Introduction
We are flooded with data regularly as a result of improved semiconductors, sensors and networking.
While data accumulation shows no signs of decreasing, our ability to act on this information is limited by our ability to analyze it quickly.
Unlocking solutions for improved performance, efficiency and speed will remain a challenge in the absence of guidance.
Machine learning—a set of techniques and software that allows computers to learn from data—can assist us in overcoming these obstacles.
Machine learning has a wide range of potential applications in data-rich areas such as water and wastewater management.q
What is Machine Learning?
Machine Learning is a mathematical approach for learning models from data that is stored in software algorithms.
These models (constructs or machines) are data-driven models that can discover patterns and correlations, identify deviations and anomalies (including events), develop rules and linkages, and so on.
Machine-learning techniques improve data processing and pattern identification, allowing the creation of predictive models that can be used to evaluate the design and behavior of parts and processes.
Unlike traditional modeling approaches, machine-learning models are not directly built by humans and are capable of changing when exposed to new training data.
These skills reduce the amount of guesswork involved in problem-solving when utilizing models and can speed up the discovery of relevant insights.
Machine learning in the water industry
The Water industry is like any industry subjected to different varieties.
That required optimizing new technologies and predicting different scenarios that may threaten the sustainability of the utility and an effective solution for these issues.
Machine learning consumes enormous, complicated data sets with more variables than humans can currently process with present technologies.
This objective, data-driven strategy overcomes human constraints such as sensitivity and bias, resulting in more accurate results that assist utilities in making better replacement decisions.
Now the question is What role can machine learning have in the water industry?
Process Design Optimization
Machine-learning algorithms can aid in the design of wastewater treatment processes by expediting process simulation—a vital stage in constructing new plants as well as enhancing the design of existing facilities.
Because of the variety of parameters affecting incoming waste streams, this process is now both time- and computationally intensive.
Because streams can contain a variety of contaminants and toxins, several biochemical, mechanical and environmental elements influence the process and must thus be computed.
Plant Operation and Control Improvement
A more efficient treatment plant can reduce the waste of vital resources such as electricity, which is frequently the single highest running expense for water and wastewater treatment facilities.
One aspect of increasing energy efficiency is accurately estimating water consumption, which is influenced by weather, consumer behavior and other interconnected factors.
Machine-learning algorithms based on sensory and weather data can assist in forecasting when demand will be high or low.
These insights can be used to automate machine controls, such as pumping schedules and time for adding or removing chemicals or microbes.
As a result, more efficient plant functioning reduces wasteful energy consumption.
Machine learning provides solutions
Assisting water utilities in dealing with dramatic changes.
Water scarcity is one of the most pressing issues confronting water utilities today.
Drought severity varies year to year due to a variety of environmental conditions, therefore having a good multi-year prediction might be crucial for long-term decision-making.
Such predictions, for example, can help make the case for investing in water recovery or desalination equipment.
They can also be crucial in assessing whether to strengthen or relax usage limitations, whether a rate increase is required to maintain the present operating budget and other factors.
Unlike depressions, sustained periods of strong economic development can result in a variety of changes in water demand that utilities may need to plan for.
This includes rises in demand as a region’s population and housing growth.
New firms entering a region can also alter demand patterns.
Water is used extensively in industries such as food processing and automobile manufacturing.
Material Exploration and Synthesis
Another area where machine learning may have an impact is in the development of membrane and filtration materials and coatings.
Better materials may be able to address problems that cause process inefficiencies, such as membrane fouling, which occurs when particles interact or attach to membrane surfaces or pores, reducing their performance.
However, discovering new materials and estimating their efficiency is difficult due to the large number of chemicals and reaction pathways involved.
Machine learning can aid in the acceleration of material discovery by building models that identify the properties of new materials at an extraordinary speed.
At leak detection
Acoustic leak detection with IoT sensors is a low-cost method of detecting growing leaks in water distribution networks.
The acoustic system detects and pinpoints leakage events with high sensitivity, but it also catches non-leakage events that may overburden the utility.
Different sounds acquired from the water distribution network by the leak sensor may indicate water loss, a fault with a water meter, or even a hidden connection.
These distinctions can be made by the program.
The more noises it analyses, the more comprehensive its database gets, and the more correctly it can detect the problem, making machine learning a useful solution for avoiding water loss, lowering the cost of pipe and water infrastructure rehabilitation and preventing infrastructure aging.
Power generation optimization
The water sector may profit from decades of refining electricity generation by implementing machine learning.
By removing manual processes Machine learning can provide dozens of predictions based on numerous scenarios in real time, eliminating the requirement for on-site experts.
Today’s technology allows for the creation of models based on the complex features of the customer while also extracting applicable insights from other utilities and water systems around the world.
References
[1] Machine Learning: What Water Utilities Can Learn from The Power Industry [online] Available at: https://www.watercom/doc/machine-learning-what-water-utilities-can-learn-from-the-power-industry-0001
[2] Droughts, Pandemics, Recessions, And More: How Machine Learning Can Help Water Utilities Prepare [online] Available at: https://www.watercom/doc/droughts-pandemics-recessions-and-more-how-machine-learning-can-help-water-utilities-prepare-0001
[3] Machine Learning Makes Water Smarter [online] Available at: https://www.wwdmag.com/smart-water/machine-learning/article/10937665/machine-learning-makes-water-smarter
