Digital twins for process optimization in water treatment plants

introduction

Water management is facing unprecedented challenges in the 21st century. With projections that 36% of the world's population lives in regions with water scarcity, a figure that could reach 52% by 2050, the optimization of water treatment plants has become a global strategic priority. In this context, digital twin technology emerges as a transformative solution that allows complex physical systems to be virtually replicated to optimize their operation in real time, reduce operating costs and improve environmental sustainability (Emeka & Chikwendu, 2025), (Editorial Portal ERP Colombia, 2025), (WEF, 2024).

Digital twins represent a significant evolution compared to traditional control and supervision systems. While conventional SCADA (Supervisory Control and Data Acquisition) systems are limited to reactive monitoring, digital twins integrate real-time data with predictive models based on artificial intelligence and machine learning, allowing proactive and adaptive management. This capacity is particularly relevant for Mexico, where more than 35 million people lack sufficient and reliable access to water (Idrica, 2024), (Larsen, 2025), (Smart Water Magazine, 2024), (Yager, 2025).

The implementation of digital twins in treatment plants can achieve reductions of up to 30% in energy consumption and improvements of 35-60% in hydraulic capacity, without the need for costly physical expansions. These figures are especially important in a country such as Mexico, where energy costs represent up to 40% of the operating expenses of treatment plants (Larsen, 2025), (Yager, 2025).

Definitions

Conceptualization of the digital twin

The concept of a digital twin is rooted in the pioneering work of Professor Michael Grieves, who in 2002 introduced the conceptual model at the University of Michigan in the context of product lifecycle management (Product Lifecycle Management, PLM) (mentioned in (Grieves, 2005). Subsequently, NASA formalized the term in 2010, expanding its application to aerospace and mission critical systems (Liu et al., 2023), (Singh et al., 2021).

The most consolidated academic definition, proposed by (Liu et al., 2023), states that a digital twin comprises five fundamental dimensions:

1. The physical entity.
2. The virtual model.
3. The data of the twin (Twin Data).
4. Bidirectional connections, and
5. The services enabled by the system.

This five-dimensional architecture provides a robust framework for implementation in diverse industrial sectors (Liu et al., 2023), (Wang et al., 2024).

In the specific context of water management, (Wagg et al., 2025) define the digital twin as “a virtual representation of a natural, engineering or social system that allows bidirectional coupling between digital and physical domains, using network-based connectivity”. This definition emphasizes the dynamic and co-evolutionary nature between the physical system and its virtual counterpart, clearly distinguishing it from traditional static simulations (Wagg et al., 2025).

Essential architectural components

The architecture of a digital twin for water treatment plants integrates several technological components (Liu et al., 2023), (Emeka & Chikwendu, 2025), (Wang et al., 2024):

• Physical layer: It comprises the actual treatment infrastructure, including filtration processes, chemical dosing, aeration systems and sludge management. This layer is equipped with IoT sensors that capture real-time operational data on parameters such as flow rates, pressure, pH levels, turbidity, dissolved oxygen and pollutant concentrations (Dellel, 2025), (Dwarakanath and others, 2023).
• Data Acquisition Layer: SCADA systems, advanced IoT sensors and continuous measurement devices transmit data from the physical plant to cloud storage platforms. The refresh rate can reach intervals of one second, allowing for almost continuous monitoring (Rika Sensor, 2025).
• Virtual modeling layer: Uses artificial intelligence (AI) and machine learning (ML) algorithms to process historical and real-time data, generating predictive models that simulate system behavior under different operating scenarios. These models incorporate computational physics, hydraulic and kinetic analysis of biochemical processes (Emeka et al., Digital Twins in Wastewater Treatment Plants: A Real-Time Optimization Framework, 2025) (Emeka & Chikwendu, 2025).
• Service layer: Provides visualization interfaces, decision support systems, predictive alerts and automated recommendations for the optimization of operating parameters (SWAN, 2022), (Wang et al., 2024).

Differentiation with previous technologies

Unlike traditional modeling and simulation systems, digital twins maintain continuous synchronization with the physical system through bidirectional feedback loops. This feature allows changes in the physical environment to be automatically reflected in the virtual model, and vice versa, facilitating the constant validation and dynamic adjustment of parameters (SIMIO STAFF, 2025), (Wagg et al., 2025).

(Wang et al., 2024) establish that digital twins in the water sector transcend mere virtual replication by integrating prescriptive analysis capabilities that not only predict future behavior, but also recommend optimal actions to achieve specific operational objectives (Wang et al., 2024), (Liu et al., 2023).

Foundations of the principles of use

Technological integration and data architecture

The effective operation of a digital twin for water treatment plants is based on the symbiotic integration of multiple enabling technologies. The architecture proposed by (Emeka & Chikwendu, Digital Twin Technology in Water Treatment: Real-Time Process Optimization and Environmental Impact Reduction, 2025) identifies four essential technological pillars:

1. Internet of Things (IoT) and intelligent sensors: The implementation requires a distributed network of sensors capable of measuring critical water quality parameters (pH, turbidity, conductivity, dissolved oxygen, residual chlorine) and operational variables (pressure, flow, energy consumption, temperature). These devices transmit data through communication protocols such as MQTT, AMQP or CoAP to centralized platforms (Dwarakanath et al., 2023), (SIMIO STAFF, 2025), (Dellel, 2025), (Rika Sensor, 2025).
2. Cloud computing and distributed storage: Massive volumes of data generated by IoT sensors require scalable storage and processing infrastructures. Cloud platforms (Cloud) such as Microsoft Azure, AWS or Google Cloud allow the deployment of intensive computational models and ensure the continuous availability of the system (Data Intelligence, 2025), (Emeka & Chikwendu, Digital Twin Technology in Water Treatment: Real-Time Process Optimization and Environmental Impact Reduction, 2025).
3. Artificial Intelligence and Machine Learning: Supervised learning algorithms (artificial neural networks, Random Forest, Support Vector Machines) and unsupervised make it possible to identify complex patterns, predict equipment failures and optimize treatment processes. Research by Chen et al. (2025) demonstrates that hybrid models that combine computational physics with ML reduce the average absolute error (MAPE) to 20.1% in predicting effluent quality (Saunders, 2024), (Barah et al., 2025), (Aparna et al., 2024), (Chen & Kao, 2025).
4. Advanced visualization and geospatial twins: The integration of geographic information systems (GIS) with BIM (Building Information Modeling) allows the creation of dynamic three-dimensional representations of infrastructure, facilitating the management of the entire life cycle of assets (KWR Water Research Institute, 2025), (Mercier, 2021).

Fundamental Operational Principles

Digital twins operate through three fundamental mechanisms established by (Grieves, 2005) and complemented by recent research (Singh et al., 2021), (Liu et al., 2023):

• Real-time monitoring: The system continuously collects operational data from the physical plant, updating the virtual model with minimum latencies (typically less than 5 seconds). This capability makes it possible to detect operational anomalies, effluent quality deviations or energy inefficiencies at early stages, before they escalate to critical failures (ATT Metrology Solutions, 2025), (Larsen, 2025), (Emeka & Chikwendu, Digital Twin Technology in Water Treatment: Real-Time Process Optimization and Environmental Impact Reduction, 2025).
• Predictive simulation and scenario analysis: The virtual model allows simulations of multiple operational scenarios to be executed without interrupting physical operations. For example, it is possible to evaluate the impact of modifying chemical dosage rates, aeration times or recirculation flows on effluent quality and energy consumption, identifying optimal configurations before implementation (Emeka & Chikwendu, Digital Twin Technology in Water Treatment: Real-Time Process Optimization and Environmental Impact Reduction, 2025), (Dalkvist, 2014).
• Autonomous optimization using AI: Optimization algorithms, such as Particle Swarm Optimization (PSO) integrated with deep learning, automatically adjust operational parameters to maximize treatment efficiency while minimizing costs and emissions. This capacity transcends the limitations of human cognitive capacity to simultaneously manage dozens of interdependent variables (Chen & Kao, 2025), (Larsen, 2025).

Predictive maintenance and asset management

A critical application of digital twins is predictive maintenance based on continuous monitoring of the condition of critical equipment (pumps, valves, membranes, aerators). (Wang et al., 2024) document that implementing predictive maintenance using digital twins can reduce downtime by up to 30% and maintenance costs by up to 25% (Wang et al., 2024), (WEF, 2024).

Predictive models analyze patterns of vibration, temperature, energy consumption and operational efficiency to identify early signs of degradation or imminent failure. This capability allows maintenance interventions to be scheduled during planned operational windows, minimizing unscheduled interruptions and extending the useful life of assets (Discovery Alert, 2024), (ATT Metrology Solutions, 2025), (Saunders, 2024).

Energy Efficiency and Sustainability

The water treatment sector is highly energy intensive, with pumping and aeration accounting for up to 60% of total energy consumption. Digital twins optimize these processes through (Emeka & Chikwendu, Digital Twin Technology in Water Treatment: Real-Time Process Optimization and Environmental Impact Reduction, 2025), (Dalkvist, 2014), (Yager, 2025):

• Determination of optimal pumping times considering variable electricity rates in real time.
• Dynamic adjustment of aeration intensities based on oxygen demands measured in real time.
• Minimization of unnecessary recirculation flows.
• Optimization of backwash sequences in filtration systems.

The company Veolia reports 35% reductions in electricity consumption and simultaneous improvements in nitrogen removal through the implementation of its Hubgrade platform based on digital twins, cf. (Larsen, 2025).

Challenges and opportunities for their application in industries/businesses in Mexico

Technical and operational challenges

The adoption of digital twins in the Mexican water sector faces several obstacles that require specific mitigation strategies:

• Data quality and availability: The effectiveness of a digital twin critically depends on the availability of high-quality and sufficiently granular historical data. Many treatment plants in Mexico lack adequate sensors or maintain disconnected systems that make it difficult to integrate data. (Conejos, 2022) identifies that “the insufficient quality of the data and its location in isolated systems that are difficult to connect” constitutes a significant barrier to the penetration of digital twins (Conejos, 2022), (VirtualPro, 2022), (SWAN, 2022).
• Legacy technological infrastructure: Integrating digital twins with obsolete SCADA and DCS (Distributed Control Systems) systems represents a significant challenge. Many facilities operate with equipment that dates back decades, designed without considering modern cybersecurity requirements or digital interoperability capabilities. The modernization of this legacy infrastructure requires substantial investments and gradual migration strategies to avoid operational interruptions (AquaTech, 2025), (Sustainability Directory, 2025), (KWR Water Research Institute, 2025).
• Digital Competency Gap: The successful implementation of digital twins requires multidisciplinary teams that combine experience in process engineering, data science, software architecture and cybersecurity. Various sources such as the Report on the Future of Employment 2025 (WEF, 2025), (IMCO, 2025), (Martínez, I., 2025), (ManpowerGroup, 2025) highlight that Mexico faces a significant shortage of professionals trained in intersectoral areas related to Information Technology (IT), data analysis and engineering. “In Mexico, 70% of employers have difficulty finding the profiles they need. Globally, the shortage of talent is most felt in medium-sized companies and the same is true in Mexico (73%)”, cf. (Manpower Group, 2025).
• Implementation costs: While the long-term benefits justify the investment, the initial costs for Industrial Internet of Things (IIoT) infrastructure development, software licensing, system integration and staff training can be prohibitive for small and medium-sized operators. The market for digital twins in Mexico reached USD 280.8 million in 2024, but access remains concentrated in large companies and metropolitan services (imarc, 2024). (OpenPR, 2025)..

Cybersecurity Risks

The interconnectivity inherent in digital twins significantly expands the cyberattack surface. INCIBE (2024) identifies three main categories of threats:

• Data manipulation: Malicious actors could alter sensor data or parameters of the virtual model, causing erroneous operational decisions that compromise the quality of treated water or the integrity of equipment.
• Infrastructure vulnerabilities: Traditional industrial communication protocols (Modbus, DNP3, OPC) often lack robust authentication and encryption mechanisms, facilitating interception or impersonation attacks.
• Denial of service attacks: Interrupted connectivity between the digital twin and the physical system could degrade monitoring and control capabilities, forcing suboptimal manual operations.

The implementation of specific cybersecurity frameworks, such as the one proposed by Homaei et al. (2025) through virtual cybersecurity departments that integrate tools such as Zabbix, Suricata and ML-based intrusion detection systems, is essential to mitigate these risks.

Strategic opportunities for Mexico

Despite the challenges, Mexico presents favorable conditions for the adoption of digital twins in the water sector:

• Driving regulatory framework: The National Water Plan 2024-2030 establishes digitalization and technological modernization as strategic pillars, with an emphasis on the optimization of water concessions and the modernization of irrigation districts. The Agency for Digital Transformation and Telecommunications and the Mexican Institute of Water Technology play key roles in promoting technological innovations.
• Growing private investment: Microsoft's announced investment of $1.3 billion over three years to improve cloud computing and AI infrastructure in Mexico catalyzes the development of technological capabilities needed for digital twins. Companies such as Idrica, Xylem-goaigua and Veolia are already implementing advanced solutions in Mexican cities.
• Proven success stories: The implementation of Xylem Vue in Monterrey Water and Drainage Services (SADM) achieved water savings of up to 37% under extreme drought conditions through intelligent monitoring and ML algorithms, demonstrating technical viability and return on investment. Similar projects in León, Ciudad Juárez, Querétaro and Saltillo show the increasing penetration of these technologies.
• Climate and water emergency: The 2022 drought in Nuevo León, which forced the declaration of a state of emergency, underlines the critical need for advanced tools to optimize limited water resources. Digital twins offer essential scenario planning capabilities and resilience to extreme weather events.
• International technology transfer: The European experience in water digitalization, exemplified by operators such as Veolia in Saltillo and Suez in other cities, facilitates the transfer of knowledge and best practices to Mexico.

Trends in managing digital twins for critical sectors

Oil and Gas Sector

The oil and gas sector represents one of the most advanced adopters of digital twin technology globally, with the market reaching USD 1.2 billion in 2024 and projections of USD 2.81 billion by 2032 (CAGR 11.9%).

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Global trends applicable to Mexico: BP implements its APEX digital twin system in operations in the Gulf of Mexico and the North Sea, allowing engineers to simulate production systems, optimize operations and predict future scenarios, resulting in increases in efficiency and production. Halliburton, in partnership with Microsoft Azure, developed integrated platforms that combine its iEnergy and Open Subsurface Data Universe with cloud-based analytics for real-time decision support.

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Mexican context: Petrleos Mexicanos (Pemex) experienced a historic increase in its refining capacity during 2024, achieving growth of 100.31% compared to 2018. This expansion is leveraged by digitalization through:

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Digital twins for refining processes: Metso developed the Geminex digital twin for real-time monitoring and optimization of critical processes such as crushing, milling and floating. Rockwell Automation presents high-definition and precision 3D models that replicate refining facilities.

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Augmented and virtual reality integration: Mexican companies incorporate 3D scanners and remote assistance glasses for staff training and facility supervision, improving safety and operational efficiency.

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Implementation challenges: Despite technical viability, the industry faces high initial investment barriers and cybersecurity upgrade needs, although the Olmeca refinery represents a successful implementation model.

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Energy Sector

The Mexican energy sector is undergoing accelerated digital transformation, with the domestic market for digital twins projected to reach USD 2,834.3 million by 2033 (CAGR 26.01%).

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Generation and distribution applications: Digital twins optimize power plant operations (hydroelectric, thermal, wind, solar) and distribution networks through:

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Simulation of operating scenarios to maximize efficiency and reduce costs

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Predictive maintenance of turbines, transformers and critical equipment

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Optimizing energy storage and demand management

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Integration of renewable sources through availability prediction based on meteorological data

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Sustainability and emission reduction: Digital twins contribute significantly to decarbonization objectives by optimizing processes to reduce energy consumption and greenhouse gas emissions, making it easier to meet climate commitments.

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Emerging trends: Kyndryl identifies that oil and gas companies are deploying digital twins to forecast production rates and simulate operations, a trend that extends to the wider energy sector, including the management of intelligent power grids.

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Mining Sector

Mining represents a sector of increasing adoption of digital twins, with the global market projected from USD 14.78 billion in 2023 to USD 240.11 billion by 2032.

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Documented Mexican cases: Innomotics reports implementations of digital twins in mines in Zacatecas and Sonora, which are currently operational. These implementations include:

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Virtual replicas of mining sites for predictive modeling and simulation

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Optimization of mineral transport routes

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Analysis of the impact of different operations on energy consumption

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Structural stability monitoring using distributed sensors

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Operational benefits: Nobahar et al. (2024) document that fully integrated digital twins significantly improve mining efficiency and sustainability by:

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Mineral processing automation and optimization

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Predictive analytics for proactive problem identification

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Reducing risks associated with capital investments in equipment

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Improving occupational safety through risk simulations

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Future trends: The integration of machine learning algorithms with AI for autonomous decision-making, combined with augmented reality for operational visualizations in real time, represents the frontier of development.

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Transport Sector

The global market for digital twins in transportation reached USD 1.9 billion in 2024, with a projection of USD 12.4 billion by 2033 (CAGR 22.5%).

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Railway infrastructure: Mexico develops significant capabilities in railway digitalization:

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Deutsche Bahn (DB) and Stadler develop complete digital twins for train fleets, allowing predictive maintenance that improves reliability

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BIM-GIS integration using IFC Rail standards facilitates efficient lifecycle management of railway infrastructures

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Indra implements the In-Mova Rail platform, a railway traffic management system based on automation, replanning and digital technologies for operational optimization

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Railway IoT network: Secure wireless communication developments interconnect devices installed in infrastructure and trains with control centers and cloud platforms, allowing for reduced safety distances between trains and virtual docking, increasing capacity and frequency.

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Road transport and logistics: Digital twins facilitate:

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Optimized fleet management through real-time monitoring

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Maintenance prediction to minimize downtime

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Optimizing supply chains

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Virtual design and testing of electric and autonomous vehicles

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Mexican automotive market: The automotive sector adopts digital twins for design, manufacturing and after-sales services, with growth driven by IoT and connected technologies.

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Applicable Use Cases in Mexico

Optimization of Urban Distribution Networks

Case: Monterrey Water and Drainage Services (SADM)

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The implementation of the Xylem Vue platform powered by GoAigua in Monterrey represents the most documented success story in Mexico. The system integrated:

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Hydrometric instrumentation in 2,864 circuits and 111 macrosectors

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Development of dashboards for visualizing key operational variables

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Deploying Machine Learning and AI algorithms for pressure optimization

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Leak detection system using advanced analytics

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Measurable results: Global water savings of 17%, reaching up to 37% in specific sectors through intelligent control of pressure, flow and consumption. The project is part of SADM's 2050 Water Supply Assurance Master Plan, serving a metropolitan population of 5.3 million inhabitants.

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Case: Metro Water Services, Nashville (applicable international reference)

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Although located in the United States, this case offers lessons that are directly transferable to the Mexican context. The distribution system comprises more than 4,800 kilometers of main pipes, two treatment plants processing 34,000 m³/day and 56 pumping stations.

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The implementation of digital twins using GoAigua technology allowed:

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-Identifying water age problems in specific reservoirs through real-time data analysis

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-Precise operational settings (reduction of one meter in the lower load limit) that stabilized water quality

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-Immediate validation of the effectiveness of changes through continuous monitoring

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-Management of Wastewater Treatment Plants
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Concept case: Tecnische Universität Berlin (TU) - Applicable to Mexico

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The TU Berlin developed a digital twin for a pumping station in its Water 4.0 facilities, demonstrating transferable capabilities to the Mexican context:

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-Partially autonomous system for detecting and eliminating blockages in pumps before obstructing flow

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-Reduction of maintenance time up to 30% and costs up to 25%

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-Integration with advanced controls, sensors and actuators for predictive management

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Mexican applicability: Mexico has treatment infrastructure for 99.99% of wastewater in metropolitan areas such as Monterrey, creating an installed base that can be improved through digital twins.

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Case: Changi Water Reclamation Plant, Singapore (best practice reference)

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Chen et al. (2025) document this implementation as a valuable reference:

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Intelligent management through real-time monitoring and data analysis

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Replication of plant operations in a digital model with predictive capacity

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Integration of process control, hydraulics, advanced analytics and operational testing

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Prediction of treated water quality 3 hours in advance, allowing proactive adjustments

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Early Detection of Contaminants and Crisis Management

Case: SARS-CoV-2 analysis in wastewater (Mexico)

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Idrica implemented its GoAigua SARS Analytics solution in Mexico during the pandemic, demonstrating the capacity of digital twins for early detection of viruses in wastewater, facilitating informed decisions by local authorities. This application demonstrates the versatility of the technology beyond traditional operational optimization.

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Case: Contamination of Mexico City wells (2024) - Negative lesson

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The contamination of wells in Mexico City in 2024, neither detected nor addressed by authorities until the multiplication of citizen complaints, underlines the urgency of continuous monitoring and early warning systems enabled by digital twins.

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Energy Optimization and Sustainability

Concept Case: GEDAI Project (Spain) - Transferable methodology

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The Technological Institute of Energy developed a methodology for rapid prototyping of digital twins to optimize productive performance, energy consumption and carbon footprint in industrial wastewater generation companies. Key elements transferable to Mexico:

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Standardized approach to democratize access to technology regardless of organizational particularities

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Rapid deployment methodology that reduces initial investment and implementation times

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Process virtualization for designing optimization scenarios

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Predicting behaviors and impact of measures before physical implementation

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Smart Agriculture and Irrigation

Mexican cases in development: Pecero (2025) documents AI-based irrigation management initiatives in:

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Calera Aquifer, Zacatecas

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Moctezuma (Valley of Mexico), Lerma and Bajo Rio Bravo basins

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These implementations provide users (mainly farmers) with access to digital platforms and intelligent irrigation tools based on climate and satellite data, optimizing the use of scarcer water resources.

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Water Infrastructure Resilient to Climate Change

Case: Canal de Isabel II (Spain) - Geospatial digital twin

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Bañales González from Esri presented a pilot project with Canal de Isabel II for comprehensive water cycle management, using ArcGIS as the base platform. Key components:

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Geospatial digital twin integrating diverse information (sanitation networks, real-time information from reservoirs via SCADA)

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Connectivity with catchment towers and control systems for real-time information on the state of reservoirs

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Ability to simulate extreme hydrometeorological event scenarios

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Applicability to Mexico: With 89% of Latin Americans concerned about the environment and 90% concerned about access to clean water, solutions of this type are strategically aligned with Mexican social and environmental priorities.

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Conclusions

Digital twin technology represents a transformative evolution for water treatment plant management, offering unprecedented capabilities for operational optimization, energy efficiency and environmental sustainability. International evidence shows reductions of up to 30% in energy consumption and improvements of 35-60% in hydraulic capacity without costly physical expansions, results that are particularly relevant for Mexico where more than 35 million people lack sufficient access to water.

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Digital twins transcend the limitations of traditional SCADA systems by integrating real-time monitoring with predictive models based on artificial intelligence, allowing proactive rather than reactive management. Its five-dimensional architecture—physical entity, virtual model, twin data, bidirectional connections, and services—provides a robust framework validated by international standards such as ISO/IEC 30186 and ISO 23247.

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For Mexico, implementation challenges include digital skills gap, legacy technological infrastructure, high initial costs, and cybersecurity risks. However, strategic opportunities emerge from the National Water Plan 2024-2030, significant private investments (USD 1.3 billion from Microsoft over three years), and demonstrated success stories such as Monterrey Water and Drainage Services with savings of up to 37% under extreme drought conditions.

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Sectorial trends show accelerated adoption in oil and gas (Pemex with digital twin Geminex), energy (a Mexican market projected at USD 2,834.3 million by 2033), mining (implementations in Zacatecas and Sonora), and transportation (In-Mova Rail platforms for railway management). This multisectoral convergence creates an ecosystem conducive to knowledge transfer and technological synergies.

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Applicable use cases range from optimization of urban distribution networks and management of treatment plants, to early detection of pollutants, energy optimization, intelligent irrigation and climate resilience. The diversity of applications underlines the versatility of the technology to address multiple dimensions of the Mexican water crisis.

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Successful implementation requires a holistic strategy that integrates technological modernization, competence development, enabling regulatory frameworks, and robust cybersecurity protocols. Collaboration between government (CONAGUA, Mexican Institute of Water Technology), private sector (Idrica, Xylem, Veolia), academia and international institutions will be essential to democratize access to this transformative technology.

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Ultimately, digital twins are not merely a technological innovation, but a strategic tool to ensure water security, environmental sustainability and resilience to climate change in Mexico and Latin America. Its accelerated adoption represents not only an opportunity, but an imperative need to face the water challenges of the 21st century.

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