Rail infrastructure operators globally have begun moves towards smart intelligent network infrastructure built upon the placement of hundreds of thousands of various sensors/device types on and along tracks as required for predictive/preventative maintenance. Deployment is currently significantly hampered by affordability and maintenance liability of suitable trackside power options. Remote cable-delivered power is too expensive and impractical and battery-powered devices are limited by battery lifetimes which means disruptive access, cost and safety challenges of regular changes and the sustainability challenge of extensive battery waste. Energy harvesting-based power has been previously considered but found to produce too little power using the approaches that could be practically implemented.
Encortec has a gamechanger energy harvesting-based power solution which solves this problem, providing an order of magnitude greater autonomous power supply than other energy harvesting approaches through a novel patented approach. Magnetic flux energy around rail tracks, generated from track return current by passing trains powered by overhead line equipment (OLE) is harvested giving industry leading levels of power. Thus, power can be supplied to sensors as the trains pass by multiple times per hour. This enables maintenance-free deployment of the full range of wireless sensors giving the frequency and range of sensed data required. Proof-of-concept/technical feasibility of the energy harvesting approach has already been established.
This proposed industrial stage R&D project aims to develop full prototype energy harvesting-powered sensor devices covering three Network Rail defined sensing scenarios (flooding, rail temperature and track vibration), combined with long-range wireless communications and data analytics and to investigate performance in the varied real-world live rail environments supported by Network Rail. Induced magnetic flux energy and therefore power varies with a number of factors which will determine real-world practicality of the solution. This R&D is a precursor to later, scaled-up trials to demonstrate the value of a complete solution.
Railways cost too much to maintain and put workers at risk while looking for track defects. Our proposal uses existing trackside assets in a new way that has the potential to do away with the need to send out workers onto the tracks looking for problems instead of solving them.
Railways are high-value national assets and are a critical part of national transport infrastructure globally. Around £9Bn is spent annually maintaining railway infrastructure to ensure safe and reliable operation -- with nearly £2B being used to locate and rectify defects. Over a million manhours were spent last year walking the track. This reliance on manual inspection and intervention puts workers in a risky environment and is not in keeping with the ambitions of the railway. Our proposed innovative solution will define and detect unique acoustic signatures using existing trackside fibre-optic cables and combines it with a novel system tracking train location to yield an automated always-on monitoring system that does away with the need to send workers onto a live railway to hunt for defects.
The FORSE project will develop and trial an innovative railway monitoring system designed to automate the detection of multiple defect types over large lengths of the network with low deployment costs compared to existing bespoke monitoring solution. Our novel concept will fuse distributed fibre-optic acoustic sensing data from trackside interrogators with train location and type data from onboard systems to generate new insights on track and train defects that cannot be detected from these systems in isolation. The resulting monitoring solution has the potential to significantly reduce the resource requirements for rail asset inspection allowing resource to be redirected and targeted towards fixing problems -- reducing safety risk for rail workers and improving asset value and availability.
This project will develop novel range finding and 3D imaging systems which will be used for driver assistance and the autonomous vehicles of the future. The cameras are based on detecting single photons (light particles) in the infra-red region of the electromagnetic spectrum. Depth information is gained by measuring the time of flight of the photons from the illuminating laser, to the object and back to the photon detector in the camera with sub-nanosecond precision. By detecting single photons, the faintest possible light signals, we will realise cameras that can 'see' further than the 3D cameras available today.
Current inspection of rail track defects utilises Network Rail's four Ultrasonic Testing Units (UTUs) that traverse the UK network, 64,000 miles of track, in 750 shifts per year. With a limitation of 30 miles per hour for rail track inspection, UTUs cannot meet the high demand and increased capacity of customers.
Every day, 4.8 million people travel by train in Britain. Around 200,000 tonnes of freight and goods are transported by rail in that same time frame, supporting businesses and consumers, productivity, and economic growth whilst taking thousands of lorries off the road, and helping in the reduction of greenhouse gasses.
A risk-free network of rail tracks across the UK is pivotal to Network Rail's long-term planning process strategy and its vision for running a safe, reliable, efficient and growing railway, in Control Period 6 and beyond. Undiscovered rail track defects lead to asset failure, unscheduled maintenance, timetable delays, accidents, and fatalities. Train delays cost passengers 3.6 million hours in 2016, whilst over £72M was claimed by passengers from operators for service disruptions in 2016/17\. With the growing demand on rail transport by passengers, there is need for commercial solutions that offers high-speed (i.e. above 60 miles per hour) high resolution, rail track inspection, and data analysis in real-time. A commercial solution with the capacity to enable UK network-wide coverage.
This RAPPID project seeks to address the challenges that the UK rail network faces regarding rapid high-speed high-resolution identification of rail track defects, data collation and analysis, enabling real-time predictive analysis, and predictive maintenance of rail tracks across the UK network and globally.
The RAPPID project is based on the novel use of Virtual Source Aperture non-destruction testing techniques in combination with artificial intelligence and deep-learning methodologies that enable real time data processing and analysis of rail track data derived via use of next generation phased-array ultrasonic testing hardware.
Over the next few years, the construction sector will witness a wave of infrastructure projects (£60 billion of spend each year over the next decade) and ground work will be undertaken to set future financial settlements. The pace of this growth, and the size of this opportunity, demands a construction sector that is the best in the world. To maximise the opportunities to drive efficiency savings across the delivery of the transport infrastructure pipeline, this proposal brings together key UK Transport Client groups, Suppliers and academic experts to establish a Transport Infrastructure Efficiency 'Living Lab' to build capability within delivery, innovation and managing construction risk.
The UK has had a modest track record of infrastructure delivery with some programmes completed late; over budget; failing to secure the benefits expected; or cancelled after a significant investment. With the increasing challenge and complexity of the government's pipeline of major projects, the capacity to deliver is being stretched. The estimation of cost and schedule can be improved and major projects and programmes are tending to avoid innovation risk. These attitudes to uncertainty and risk are deeply engrained and cultural, with inconsistencies across Departments and ALBs. Together, they create barriers to the greater uptake of Modern Methods of Construction and driving productivity. This proposal offers a strategic, scalable and sector wide approach with Government, Client Groups, Suppliers and Academia working in partnership.
To overcome these challenges, the 'Living lab' will work in collaboration with i3P and the CIH to tackle the systemic issues that still obstruct the use, integration and adoption of innovations that could drive productivity and wider social benefits through major construction schemes. It will be a catalyst for cultural change, shifting focus within infrastructure delivery decision-making from the costs of construction to an understanding of its whole life value.
Statement from Professor Lord Robert Mair, Cambridge University, Chair of the DFT Science Advisory Council and Member, Transport Research & Innovation Board:
"This demonstrator is a transformative collaboration. It uses data, technology and Modern Methods of Construction within live transport infrastructure projects to showcase the value of data visualisation through real-time data control rooms and demonstrates where we can drive even greater productivity and efficiency through innovation transfer. By implementing advanced construction and engineering techniques on live projects, we will deliver significantly better outcomes for society and provide the evidence needed to scale how we drive productivity across the transport infrastructure sector."
"The Innovate funded Prometheus project will develop a fully autonomous robot capable of geo-technical surveys in unknown voids for use in the mining, water infrastructure monitoring and offshore industries. This robot will be able to be automatically deployed and recovered through a standard restricted access bore of 150mm diameter, significantly increasing potential use cases over existing systems. Key demonstrations will be carried out during the project in conjunction with Network Rail - to explore and map mine workings that extend under existing rail infrastructure.
Further, applications are also within the water industry with aging water infrastructure. This is presenting major issues to societies, in terms of leakages, burst water mains, flooding, contamination, etc. This is resulting in significant costs to infrastructure providers in terms of fines, legal fees, and complex repairs.
The system itself will be designed, built and tested by a consortium led by Headlight -- an SME working with leading edge sensor and data processing technologies. Partners include Callen-Lenz, an SME with expertise in airborne robotic systems development and deployment. They will work closely with the Universities of Manchester, Royal Holloway and Bristol to integrate the latest sensors, control and manufacturing techniques into a truly novel and highly capable platform. This will include sensors and adaptive sensing software provided by both Thales and Headlight.
The joint requirements of fully autonomous operation beyond visual line of sight (BVLOS), combined with deployment through a limited access 150-diameter borehole will be demonstrated both in a university lab environment and at key milestone demonstrations in conjunction with Network Rail. This will be an excellent illustration of robotics, autonomy and AI in extreme environments with widespread application. The final system will demonstrate a step change in autonomous capability, highly flexible operation and deployment, meeting a real and existing industrial need for rapid inspection of areas that are difficult to access and complex to navigate."
"In this 18-month project, self-powered Sensors for monitoring key parameters affecting the performance of the railway infrastructure (load, temperature, shock, etc.) will be developed, deployed, tested and evaluated (move from TRL2-3 to TRL7). The Sensors will combine Ilika's Stereax(r) solid-state battery technology and Smart Component Technologies Ltd (SCT) novel ultra-low power sensor platform and will be wirelessly connected to Network Rail's existing condition monitoring platform (Intelligent Infrastructure). The self-powered sensors will be maintenance free and will generate data 24/7, 365 days a year.
The solid-state battery powered sensors will be the first of this type developed and tested for the railway industry. Solid-state batteries offer substantial benefits over currently used lithium-ion batteries, including; low leakage currents, compact design with twice the volumetric energy density of Lithium-ion batteries, high power density and cycle life of 5,000 cycles (equivalent to a 10-year lifespan).
The self-powered Sensors will be demonstrated on live Network Rail infrastructure as part of a 6-month trial deployment. Three representative Network Rail trial sites will be chosen, specifically targeting known problematic sites, as well as high speed and high frequency lines. At each trial site, Sensors will be distributed at 10m intervals, on strategic components (e.g. points motor, crossing nose, stretcher bar, etc.).
The project directly addresses challenges identified by Network Rail for 'Reliable and Resilient Switches' and will enable the widespread deployment of sensor technology in the railway industry.
The project consortium consists of Ilika (lead), Smart Component Technologies Ltd and Network Rail."
Switches & Crossings' (S&C's) are the costliest and most safety critical asset within the rail infrastructure to maintain.
Smart Component Technologies (SCT), and their collaborators University of Birmingham, CHG Electrical Ltd and Network Rail, will develop through this project a technology that can detect track voids and measure nose impact at S&C's, as well as condition of the switch mechanism/drive.
This device, which sits within a wider suite of products that have already been developed, will additionally be able to report on the overall health of the S&C, especially when this data is combined with other data inputs gained from additional asset monitoring technologies.
The objective is to create a solution that helps rail infrastructure owners, such as Network Rail, to detect and remedy problems at S&C's in a cost-effective manner before they become a critical failure on the network.
The project directly addresses challenges identified by Network Rail for 'Reliable and Resilient Switches'. The project outputs will improve automated inspection methods, helping predictive maintenance, whilst furthering understanding of precursors to switch wear and damage.
Achieving the above will help keep passenger and freight trains running smoothly, as well as prevent catastrophic events, such as derailments.
Only a third of passengers on the UK's railways seem happy with the wifi service they receive on the train. People increasingly expect free and reliable internet wherever they go and are frustrated that train journeys interrupt the entertainment and communications they value for lifestyle and work.
The problem is not for lack of attention. The Government knows how much this matters to people and has called for rail franchises to include free wifi from 2020\. Although the initiative itself will guarantee no more than 1 Mbps per head, it sends a clear signal to the train operators who have so far found it difficult to justify investment in the infrastructure needed over the timeframe of the franchise contracts. Network Rail Infrastructure Limited (NRIL) is the owner and operator of most of the rail infrastructure in Great Britain and is at the forefront of attempts to find innovative solutions for this market failure.
HS2 is the next major railway infrastructure project in the UK and an excellent opportunity to prove high bandwidth, reliable internet services for entire journeys. VILIRI's approach is based on Optical Wireless Communications (OWC) that uses Infra-Red (IR) frequencies to send data between train and trackside at 10Gigabits per second (10Gbps). This is more than enough for all types of internet browsing, real time communications and effectively unlimited capacity for each passenger.
The innovation is to use advanced optics with high levels of concentration and carefully matched sensors that can take low power IR signals and process them reliably in real time. This needs transceivers on the train and along the track so that data is transferred by line of sight from each transceiver in turn. The quality of service achievable will support operational communications and security CCTV picture transfers, for example, as well as information and internet for the passengers.
The project will enhance the capability of the optical links by advanced signal processing and multiple channels where needed. Care will be taken to ensure that the optics remain clean and clear in all weathers. Most importantly, the business case for installing the system as part of the railway infrastructure will be covered by the revenue that additional internet services will bring while passengers enjoy the basic internet service without direct charges. Capital costs will be reduced by internet connection within the provision of optical fibre that has to be laid for signalling and other functions.
The demand for train-to-track communications are proliferating; with in-cab signalling, passenger WiFi, remote CCTV, passenger counting, e-ticketing and remote condition monitoring (RCM), passenger information systems (PIS), seat reservation systems, driver advisory systems and many other applications jostling for bandwidth.
Many of these applications require guaranteed priority, security, and/or safety integrity. Traditionally to satisfy this, these applications have dedicated roof antennas, routers and networking but space on trains is restricted and duplication is costly.
The cost and complexity for Train Operating Companies (TOCs) to provide digital connectivity creates a barrier to adoption of new digital data-centric services to make train operation more intelligent.
SAFRON will create a prototype for a shared communications architecture such that any data-centric system can use the same connection from train-to-trackside at an assured level of priority, safety and security. This will reduce the cost for the TOC by eliminating duplicate connections and enabling them to make better use of data. We will explore several classifications of train-to-trackside data:
* Public
* Private
* Mission-critical
* Safety-related (SIL2)
We will explore the requirements for each of these application types, including security, quality of service, bandwidth, and data timeliness. Furthermore, due to approaching obsolescence of the existing lineside GSM-R network, we will assess the suitability of this architecture for safety-critical train control (SIL4). Communications technologies we will consider include 4G LTE and emerging 5G, with the option for falling-back on 3G and GPRS technologies too for resilience.
A hardware and software system that can accommodate these application types will be developed and demonstrated to satisfy their respective safety, security, and performance requirements.
We will evaluate their performance in the lab in indicative operational and degraded scenarios. A safety and security review will be carried out on the proposed solution, and an approval strategy will be developed for certification and product approval.
SAFRON will allow all players in the railway industry to use this fast and ubiquitous network to support the train and trackside to become more closely integrated - working together as one system.
SAFRON will be delivered in partnership between Apollo Rail Ltd (Apollo), TeleRail Networks Limited (TeleRail Networks), University of Surrey (Surrey), specifically the Surrey Centre for Cyber Security (SCCS) and the 5G Innovation Centre (5GIC) and Network Rail Telecoms (NR).
We have letters of support from project stakeholders including Department for Transport (DfT), Heathrow Express (HEx), Railway Safety and Standards Board (RSSB), and Network Rail Signalling.
RODIO is an industrial research project developing a scalable solution to address the complex challenges of remote condition monitoring (RCM) of rail tracks. The RCM challenges identified by Network Rail include tree fall, rock fall, trespassing, animal intrusion, landslide and subsidence. The emerging internet-of-things (IoT) technologies can offer low-power and remote sensing and data communication for the RCM problem. However, technical and scalability challenges include powering and maintenance of sensors and wireless connectivity in network wide, remote and harsh environment. The project aims to develop near real-time detection and identification of intrusions and obstructions on rail tracks. The system performance will be analysed and evaluated through a live pilot deployment in TATA Steel Port Talbot (Wales). Exploitation and deployment of low-power IoT solutions, artificial intelligence and edge computing on the existing rail network will deliver state-of the-art RCM systems which will be beneficial to the UK rail infrastructure, train operators, passengers and freight customers. The project team including IOTICS, Vortex-IOT, Fincore, TATA Steel UK and Network Rail Infrastructure, brings together multi-disciplinary expertise, resources and skills from IoT, Machine Learning and Edge Computing to heavy industry and key rail-sector partners.
Precise train positioning is key to the vision for an innovative railway presented by the Rail Capability Delivery Plan 2017\.
GPS is problematic for rail and currently the industry relies heavily on signs, transponders and balises in the track to locate a train at a specific point on its path. These are expensive to install and maintain. Each new application that needs them increases the complexity and management burden of the infrastructure.
This project takes a completely new, lower cost, approach. Adapting recent developments in technology for autonomous cars, a train will be able to locate itself without needing any infrastructure equipment. Instead of a physical transponder or balise in the track, the system creates a virtual 'Video Balise' - Valise, which is stored in a forward facing camera mounted in the windscreen. The Valise is 'read' as the camera recognises that the train is passing the stored location.
The Valise will unlock the potential for many high value applications which cannot economically be delivered with today's expensive physical positioning infrastructure.
The project is being delivered by RDS in collaboration with Nottingham Scientific Ltd, Omnicom Balfour Beatty, First Group and Network Rail.
RDS will develop the Valise technology and integrate it with its Video Train Positioning System (VTPS) to provide an autonomous low cost system suitable for deployment in service trains. The technology will be trialled in a real-time demonstrator on a First Group train, integrated with the RDS Driver Support System (DSS). The demonstator will show how the technology would be deployed for platform stopping and 'virtual' temporary speed restrictions.
Balfour Beatty and Network Rail will validate the performance of the Valise against existing positioning systems and demonstate its use for remote monitoring and survey applications.
NSL will bring its R&D experience in 'virtual balise' technology using satellite postioning (GNSS). With this, the project team will develop a safety approach for integerating GNSS and video virtual balises in order to realise a dependable virtual balise capable of use in safety related applications.
Industrial research of disruptive technologies to monitor the health of rail subsurface infrastructure (hidden) assets in real time using innovative radar and visualisation technologies -- Infrastructure Monitoring System (INFRAMONIT).
This project simply addresses the global challenge of how to maintain subsurface infrastructure. It focuses on Intelligent Maintenance based on trainborne inspection which provides accurate, timely information for condition-based intervention and reduces the need for workers to be on or about the operating railway. INFRAMONIT can see below the surface to inspect rail substructure, rail ballast, drainage systems, subgrade, water pipes and utilities, tunnel linings, retaining walls and bridge surface structures. Rail infrastructure must meet the future needs of passenger and freight customers and should be more reliable, more readily available and easier to maintain.
INFRAMONIT provides the innovations that monitor the health of the infrastructure assets in real time using smart, built-in sensors and novel radar technology. The planned research is a critical investigation for the purpose of product development leading to an improvement in the current state-of-the-art. It will create a prototype demonstrator which will be tested in a laboratory with simulated infrastructure failures for technology validation. It targets a better way of monitoring and visualising the status of infrastructure to avoid disrupting train services by developing a technology that automatically delivers preventive maintenance plans so that maintenance can be completed before a failure occurs.
This project will provide a new smart technology to inspect and maintain resilient integrated rail infrastructure. The system is innovative, unique and the transport infrastructure managers involved from Network Rail are confident it has a large market potential. The SMEs will use the project to commence the commercial development of this disruptive technology of infrastructure inspection radar.
There are two main foci of innovation, the first is the novel rotational kinematics of the antennae, which allows data to be collected continuously from a three-hundred sixty-degree swath about the longitudinal axis of the vehicle. This offers a significant competitive advantage over current state of the art systems, which consists of several vertically fixed antennae uniformly positioned along the vehicle's lateral axis. The second focus is on fundamental processing software to transform the raw radar data into subsurface visualisations, which will be developed in parallel with the prototype.
Key words: radar, inspection, visualisation, preventative maintenance, rail infrastructure, resilience, capacity, failure, repair, planned maintenance.
Today's increasingly complex high value engineering parts are too valuable to discard when
they are damaged. Unfortunately, current repair methods, often based on a series of complex
manual operations, are difficult to control to ensure that high performance, often safety
critical, parts have the required level of integrity. This problem is exacerbated when parts are
repaired in remote, often inhospitable and hazardous locations, where it is difficult to provide
the required level of repair skill.
There is an urgent need for a flexible, efficient and automated method of repairing high value
metallic parts, close to the point of use. Hybrid Additive Manufacturing (AM) processes,
combining Directed Energy Deposition (DED) AM processes, with conventional 5 axes
machining are already in production. Although very effective, this solution is limited to
relatively small parts.
In CONFIGURE a flexible automated repair unit, encompassing every stage of the repair
operation (damage assessment, repair, finishing and final inspection) will be developed and
demonstrated. Using a robotic platform with interchangeable inspection, deposition and
machining heads it will allow parts of almost unlimited size to be repaired in a seamless
operation. The new unit can be provided in portable and even mobile configuration, enabling it
to be positioned wherever required.
Digital manufacturing technology is a key aspect of the project. An array of sensors will be
incorporated into the repair unit to provide both process and part quality data. This
information, used to enable the unit to operate in an autonomous mode, will also be logged to
allow detailed analysis to be performed and provide traceability. The CONFIGURE unit will be
demonstrated in two end-use sectors, repair of damaged rail track and high value mining
equipment but there are a wide range of potential cross-sector applications, including new part
production.
QT-PRI is a collaboration between RSK, Atkins, Network Rail and the University of Birmingham (UoB) to establish the Quantum Technology (QT) gravity sensor market opportunities against assessment of current geophysical technologies to detect and assess the condition of assets buried below the railway network, in particular drains, as well as water flow through the railway earthworks. There are over 190,000 railway earthworks and over 6000km buried assets. The incomplete asset inventory significantly limits the development of a framework to allow proactive condition assessment thereby maximising the limited resources and keeping the rail network operational. Currently, geophysical sensors are commercially used to detect the location of the ducts and pipes in roads and with limited success on the railways, but are rarely used to detect the asset condition or the condition of the parent asset (earthwork) itself. QT-PRI will open up a new market for QT gravity sensors by: 1) Assessing in detail the capability and limitations of QT gravity sensors benchmarked against current geophysical sensors for the railway environment; 2) Increasing the marketplace for the sensors by engagement with the client base, excellent dissemination activities, and practical field demonstrations.
The rail network relies on an extensive system of trackside drains to remove surface water and minimise the risk of flooding and damage to the network. Failure to maintain the drainage infrastructure can have significant cost and safety implications for the parent asset; such as delay minutes, poor track geometry, line closures and the likelihood of earthwork failures. Our focus is on improving the performance of the rail infrastructure's drainage system - a critical, yet often overlooked element of the network infrastructure in order to help "design, build and operate railway infrastructure at reduced cost". Our approach is to leverage previous work on an innovative self-learning system for maintaining drainage networks in the highways sector and adapt this technology – including the IoT sensor network, data models and decision support system - for use in the rail sector. This represents a major advance in the state of the art as it will address key challenges identified by Network Rail and enable proactive maintenance of trackside drainage assets. Our consortium includes Network Rail as a challenge owner, InTouch Ltd as a technology supplier and primary route to market, and a strong science base consisting of the Transport Systems Catapult and Lancaster University. The resulting system will be tested on 14 miles of Network Rail test track.
RAPPORT is an industrial research project developing technology solutions to reduce disruption and delays in UK rail. Working in collaboration with key industry partners and academic institutions including Network Rail, Arriva Rail North, Icomera UK and Leeds University Institute for Transport Research, the RAPPORT project will develop and help bring to market a suite of innovative and revolutionary technology tools that will transform operational awareness of train locations and movements. Through the exploitation of enhanced location information and interactive mapping, RAPPORT will deliver useable tools with practical operational benefits. These include accelerating accident and emergency response times to rail incidents; providing supplementary information to signallers at User-Worked Crossings to enable more accurate train location awareness and better decision making; improving service recovery procedures by presenting deeper insights into real-time delays caused by incidents. By introducing innovative technology to existing systems provided by collaboration partners, the project will deliver a live trial of high value, low cost, state-of-the-art products with immediate use and benefit to the rail network, its users and moreover to the UK economy as a whole.
Knowledge Transfer Partnership
To develop and implement ergonomic enhancements for the knowledge management capability.
Knowledge Transfer Partnership
To develop a model that predicts wear and fatigue of overhead electric line and validate it using monitored data.
This project aims to prove the feasibility of producing a high impact advance in surveying of the railway network through the development of a novel device capable of high speed asset monitoring and automated asset identification for the railways. It is aimed to support the work of Network Rail and their sub-contractors who require detailed asset maps of the rail infrastructure. The project builds on recently patented IP from Oxford University Mobile Robotics Group. The combination of Laser Scanning and HD camera hardware will combine with satellite navigation systems to create a 3D topometrically correct asset map of the rail network which is automatically analysed with the latest visual analytics techniques. Trial units will be developed and outputs displayed.
This proposal addresses directly an enhancement of the efficiency, export potential and energy utilization of the UK concrete industry, as cement is the second most used substance on the planet after water and the manufacturing process produces large amounts of CO2 per year. The proposed technology will enable concrete manufacturers to increase the quantities of low carbon cement they use by developing a highly cost-effective continuous, conveyor-based solid-state microwave-based system for the production of low-carbon concretes incorporating higher levels of waste products, with lower energy use and lower carbon footprint, yet retaining high mechanical performance. The significant reduction in the carbon footprint is expected to lead to rapid environmental, social and economic impact and hence market opportunities.