Smart Water Quality Monitoring and Treatment Using IoT Technology

The primary purpose of this study is to design and build a water quality monitoring and treatment system with the concept of the Internet of Things (IoT) using Arduino Mega as a core controller, an esp8266 wifi module to connect to the internet, and smartphones to control and monitor the system. The system utilized a water level sensor to monitor the water level in the tank to avoid possible overflow, and to monitor the essential parameters of water quality such as PH, turbidity, total dissolved solids, and chlorine content, and to perform chlorination as a treatment process to maintain chlorine residuals in water. The Blynk App framework was used to integrate the system into the Internet of Things. In addition, a liquid crystal display (LCD) is employed to view the parameters of water quality, if the internet is not available. These sensors wirelessly send the data they continuously collect on water quality indicators to a centralized server for processing. The server responds to requests for smartphones that the Blynk Apps has installed. The automated control mechanisms are put into place at the water treatment control stage based on the data analysis. Additionally, the smart water monitoring and treatment system has user interfaces that can be accessed via web and mobile applications. These interfaces give stakeholders remote access to real-time information about the quality of the water as well as personalized alarms. Such accessibility improves the ability to make decisions and enables quick responses to crucial water quality occurrences. From the results and findings of the research, the IoT-based Water Quality Monitoring and Treatment System is efficient and effective in detecting water quality and provides treatment using automatic chlorinatio

An internet of things approach for water efficiency: A case study of the beverage factory

There is a lack of knowledge among food manufacturers about adopting the Internet of Things (IoT)-based water monitoring system and its ability to support water minimisation activities. It is therefore necessary to investigate the applicability of IoT-based real-time water monitoring systems in a real food manufacturing environment to pursue water-saving opportunities accordingly. This article aims to propose an architecture of an IoT-based water-monitoring system needed for real-time monitoring of water usage, and address any water inefficiencies within food manufacturing. This article looks at a study conducted in a food beverage factory where an IoT-based real-time water monitoring system is implemented to analyse the complete water usage in order to devise solutions and address water overconsumption/wastage during the manufacturing process. The successful implementation of an IoT-based real-time water monitoring system offered the beverage factory a detailed analysis of the water consumption and insights into the water hotspots that needed attention. This action initiated several water-saving project opportunities, which contributed to the improvement of water sustainability and led to an 11% reduction in the beverage factory’s daily water usage

ACWA: An AI-driven Cyber-Physical Testbed for Intelligent Water Systems

This manuscript presents a novel state-of-the-art cyber-physical water testbed, namely: The AI and Cyber for Water and Agriculture testbed (ACWA). ACWA is motivated by the need to advance water supply management using AI and Cybersecurity experimentation. The main goal of ACWA is to address pressing challenges in the water and agricultural domains by utilising cutting-edge AI and data-driven technologies. These challenges include Cyberbiosecurity, resources management, access to water, sustainability, and data-driven decision-making, among others. To address such issues, ACWA consists of multiple topologies, sensors, computational nodes, pumps, tanks, smart water devices, as well as databases and AI models that control the system. Moreover, we present ACWA simulator, which is a software-based water digital twin. The simulator runs on fluid and constituent transport principles that produce theoretical time series of a water distribution system. This creates a good validation point for comparing the theoretical approach with real-life results via the physical ACWA testbed. ACWA data are available to AI and water domain researchers and are hosted in an online public repository. In this paper, the system is introduced in detail and compared with existing water testbeds; additionally, example use-cases are described along with novel outcomes such as datasets, software, and AI-related scenarios

Short message service (SMS) based system for monitoring free chlorine concentration and Ph in the water reservoirs

A Project Report Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Science in Embedded and Mobile Systems of the Nelson Mandela African Institution of Science and TechnologyChlorine is the most common and affordable chemical reagent used for killing harmful micro organisms in the water to eliminate the spread of waterborne diseases such as cholera and diarrhoea. According to World Health Organization, a free chlorine level within 0.5~1 mg/l and a pH level within 6.5~8.5 is recommended for human consumption. It is essential to monitor water quality parameters such as free chlorine in the water since a lower value will not disinfect water efficiently while extreme values of free chlorine may affect the community’s health. Zanzibar Water Authority (ZAWA) employs colorimetric approach to manually measure free chlorine in the water reservoirs and submit the results into the kobocollect mobile application. However, this approach is prone to human errors, ineffective and time consuming. The objective of this current project was to develop a SMS-based system for monitoring free chlorine concentration and pH in the water reservoirs. Extreme Programming (XP) agile development methodology accompanied with V-Model were employed for developing and testing of the proposed system. The developed system consists of hardware device and dashboard for remote monitoring of water quality parameters such as free chlorine concentration and pH. The hardware device sends sensor data and notification alerts as SMS. The device is energy efficient since it uses SMS to send sensor data. The device can be deployed in a noisy environment since it uses RS485 communication protocol to communicate with the free chlorine sensor. The developed dashboard is used for remote monitoring of the sensor data. The dashboard is user friendly and practical since it was designed with the end users. The system was tested and validated by the end users to ensure all requirements were met. The developed system has a potential capability to improve management of water reservoirs by enhancing transparency, cooperation, and accountability among stakeholders

Municipal Wastewater Management

Taking the papers’ collection of this Special Issue as a whole, it is clear that “Municipal Wastewater Management” is an ongoing field of research with the ability to incorporate current environmental and human health challenges. The use of municipal sewage to monitor COVID-19 virus circulation in communities and the estimation of possible outbreaks, even before clinical cases have been identified, is a fact that justifies this. In light of the Coronavirus pandemic, the interest of the impact that research on municipal wastewater management can have on improving humans’ health and protecting the environment is being rethought. In respect to this, there is an essential need for scientific publications that present varieties of case studies and discuss best practices, so as wastewater treatment plants to be seen not only as sites of pollutants removal but also as places where energy is efficiently used and environmental sustainability is being practiced, in close relation to the needs of the community. Viewed in this way, the papers’ collected in this Special Issue are looking forward to reach a broad readership that can gain awareness and understanding of their topics and be stimulated into future research and collaborations that would improve all stakeholders engagement in promoting a sustainable municipal wastewater management

Developing Multi-Scale Models for Water Quality Management in Drinking Water Distribution Systems

Drinking water supply systems belong to the group of critical infrastructure systems that support the socioeconomic development of our modern societies. In addition, drinking water infrastructure plays a key role in the protection of public health by providing a common access to clean and safe water for all our municipal, industrial, and firefighting purposes. Yet, in the United States, much of our national water infrastructure is now approaching the end of its useful life while investments in its replacement and rehabilitation have been consistently inadequate. Furthermore, the aging water infrastructure has often been operated empirically, and the embracement of modern technologies in infrastructure monitoring and management has been limited. Deterioration of the water infrastructure and poor water quality management practices both have serious impacts on public health due to the increased likelihood of contamination events and waterborne disease outbreaks. Water quality reaching the consumers’ taps is largely dependent on a group of physical, chemical, and biological interactions that take place as the water transports through the pipes of the distribution system and inside premise plumbing. These interactions include the decay of disinfectant residuals, the formation of disinfection by-products (DBPs), the corrosion of pipe materials, and the growth and accumulation of microbial species. In addition, the highly dynamic nature of the system’s hydraulics adds another layer of complexity as they control the fate and transport of the various constituents. On the other hand, the huge scale of water distribution systems contributes dramatically to this deterioration mainly due to the long transport times between treatment and consumption points. Hence, utilities face a considerable challenge to efficiently manage the water quality in their aging distribution systems, and to stay in compliance with all regulatory standards. By integrating on-line monitoring with real-time simulation and control, smart water networks offer a promising paradigm shift to the way utilities manage water quality in their systems. Yet, multiple scientific gaps and engineering challenges still stand in the way towards the successful implementation of such advanced systems. In general, a fundamental understanding of the different physical, chemical, and biological processes that control the water quality is a crucial first step towards developing useful modeling tools. Furthermore, water quality models need to be accurate; to properly simulate the concentrations of the different constituents at the points of consumption, and fast; to allow their implementation in real-time optimization algorithms that sample different operational scenarios in real-time. On-line water quality monitoring tools need be both reliable and inexpensive to enable the ubiquitous surveillance of the system at all times. The main objective of this dissertation is to create advanced computational tools for water quality management in water distribution systems through the development and application of a multi-scale modeling framework. Since the above-mentioned interactions take place at different length and time scales, this work aims at developing computational models that are capable of providing the best description of each of the processes of interest by properly simulating each of its underlying phenomena at its appropriate scale of resolution. Molecular scale modeling using tools of ab-initio quantum chemical calculations and molecular dynamics simulations is employed to provide detailed descriptions of the chemical reactions happening at the atomistic level with the aim of investigating reaction mechanisms and developing novel materials for environmental sensing. Continuum scale reactive-transport models are developed for simulating the spatial and temporal distributions of the different compounds at the pipe level considering the effects of the dynamic hydraulics in the system driven by the spatiotemporal variability in water demands. System scale models are designed to optimize the operation of the different elements of the system by performing large-scale simulations coupled with optimization algorithms to identify the optimal operational strategies as a basis for accurate decision-making and superior water quality management. In conclusion, the computational models developed in this study can either be implemented as stand-alone tools for simulating the fundamental processes dictating the water quality at different scales of resolution, or be integrated into a unified framework in which information from the small scale models are propagated into the larger scale models to render a high fidelity representation of these processes

Artificial Intelligence-Based Optimization of Industrial Membrane Processes

AbstractArtificial intelligence (AI) is gaining acceptance for modern control systems in various applications in daily life including the Chemical process industry. Above all, application of AI is increasing in the field of membrane-based treatment where it shows great potential until now. Membrane separations are generally recognized as energy-efficient processes. In particular, membrane desalination, forward osmosis, energy generation, and biomass treatment have shown substantial potential in modern industries, such as wastewater treatment, pharmaceuticals, petrochemicals, and natural products. All these industries consume more than 20% of total energy consumption in the world. Moreover, the laboratory research outcomes illuminate the way to better membrane design and development, including advanced process control and optimization. The membrane processes with existing technologies for a sustainable environment could be integrated with the AI model. This review summarizes several membrane-based water treatment designs and plant performances where artificial intelligence is being used to minimize waste generation and lead to cleaner production

The Impact Of Community-Based Training Outreach In Improving Water Quality, Health, And Sanitation In Kenya

Water is an important component of the human body. A greater percentage of the body comprises of water. Digestion, brain function, movement, and sweating among others are the basic functions of the human body that require water. Often, water quality is neglected in developing countries such as Kenya, Uganda, and Tanzania. Many individuals in these countries understand the importance of having accessible water but do not prioritize the quality and the sources of the water available to them. Public health scholars endorse education, training, and sanitary infrastructure to promote awareness and importance of water quality. To address an existing waterborne disease crisis in a school in Kenya, the researcher conducted a needs assessment and tested the applicability and modified a water purification technology (WaterPOD) along Menomonee River in WI. Upon success of the simulation, the researcher used the Health Belief Model, Diffusion of Innovations, and Assessment, Design, Implementation, and Evaluation model (ADDIE) of instructional design to develop a water health-training program, which she later implemented and evaluated in 5 locations in Kenya. The results showed that the training program increased public awareness, perceived severity of waterborne diseases; water treatment and source protection. It also increased self-efficacy on household water health and sanitation