on the application and development of ML in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.