Early indications from the project have shown potential savings of £10 million per annum against historic costs.
Thames Water Utilities Limited (TWUL) is the UK’s largest water and wastewater company providing services to over 16 million customers. Thames Water supplies on average 2.6 billion litres of drinking water per day across London and the Thames Valley, and treats an average of more than 4.4 billion litres per day of wastewater.
Given the complexity of end-to-end planning between 350 sewage works to 32 sludge treatment centres and the day to day operational constraints of these sites, Thames Water recognised the need for a Decision Support Tool (DST) that would provide an optimal intersite transport and sludge treatment plan.
Traditionally, Thames Water managed their intersite sludge activities via a central logistics management desk. This used a crude average haulage rate and cost-to-treat to try and optimise the logistics whilst maximising generation potential and minimising overall operational (OPEX) costs. Thames Water recognised that by embedding leading-edge prescriptive analytics into business as usual decision-making, significant operational efficiencies could be achieved.
Thames Water appointed Business Modelling Associates (BMA) to build a sludge supply and demand Decision Support Tool (DST) that provides an optimised, lowest OPEX cost, end-to-end plan of how to store, transport, treat and dispose of sludge.
The sludge optimisation DST provides daily (14 day look-ahead), weekly (12 week), monthly (12 month) and quarterly (5 year AMP period) plans across the full sludge asset base to optimise for lowest operational expenditure. In addition, the plans maximise throughput based on asset availability and focus investment and operational change to achieve a calmer and less reactive operational system by having the capability to re-plan quickly.
The power of this type of decision support engine is the ability to objectively quantify the sludge process, outside of existing business silos. Unlike effluent processes sludge doesn’t have constrained boundaries and can be moved by tanker or truck to the most optimum location. By looking across the end to end of the sludge value chain, each constituent part and process can be evaluated.
Historically sludge had been retained locally being sent to the nearest sludge centre; the DST has suggested that hauling some sludge slightly further can actually reduce overall costs. The impact of dry solids performance or site throughput constraints can be understood on the whole network, rather than on each individual site. The DST has identified significant financial savings through improving site throughputs and opportunities to reduce demurrage charges due to the reactive nature of the intersite operations.
A unique feature of the DST developed by BMA is the optimisation engine that drives it. The prescriptive analytics solution provides actionable outcomes rather than forecasts, as are often provided with predictive analytics tools. This enables Thames Water to have confidence in the actions to take to achieve optimal performance.
One of the key requirements of the sludge optimisation DST was the ability to run the plan and related scenarios in less than 10 minutes. This allows scenarios and plans to be quickly amended and re-run based on emerging questions or business challenges, especially where sites have suddenly become unavailable due to unforeseen outages.
The modelling technology, Enterprise Optimizer (EO), ensured that all constraints and interdependencies were represented to give an optimal solution for the whole value chain. EO enabled operational, asset, process, investment and financial information to be modelling within a single optimisation.
The DST has enabled Thames Water to make more considered decisions with a clearer understanding of the performance and costs associated with the sludge process. By considering the sludge treatment and recycling process as a system, rather than a sub-set of waste water treatment or as individual assets, TWUL have identified potential OPEX savings of up to £10 million per annum against historic costs. The tool is also already being used to support PR19 decision making. Whilst the project objective was to provide an optimised operational plan, the project also revealed more immediate opportunities to reduce OPEX.