Bibliometric analysis and systematic review of AI applied to wastewater treatment. Wastewater treatment technology, economy, management, and reuse were discussed. Prediction accuracy of AI technologies on pollutant removal ranged 0.64–1.00. Application of AI technology could reduce operational costs by up to 30 %. Combined AI methods could provide higher accuracy and lower error. Wastewater treatment is an important step for pollutant reduction and the promotion of water environment quality.
Digitization is essential for delivering these centralized water collection services and supporting efficient urbanization. It allows networks to benefit from online connectivity and management platforms that feed on information (Big Data) and can handle data far more effectively than can human operators. Commonly known as Artificial Intelligence (AI) systems, these revolutionary processes have advanced the way wastewater networks can be managed, helping to protect Wastewater Treatment Plants from damage, maximizing process efficiencies and enabling expanded water reuse projects. Digitizing a city's wastewater networks through AI starts with good data. To understand how such a system is able to provide operational wastewater intelligence, it is important to understand Big Data, AI, Machine Learning and their applications.
Streets swamped by muddy water with garbage floating by, roads impassable. As in previous years, Diamniadio Lake City has not escaped the series of floods that affect some cities in Senegal each rainy season. Indeed, this urban centre is preparing to test, thanks to Artificial Intelligence (AI), a new way of managing urban development. "By taking the Digital Technologies Park of Diamniadio as a reference site, we have carried out modelling and worked on water runoff scenarios in order to channel them and solve these flood problems," Bassirou Abdoul Ba, coordinator of the Digital Technologies Park, told Scidev.Net. This park, covering 25 hectares, is the first experimental phase of the "smart city" under construction 35km from Dakar, the Senegalese capital.
Despite its name, wastewater is a critical type of water that must be understood regardless of the source of discharge because freshwater is a limited resource on this planet. As municipalities and wastewater treatment plants grapple with needing to do more with less resources -- and major concerns when it comes to potential fines -- many are starting to realize the immense value of reusing water and driving efficiencies. However, first they need to have a firm understanding of water composition when it comes to reuse--not just from a mandated compliance standpoint but rather based on how the treated water could be used for several other purposes. Reuse comes in many forms -- from drinking to commercial needs like irrigation, golf courses, etc. -- and has the potential to drive additional revenue, optimize operational efficiencies and potentially reduce regulatory limits. Unfortunately, most industries simply have not changed their mindset enough to invest in continuous monitoring to proactively understand what actually is in their water for safe reuse.
The vast networks of buried water, wastewater and storm water infrastructure are the veins and arteries feeding our people, our cities and protecting our environment. Without sustainable and viable water, waste and storm water solutions, our quality of life is in peril. Society's water, wastewater and storm water systems have played significant roles in eliminating disease, the safeguarding the environment and protecting communities. Thanks to substantial post-depression and post-World War II investments, most in the U.S. have grown up without the need to give this infrastructure a second thought. We open the taps and a clean, safe and seemingly unlimited supply of water is available to us; our waste is whisked away, treated and returned to the environment; and storm events rarely interrupt our daily lives.
From leak detection to forecasting usage, artificial intelligence has the potential to completely transform the water utilities sector. A sea change in the way utilities operate is underway thanks to the sector's tech-savvy players, who are adopting artificial intelligence (AI), machine-learning and new data analytics to bring better efficiency and resilience to the water utility value chain. While a wait-and-see approach predominates, those taking the plunge are moving quickly to overhaul utilities from the ground up and drive the understanding and adoption of digital solutions through exciting initiatives. It's about creating value, not just putting forward things thought to be technologically cool Incredible computational power and advanced algorithms are helping to translate data into targeted, actionable intelligence. Meanwhile, the worldwide appetite for AI is projected only to grow.
Throughout history, there have been moments when the progress of technology has taken great steps forward, when a combination of the right tools, a capacity for innovation, and sparks of ingenuity lead to breakthroughs that transform how we live our lives. How we produce and process information is critical to innovation – and our methods of recording and communicating information have themselves undergone great leaps. From the development of writing, to Gutenberg's printing press – which advanced the spread of knowledge to the masses and ushered in the enlightenment and scientific revolution – to the first programmable digital computer Colossus, the cost of reproducing and communicating information, or data, has fallen again and again. At the same time, tools for processing and making sense of large quantities of data have developed exponentially – with artificial intelligence (AI) representing the latest leap. In the same way that Gutenberg's press ushered in a new era of growth, data-driven technologies such as AI will underpin our future prosperity. There is no doubt that machine learning and AI is already improving peoples' lives, from intelligent personal assistants that can prepare us for changes in the weather, to systems that protect our money from criminals, or devices that offer medical advice from the comfort of our own home. And this is only the start; the potential of AI is undeniable. Our next challenge will be to harness this technology to transform how we diagnose diseases, manufacture goods and build our homes. Using advanced algorithmic techniques such as'deep learning', AI has the potential to solve complex problems fast, and in so doing, free up time and raise productivity. But we also need to make sure AI benefits everyone in the UK, which is why – in addition to this Sector Deal – the government is establishing a Centre for Data Ethics and Innovation to advise on the ethical use of data, including for AI. The huge global opportunity AI presents is why the Industrial Strategy white paper identified AI and data as 1 of 4 Grand Challenges – in which the UK can lead the world for years to come.
Municipalities, just like the industrial and commercial sectors, are coming under increased pressure to reduce their energy consumption and outputs, not to mention the need to reduce costs overall. Municipal buildings and services have a huge energy savings potential, which can reduce their overall energy consumption and energy costs. At Maximpact our expert teams have assisted municipalities all over the world to identify their energy saving capacity in various sectors. As cities around the world become more urbanised and populations grow, the pressure of cities to find sustainable solutions to serve their communities is only going to increase. Changes to municipalities in becoming more energy efficient and using artificial intelligence to manage energy resources are part of a global trend of developing smart cities. Smart cities are looking to the future to redefine their energy outputs in cleaner, more sustainable and more cost-efficient ways.
This paper presents a new clustering algorithm for space-time data based on the concepts of topological data analysis and in particular, persistent homology. Employing persistent homology - a flexible mathematical tool from algebraic topology used to extract topological information from data - in unsupervised learning is an uncommon and a novel approach. A notable aspect of this methodology consists in analyzing data at multiple resolutions which allows to distinguish true features from noise based on the extent of their persistence. We evaluate the performance of our algorithm on synthetic data and compare it to other well-known clustering algorithms such as K-means, hierarchical clustering and DBSCAN. We illustrate its application in the context of a case study of water quality in the Chesapeake Bay.