Trends such as online shopping, fast pace of lifestyle and wellness issues are key drivers for consumers' preferences of shopping activities and product selection. There is evidence that food retail has shifted towards smaller in size stores and ready meals or food products which require less time for cooking. In fact, the frozen food market has increased recently and is projected to rise by 27% by 2020. This study focuses on energy efficiency of small size frozen food supermarkets. The investigation started with in-situ monitoring of energy use and environmental conditions in two frozen food stores with different HVAC but same refrigeration systems and store operation schedules. A dynamic thermal model of frozen food stores was developed using EnergyPlus and validated using the monitored data. The model takes into account interlinked heat exchanges between building, HVAC and refrigeration systems and was used to investigate energy efficiency improvements. Two HVAC systems were examined; coupling heating, air-conditioning and ventilation (coupled system) and separating heating and air-conditioning from ventilation (decoupled system). A number of refrigeration systems (remote, centralised, cascade, transcritical CO2 booster) and working fluids were investigated. Analysis of the monitored data has shown that energy use of frozen supermarkets is at the upper range of published supermarkets energy use benchmarks (1085 kWh/m2/annum). It was also shown that sales area temperature is highly affected by HVAC controls, refrigeration equipment and transient customers' pattern. The computational study has identified energy performance of sub-systems and their interactions. Results indicate that 61% of total energy use is due to the refrigeration system while HVAC and lighting are the next most energy intensive systems. Apart from lighting upgrade to LED which offers high energy savings (23%), energy efficiency can be improved for both coupled and decoupled HVAC systems by incorporating night ventilative cooling and operating remote LT cabinets with lower ambient temperature. Night ventilative cooling can lead to reduction of 3.6% in total energy use. Centralised refrigeration systems change the heating/cooling balance and can reduce the total energy use by up to 20% for a CO2 centralised system. The results of this research project are a contribution towards better understanding of energy use in food dominant supermarkets and their energy savings potential.

It is demonstrated that by using a miniature chemical reaction vessel under adaptive mechatronic control, it is possible to design and construct a low-cost carbon dioxide measurement system. With further development such a system would be potentially suitable for low-cost commercial application, in particular as sacrificial, single-mission instrumentation packages in horticultural cargo monitoring. Current instrumentation systems for carbon dioxide (CO2) gas measurement are reviewed and their limitations with respect to low cost commercial applications determined. These utilise technology intended for laboratory measurements. In particular the optical energy absorbance of CO2 in the infra-red electromagnetic spectrum. These systems require large optical paths (typically 10cm) in order to measure small CO2 concentrations. This in turn has a large impact on the physical size of the sensing system. Of the many applications requiring online CO2 sensing packages (such as medical, petroleum, environmental and water treatment)the horticultural industry is the primary focus for this research. CO2 sensing systems are primarily used in horticulture to monitor the produce environment and help extend storage time. For these applications CO2 concentrations are typically low (in the range 0 to 1%) and the paramount need is for low-cost (and possibly disposable) sensing packages. The basis of the measurement technique is the use of bulk (but small volume) aqueous chemical reaction under mechatronic control. Unlike thin film technologies where very thin membranes are passively exposed to the gaseous sample, here a small volume (approximately 2mL) of simple and very cheap liquid chemical indicator (calcium hydroxide solution) is used to produce an opaque precipitate. CO2 concentration is then assessed by low-cost optical attenuation measurements of the developing opacity of the solution. The instrumentation package comprises pumps, flowmeter, reaction cell and infra-red optics for the turbidity measurement, plus reagent and waste vessels, pipelines and electronics. During each measurement cycle, the reaction cell is flushed, with fresh chemical indicator and a sample of gas admitted. The indicator and the sample gas are then vigorously mixed and the change in the indicators optical properties measured at regular intervals. An embedded 8-bit microcontroller performs the necessary analysis to deduce the CO2 concentration (as percentage by volume) for the sample gas by reference to one or more of five ``Time-To-Threshold'' calibration models. These models evaluate the trend in turbidity development as precipitate is formed. First and second prototypes of the measurement system have been constructed and their (low-cost) components and overall performance evaluated, the first a `proof-of-concept' and the second to investigate methodology shortcomings. As a result the design of a third prototype is outlined. The measurement systems have been shown to work adequately well within expected limitations, resulting in a usable low-cost measurement technique. The current prototypes have a useful range of at least 5% to 100% CO2 with a discrimination of typically +-6%. Deficiencies, particularly performance at low concentrations, are identified and potential enhancements for future prototypes proposed.

The data-driven models used for the design of powertrain controllers are typically based on the data obtained from steady-state experiments. However, they are only valid under stable conditions and do not provide any information on the dynamic behaviour of the system. In order to capture this behaviour, dynamic modelling techniques are intensively studied to generate alternative solutions for engine mapping and calibration problem, aiming to address the need to increase productivity (reduce development time) and to develop better models for the actual behaviour of the engine under real-world conditions. In this thesis, a dynamic modelling approach is presented undertaken for the prediction of NOx emissions for a 2.0 litre Diesel engine, based on a coupled pre-validated virtual Diesel engine model (GT- Suite ® 1-D air path model) and in-cylinder combustion model (CMCL ® Stochastic Reactor Model Engine Suite). In the context of the considered Engine Simulation Framework, GT Suite + Stochastic Reactor Model (SRM), one fundamental problem is to establish a real time stochastic simulation capability. This problem can be addressed by replacing the slow combustion chemistry solver (SRM) with an appropriate NOx surrogate model. The approach taken in this research for the development of this surrogate model was based on a combination of design of dynamic experiments run on the virtual diesel engine model (GT- Suite), with a dynamic model fitted for the parameters required as input to the SRM, with a zonal design of experiments (DoEs), using Optimal Latin Hypercubes (OLH), run on the SRM model. A response surface model was fitted on the predicted NOx from the SRM OLH DoE data. This surrogate NOx model was then used to replace the computationally expensive SRM simulation, enabling real-time simulations of transient drive cycles to be executed. The performance of the approach was validated on a simulated NEDC drive cycle, against experimental data collected for the engine case study. The capability of methodology to capture the transient trends of the system shows promising results and will be used for the development of global surrogate prediction models for engine-out emissions.

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