We’re pleased to share our recent work published in Expert Systems with Applications (Elsevier), on a Decision Support System (DSS) that models industrial water networks as process networks consisting of:
• Freshwater and wastewater sources
• Treatment processes
• Connection links with capacity, quality, and reduction-rate attributes
The DSS is powered by a compact mathematical model automatically generated from operational data through our optimisation engine -optEngine- optimising cost, energy, freshwater intake, and wastewater treatment.
Crucially, the system supports both design-time and operational decision-making, enabling:
• Evaluation of alternative network design investments
• Day-to-day optimisation of water flows
• Rapid response to unexpected operational events
Validated on large-scale industrial case studies, the framework demonstrates how advanced optimisation can be translated into practical, real-time decision support for complex water networks.
Optiscale: a decision support system for optimised industrial water management
Industrial water networks are highly complex. They involve intricate process topologies, multiple water quality constraints, and frequent disruptions such as valve malfunctions, pipeline blockages, and demand fluctuations. Managing freshwater intake and reuse efficiently under these conditions remains a major industrial challenge.
OptEngine in action
Our optimisation engine -OptEngine- operates daily or on-demand, translating network data into mathematical programming models and proposing optimised (waste)water flow configurations across the network.
️ Performance highlights
• Near-optimal solutions delivered within seconds
• Tested on networks with 5–15 components and 5–10 quality parameters
• Suitable for real-time operational support
Validated on real industrial case studies
The framework was validated using data inspired by three large-scale industrial applications, including:
· An oil refinery (Tüpraş, Izmit, Turkey)
· Two chemical industries (DOW, Bohlen, Germany & Solvay, Livorno, Italy)
Measured impact
· Water reuse up to 90% (~2.3 mcm/year)
· Freshwater intake reduction up to 18% (~1.2 mcm/year)
· Support for selecting the most cost-effective network design investments
Why this matters
Unlike traditional, case-specific optimisation solutions, this work delivers a generalised, reusable decision-support framework—bridging advanced optimisation theory with practical industrial water management. From design to operations, this is how decision support systems enable resilient, efficient, and sustainable industrial water networks—without compromising production.
