Water treatment plant

Plant Optimiser

Predictive water treatment optimisation software combining deterministic modelling and machine learning to improve the performance of municipal and industrial water treatment plants.

Plant Optimiser

Deterministic modelling and machine learning

Deterministic modelling is a digital simulation approach based on the physics and chemistry of a treatment plant – the principles upon which engineers would typically design a plant.    

Machine learning is a branch of artificial intelligence and computer science which uses algorithms and statistical models to draw inferences from patterns in data, to ‘learn’ how the plant operates and deliver accurate near-term forecasting.

PRODUCT APPLICATIONS

Make the right decision at the right time to ensure a safe, reliable and efficient operation.

Uniquely powered by deterministic modelling and machine learning, Envirosuite’s Plant Optimiser helps operators real-time monitor and predict plant performance.

By quickly adapting operational settings to specific circumstances such as changes in feed water quality or quantity, operators can effectively mitigate risks, improve efficiency, reduce chemical and energy costs and extend the life of their assets while maintaining water quality.

Drinking water

Drinking water is essential and critical in our daily life. Ensuring safe and reliable water supply is the primary mission of treatment plant operators.

As feed water variability can be a pressing, Plant Optimiser enables operators to optimise chemical dosing quickly and predictively, adapting to feed water changes to achieve consistent and reliable water quality while driving down operating costs.

Desalination

While desalination is a way to lessen the stress of water scarcity, the water and energy nexus, along with operating costs, is a constant challenge for water utilities using this technology.

By recommending the most efficient operating modes, Plant Optimiser helps reduce energy and chemical costs, reducing membrane fouling, and optimise maintenance cycles, reducing both operating and capital costs at desalination plants.

Industrial process water

Improving water conservation and environmental sustainability has placed an importance on the optimisation of industrial water treatment processes. Many companies in oil & gas, chemical processing, mining, power generation, etc. started to implement solutions to ensure no discharge of industrial wastewater into the environment.

Plant Optimiser can forecast and manage environmental risks and recommend the most efficient operating and dosing strategy to achieve your zero liquid discharge objective while delivering cost savings.

Inside the product

Distilling complex data into highly accurate, simple and daily recommendations

EVS Water Plant Optimiser enables efficient communication and rapid and confident decision making across different levels of the organisation.

EVS Water Plant Optimiser runs plant-wide simulation

Run plant-wide simulation

Plant-wide simulation and forecasts of feed water quality, flow, process chemistry, material balances and process performance.

Plant Optimiser's daily automatically generated email advice

Daily automatically generated email advice

The key setpoints needed to drive down operating cost and improve water quality are provided automatically by email to plant operators and engineers.

Add scenarios with “hypothetical” data

Assessment of possible outcomes for the modelled influent conditions allowing potential risks to be identified.

EVS Water Plant Optimiser

Hour-by-hour forecasts of optimum plant settings for up to 24 hours in advance

Every day, or every hour, Plant Optimiser automatically considers business-as-usual and alternative operating scenarios, up to 24 hours ahead, and identifies the most efficient mode of operation for your plant.

Customer service

World-class service

Operational and design data can be taken from the existing monitoring network or process monitoring systems.

On-site or web-based training is provided to every customer as part of subscription fees.  

Ongoing support is provided via our 24-hour global support portal, which includes in-house water industry experts to guide you in the most efficient use of the system.

Getting started

1

Talk to us

Our team of specialists can answer any questions you have about the design, operational or environmental challenges that you are experiencing.

2

Solution design

Let us map out your priorities to understand challenges you are facing and help you select the best solution for your needs.

3

Implement

We will work with you to implement the best solution, demonstrate performance and ensure the foundations are set for a successful ongoing collaboration.

Ready to talk?

FAQs

Here are answers to common questions about Plant Optimiser. Reach out to our team today if you don't see the answer you are looking for.

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 recurrent neural networks 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.

Although technically feasible to connect with DCS/SCADA systems ​​​it is recommended that data for Plant Optimiser are obtained from the data historian rather than the DCS/SCADA system. Latency between DCS/SCADA systems is not typically of concern in applications of this type, which require approximately hourly resolution in data.

Further DCS/SCADA connected applications are typically associated with much longer timeframes associated with permissions and policies related to accessing the data stored there. Data historians (e.g. PI) are designed to interact with a wide variety of stakeholders including 3rd party vendors and we are confident that one of our three preferred methods for data management (code to Envirosuite’s API, ftp transfer to a WSD site, or ftp transfer to an Envirosuite site) will be accepted. Alternatives to this design will require a variation to the scope of work in this proposal.

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.

1. Forecast water quality for each nominated feed stream
2. Forecast dosing requirements
3. Forecast energy requirements
4. Simulated forecast of the treatment operation as per configured digital twin
5. Daily advice of operating setpoints to achieve operational and environmental targets
6. Red flags at critical control points / operation outside equipment guidelines

The Plant Optimiser solution is designed to cover the whole water or wastewater treatment process and provide recommendations related to the operating and environmental objectives at that particular plant. Forecast recommendations are then focused on particular unit operations likely to deliver the greatest savings or best improvements in water quality. Having said this, key dollar savings typically relate to energy consumption or chemical/consumable consumption. 

The system is currently an advisory system and does not connect directly to devices. Recommendations still need to be manually implemented by the operating team. Any unauthorised personnel would not have remote access to the customers' chemical control systems.

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