Evaluation of data-driven models for predicting the service life of concrete sewer pipes subjected to corrosion

Optimizing wastewater treatment through artificial intelligence: recent advances and future prospects

Artificial intelligence (AI) is increasingly being applied to wastewater treatment to enhance efficiency, improve processes, and optimize resource utilization. This review focuses on objectives, advantages, outputs, and major findings of various AI models in the three key aspects: the prediction of removal efficiency for both organic and inorganic pollutants, real-time monitoring of essential water quality parameters (such as pH, COD, BOD, turbidity, TDS, and conductivity), and fault detection in the processes and equipment integral to wastewater treatment. The prediction accuracy (R2 value) of AI technologies for pollutant removal has been reported to vary between 0.64 and 1.00. A critical aspect explored in this review is the cost-effectiveness of implementing AI systems in wastewater treatment. Numerous countries and municipalities are actively engaging in pilot projects and demonstrations to assess the feasibility and effectiveness of AI applications in wastewater treatment. Notably, the review highlights successful outcomes from these initiatives across diverse geographical contexts, showcasing the adaptability and positive impact of AI in revolutionizing wastewater treatment on a global scale. Further, insights on the ethical considerations and potential future directions for the use of AI in wastewater treatment plants have also been provided.

A multi-tier deterioration assessment models for sewer and stormwater pipelines in Hong Kong

Sewerage and stormwater networks are subjected to several deterioration factors, including aging, environmental conditions, and traffic. Maintaining these critical assets in good condition is essential to avoid harmful consequences, such as environmental contamination and negative implications on other infrastructure systems (e.g., water and road networks). Deterioration assessment models are effective and cost-efficient means for proactive management systems that can reduce such consequences. In this connection, this study aims to develop deterioration assessment models for sewer and stormwater pipelines in Hong Kong. First, critical factors that impact the deterioration process of these pipelines were identified. Data for these factors were then collected from the Drainage Services Department (DSD) and open-source data provided by the Hong Kong government. To improve prediction accuracy, a multi-tier concept was utilized in building the models. The first tier categorized pipelines into two groups: fail and not fail, whereas the second tier assigned a grade range from 1 to 3 to the "not fail" pipelines. Several artificial intelligence approaches, such as random forest, neural network, and SVM, were tested. Random forest achieved the highest accuracy in predicting pipelines condition, followed by neural networks. A sensitivity analysis was carried out to investigate the combined impact of two factors, with age being one of them, on the pipeline's performance. The findings of this study provide a robust decision-making tool that DSD authorities and consultants can use to optimize inspection and maintenance activities.

Review on Microbially Influenced Concrete Corrosion

Microbially influenced concrete corrosion (MICC) causes substantial financial losses to modern societies. Concrete corrosion with various environmental factors has been studied extensively over several decades. With the enhancement of public awareness on the environmental and economic impacts of microbial corrosion, MICC draws increasingly public attention. In this review, the roles of various microbial communities on MICC and corresponding protective measures against MICC are described. Also, the current status and research methodology of MICC are discussed. Thus, this review aims at providing insight into MICC and its mechanisms as well as the development of protection possibilities.

Predicting the concentrations of enteric viruses in urban rivers running through the city center via an artificial neural network

Viral waterborne diseases are widespread in cities due largely to the occurrence of enteric viruses in urban rivers, which pose a significant concern to human health. Yet, the application of rapid detection technology for enteric viruses in environmental water remains undeveloped globally. Here, multiple linear regression (MLR) modeling and artificial neural network (ANN) modeling, which used frequently measured physicochemical parameters in river water, were constructed to predict the concentration of enteric viruses including human enteroviruses (EnVs), rotaviruses (HRVs), astroviruses (AstVs), noroviruses GⅡ (HuNoVs GⅡ), and adenoviruses (HAdVs) in rivers. After training, testing, and validating, ANN models showed better performance than any MLR model for predicting the viral concentration in Jinhe River. All determined R-values for ANN models exceeded 0.89, suggesting a strong correlation between the predicted and measured outputs for target enteric viruses. Furthermore, ANN models provided a better congruence between the observed and predicted concentrations of each virus than MLR models did. Together, these findings strongly suggest that ANN modeling can provide more accurate and timely predictions of viral concentrations based on frequent (or routine) measurements of physicochemical parameters in river water, which would improve assessments of waterborne disease prevalence in cities.

Hybrid Differential Evolution-Based Regression Tree Model for Predicting Downstream Dam Hazard Potential

There are a large number of dams throughout the United States, and a considerable portion of them are categorized as having high hazard potential. This state of affairs constitutes a challenge, especially when coupled with their rapid deterioration. As such, this research paper proposes an optimized data-driven model for the fast and efficient prediction of dam hazard potential. The proposed model is envisioned on two main components, namely model development and model assessment. In the first component, a hybridization of the differential evolution algorithm and regression tree to forecast downstream dam hazard potential is proposed. In this context, the differential evolution (DE) algorithm is deployed to: (1) automatically retrieve the optimal set of input features affecting dam hazard potential; and (2) amplify the search mechanism of regression tree (REGT) through optimizing its hyper parameters. As for the second component, the developed DE-REGT model is validated using four folds of comparative assessments to evaluate its prediction capabilities. In the first fold, the developed DE-REGT model is trialed against nine highly regarded machine learning and deep learning models. The second fold is designated to structure, an integrative ranking of the investigated data-driven models, counting on their scores in the performance evaluation metrics. The third fold is used to study the effectiveness of using differential evolution for the hyper parameter optimization of regression tree. The fourth fold aims at testing the usefulness of using differential evolution as a feature extractor algorithm. Performance comparative analysis demonstrated that the developed DE-REGT model outperformed the remainder of the data-driven models. It accomplished mean absolute percentage error, relative absolute error, mean absolute error, root squared error, root mean squared error and a Nash–Sutcliffe efficiency of 9.62%, 0.27, 0.17, 0.31, 0.41 and 0.74, respectively. Results also revealed that the developed model managed to perform better than other meta-heuristic-based regression tree models and classical feature extraction algorithms, exemplifying the appropriateness of using differential evolution for hyper parameter optimization and feature extraction. It can be argued that the developed model could assist policy makers in the prioritization of their maintenance management plans and reduce impairments caused by the failure or misoperation of dams.

Data-driven estimation of COVID-19 community prevalence through wastewater-based epidemiology

Wastewater-based epidemiology (WBE) has been regarded as a potential tool for the prevalence estimation of coronavirus disease 2019 (COVID-19) in the community. However, the application of the conventional back-estimation approach is currently limited due to the methodological challenges and various uncertainties. This study systematically performed meta-analysis for WBE datasets and investigated the use of data-driven models for the COVID-19 community prevalence in lieu of the conventional WBE back-estimation approach. Three different data-driven models, i.e. multiple linear regression (MLR), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS) were applied to the multi-national WBE dataset. To evaluate the robustness of these models, predictions for sixteen scenarios with partial inputs were compared against the actual prevalence reports from clinical testing. The performance of models was further validated using unseen data (data sets not included for establishing the model) from different stages of the COVID-19 outbreak. Generally, ANN and ANFIS models showed better accuracy and robustness over MLR models. Air and wastewater temperature played a critical role in the prevalence estimation by data-driven models, especially MLR models. With unseen datasets, ANN model reasonably estimated the prevalence of COVID-19 (cumulative cases) at the initial phase and forecasted the upcoming new cases in 2-4 days at the post-peak phase of the COVID-19 outbreak. This study provided essential information about the feasibility and accuracy of data-driven estimation of COVID-19 prevalence through the WBE approach.

Comparative life cycle assessment of sewer corrosion control by iron salts: Suitability analysis and strategy optimization

Sewer deterioration caused by sulfide-induced concrete corrosion is spreading worldwide. Within the strategies to overcome this problem, dosing iron salts into the pipeline has attracted more attention. However, there is not yet research that evaluates this method whether it is overall environmentally friendly. Here, we conducted a comparative Life Cycle Assessment (LCA) to adjudge the benefits of dosing ferric chloride over non-dosing option in three different H₂S concentration levels (High, Medium, Low). Compared with taking no precautions, dosing ferric chloride performs better for all impact categories only in High H₂S situation, which can reduce the environmental impacts by 10% to 50%. In Medium H₂S situation, dosing ferric chloride shows lower environmental impacts of Global Warming, Fossil Fuel Depletion, Acidification, and Eutrophication, while leads to the deterioration of Human Toxicity and Freshwater Ecotoxicity by 10% and 13%, respectively. In Low H₂S situation, dosing ferric chloride performs even worse for all impact categories. Therefore, from an LCA perspective, this study recommends iron salts dosing technology to be applied in severe corrosion conditions caused by high H₂S concentrations. Contribution analysis shows that asphalt and diesel consumed during the sewer construction and renovation dominate all impact categories for non-dosing option, whereas the main contributor of Human Toxicity and Freshwater Ecotoxicity is shifted to ferric chloride production in dosing option, average at around 50%. Sensitivity analysis on the length of pipes protected by iron salts confirms that the initial dosing location is more preferable to be set at upstream of the sewer system. From an LCA perspective, as alternatives to ferric chloride, ferrous chloride is superior in all impact categories, and ferric sulfate could reduce the toxicity-related impacts and other effects at the expense of exacerbation of acidification. In the end, a systematic optimization of salts dosing should be considered in urban sewer management practice.

Durability of Fibre-Reinforced Calcium Aluminate Cement (CAC)-Ground Granulated Blast Furnace Slag (GGBFS) Blended Mortar after Sulfuric Acid Attack

Concrete wastewater infrastructures are important to modern society but are susceptible to sulfuric acid attack when exposed to an aggressive environment. Fibre-reinforced mortar has been adopted as a promising coating and lining material for degraded reinforced concrete structures due to its unique crack control and excellent anti-corrosion ability. This paper aims to evaluate the performance of polyethylene (PE) fibre-reinforced calcium aluminate cement (CAC)-ground granulated blast furnace slag (GGBFS) blended strain-hardening mortar after sulfuric acid immersion, which represented the aggressive sewer environment. Specimens were exposed to 3% sulfuric acid solution for up to 112 days. Visual, physical and mechanical performance such as water absorption ability, sorptivity, compressive and direct tensile strength were evaluated before and after sulfuric acid attack. In addition, micro-structure changes to the samples after sulfuric acid attack were also assessed by X-Ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) to further understand the deterioration mechanism. The results show that overall fibre-reinforced calcium aluminate cement (CAC)-based samples performed significantly better than fibre-reinforced ordinary Portland cement (OPC)-based samples as well as mortar samples in sulfuric acid solution in regard to visual observations, penetration depth, direct tensile strength and compressive reduction. Gypsum generation in the cementitious matrix of both CAC and OPC-based systems was the main reason behind the deterioration mechanism after acid attack exposure. Moreover, laboratory sulfuric acid testing has been proven for successfully screening the cementitious material against an acidic environment. This method can be considered to design the service life of concrete wastewater pipes.

Evaluating Physical and Fiscal Water Leakage in Water Distribution System

With increasing population, the need for research ideas on the field of reducing wastage of water can save a big amount of water, money, time, and energy. Water leakage (WL) is an essential problem in the field of water supply field. This research is focused on real water loss in the water distribution system located in Ethiopia. Top-down and bursts and background estimates (BABE) methodology is performed to assess the data and the calibration process of the WL variables. The top-down method assists to quantify the water loss by the record and observation throughout the distribution network. In addition, the BABE approach gives a specific water leakage and burst information. The geometrical mean method is used to forecast the population up to 2023 along with their fiscal value by the uniform tariff method. With respect to the revenue lost, 42575 Br and 42664 Br or in 1562$ and 1566$ were lost in 2017 and 2018, respectively. The next five-year population was forecasted to estimate the possible amount of water to be saved, which was about 549627 m3 and revenue 65,111$ to make the system more efficient. The results suggested that the majority of losses were due to several components of the distribution system including pipe-joint failure, relatively older age pipes, poor repairing and maintenance of water taps, pipe joints and shower taps, negligence of the consumer and unreliable water supply. As per the research findings, recommendations were proposed on minimizing water leakage.