Low carbon manufacturing: Fundamentals, methodology and application case studies

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.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 work is financially supported by The Royal Thai Government

Intelligent Multi-Attribute Decision Making Applications: Decision Support Systems for Performance Measurement, Evaluation and Benchmarking

Efficiency has been and continues to be an important attribute of competitive business environments where limited resources exist. Owing to growing complexity of organizations and more broadly, to global economic growth, efficiency considerations are expected to remain a top priority for organizations. Continuous performance evaluations play a significant role in sustaining efficient and effective business processes. Consequently, the literature offers a wide range of performance evaluation methodologies to assess the operational efficiency of various industries. Majority of these models focus solely on quantitative criteria omitting qualitative data. However, a thorough performance measurement and benchmarking require consideration of all available information since accurately describing and defining complex systems require utilization of both data types. Most evaluation models also function under the unrealistic assumption of evaluation criteria being dependent on one another. Furthermore, majority of these methodologies tend to utilize discrete and contemporary information eliminating historical performance data from the model environment. These shortcomings hinder the reliability of evaluation outcomes leading to inadequate performance evaluations for many businesses. This problem gains more significance for business where performance evaluations are tied in to important decisions relating to business expansion, investment, promotion and compensation. The primary purpose of this research is to present a thorough, equitable and accurate evaluation framework for operations management while filling the existing gaps in the literature. Service industry offers a more suitable platform for this study since the industry tend to accommodate both qualitative and quantitative performance evaluation factors relatively with more ease compared to manufacturing due to the intensity of customer (consumer) interaction. Accordingly, a U.S. based food franchise company is utilized for data acquisition and as a case study to demonstrate the applications of the proposed models. Compatible with their multiple criteria nature, performance measurement, evaluation and benchmarking systems require heavy utilization of Multi-Attribute Decision Making (MADM) approaches which constitute the core of this research. In order to be able to accommodate the vagueness in decision making, fuzzy values are also utilized in all proposed models. In the first phase of the study, the main and sub-criteria in the evaluation are considered independently in a hierarchical order and contemporary data is utilized in a holistic approach combining three different multi-criteria decision making methods. The cross-efficiency approach is also introduced in this phase. Building on this approach, the second phase considered the influence of the main and sub-criteria over one another. That is, in the proposed models, the main and sub-criteria form a network with dependencies rather than having a hierarchical relationship. The decision making model is built to extract the influential weights for the evaluation criteria. Furthermore, Group Decision Making (GDM) is introduced to integrate different perspectives and preferences of multiple decision makers who are responsible for different functions in the organization with varying levels of impact on decisions. Finally, an artificial intelligence method is applied to utilize the historical data and to obtain the final performance ranking. Owing to large volumes of data emanating from digital sources, current literature offers a variety of artificial intelligence and machine learning methods for big data analytics applications. Comparing the results generated by the ANNs, three additional well-established methods, viz., Adaptive Neuro Fuzzy Inference System (ANFIS), Least Squares Support Vector Machine (LSSVM) and Extreme Learning Machine (ELM), are also employed for the same problem. In order to test the prediction capability of these methods, the most influencing criteria are obtained from the data set via Pearson Correlation Analysis and grey relational analysis. Subsequently, the corresponding parameters in each method are optimized via Particle Swarm Optimization to improve the prediction accuracy. The accuracy of artificial intelligence and machine learning methods are heavily reliant on large volumes of data. Despite the fact that several businesses, especially business that utilize social media data or on-line real-time operational data, there are organizations which lack adequate amount of data required for their performance evaluations simply due to the nature of their business. Grey Modeling (GM) technique addresses this issue and provides higher forecasting accuracy in presence of uncertain and limited data. With this motivation, a traditional multi-variate grey model is applied to predict the performance scores. Improved grey models are also applied to compare the results. Finally, the integration of the fractional order accumulation along with the background value coefficient optimization are proposed to improve accuracy

Integrated grey relational analysis and multi objective grey linear programming for sustainable electricity generation planning

Sustainable energy generation is a key feature in sustainable development and among various sources of energy electricity due to some unique characteristics seems particularly important. Optimising electricity generation mix is a highly complex task and requires consideration of numerous conflicting criteria. To deal with uncertainty of experts’ opinions, inaccuracy of the available data and including more factors, some of which are difficult to quantify, in particular for environmental and social criteria, we applied grey relational analysis (GRA) with grey linguistic, and grey interval values to obtain the rank of each system. Then the obtained ranking were used as coefficients for a multi objective decision making problem, aimed to minimize the cost, import dependencies and emissions as well as maximizing the share of generation sources with better ranking. Due to existence of interval variables multi objective grey linear programming (MOGLP) method was used to solve the problem. Our results for the UK as a case study suggest increased role for all low carbon energy technologies and sharp reduction in the use of coal and oil. We argue that the integrated GRA–MOGLP approach provides an effective tool for the evaluation and optimisation of complex sustainable electricity generation planning. It is particularly promising in dealing with uncertainty and imprecisions, which reflect real-life scenarios in planning processes

Effects of sustainable governance to sustainable development

Sustainable development advocates effective and efficient planning of both present and future use of resources. Governance, on the other hand, is based on the joint and coordinated management of multidimensional variables, which is the basis of the sustainability approach. This study aims to determine how much sustainable governance influences the fulfillment of multidimensional sustainable development. Multiple regression analysis was used to determine the variables that reveal the impact of governance on development in terms of sustainability while the gray relational analysis method was used to rank the countries. The results reveal that increases in the number of people using the internet in society, as well as in the levels of developments in e-government and human development, environmental performance, and political reform, all assist countries achieve their SDGs. Furthermore, it was found that governance has a positive and significant impact on SDGs. In addition, an MCDM model consisting of BWM and gray relational analysis was used to evaluate countries based on their performance in sustainable development, the economic, governance and environment. The gray relational analysis results, on the other hand, revealed that developed and wealthy countries ranked first, while underdeveloped countries experiencing instability, such as war and conflict, ranked last. The Nordic countries outperform other countries in terms of governance and sustainability, depending on the strength of their democracy and executive capacity. © 2022 by the authors

GIS-Based Site Suitability Analysis for Wind and Solar Photovoltaics Energy Plants in Central North Region, Namibia

Increasing urbanisation and population growth are making it difficult for governments to achieve sustainable development. Provision of clean energy is among the seventeen sustainable development goals, as it reduces reliance on fossil fuels. In recent years, Namibia has rapidly increased her reliance on sustainable energy. The renewable energy sources (RESs), including wind and solar energy, can be described as clean sources which have lesser negative environmental impact compared to conventional energy sources. Amongst the pressing challenges today is finding solutions on efficient solar and wind energy production. It is imperative to work out the optimum location of RESs before installing them. This can significantly improve performance and establishes the foundation for studying both solar and wind power in a site selection problem. This study aims to determine potential locations for wind and solar photovoltaic (PV) energy plants installation using one of the multi-criteria decision-making (MCDM) methods, the analytical hierarchy process (AHP), and a geographic information system (GIS) within the Central North Regional Electricity Distributor (CENORED) supply area. Combining GIS with MCDM results in a powerful technique for selecting potential sites, since GIS provides effective analysis, manipulation, and visualization of geospatial data, whereas MCDM provides consistent weighing of criteria. In the evaluations of the location: topographical, environmental, climatic and regulations constraints were considered as factors that may facilitate or hinder the deployment of solarwind energy power plants. For solar PV energy plant, the highest potential areas are in the north-west, south-west and study area's southern regions, whereas for the wind power plant, only the northwest part is a highly suitable location for wind energy plants installation. These findings can be used to determine most favourable location of interest for solar PV and wind power plant development or to support the integration of electrical grid expansion and off-grid electrification strategies