A plan-do-check-act framework for WEEE and RoHS : a model for implementing WEEE and RoHS by integrating eco-design factors and activities into business operation and strategy.
AuthorEl-Gomla, Randa A.M.
SupervisorAbd-Alhameed, Raed A.
Environmentally Conscious Manufacturing (ECM)
Environmentally Conscious Design (ECD)
Life Cycle Analysis (LCA)
Environmentally Conscious Production (ECP)
Life cycle thinking
Environmental Management System (EMS)
Waste of Electrical and Electronic Equipment (WEEE) directive
The University of Bradford theses are licenced under a Creative Commons Licence.
InstitutionUniversity of Bradford
DepartmentSchool of Engineering, Design and Technology
MetadataShow full item record
AbstractEco-design is relatively new and fast growing field of research due to its vital importance to the manufacturing industry and its related environmental issues such as reducing waste, and CO2 emission. A major EU programme relating to the environment is the waste of Electrical and Electronic Equipment (WEEE) directive. The (WEEE) directive specifies ten categories and a voltage range which is up to 1.000 volts AC or 1.500volts DC. The developed framework came for the implementation of Eco-design principles that helps to take into account the adaption of the (WEEE) directive and the restriction of hazard substances (RoHS) used in electrical and electronic equipments. As a result of identify gaps and needs such as a lack of a comprehensive Eco-design framework and the need to integrate it to the normal business operation. In this research the PDCA framework for Eco-design and WEEE directive will be discussed. The framework will encompass all of the Eco-design¿s implementation and integration factors and activities such as WEEE and RoHS directives, Eco-design management, Environmental legislations, Eco-design tools and considerations. The literature review covers the topic of Eco-design¿s related issues, and WEEE and RoHS directives rules. Based on comprehensive questionnaire survey of Eco-design, WEEE and RoHS issues and activities among a sample of environmentally aware companies, statistical analysis is carried out using SPSS software. Then the findings of the survey triangulated with the findings of the literature review formed the basis of the design and implementation plan of the proposed framework
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Design and Operation of Multistage Flash (MSF) Desalination: Advanced Control Strategies and Impact of Fouling. Design operation and control of multistage flash desalination processes: dynamic modelling of fouling, effect of non-condensable gases on venting system design and implementation of GMC and fuzzy controlMujtaba, Iqbal M.; Alsadaie, Salih M.M. (University of BradfordFaculty of Engineering and Informatics, 2017)The rapid increase in the demand on fresh water due the increase in the world population and scarcity of natural water puts more stress on the desalination industrial sector to install more desalination plants around the world. Among these desalination plants, multistage flash desalination process (MSF) is considered to be the most reliable technique of producing potable water from saline water. In recent years, however, the MSF process is confronting many problems to cut off the cost and increase its performance. Among these problems are the non-condensable gases (NCGs) and the accumulation of fouling which they work as heat insulation materials. As a result, the MSF pumps and the heat transfer equipment are overdesigned and consequently increase the capital cost and decrease the performance of the plants. Moreover, improved process control is a cost effective approach to energy conservation and increased process profitability. Thus, this study is motivated by the real absence of detailed kinetic fouling model and implementation of advance process control (APC). To accomplish the above tasks, commercial modelling tools can be utilized to model and simulate MSF process taking into account the NCGs and fouling effect, and optimum control strategy. In this research, gPROMS (general PROcess Modeling System) model builder has been used to develop the MSF process model. First, a dynamic mathematical model of MSF is developed based on the basic laws of mass balance, energy balance and heat transfer. Physical and thermodynamic properties of brine, distillate and water vapour are included to support the model. The model simulation results are validated against actual plant data published in the literature and good agreement with these data is obtained. Second, the design of venting system in MSF plant and the effect of NCGs on the overall heat transfer coefficient (OHTC) are studied. The release rate of NCGs is studied using Henry’s law and the locations of venting points are optimised. The results reveal that high concentration of NCGs heavily affects the OHTC. Furthermore, advance control strategy namely: generic model control (GMC) is designed and introduced to the MSF process to control and track the set points of the two most important variables in the MSF plant; namely the Top Brine Temperature (TBT) which is the output temperature of the brine heater and the Brine Level (BL) in the last stage. The results are compared to conventional Proportional Integral Derivative Controller (PID) and show that GMC controller provides better performance over conventional PID controller to handle a nonlinear system. In addition, a new control strategy called hybrid Fuzzy-GMC is developed and implemented to control the same aforementioned loops. Its results reveal that the new control outperforms the pure GMC in some areas. Finally, a dynamic fouling model is developed and incorporated into the MSF dynamic process model to predict fouling at high temperature and high velocity. The proposed dynamic model considers the attachment and removal mechanisms of calcium carbonate and magnesium hydroxide with more relaxation of the assumptions. Since the MSF plant stages work as a series of heat exchangers, there is a continuous change of temperature, heat flux and salinity of the seawater. The proposed model predicts the behaviour of fouling based on the physical and thermal conditions of every single stage of the plant.
MSF process modelling, simulation and optimisation : impact of non-condensable gases and fouling factor on design and operation. Optimal design and operation of MSF desalination process with non-condensable gases and calcium carbonate fouling, flexible design operation and scheduling under variable demand and seawater temperature using gPROMS.Mujtaba, Iqbal M.; Said, Said Alforjani R. (University of BradfordSchool of Engineering, Design and Technology, 2013-11-27)Desalination is a technique of producing fresh water from the saline water. Industrial desalination of sea water is becoming an essential part in providing sustainable source of fresh water for a large number of countries around the world. Thermal process being the oldest and most dominating for large scale production of freshwater in today¿s world. Multi-Stage Flash (MSF) distillation process has been used for many years and is now the largest sector in the desalination industry. In this work, a steady state mathematical model of Multistage Flash (MSF) desalination process is developed and validated against the results reported in the literature using gPROMS software. The model is then used for further investigation. First, a steady state calcium carbonate fouling resistance model has been developed and implemented in the full MSF mathematical model developed above using gPROMS modeling tool. This model takes into consideration the effect of stage temperature on the calcium carbonate fouling resistance in the flashing chambers in the heat recovery section, heat rejection section, and brine heaters of MSF desalination plants. The effect of seasonal variation of seawater temperature and top brine temperature on the calcium carbonate fouling resistance has been studied throughout the flashing stage. In addition, the total annual operating cost of the MSF process is selected to minimise, while optimising the operating parameters such as seawater rejected flow rate, brine recycle flow rate and steam temperature at different seawater temperature and fouling resistance. Secondly, an intermediate storage between the plant and the client is considered to provide additional flexibility in design and operation of the MSF process throughout the day. A simple polynomial based dynamic seawater temperature and different freshwater demand correlations are developed based on actual data. For different number of flash stages, operating parameters such as seawater rejected flow rate and brine recycle flow rate are optimised, while the total annual operating cost of the MSF process is selected to minimise.The results clearly show that the advantage of using the intermediate storage tank adds flexible scheduling in the MSF plant design and operation parameters to meet the variation in freshwater demand with varying seawater temperatures without interrupting or fully shutting down the plant at any time during the day by adjusting the number of stages. Furthermore, the effect of non-condensable gases (NCG) on the steady state mathematical model of MSF process is developed and implemented in the MSF model developed earlier. Then the model is used to study effect of NCG on the overall heat transfer coefficient. The simulation results showed a decrease in the overall heat transfer coefficient values as NCG concentrations increased. The model is then used to study the effect of NCG on the design and operation parameters of MSF process for fixed water demand. For a given plant configuration (fixed design) and at different seawater and steam temperatures, a 0.015 wt. % of NCG results in significantly different plant operations when compared with those obtained without the presence of NCG. Finally, for fixed water demand and in the presence of 0.015 wt. % NCGs, the performance is evaluated for different plant configurations and seawater temperature and compared with those obtained without the presence of NCG.
Engineering System Design for Automated Space Weather Forecast. Designing Automatic Software Systems for the Large-Scale Analysis of Solar Data, Knowledge Extraction and the Prediction of Solar Activities Using Machine Learning Techniques.Qahwaji, Rami S.R.; Ipson, Stanley S.; Alomari, Mohammad H. (University of BradfordSchool of Computing, Informatics & Media, 2010-03-03)Coronal Mass Ejections (CMEs) and solar flares are energetic events taking place at the Sun that can affect the space weather or the near-Earth environment by the release of vast quantities of electromagnetic radiation and charged particles. Solar active regions are the areas where most flares and CMEs originate. Studying the associations among sunspot groups, flares, filaments, and CMEs is helpful in understanding the possible cause and effect relationships between these events and features. Forecasting space weather in a timely manner is important for protecting technological systems and human life on earth and in space. The research presented in this thesis introduces novel, fully computerised, machine learning-based decision rules and models that can be used within a system design for automated space weather forecasting. The system design in this work consists of three stages: (1) designing computer tools to find the associations among sunspot groups, flares, filaments, and CMEs (2) applying machine learning algorithms to the associations¿ datasets and (3) studying the evolution patterns of sunspot groups using time-series methods. Machine learning algorithms are used to provide computerised learning rules and models that enable the system to provide automated prediction of CMEs, flares, and evolution patterns of sunspot groups. These numerical rules are extracted from the characteristics, associations, and time-series analysis of the available historical solar data. The training of machine learning algorithms is based on data sets created by investigating the associations among sunspots, filaments, flares, and CMEs. Evolution patterns of sunspot areas and McIntosh classifications are analysed using a statistical machine learning method, namely the Hidden Markov Model (HMM).