Water is a scarce and valuable resource. Community expectations around water treatment and environmental management continue to increase. Water treatment plants and infrastructure are often decades old and operate inefficiently, while water quality incidents are difficult to predict, costly and damaging to reputation.
EVS Water is a digital twin that combines water modelling and machine learning. It's a powerful solution for engineers, plant operators and asset managers to improve both operational and environmental performance.
EVS Water uses a combination of machine learning and best-practice water modelling approaches consistent with Envirosuite’s philosophy of embedding the world’s leading science in technology that is useful for decision makers.
Our web-based interface provides automated optimisation tools, collaborative change control and audit trail capabilities, significantly speeding up design times for engineering teams.
Improve capital and operational efficiencies with virtual representations to perform digital what-if scenarios of your processes and plant. Know water quality is compliant before releasing it into waterways.
Hour-by-hour forecasts for optimal plant settings over the next 24-hours to achieve operational and water quality goals.
Use the power of water modelling to achieve compliance and manage water treatment assets.
Manage processes from boiler feeds and makeup, cooling water makeup and blowdown, as well as any water-based application used in industrial manufacturing of semiconductors, pharmaceuticals, food & beverage.
EVS Water Products
Advanced water treatment software to unlock value beyond environmental compliance to make confident decisions that optimise operational and environmental outcomes.
Water Modelling Configuration
EVS Water is packed with over 60 different process units for modelling conventional, membrane, ion exchange and thermal treatment processes as well as traditional biological nutrient removal processes. The water treatment software covers more than 60 different parameters measured or regulated in effluent including biological, inorganic, pathogens and micropollutants.Discover EVS Water
Biological nutrient removal, conventional, membrane, ion exchange and thermal treatment processes.
Equipment sizing, operational parameter prediction and stream properties calculation for each unit ops.
EVS Water is suitable for drinking water, industrial and traditional biological processes.
30+ years of process engineering and water treatment design experience embedded in our product suite.
Our team of specialists can answer any questions you have about the design, operational or environmental challenges that you are experiencing.
Let us map out your priorities to understand challenges you are facing and help you select the best solution for your needs.
We will work with you to implement the best solution, demonstrate performance and ensure the foundations are set for a successful ongoing collaboration.
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Backed by Science
EVS Water is designed with Envirosuite’s philosophy of embedding the world’s best science in products that help businesses make better decisions.
Our solutions are focused on delivering practical, easily-understood information to decision makers. Our technology is supported with high-quality modelling and analytics, which is delivered to operators, engineers and asset managers quickly and reliably.
Our suite of Environmental Intelligence solutions are designed to help you address your environmental challenges, expand your operations and build trust with your stakeholders and surrounding communities.
Here are answers to common questions about EVS Water. Reach out to our team today if you don't see the answer you are looking for.
Currently, EVS Water operates only in Amazon Web Services (AWS) infrastructure, as it is tightly coupled to AWS capabilities for machine learning and data science. There are some possibilities for hosting some data locally, but these are highly dependent on specific project requirements, would be considered and would need to be discussed with our data management team.
EVS Water uses recurrent neural networks (RNN) powered by TensorFlow and Keras to calibrate models around a deterministic baseline. These tools have been chosen as they are widely accepted frameworks for applying RNN to challenges requiring identification of patterns in complex data sets. These frameworks are production-focused and integrated into the source code to enable accurate forecasts for EVS Water applications and allow for relatively rapid implementation and processing times. Input features to the RNN may include Feed/Coagulation pH, DOC, UV254, Temperature, Feed/Lamella Overflow Turbidity and coagulant dose, using both existing and a vendor installed instruments.
Sorption Capacity used in the deterministic model is also further calibrated by this RNN model to site specific conditions. The detailed design of the RNN models is developed as a result of close engagement with the customer and site personnel and depends on the design of the plant and available monitoring data.
We use a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. It is also robust to missing data and shifts in the trend, and typically handles outliers well.
The deterministic baseline for coagulation recommendations are based on a number of key models including a modified version of the Edwards (1997) Langmuir based coagulation model which is developed specifically for representing enhanced coagulation using aluminium and ferric salt based coagulants, considered the most accurate of the available models for DOC removal during coagulation (Tseng and Edwards, 1999).
The key inputs for this model are feed DOC [mg/L] and feed/coagulation pH which lead to a prediction of coagulant dose [mg/L], based on a recent inorganic analysis or online alkalinity measurement if available.
PIDs, PFDs, monitoring points, representative monitoring data, dosing locations, equipment datasheets will be required to be shared at the start of the project. Data for nominated monitoring points will need to be shared at least daily during the operational phase of the project with protocols agreed at the start of the project.
To facilitate an evaluation of financial benefits, average unit cost of chemicals, electricity consumption, solid waste disposal and labour cost for the plant will be requested at the start of the project.