The requirement and awareness of the carbon emissions reduction in several scales and application of sustainable manufacturing have been now critically reviewed as important manufacturing trends in the 21st century. The key requirements for carbon emissions reduction in this context are energy efficiency, resource utilization, waste minimization and even the reduction of total carbon footprint. The recent approaches tend to only analyse and evaluate carbon emission contents of interested engineering systems. However, a systematic approach based on strategic decision making has not been officially defined with no standards or guidelines further formulated yet. The above requirements demand a fundamentally new approach to future applications of sustainable low carbon manufacturing. Energy and resource efficiencies and effectiveness based low carbon manufacturing (EREEbased LCM) is thus proposed in this research. The proposed EREE-based LCM is able to provide the systematic approach for integrating three key elements (energy efficiency, resource utilization and waste minimization) and taking account of them comprehensively in a scientific manner. The proposed approach demonstrates the solution for reducing carbon emissions in manufacturing systems at both the machine and shop floor levels. An integrated framework has been developed to demonstrate the feasible approach to achieve effective EREE-based LCM at different manufacturing levels including machine, shop floor, enterprise and supply chains. The framework is established in the matrix form with appropriate tools and methodologies related to the three keys elements at each manufacturing level. The theoretical model for EREE-based LCM is also presented, which consists of three essential elements including carbon dioxide emissions evaluation, an optimization method and waste reduction methodology. The preliminary experiment and simulations are carried out to evaluate the proposed concept. The modelling of EREE-based LCM has been developed for both the machine and shop floor levels. At the machine level, the modelling consists of the simulation of energy consumption due to the effect of machining set-up, the optimization model and waste minimization related to the optimized machining set-up. The simulation is established using sugeno type fuzzy logic. The learning method uses on experimental data (cutting trials) while the optimization model is created using mamdani type fuzzy logic with grey relational grade technique. At the shop floor level, the modelling is designed dependent on the cooperation with machine level modelling. The determination of the work assignment including machining set-up depends on fuzzy integer linear programming for several objectives with the evaluation of energy consumption data from machine level modelling. The simulation method is applied as the part of shop floor level modelling in order to maximize resource utilization and minimize undesired waste. The output from the shop floor level modelling is machine production a planning with preventive plan that can minimize the total carbon footprint. The axiomatic design theory has been applied to generate the comprehensive conceptual model E-R-W-C (energy, resource, waste and carbon footprint) of EREE-based LCM as a generic perspective of the systematic modelling. The implementation of EREE-based LCM on both the machine and shop floor levels are demonstrated using MATLAB toolbox and ProModel based simulation. The proposed concept, framework and modelling have been further evaluated and validated through case studies and experimental results.

This thesis aims to examine energy demand within UK industry and assess the improvement potential available through efficiency measures. The techniques employed throughout the work have been mainly engineering based, drawing on thermodynamics. Alongside this approach, an assessment of drivers and barriers to the technical potential was undertaken. Data availability was a key challenge in the current work. The variety in energy uses meant the use of publically available datasets was limited. A database was constructed utilising site level emissions data, and employed a subsector disaggregation that facilitated energy analysis. The database was used for an analysis of waste heat recovery options. Opportunities were identified in low temperature recovery, heat-ta-power technology, and the transport of heat. Each of these options would require further research and support to be fully realised. It was found that splitting the industrial sector into an energy-intensive and non-energy- intensive subsector, where the grouping was based on the drivers to energy efficiency, allowed generalisations to be made regarding future improvement potential. Based on analysis of past trends, it was found that the energy-intensive subsector has limited potential for further efficiency gains through currently used processes. To make significant improvements radical changes in current processes will be required. A study of the energy-intensive Cement subsector concurred with these findings. Future efficiency improvements in this subsector are likely limited without a shift to alternative cement production. The non-energy-intensive subsector was thought to have relatively greater improvement potential through existing processes. The analysis of these processes is limited by lack of data however. An analysis of the non-energy-intensive Food and drink subsector therefore focussed on improvements in supplying low temperature heat, rather than the efficiency of specific processes. Opportunities through improving steam systems, increasing combined heat-and-power use, and the adoption of heat pumps were found to offer similar improvement potentials.