Water is a natural resource that is now required for practically every daily activity.
Water, due to its multi-utilitarian character, can be used for a variety of industrial, commercial and non-commercial applications.
The irony of water is that it is both the most abundant and the scarcest resource in nature.
According to current trends in human water consumption and disposal, 47% of the world’s population would be living in areas of significant water stress by 2030.
The regions that are already facing water stress will confront further acute water stress, and most governments and local civic organizations will consider access to safe drinking water and suitable sanitation facilities.
At this point, “Water Innovations,” or better methods of water management and treatment, are thus the main concerns for debate on the round-tables of most corporations in the concerned industry.
Water purification and wastewater treatment methods in residential, commercial and institutional settings are now aimed toward conservation and sustainability through the use of modern technologies.
The new drinking water treatment plants (DWTPs) management developments intend to address a prevalent issue in it, such as process fragmentation, which is affected by the use of various technologies and operating modes.
The team faces a challenge in upgrading and interconnecting the many SCADAs, which generally redesign and expand new processes, making it difficult to optimize the DWTP.
As a result, and because of the importance of these facilities for the delivery of drinking water in terms of quantity and quality, their operations tend to rely heavily on human resources.
Trends in drinking water treatment management
Model predictive control (MPC) is a widely accepted and useful control strategy for many water treatment applications as a result of its ability to solve the control problem as optimization and establish satisfactory implementation when satisfying operational and safety constraints.
However, MPC is a useless approach to designing local controllers for systems with strong nonlinearities and sudden changes in operating conditions.
Chlorination process control
The specified quantity of chlorine may be modified in line with the statistics amassed through the analyzer.
The next step, superior predictive management, is the connection of the manner and its versions over time.
The system learns and calculates the precise level of every parameter so that their modifications do not depend upon human decision-making.
Coagulation process control
Coagulation is a critical operation process in water treatment plants.
Coagulation is defined by complicated chemical reactions and the coagulation chemical dosing unit exhibits nonlinear behavior.
Thus, the coagulation mechanism is quite difficult.
Also, in the processes of chemical dosing unit, abnormal variants in the water quality and needs, depletion of chemicals stock, system errors and plant operators’ errors often lead to multiple operating regimes resulting in either underdose, normal, or overdose operating conditions.
At present, this form of control is being carried out on a pilot foundation for a few instances of use.
However, the trend is for it to be conventional into typical plant management of the water accrued, mechanizing treating for coagulation, imitating the properties of the chemical stocked, tracking decanters, enhancing filtration and pumping, tracking water as a product and calculating microbiological risks.
For this variation to work, human beings want to accept it as true within automation because of the quality manner to get rid of accidental human error, and as an extra dependable choice than guide measuring.
All relevant data must be integrated and analyzed
To accomplish complete plant control, all data that may have an impact on its functioning must be integrated.
External data such as weather forecasts are examples of this.
This approach may occasionally necessitate manually entering data into the system until the right tools can be introduced.
The integration of all information on a single platform guarantees that everything is in place to automatically make judgments and predict processes, with recommendations for action supplied as needed.
For example, if a storm is forecast within the next 24 hours, operators are notified so that the coagulant dosage can be adjusted before the turbidity increases.
It will then be up to the operator to determine whether to perform these operational strategies manually or to automate their solution.
This new approach to plant management, which is critical for the smooth operation of processes, is powered by advances in mechanical, hybrid and AI methods.
The water industry anticipates that technological solutions that group information and issue subsequent recommendations will be modular, agnostic, interconnected and scalable, allowing them to be customized to cater to the reality of each plant – and sometimes to the reality of more than one.
Threat detection in drinking water treatment
We will observe how systems in the future increasingly take into account the detection of potential dangers to the public via drinking water.
Variable monitoring will help to prevent virus and bacteria-based crises (SARS-CoV-2, Legionnaire’s illness, and so on), as well as any other occurrence that could endanger water security.
Asset lifecycle management of drinking water treatment
Predictions, rather than manufacturer recommendations, are being used to identify the optimal time to replace an asset or change its maintenance schedule, by a shift toward greater sustainability.
Furthermore, new technology solutions will allow corrective actions to be conducted when necessary, resulting in predictive, proactive maintenance, because the system will recognize trends and provide precise recommendations to operators to extend asset life.
References
[1] Bello, O., Hamam, Y., & Djouani, K. (2014). Coagulation process control in water treatment plants using multiple model predictive control. Alexandria Engineering Journal, 53(4), 939-948.
[2] Drewa, M., Brdys, M. A., & Cimiński, A. (2007). Model predictive control of integrated quantity and quality in drinking water distribution systems. IFAC Proceedings Volumes, 40(5), 95-100.
