A control-oriented anfis model of evaporator in a 1-kwe organic rankine cycle prototype

This paper presents a control-oriented neuro-fuzzy model of brazed-plate evaporators for use in organic Rankine cycle (ORC) engines for waste heat recovery from exhaust-gas streams of diesel engines, amongst other applications. Careful modelling of the evaporator is both crucial to assess the dynamic performance of the ORC system and challenging due to the high nonlinearity of its governing equations. The proposed adaptive neuro-fuzzy inference system (ANFIS) model consists of two separate neuro-fuzzy sub-models for predicting the evaporator output temperature and evaporating pressure. Experimental data are collected from a 1-kWe ORC prototype to train, and verify the accuracy of the ANFIS model, which benefits from the feed-forward output calculation and backpropagation capability of the neural network, while keeping the interpretability of fuzzy systems. The effect of training the models using gradient-descent least-square estimate (GD-LSE) and particle swarm optimisation (PSO) techniques is investigated, and the performance of both techniques are compared in terms of RMSEs and correlation coefficients. The simulation results indicate strong learning ability and high generalisation performance for both. Training the ANFIS models using the PSO algorithm improved the obtained test data RMSE values by 29% for the evaporator outlet temperature and by 18% for the evaporator outlet pressure. The accuracy and speed of the model illustrate its potential for real-time control purposes

The Application of ANN and ANFIS Prediction Models for Thermal Error Compensation on CNC Machine Tools

Thermal errors can have significant effects on Computer Numerical Control (CNC) machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This thesis first reviews different methods of designing thermal error models, before concentrating on employing Artificial Intelligence (AI) methods to design different thermal prediction models. In this research work the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as the backbone for thermal error modelling. The choice of inputs to the thermal model is a non-trivial decision which is ultimately a compromise between the ability to obtain data that sufficiently correlates with the thermal distortion and the cost of implementation of the necessary feedback sensors. In this thesis, temperature measurement was supplemented by direct distortion measurement at accessible locations. The location of temperature measurement must also provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results. In this thesis, a new intelligent system for reducing thermal errors of machine tools using data obtained from thermography data is introduced. Different groups of key temperature points on a machine can be identified from thermal images using a novel schema based on a Grey system theory and Fuzzy C-Means (FCM) clustering method. This novel method simplifies the modelling process, enhances the accuracy of the system and reduces the overall number of inputs to the model, since otherwise a much larger number of thermal sensors would be required to cover the entire structure. An Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means clustering (ANFIS-FCM) is then employed to design the thermal prediction model. In order to optimise the approach, a parametric study is carried out by changing the number of inputs and number of Membership Functions (MFs) to the ANFIS-FCM model, and comparing the relative robustness of the designs. The proposed approach has been validated on three different machine tools under different operation conditions. Thus the proposed system has been shown to be robust to different internal heat sources, ambient changes and is easily extensible to other CNC machine tools. Finally, the proposed method is shown to compare favourably against alternative approaches such as an Artificial Neural Network (ANN) model and different Grey models

Novel Tornado-Like Vortex Generator with Intelligent Controller

Cooking fumes may cause multiple adverse health effects, and range hoods play central roles in controlling indoor air pollution caused by cooking fumes. However, the traditional design of the range hoods has a low efficiency due to its working principle, and the efficiency decreases rapidly as the mounting height of the exhaust hood increases. This thesis is aimed at design and building a novel tornado-like vortex generator (TLVG) with an intelligent controller to enhance the efficiency of traditional range hood. Both experimental results and numerical simulation indicate that most of the cooking fumes are spreading to surrounding areas when the traditional range hood is working alone, while the cooking fumes are drawn into the tornado-like vortex and exhausted through the range hood when the novel TLVG is on. The effects of various factors on the efficiency of sucking cooking fumes are analyzed by orthogonal experiment design. The results show that the key factor affecting the performance of the TLVG is the horizontal jet angle. A higher jet velocity results in a lower negative pressure, which helps concentrate and exhaust the fume. The results also reveal that the exhaust flow velocity marginally affects the pressure around the source of cooking fumes, but the tornado-like vortex cannot be produced when the value of the exhaust flow velocity is too high. In addition, the figures of the velocity field, pressure field, and tracking particle field are plotted and analyzed. In this thesis, an intelligent controller of TLVG is designed and simulated to adapt to various types of range hoods. Adaptive-Network-based Fuzzy Inference System (ANFIS) is used in this intelligent controller, which combines the merits of both Fuzzy Inference Systems and Neural Networks. The results from the numerical simulation of the TLVG can be used to train and test the neural fuzzy system. Besides, Particle Swarm Optimization (PSO) is used for effective training in ANFIS networks. Digital simulation results demonstrate that the designed ANFIS-Swarm controller realizes a better prediction of the checking data than that from a basic ANFIS controller. This study provides information for improving the kitchen environment, and it can also be applied to different types of range hood, exhaust ventilation system, and air pollution control

Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China

© 2018 Elsevier B.V. With atmospheric environmental pollution becoming increasingly serious, developing an early warning system for air quality forecasting is vital to monitoring and controlling air quality. However, considering the large fluctuations in the concentration of pollutants, most previous studies have focused on enhancing accuracy, while few have addressed the stability and uncertainty analysis, which may lead to insufficient results. Therefore, a novel early warning system based on fuzzy time series was successfully developed that includes three modules: deterministic prediction module, uncertainty analysis module, and assessment module. In this system, a hybrid model combining the fuzzy time series forecasting technique and data reprocessing approaches was constructed to forecast the major air pollutants. Moreover, an uncertainty analysis was generated to further analyze and explore the uncertainties involved in future air quality forecasting. Finally, an assessment module proved the effectiveness of the developed model. The experimental results reveal that the proposed model outperforms the comparison models and baselines, and both the accuracy and the stability of the developed system are remarkable. Therefore, fuzzy logic is a better option in air quality forecasting and the developed system will be a useful tool for analyzing and monitoring air pollution

Modelling and control of waste heat recovery systems for heavy-duty applications

Internal combustion engines (ICEs) are likely to be used in heavy-duty applications for many years and it is important to continue improving their efficiency. Undesirable emissions in internal combustion engines are of major concern due to their negative effect on the human health and global warming. One approach is to recover waste heat from the exhaust of heavy-duty diesel engines (HDDEs) using waste heat recovery (WHR) technologies. WHR based on organic Rankine cycle (ORC) is a promising technology, which offers potential to reduce the fuel consumption of HDDEs by converting the wasted thermal energy to alternative useful electrical or mechanical energy. In the ORC, the evaporator is considered the most critical component of the system. Careful modelling of the evaporator unit is both crucial to assess the dynamic performance of the ORC system and challenging due to the high nonlinearity of its governing equations. This study uses an Adaptive Network-based Fuzzy Inference System (ANFIS) modelling technique to provide efficient control-oriented evaporator models for prediction of heat source and refrigerant temperatures at the evaporator outlet. The ANFIS model benefits from feed-forward output calculation and backpropagation capability of neural network, while keeping the interpretability of fuzzy systems. The effect of training the models using hybrid gradient-descent least-square estimate (GD-LSE) and particle swarm optimisation (PSO) techniques is investigated and the performance of both techniques are compared in terms of RMSE and correlation coefficients. The simulation results indicate strong learning ability and high generalisation performance for both techniques beyond capability of numerical models. However, a better accuracy is achieved for the models trained using the PSO algorithm. Experimentally-measured data is collected from a 1-kWe ORC prototype developed in Clean Energy Processes (CEP) laboratory at Imperial College London and the proposed ANFIS techniques is applied in order to investigate the application of the neuro-fuzzy technique for modelling the evaporator unit. Comparison of the experimental data and the neuro-fuzzy models predictions reveals an acceptable accuracy in predicting the evaporator outlet temperature and pressure. A novel control approach is also proposed to ensure the safe operation of ORC waste heat recovery system and stabilize its work output when subjected to transient heat sources in a range of waste heat from heavy-duty diesel engines. The control strategy comprises a neuro-fuzzy controller based on the inverse dynamics of the ORC system to control the superheating at the evaporator outlet by adjusting the pump speed and a PI controller to maintain the expander work output by regulating the mass flow rate at the expander inlet. The performance of the control strategy is investigated with respect to set-point tracking and its robustness is tested in the presence of noise. The simulation results indicate an enhancement in the controller performance by combination of feedforward and feedback controllers based on neuro-fuzzy techniques. The proposed control scheme not only can obtain satisfactory transient response under various loading conditions, but also can achieve desirable disturbance rejection performance

Application of Power Electronics Converters in Smart Grids and Renewable Energy Systems

This book focuses on the applications of Power Electronics Converters in smart grids and renewable energy systems. The topics covered include methods to CO2 emission control, schemes for electric vehicle charging, reliable renewable energy forecasting methods, and various power electronics converters. The converters include the quasi neutral point clamped inverter, MPPT algorithms, the bidirectional DC-DC converter, and the push–pull converter with a fuzzy logic controller

Interval type-2 Atanassov-intuitionistic fuzzy logic for uncertainty modelling

This thesis investigates a new paradigm for uncertainty modelling by employing a new class of type-2 fuzzy logic system that utilises fuzzy sets with membership and non-membership functions that are intervals. Fuzzy logic systems, employing type-1 fuzzy sets, that mark a shift from computing with numbers towards computing with words have made remarkable impacts in the field of artificial intelligence. Fuzzy logic systems of type-2, a generalisation of type-1 fuzzy logic systems that utilise type-2 fuzzy sets, have created tremendous advances in uncertainty modelling. The key feature of the type-2 fuzzy logic systems, with particular reference to interval type-2 fuzzy logic systems, is that the membership functions of interval type-2 fuzzy sets are themselves fuzzy. These give interval type-2 fuzzy logic systems an advantage over their type-1 counterparts which have precise membership functions. Whilst the interval type-2 fuzzy logic systems are effective in modelling uncertainty, they are not able to adequately handle an indeterminate/neutral characteristic of a set, because interval type-2 fuzzy sets are only specified by membership functions with an implicit assertion that the non-membership functions are complements of the membership functions (lower or upper). In a real life scenario, it is not necessarily the case that the non-membership function of a set is complementary to the membership function. There may be some degree of hesitation arising from ignorance or a complete lack of interest concerning a particular phenomenon. Atanassov intuitionistic fuzzy set, another generalisation of the classical fuzzy set, captures this thought process by simultaneously defining a fuzzy set with membership and non-membership functions such that the sum of both membership and non-membership functions is less than or equal to 1. In this thesis, the advantages of both worlds (interval type-2 fuzzy set and Atanassov intuitionistic fuzzy set) are explored and a new and enhanced class of interval type-2 fuzzy set namely, interval type-2 Atanassov intuitionistic fuzzy set, that enables hesitation, is introduced. The corresponding fuzzy logic system namely, interval type-2 Atanassov intuitionistic fuzzy logic system is rigorously and systematically formulated. In order to assess this thesis investigates a new paradigm for uncertainty modelling by employing a new class of type-2 fuzzy logic system that utilises fuzzy sets with membership and non-membership functions that are intervals. Fuzzy logic systems, employing type-1 fuzzy sets, that mark shift from computing with numbers towards computing with words have made remarkable impacts in the field of artificial intelligence. Fuzzy logic systems of type-2, a generalisation of type-1 fuzzy logic systems that utilise type-2 fuzzy sets, have created tremendous advances in uncertainty modelling. The key feature of the type-2 fuzzy logic systems, with particular reference to interval type-2 fuzzy logic systems, is that the membership functions of interval type-2 fuzzy sets are themselves fuzzy. These give interval type-2 fuzzy logic systems an advantage over their type-1 counterparts which have precise membership functions. Whilst the interval type-2 fuzzy logic systems are effective in modelling uncertainty, they are not able to adequately handle an indeterminate/neutral characteristic of a set, because interval type-2 fuzzy sets are only specified by membership functions with an implicit assertion that the non-membership functions are complements of the membership functions (lower or upper). In a real life scenario, it is not necessarily the case that the non-membership function of a set is complementary to the membership function. There may be some degree of hesitation arising from ignorance or a complete lack of interest concerning a particular phenomenon. Atanassov intuitionistic fuzzy set, another generalisation of the classical fuzzy set, captures this thought process by simultaneously defining a fuzzy set with membership and non-membership functions such that the sum of both membership and non-membership functions is less than or equal to 1. In this thesis, the advantages of both worlds (interval type-2 fuzzy set and Atanassov intuitionistic fuzzy set) are explored and a new and enhanced class of interval type-2 fuzz set namely, interval type-2 Atanassov intuitionistic fuzzy set, that enables hesitation, is introduced. The corresponding fuzzy logic system namely, interval type-2 Atanassov intuitionistic fuzzy logic system is rigorously and systematically formulated. In order to assess the viability and efficacy of the developed framework, the possibilities of the optimisation of the parameters of this class of fuzzy systems are rigorously examined. First, the parameters of the developed model are optimised using one of the most popular fuzzy logic optimisation algorithms such as gradient descent (first-order derivative) algorithm and evaluated on publicly available benchmark datasets from diverse domains and characteristics. It is shown that the new interval type-2 Atanassov intuitionistic fuzzy logic system is able to handle uncertainty well through the minimisation of the error of the system compared with other approaches on the same problem instances and performance criteria. Secondly, the parameters of the proposed framework are optimised using a decoupledextended Kalman filter (second-order derivative) algorithm in order to address the shortcomings of the first-order gradient descent method. It is shown statistically that the performance of this new framework with fuzzy membership and non-membership functions is significantly better than the classical interval type-2 fuzzy logic systems which have only the fuzzy membership functions, and its type-1 counterpart which are specified by single membership and non-membership functions. The model is also assessed using a hybrid learning of decoupled extended Kalman filter and gradient descent methods. The proposed framework with hybrid learning algorithm is evaluated by comparing it with existing approaches reported in the literature on the same problem instances and performance metrics. The simulation results have demonstrated the potential benefits of using the proposed framework in uncertainty modelling. In the overall, the fusion of these two concepts (interval type-2 fuzzy logic system and Atanassov intuitionistic fuzzy logic system) provides a synergistic capability in dealing with imprecise and vague information

An integrated approach to value chain analysis of end of life aircraft treatment

Dans cette thèse, on propose une approche holistique pour l’analyse, la modélisation et l’optimisation des performances de la chaîne de valeur pour le traitement des avions en fin de vie (FdV). Les recherches réalisées ont débouché sur onze importantes contributions. Dans la première contribution, on traite du contexte, de la complexité, de la diversité et des défis du recyclage d’avions en FdV. La seconde contribution traite du problème de la prédiction du nombre de retraits d’avions et propose une approche intégrée pour l’estimation de ce nombre de retraits. Le troisième et le quatrième articles visent à identifier les parties prenantes, les valeurs perçues par chaque partenaire et indiquent comment cette valeur peut affecter les décisions au stade de la conception. Les considérations relatives à la conception et à la fabrication ont donné lieu à quatre contributions importantes. La cinquième contribution traite des défis et opportunités pouvant résulter de l’application des concepts de la chaîne logistique verte, pour les manufacturiers d’avions. Dans la sixième contribution, un outil d’aide à la décision a été développé pour choisir la stratégie verte qui optimise les performances globales de de toute la chaîne de valeur en tenant compte des priorités et contraintes de chaque partenaire. Dans la septième contribution, un modèle mathématique est proposé pour analyser le choix stratégique des manufacturiers en réponse aux directives en matière de FdV de produits comme le résultat des interactions des compétiteurs dans le marché. La huitième contribution porte sur les travaux réalisés dans le cadre d’un stage chez le constructeur d’avions, Bombardier. Cette dernière traite de l’apport de « l’analyse du cycle de vie » au stade de la conception d’avions. La neuvième contribution introduit une méthodologie d’analyse de la chaîne de valeur dans un contexte de développement durable. Finalement, les dixième et onzième contributions proposent une approche holistique pour le traitement des avions en FdV en intégrant les concepts du « lean », du développement durable et des contraintes et opportunités inhérentes à la mondialisation des affaires. Un modèle d’optimisation intégrant les modèles d’affaires, les stratégies de désassemblage et les structures du réseau qui influencent l’efficacité, la stabilité et l’agilité du réseau de récupération est proposé. Les données requises pour exploiter le modèle sont indiquées dans l’article. Mots-clés: Fin de vie des avions, analyse de la chaîne de valeurs, développement durable, intervenants.The number of aircrafts at the end of life (EOL) is continuously increasing. Dealing with retired aircrafts considering the environmental, social and economic impacts is becoming an emerging problem in the aviation industry in near future. This thesis seeks to develop a holistic approach in order to analyze the value chain of EOL aircraft treatment in the context of sustainable development. The performed researches have led to eleven main contributions. In the first contribution, the complexity and diversity of the EOL aircraft recycling including the challenges and problem context are discussed. The second contribution addresses the challenges for estimation of retired aircrafts and proposes an integrated approach for prediction of EOL aircrafts. The third and fourth contributions aim to identify the players involved in EOL recycling context, values perceived by different shareholders and formulate that how such value can affect design decisions. Design stage consideration and manufacture’s issues are discussed and have led to four main contributions. The fifth contribution addresses the opportunities and challenges of applying green supply chain for aircraft manufacturers. In the sixth contribution, a decision tool is developed to aid manufactures in early stage of design for their green strategy choices. In the seventh contribution, a mathematical model is developed in order to analyze the strategic choice of manufacturers in response to EOL directives as the result of the interaction of competitors in the market. An internship project has been also performed in Bombardier and led to the eighth contribution, which addresses life cycle approach and incorporating the sustainability in early stage of design of aircraft. The ninth contribution introduces a methodology for analyzing the value chain in the context of sustainable development. Finally, the tenth and eleventh contributions propose a holistic approach to EOL aircraft treatment considering lean principals, sustainable development, and global business environment. An optimization model is developed to support decision making in both strategic and managerial level. The analytical approaches, decision tools and step by step guidelines proposed in this thesis will aid decision makers to identify appropriate strategies for the EOL aircraft treatment in the sustainable development context. Keywords: End of life aircraft, value chain analysis, sustainable development, stakeholders