Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Over recent years the need to reduce both fuel consumption and emissions of carbon dioxide has become an increasing preoccupation, as well as ever stringent emission legislation. Intensive research performed by the automotive industry and academia is in progress, centred on ways to reduce exhaust emissions from IC engines on the one hand, and fuel efficient vehicles on the other. Fast progress in meeting future emission and fuel economy regulations has been hampered by the commonly accepted trade-offs between reduction in exhaust emissions and improvements in fuel economy, as well as by the customers demand for better torque output and driveability.A novel poppet valve 2-stroke controlled auto-ignition combustion engine has been proposed by Brunel and Brighton Universities. The purpose of this proposal is to penetrate and understand the key in-cylinder phenomena and processes involved in the newly proposed poppet valve 2-stroke auto-ignition combustion engine. This will enable the assessment of its potential for leapfrog improvements in performance, fuel economy, and exhaust emissions, as compared to current gasoline engines. Such a programme demands leading-edge expertise in engine technology, computational fluid dynamics, autoignition chemical kinetics, chemically selective in-cylinder diagnostics, and industrial practice. The proposed programme involves four universities supported by relevant industrial companies, taking a multi-disciplinary approach to the study of the underlying processes and technologies for the next generation of gasoline engines. It is the first time that a novel and detailed methodology has been proposed to achieve significantly extended and better controlled auto-ignition combustion operation in the current poppet valved engine without the pitfalls of the traditional crankcase scavenged ported two-stroke engines. The single cylinder poppet valve 2-stroke camless engine offers the ideal research tool to experiment with the proposed methodology. In addition, new and novel experimental techniques, such as the high-speed in-cylinder residual gas mapping and in-cylinder temperature imaging, are to be developed and applied to obtain the much-needed better understanding of underlying physical and chemical processes involved in the new combustion engine. This is complemented by the development and application of sophisticated chemistry CFD engine simulation with the state-of-the-art autoignition combustion prediction capability and refined fuel spray and evaporation models. Such a systematic and comprehensive programme of exploration and research on CAI combustion for achieving superior 2-stroke part-load fuel economy and emissions is imperative for the future development of a new frontier gasoline engine with leapfrog improvements in performance, fuel economy, and exhaust emissions.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Batch processes are gaining ever increasing importance in manufacturing industries. They are particularly prevalent in the polymer, pharmaceutical and specialty chemical industries where the focus is on the production of low-volume, high-value added products. Yet, while advanced control of continuous processes has progressed significantly over the last few decades, the characteristics associated with batch processes make them particularly challenging to control. These include presence of nonlinear and time-varying dynamics, lack of on-line sensors for product quality variables, frequent operation close to process constraints and an abundance of unmeasured disturbances.In batch processing the objective for the control system can be divided into Batch End /Point Control and Trajectory Tracking Control problems. The fundamental difference between these two types of control problems is that an end-point controller is concerned with ensuring that the quality of the product at the end of a batch meets target specifications, whilst trajectory tracking involves the regulation of product quality to a, typically, time-varying set-point as a batch progresses. Another highly relevant control problem that has not yet been effectively addressed by the academic community is the reduction of batch run length. In fact, the ability to reduce batch run length, while also ensuring that the final product conforms to stringent quality specifications, is arguably the most critical business driver in batch processing industries. The aim of the proposed project is to develop a novel Model Predictive Controller that is capable of addressing a critical operational objective in industrial batch processing, which is real-time reduction of the batch run length. The MPC controller will employ a multivariate statistical data-driven prediction model and will also be applicable to both trajectory tracking and batch end-point control problems for processes that exhibit variable batch run lengths and contain irregular measurements of the controlled variables.The novelty of the proposed project stems from the fact that none of the existing advanced control techniques provide solutions to both the trajectory tracking and batch end-point control while dealing with variable batch run lengths and irregular measurements of the controlled variables. Also, none of the existing controllers address the critical control problem of batch run length minimisation. In contrast, the controllers developed in the proposed project will address all three control problems (trajectory tracking, batch end-point control and batch run length control) while also tolerating the presence of variable batch run lengths and irregular measurements of the controlled variables.