Water Trends in automation for 2023: Improving operability and management 

The water sector is set to play a pivotal role in combating climate change, with enhanced water management benefiting a whole host of areas such as health, energy, agriculture, food security and people’s livelihoods in general (UNESCO).

Process automation is one of the most important trends for 2023 and is one of the promising alternatives hailed to improve the current water scenario. Comprehensive data integration in innovative, vendor-agnostic technological solutions is the first stepping stone towards more proactive, automated water-cycle management.

Automation brings significant benefits to water utilities, such as better water cycle operability and management, cost reductions, and an increase in customer perceived value. According to Global Water Intelligence’s Accelerating the digital water utility whitepaper (2019), the ROI from automating operations can be as high as 14%.

Integrated water cycle management encompasses the complex task of managing the systems and processes involved in urban supply, resource reuse, sanitation and irrigation. Automation optimizes the use of water in all of these phases.

Automation

Extreme events

Droughts, storms and floods, all of which are water-related phenomena, top the list of catastrophes recorded over the past 50 years. For this reason, more and more water utilities are turning to intelligent technological solutions to reduce the damage and operational costs caused by these episodes. These solutions can predict and provide early warnings about these events thanks to historical knowledge and data integration.

These solutions identify potential incidents based on exceeded thresholds by installing sensors, analyzing historical data and using meteorological and hydrological prediction models. This internal and external data, combined with mathematical models, can be used to perform analyses aimed at simulating scenarios and supporting decision-making. Automation is a key element of early warning systems, which bring returns on investment of up to 10 times their cost.

DWTPs

In 2023, and in the coming years, drinking water treatment plants will move towards automated management, improving plant operations.

Digital transformation paves the way to centralized data management as opposed to the control of different DWTP processes in isolation. In advanced predictive control, the different algorithmic models learn and solve issues by predicting and optimizing processes in the DWTP, and automate them without human intervention.

Use cases include predicting the quality of the water collected, automating coagulation dosing, simulating the properties of stored chemicals, monitoring decanters, and optimizing filtration and pumping.

In addition, in the future we will see progress being made in the detection of potential events in supply networks, from the time water leaves the treatment plant and/or reservoir to the final point of consumption, including incidents that may pose a threat to the population. This will be achieved by automating and monitoring the main water quality parameters guaranteeing optimum cleanliness and hygiene conditions for safe consumption. Thus, water sanitation will be broadened to include other criteria over and above viruses and bacteria (SARS-CoV-2, Legionella, etc.).

The first step to achieve this is to bring together the myriad of technologies and operations that exist in DWTPs. Digital transformation based on specific needs, without being part of an overall strategic plan, has led to widespread fragmentation of sources and data in drinking water treatment plants. Accordingly, digital platforms must be brought in to integrate and analyze dispersed data, breaking down operational silos, moving towards centralized, automatic plant control, and facilitating decision-making.

WWTPs

Wastewater treatment plants (WWTPs) are facing new challenges that test their resilience, requiring progress in sensors, digital transformation, and automated decision-making. More stringent quality requirements for plant effluent and sludge quality, together with demands to reduce greenhouse gas emissions, odors and noise, are making WWTP management more complex.

In this context, Industry 4.0 opens up new opportunities in terms of improved connectivity and operations, boosting work centralization. Installing sensors in infrastructure and subsequently integrating internal data (LIMS, CMMS, SCADA, field devices, etc.) and external data (meteorological data, social networks) will facilitate the automation of WWTP processes.

The use of algorithms and mathematical models applied to this data will provide operators with recommendations on what actions they need to carry out next, anticipating potential hurdles and optimizing plant processes. Likewise, the tendency is to implement systems that can automatically trigger any actions required in the plant, resulting in operational improvements, robustness and cost reductions

Smart irrigation

Automating irrigation in farming is another of the trends for 2023, aiming to reduce its water footprint, promote environmental sustainability and optimize costs.

The starting point is smart irrigation, which means watering crops according to their real needs, taking into account soil types and moisture, climate conditions and weather forecasts. Technological solutions indicate when and how much water is required based on remote sensing and information from sensors installed in the fields to calculate the water balance.

However, the digital adjustment of irrigation scheduling goes one step further, with more and more irrigation associations automating this process. One of the main advantages is enhanced environmental sustainability, since streamlining water consumption also reduces the amount of energy used.

To achieve this aim, all network assets must have sensors installed so they can be monitored. In 2023, irrigation associations are expected to digitally transform their infrastructure and introduce new technological solutions to exploit data in the interests of greater sustainability.

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