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Efficient city-wide water distribution systems (WDS) are vital to the health and financial wellbeing of the cities' inhabitants. The impact of research on evolutionary algorithms and heuristics at Exeter has been to provide efficient water distribution system designs on a city-wide basis for a large city, the City of Ottawa. As part of a programme of building and upgrading infrastructure costing in excess of $225M CAD, the City has implemented large-scale capital projects based on designs produced by genetic algorithm technology, made possible by utilising the heuristics and modular optimisation developed by Keedwell and colleagues at the University of Exeter.
Collaborations funded through EPSRC Interact and RCUK UK-China Science Bridge resulted in QUB's advanced control research having important economic and environmental impact in China, Pakistan, Vietnam. This includes the creation of new core modules for the Shanghai Automation Instrumentation Co (SAIC) SUPMAX Distributed Control System series of products now in use for whole plant monitoring and control to maximise energy efficiency and reduce pollutant emissions. These products have since 2008 increased SAIC's revenue by over $50M p.a. Related networked monitoring technologies have been successfully deployed in Baosteel's hot-rolling production lines and in the Nantong Water Treatment Company that treats 20,000 tonnes of industrial waste water daily.
The application of advanced control algorithms has generated an impact on the economy and the environment through increased precision and reduced cost of operation of fast mechanical systems. A reduction in fuel consumption and CO2 emissions has been achieved in the transportation industry by the implementation of novel advanced control algorithms for advanced cruise control systems.
Prof Irving and Prof Sterling of the Institute of Power Systems at Brunel University collaborated with National Grid (NG) to develop and deploy a Sparse Dual Revised Simplex (SDRS), optimisation engine for real-time power allocation of all generators that were controlled by the NG. Since 2005-6 NG has been using the algorithms to aid in operation of their Balancing Mechanism, which provides a means of adjusting the level of production or consumption of individual generators or demands in the British Electricity Trading and Transmission Arrangements (BETTA). The algorithms enable the Balancing Mechanism (BM) to efficiently adjust outputs of generators in real time in order to balance the demand for electricity at minimum cost. Therefore, providing economic balancing of the transmission system at a scale of 2-3% of the £5bn annual electricity market (approximately £100M-200M per annum), hence about £800 million has been optimally traded in total in the BM since 2008. It is also important to acknowledge the reliability of the algorithms and SDRS optimisation engine from 2006 to present day, as periods of software outage carry high operational costs. The algorithms developed at Brunel continue to have very significant real world impact in terms of financial volume and its reach, such that every transmission scale power generator in the UK participates in the balancing mechanism and by implication every electricity-user benefits.
Work undertaken at the Applied DSP and VLSI Research Group since the early/mid nineties, has led to a number of significant contributions underpinning the development and commercial exploitation by industry of power efficient and complexity reduced integrated Digital Signal Processing (DSP) systems and products. These developments have paved the way for a new paradigm in the design of complexity reduced electronic systems aiding the emergence of numerous new commercial application areas and products in a diversity of fields. Indeed, these developments continue their currency and applicability in today's electronic products sector and thus shall be at the core of this case study.
Research undertaken at ERPE on the unsteady flow and air pressure regime in building drainage networks led to the development of the Positive Air Pressure Attenuator — PAPATM (http://www.studor.net/papa-system) and DyteqtaTM (http://www.dyteqta.com/introduction.html) devices which reduce the risk to health presented by the potential for cross-transmission of aerosolised pathogenic micro-organisms e.g. SARS. Since 2008, the PAPATM has been installed in 300 plus buildings in 15 countries, reducing the risk of infection and improving air-quality for an estimated 20,000 people. Studor, who employ 9 people to market these devices, have increased turnover [text removed for publication].
Like using glasses to improve eyesight, or the corrective lens of the Hubble telescope, the development of a stable deconvolution algorithm for oil well pressure data has increased the amount of information that can be extracted from well test analyses. The method specifically allows the volume of the reservoir connected to the well to be determined. Several oil and gas companies attest to an increase in their estimates of reserves by more than 20% using deconvolution, with one company indicating a doubling of reserves. The research has led to better design of recovery, better financial planning and more informed investment decisions in the oil and gas industry.
Exeter Engineering's Centre for Water Systems (CWS) undertakes internationally leading fundamental and applied research in the $500bn global water sector. EPSRC-funded research has underpinned impacts with both reach and significance in the areas of practitioner and professional services and economic impact. CWS staff have co-authored authoritative best practice guides with highly respected practitioner publishers: the Construction Industry Research and Information Association (CIRIA), the Building Research Establishment (BRE) and Spon Press. These have been widely used in the water sector, and construction and built environment sector. CWS software and knowhow have been used extensively by water service providers (such as Scottish Water) and their consultants (including SEAMS, originally an Exeter spinout) to enhance business performance by identifying efficiencies, saving costs and improving operation. Optimisation software has been made freely available and has hundreds of users worldwide including consultants and financial organisations.
Model Predictive Control (MPC) is a controller design methodology involving on-line dynamic optimisation of a user-defined objective. The research of Prof. D.Q. Mayne FRS and his colleagues at Imperial College has resulted in the first MPC algorithms capable of dealing with both linear and nonlinear systems and hard constraints on controls and states, thus making MPC a viable technique for industrial applications. His research in linear and nonlinear MPC has been exploited by multinational companies such as Honeywell and ABB. Evidence of impact is found in: 1) ethylene production by Basell Polyolefins GmbH resulting in economic benefits in millions of dollars annually; 2) Sinopec's JinShan power plant efficiency, reducing fuel consumptions of 500 tons of coal and 1,700 tons of coke per annum; 3) automotive powertrain design creating new business for Honeywell (based on OnRAMP design suite); 4) ABB's cpmPlus Expert Optimizer tools used for cement manufacturing, affecting companies such as Untervaz (Switzerland), Lägerdorf (Germany) and Buzzi (Italy); 5) ABB's BoilerMaz system for optimising boiler start-up mechanism resulting in energy savings per start-up of around 15%.
Transport crew scheduling research at Leeds University since 1994 produced optimising algorithms and industry-ready software that led to the spinning out of Tracsis in 2004. The software, including upgrades, is used by over 40 bus and train companies who previously relied on manual processes. A minimum estimate of a £230 million saving in crew costs has been achieved in the UK alone over 2008-31.7.2013. Since 2008, the software has been routinely used by bidders in all UK rail franchise tenders, contributing to cost effective, efficient and reliable rail transport. Success led to the Tracsis floatation in November 2007 (market capitalisation: £46.7 million on 22/5/2013).