Co-firing biomass with coal at existing power plant is widely adopted as one of the main technologies for reducing CO2 emissions in the UK and the rest of the world. Despite various advances in developing the co-firing technology, a range of technological issues remain to be resolved due to the inherent differences in the physical and combustion properties between biomass and coal. Typical problems associated with co-firing include poor flame stability, low thermal efficiency, and slagging and fouling. This project aims to achieve the optimisation of biomass/coal co-firing processes through a combination of advanced fuel characterisation, integrated measurement and computational modelling. In the area of fuel characterisation, both thermo-gravimetric analysis and automated image analysis techniques in conjunction with conventional fuel analysis methods will be combined to achieve comprehensive characterisation of biomass and biomass/coal blends from a wide range of sources. Because of the physical differences between biomass and coal the fluid dynamics of the biomass/coal/air three-phase flow in the fuel lines feeding the burners is rather complex and very little is known in this area of science. It is proposed in this project to develop an instrumentation technology capable of measuring the basic parameters of the biomass/coal particles in the fuel lines on an on-line continuous basis. The system will allow the monitoring and optimisation of the fuel delivery to the burners. The instrumentation technology combines novel electrostatic sensing and digital imaging principles and embedded system design methodology. The flow parameters to be measured include particle size distribution, velocity and concentration of biomass/coal particles as well as biomass proportion in the blend. It is known that biomass addition and variations in coal diet can have a significant impact on combustion stability and co-firing efficiency. As part of this project, a system incorporating digital imaging devices and solid state optical detectors will be developed for the continuous monitoring of the burner conditions and flame stability under co-firing conditions. Computational modelling provides a powerful supplementary tool to experimental measurement in the studies of three-phase flow and combustion flame characteristics. Computational Fluid Dynamic (CFD) modelling techniques will be applied in this project to investigate the dynamic behaviours of irregular biomass particles and their blends with pulverised coal in the fuel lines and associated combustion characteristics particularly flame stability. CFD modelling techniques will also be applied to study the impact of biomass addition on ash deposition and formation of slagging and fouling. The measurements from the flow metering and flame monitoring systems will be integrated to establish and validate the CFD models. Meanwhile, the modelling results will be used to interpret the practical measurements under a wide range of conditions.The project consortium comprises three academic centres of expertise including Kent, Leeds and Nottingham. Collaborative arrangements with three leading research centres in China have been established in addition to support from power generation organizations in the UK and China. Following the design and implementation of the instrumentation systems and computational modeling work, experimental work will be performed on combustion test rigs in both countries. The instrumentation systems and computational models will then be scaled up for full scale power stations. Demonstration trials will be undertaken to assess the efficacy of the advanced fuel characterisation techniques, the performance and operability of the instrumentation systems, and the validity of the computational models under a range of co-firing conditions. Recommendations for the optimization of co-firing processes at existing power plant and on the design of new plant will be reported.

Co-firing biomass with coal at existing power plant is widely adopted as one of the main technologies for reducing CO2 emissions in the UK and the rest of the world. Despite various advances in developing the co-firing technology, a range of technological issues remain to be resolved due to the inherent differences in the physical and combustion properties between biomass and coal. Typical problems associated with co-firing include poor flame stability, low thermal efficiency, and slagging and fouling. This project aims to achieve the optimisation of biomass/coal co-firing processes through a combination of advanced fuel characterisation, integrated measurement and computational modelling. In the area of fuel characterisation, both thermo-gravimetric analysis and automated image analysis techniques in conjunction with conventional fuel analysis methods will be combined to achieve comprehensive characterisation of biomass and biomass/coal blends from a wide range of sources. Because of the physical differences between biomass and coal the fluid dynamics of the biomass/coal/air three-phase flow in the fuel lines feeding the burners is rather complex and very little is known in this area of science. It is proposed in this project to develop an instrumentation technology capable of measuring the basic parameters of the biomass/coal particles in the fuel lines on an on-line continuous basis. The system will allow the monitoring and optimisation of the fuel delivery to the burners. The instrumentation technology combines novel electrostatic sensing and digital imaging principles and embedded system design methodology. The flow parameters to be measured include particle size distribution, velocity and concentration of biomass/coal particles as well as biomass proportion in the blend. It is known that biomass addition and variations in coal diet can have a significant impact on combustion stability and co-firing efficiency. As part of this project, a system incorporating digital imaging devices and solid state optical detectors will be developed for the continuous monitoring of the burner conditions and flame stability under co-firing conditions. Computational modelling provides a powerful supplementary tool to experimental measurement in the studies of three-phase flow and combustion flame characteristics. Computational Fluid Dynamic (CFD) modelling techniques will be applied in this project to investigate the dynamic behaviours of irregular biomass particles and their blends with pulverised coal in the fuel lines and associated combustion characteristics particularly flame stability. CFD modelling techniques will also be applied to study the impact of biomass addition on ash deposition and formation of slagging and fouling. The measurements from the flow metering and flame monitoring systems will be integrated to establish and validate the CFD models. Meanwhile, the modelling results will be used to interpret the practical measurements under a wide range of conditions.The project consortium comprises three academic centres of expertise including Kent, Leeds and Nottingham. Collaborative arrangements with three leading research centres in China have been established in addition to support from power generation organizations in the UK and China. Following the design and implementation of the instrumentation systems and computational modeling work, experimental work will be performed on combustion test rigs in both countries. The instrumentation systems and computational models will then be scaled up for full scale power stations. Demonstration trials will be undertaken to assess the efficacy of the advanced fuel characterisation techniques, the performance and operability of the instrumentation systems, and the validity of the computational models under a range of co-firing conditions. Recommendations for the optimization of co-firing processes at existing power plant and on the design of new plant will be reported.

UK manufacture accounts for 13% of GDP, 50% of exports and directly employs 2.5 million people. Parallel Kinematic Machines (PKM) are a new type of machine tools and have been identified as a key technology that fills in the gap between computer numerical controlled machines and industrial robots due to their superior dynamic performance, flexibility and versatility to large-scaled parts machining. The use of PKMs creates more flexibility and dexterity in manufacturing processes while achieving high precision and high speed. This contributes significantly to the economy by improving efficiency, reducing product defects, and saving time/money/energy. The PKM integrated manufacturing system would inevitably introduce errors due to stiffness and motion of the components in the system. These errors will be accumulated through the production chain, and influence the geometrical quality of the machined parts. Predicting part quality based on error propagation in the PKM manufacturing processes represents a step change in managing production processes, as it removes the current cumbersome trial-and-error processes and enables rapid reconfiguration of production systems. Other benefits would include 20% reduction of part defects and rework, leading to a significant cost saving. Part quality resulted from interaction of manufacturing systems and machining processes, with intertwined machining errors and their propagation through multiple operations, machine tools, and fixtures and jigs. At the moment, there is no robust industrial or international standard to evaluate machining capability of PKM tools with these errors. Current trial-and-error based approach that requires a large amount of time, materials and energy, is not sustainable and suitable for future smart factories to meet frequent changes with reconfigurability. Therefore new analytical methods are urgently needed. The proposed research is adventurous in creating a new quality prediction capability for PKM based flexible manufacturing processes by revealing the relationship between manufacturing system errors and part or assembly quality. This leads to an effective error discrimination control strategy to achieve a better process control while ensuring the required product quality. Error propagation in a production process is to be explored by investigating the role of stiffness characteristics of a PKM in influencing the machining process. This will lead to the development of machining load-models in both milling and drilling on a specific machining process. Experiments are to be implemented at QUB's PKM laboratory and KCL PKM laboratory, and a map between errors and part quality is to be created through modeling and testing. This will deliver an enhanced understanding of errors and their propagation mechanism thereby leading to the identification of potential strategies for reducing individual, propagated, and residual errors. An integrated validation system that consists of a kinematic/dynamic analysis module, kinetostatic model, CAD module, and FEM module will be implemented in a virtual environment and in a manufacturing site. The project will access expertise from world-leading groups in advanced PKM machining processes. The research is highly transformative in its nature of connecting academic cutting-edge research to the practical issues encountered in complex PKM manufacture processes. Key results are to be generated and fundamental science is to be revealed in the collaborative work, training and workshops with support of AMRC, MTC and Tianjin University. The research will benefit the academic community in manufacture and robotics, and industrial sectors who will gain knowledge for reduction of errors particularly propagated errors in manufacturing processes integrated with PKMs.

Co-firing biomass with coal at existing power plant is widely adopted as one of the main technologies for reducing CO2 emissions in the UK and the rest of the world. Despite various advances in developing the co-firing technology, a range of technological issues remain to be resolved due to the inherent differences in the physical and combustion properties between biomass and coal. Typical problems associated with co-firing include poor flame stability, low thermal efficiency, and slagging and fouling. This project aims to achieve the optimisation of biomass/coal co-firing processes through a combination of advanced fuel characterisation, integrated measurement and computational modelling. In the area of fuel characterisation, both thermo-gravimetric analysis and automated image analysis techniques in conjunction with conventional fuel analysis methods will be combined to achieve comprehensive characterisation of biomass and biomass/coal blends from a wide range of sources. Because of the physical differences between biomass and coal the fluid dynamics of the biomass/coal/air three-phase flow in the fuel lines feeding the burners is rather complex and very little is known in this area of science. It is proposed in this project to develop an instrumentation technology capable of measuring the basic parameters of the biomass/coal particles in the fuel lines on an on-line continuous basis. The system will allow the monitoring and optimisation of the fuel delivery to the burners. The instrumentation technology combines novel electrostatic sensing and digital imaging principles and embedded system design methodology. The flow parameters to be measured include particle size distribution, velocity and concentration of biomass/coal particles as well as biomass proportion in the blend. It is known that biomass addition and variations in coal diet can have a significant impact on combustion stability and co-firing efficiency. As part of this project, a system incorporating digital imaging devices and solid state optical detectors will be developed for the continuous monitoring of the burner conditions and flame stability under co-firing conditions. Computational modelling provides a powerful supplementary tool to experimental measurement in the studies of three-phase flow and combustion flame characteristics. Computational Fluid Dynamic (CFD) modelling techniques will be applied in this project to investigate the dynamic behaviours of irregular biomass particles and their blends with pulverised coal in the fuel lines and associated combustion characteristics particularly flame stability. CFD modelling techniques will also be applied to study the impact of biomass addition on ash deposition and formation of slagging and fouling. The measurements from the flow metering and flame monitoring systems will be integrated to establish and validate the CFD models. Meanwhile, the modelling results will be used to interpret the practical measurements under a wide range of conditions.The project consortium comprises three academic centres of expertise including Kent, Leeds and Nottingham. Collaborative arrangements with three leading research centres in China have been established in addition to support from power generation organizations in the UK and China. Following the design and implementation of the instrumentation systems and computational modeling work, experimental work will be performed on combustion test rigs in both countries. The instrumentation systems and computational models will then be scaled up for full scale power stations. Demonstration trials will be undertaken to assess the efficacy of the advanced fuel characterisation techniques, the performance and operability of the instrumentation systems, and the validity of the computational models under a range of co-firing conditions. Recommendations for the optimization of co-firing processes at existing power plant and on the design of new plant will be reported.

The SPECTRA programme seeks to enhance the sustainable development of one of the poorest regions of China, Guizhou, through cutting edge critical zone science undertaken by integrated, complementary and multidisciplinary teams of Chinese and UK scientists. The key question for management of the karst landscapes of SW China is "how can the highly heterogeneous critical zone resources be restored, to enable sustainable delivery of ecosystem services?" We know little about the geological, hydrological and ecological processes which control soil fertility and soil function in these landscapes and how best to manage them to maximise ecosystem service delivery. SPECTRA has been designed to address these questions through a suite of 4 interlinked workpackages. The CZ will span a gradient from undisturbed natural vegetation through to human perturbed and highly degraded landscapes. Using cutting-edge approaches we will integrate measurements of: (1) the three-dimensional distribution of plants (including roots), soil, fungi, and microbes; (2) rates of rock weathering, elemental release and soil formation processes; (3) rates of erosion and soil redistribution; and, (4) pools and fluxes of soil organic C (SOC), nitrogen (N) and phosphorus (P). This will allow us to identify the biological controls on nutrient availability, soil formation and loss in the CZ and their response to perturbation, providing the rich evidence base needed to inform land management decision-making in the Guizhou province. In doing so, SPECTRA will directly address the Newton Fund objective of enhancing economic development and social welfare by providing rigorous applied scientific knowledge that will underpin the development of strategies to improve net ecological service delivery from the karst landscape, informing realistic economic and ecological compensation plans to alleviate poverty, particularly for the households that rely on fragile soils for a living. The project is also designed to maximise the benefits to the science communities of both countries, thereby bringing significant institutional benefits to all partners. Training of Chinese Early Career Researchers in state-of-the-art approaches and techniques in leading UK laboratories is an absolute priority of the scientific partnership, and combined with the networking opportunities between project partners in the global CZ community, will contribute significantly to meeting the Newton Fund objective of building the capacity for CZ Science in China. The ultimate beneficiaries of this project will be the people of Guizhou karst region (population 35 million), which is one of the poorest regions in China with a GDP less than 50% of the national average. In response to the environmental deterioration and changing social conditions in the Guizhou karst region, the Chinese government has intervened to promote the abandonment of the most degraded cultivated land and its succession to grassland, shrub and forest. This strategy has met with mixed success and is not yet underpinned by well-developed plans to repay landowners for rational and sustainable use of land resources. This must be informed by science that quantifies current and potential ecosystem service delivery. There is significant potential for our research on the response, resilience and recovery of the karst critical zone to perturbation to inform improved land management strategies that will meet these demands, leading in turn to improved delivery of ecosystem services to the communities in this region and higher environmental quality, addressing poverty and the welfare of the population through development of long-term sustainable economic development.