This project was undertaken collaboratively with the University of Sheffield and University of Bradford. In respect of water services, and hence sewerage provision, in England and Wales the Regulator seeks to ensure effective and efficient expenditure by service providers on service delivery whilst protecting the environment, meeting standards, and achieving social goals. This is against a background of seeking to minimise the cost to customers and maintain reasonable levels of shareholder returns (to guarantee financial viability for the businesses). However, in respect of the investment in the renewal, maintenance and operation of the underground sewerage assets there is a need for better economic justification.

Funding body: EPSRC and Ewan Optimal Solutions Ltd (CASE studentship)

A major challenge to the UK water industry is to improve the performance of sewer systems. Flooding of properties and roads, and excessive spill frequencies are problems that the UK water companies are now required to address. Indeed, the water industry is investing large sums of money to rectify these problems, starting with the creation of Drainage Area Plans. These first define the networks and then identify the problems. The third stage is the development of a catchment strategy or 'optioneering', which involves proposing how the problems can be overcome and at what cost.

Funding body: Teaching Company Directorate, DTI and Ewan Optimal Solutions Ltd.

The privatisation-led drive in the UK water industry towards increasing efficiency and effectiveness has led to the water companies requiring the optimal performance from their assets. The regulator has also placed particular emphasis on improving combined sewer overflow (CSO) discharges during the AMP3 Period. These market pressures have led to the introduction of novel computing techniques that improve the decision making process. One such technique is the genetic algorithm (GA), whose potential to optimise urban drainage systems was identified by Rauch and Harremos (1998). Genetic algorithms are general artificial evolution search methods based on natural selection and mechanisms of population genetics. They emulate nature's very effective optimisation techniques of evolution, which are based on preferential survival and reproduction of the fittest members of the population, the maintenance of a population with diverse members, the inheritance of genetic information from parents, and the occasional mutation of genes.