The operation of urban water treatment plants: a review of smart dashboard frameworks

By locating useful characteristics and determining the perfect circumstances to meet ideal water quality criteria, this study seeks to improve the operation of a water treatment facility. The research comprises gathering data from personnel and exposure to system events, as well as from explicit and tacit knowledge sources. The problem at hand is a multi-objective, multi-criteria problem with many variables in spatial and temporal dimensions, requiring the use of powerful tools for analysis. All engineering problems have an objective function consisting of smaller sub-functions, typically in the form of cost or error minimization. To solve such problems, optimization methods based on natural patterns have been introduced, including genetic algorithms, evolutionary algorithms, and particle mass optimization. By optimizing the operation process of the water treatment plant, the quality of the water provided can be improved to meet standards set by organizations such as Iran 1053, WHO, and EPA. The study's findings could be used to implement changes to the plant's management and operation processes to achieve more ideal water quality conditions. Ultimately, the optimization of water treatment plant processes could have significant positive impacts on public health and well-being, as well as the environment

Design of a Decision Support System to Operate a NO₂ Gas Sensor Using Machine Learning, Sensitive Analysis and Conceptual Control Process Modelling

The most advantageous method for detecting dangerous gases and reducing the risk of potential environmental toxicity effects is the use of innovative gas sensing systems. However, designing effective sensors requires a complex process of synthesizing functional nanoparticles, which is a costly process. Additionally, practical operation of the toxic gas sensors always carries a significant cost along with a considerable risk of hazardous gas emissions. Machine learning algorithms may be used to accurately automate the behavior of the sensors to eliminate the abovementioned deficiencies. In the present research, there are three different factors involved in the optimization of NO2 sensing by means of the response surface methodology (RSM). Two main functions of sensor efficiency, namely sensitivity and response time, are predicted according to the Fe3O4 additive (%), input NO2 (ppm), and response time/sensitivity, and moreover, the execution of a controlling system of the sensor network using the Jacobson model is proposed. The machine learning computations are implemented by Meta.RegressionByDiscretization, M5.Rules, Lazy KStar, and Gaussian Processes algorithms. The outcomes illustrate that the best gas sensor efficiency predictions are related to M5.Rules and Lazy KStar, with a correlation coefficient of more than 96%. The best performance of machine learning computations can be found in the range of 8–10-fold in training and testing arrangements. Meanwhile, the ANOVA assessment confirmed that the most important features in the prediction of response time and sensitivity are NO2 concentration and response time, respectively, with the lowest p-value recorded. The outcomes illustrated that with combinations of RSM, machine learning, and the Jacobson model as a controller, a decision support system can be presented for the NO2 gas sensor system

A risk-based soft sensor for failure rate monitoring in water distribution network via adaptive neuro-fuzzy interference systems

Abstract Water Distribution Networks (WDNs) are considered one of the most important water infrastructures, and their study is of great importance. In the meantime, it seems necessary to investigate the factors involved in the failure of the urban water distribution network to optimally manage water resources and the environment. This study investigated the impact of influential factors on the failure rate of the water distribution network in Birjand, Iran. The outcomes can be considered a case study, with the possibility of extending to any similar city worldwide. The soft sensor based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) was implemented to predict the failure rate based on effective features. Finally, the WDN was assessed using the Failure Modes and Effects Analysis (FMEA) technique. The results showed that pipe diameter, pipe material, and water pressure are the most influential factors. Besides, polyethylene pipes have failure rates four times higher than asbestos-cement pipes. Moreover, the failure rate is directly proportional to water pressure but inversely related to the pipe diameter. Finally, the FMEA analysis based on the knowledge management technique demonstrated that pressure management in WDNs is the main policy for risk reduction of leakage and failure