Design of Nonlinear PID Controllers and Their Application to a Heat Exchanger System for LNG-fuelled Marine Engines

Excessive use of fossil fuels resources is adding several types of greenhouse gases which make the earth warmer. Emissions from ship's exhausts contribute to global climate change, too. The International Maritime Organization (IMO) has adopted regulations to reduce the emission of air pollutants from international shipping, such as major air pollutants, carbon dioxide (CO2), nitrogen oxides (NOx), and sulphur oxides (SOx) under Annex VI of the 1997 MARPOL protocol. Likewise, as regulations on the emission of major air pollutants have become internationally strict, the development of environmentally friendly vessels and engines is required. One of the globally accepted means of reducing emission gases is the use of more eco-friendly fuel, LNG (Liquefied Natural Gas). LNG as a marine fuel reduces air pollutants as referred compared to traditional heavy fuel oil (HFO). Recently, large engine manufacturers are developing LNG-fuelled marine engines. In order to use this cryogenic LNG as a fuel, it is necessary to change it back to a gaseous state. A heat exchanger is used to regasify LNG. The heat exchange takes place between LNG and glycol on the primary loop, and heat exchange occurs between glycol and steam on the secondary loop. These series of processes are called LNG regasification. To control the temperature of the heat exchanger, it is necessary to model the heat exchanger. However, it is not easy to obtain an accurate mathematical model because the heat exchanger has non-linearity and time-varying characteristics. In addition, a fixed-gain controller is bound to have a limitation in its function if parameters of the heat exchanger are changed. Thus, various techniques have been studied to improve the adaptability and robustness of the controller. Recently, there has been using nonlinear PID (NPID) controller for the controlled system which have highly nonlinear and time-varying characteristics during operation. Therefore, this thesis proposes two types of the nonlinear proportional, integral, derivative (NPID) controllers to control the glycol temperature of the regasification system for LNG-fuelled marine engines. The Fully-Nonlinear PID (F-NPID) controller has a structure that the error between the set-point (or reference input) and output (or the measured output) is scaled nonlinearly, and input into the controller to derive proportional, integral, and derivative controllers. The Partial-Nonlinear PID (P-NPID) controller uses the conventional linear PD controller and only I controller uses the method of F-NPID controller. In this case, the nonlinear functions are implemented by the Fuzzy model of Takagi-Sugeno (T-S) type. In addition, the error is continuously scaled so that outstanding control performance can be maintained even when the operating environment is changed, thereby improving the swiftness and the closeness of responses. Also, the parameters of the two proposed controllers are optimally tuned in terms of minimizing the integral of the absolute error (IAE) objective function based on the genetic algorithm (GA). Meanwhile, it is necessary to examine the stability of overall feedback system that can be caused by introducing nonlinear functions during controller design. For this, the stability of the overall feedback system is analyzed by applying the circle stability theorems, which is often used for stability analysis of nonlinear problems. The proposed controllers are verified their performances which are the set-point tracking, robustness against noise and parameter changes, disturbance rejection performances by comparing with two conventional PID controllers and a conventional NPID controller.Chapter 1. Introduction 1 1.1 Research background and trends 1 1.2 Research content and composition 6 Chapter 2. LNG-fuelled Marine Engines 8 2.1 Changes of LNG-fuelled marine engines 8 2.2 Fuel injection of LNG-fuelled marine engines 10 2.3 Fuel supply system of LNG-fuelled marine engines 13 Chapter 3. Modeling of LNG Regasification System 17 3.1 Heat exchanger 17 3.2 LNG regasification system 18 3.3 Modeling of the secondary loop heat exchanger of LNG regasification system 19 3.3.1 Model of an I/P converter 19 3.3.2 Model of a pneumatic control valve 20 3.3.3 Model of a heat exchanger 23 3.3.4 Model of a disturbance 27 3.3.5 Model of a RTD sensor 28 3.3.6 Model of a time delay 29 3.3.7 Open-loop control system 30 Chapter 4. Surveys of Existing PID Controllers 32 4.1 Linear PID controller 32 4.1.1 Structure of the conventional PID controller 32 4.1.2 Characteristics of control actions 33 4.1.3 Effects of PID controller gains 36 4.2 Gain tuning of the conventional PID controller 37 4.2.1 Ziegler-Nichols tuning method 37 4.2.2 Tyreus-Luyben tuning method 40 4.3 Practical PID controller 41 4.4 Existing nonlinear PID controllers 44 4.4.1 Seraji’s NPID controller 45 4.4.2 Korkmaz’s NPID controller 48 Chapter 5. Suggestion of the Proposed Nonlinear PID Controllers 52 5.1 Fully-nonlinear PID controller 52 5.1.1 Nonlinear P block 53 5.1.2 Nonlinear D block 57 5.1.3 Nonlinear I block 57 5.1.4 Relationship between and 60 5.2 Partially-nonlinear PID controller 62 5.2.1 Linear PD block 63 5.2.2 Nonlinear I block 63 5.3 Feedback control systems 63 5.3.1 Modified F-NPID control system 63 5.3.2 P-NPID control system 66 5.4 Tuning of the controller parameters 68 5.4.1 Genetic algorithm 68 5.4.2 Optimal tuning of the controller parameters 73 Chapter 6. Stability Analysis 75 6.1 System description 75 6.2 Basic definitions and theorems 76 6.3 Stability of the NPID control systems 86 6.3.1 Sector condition of nonlinear block 86 6.3.2 Stability analysis of F-NPID control system 87 6.3.3 Stability analysis of P-NPID control system 88 Chapter 7. Simulation and Discussion of Results 90 7.1 Controller parameter tuning 90 7.2 Reponses to set-point changes 91 7.3 Reponses to noise rejection 94 7.4 Reponses to system parameter changes 95 7.5 Reponses to disturbance changes 97 Chapter 8. Conclusion 99 References 101Docto

Knowledge based recursive non-linear partial least squares (RNPLS)

Producción CientíficaSoft sensors driven by data are very common in industrial plants to perform indirect measurements of difficult to measure critical variables by using other variables that are relatively easier to obtain. The use of soft sensors implies some challenges, such as the colinearity of the predictor variables, the time-varying and possible non-linear nature of the industrial process. To deal with the first challenge, the partial least square (PLS) regression has been employed in many applications to model the linear relations between process variables, with noisy and highly correlated data. However, the PLS model needs to deal with the other two issues: the non-linear and time-varying characteristics of the processes. In this work, a new knowledge-based methodology for a recursive non-linear PLS algorithm (RNPLS) is systematized to deal with these issues. Here, the non-linear PLS algorithm is set up by carrying out the PLS regression over the augmented input matrix, which includes knowledge based non-linear transformations of some of the variables. This transformation depends on the system’s nature, and takes into account the available knowledge about the process, which is provided by expert knowledge or emulated using software tools. Then, the recursive exponential weighted PLS is used to modify and adapt the model according to the process changes. This RNPLS algorithm has been tested using two case studies according to the available knowledge, a real industrial evaporation station of the sugar industry, where the expert knowledge about the process permits the formulation of the relationships, and a simulated wastewater treatment plant, where the necessary knowledge about the process is obtained by a software tool. The results show that the methodology involving knowledge regarding the process is able to adjust the process changes, providing highly accurate predictions.Este trabajo forma parte del proyecto de investigación: MINECO/FEDER: DPI2015-67341-C2-2-R

An optimized attack tree model for security test case planning and generation

Securing software assets via efficient test case management is an important task in order to realize business goals. Given the huge risks web applications face due to incessant cyberattacks, a proactive risk strategy such as threat modeling is adopted. It involves the use of attack trees for identifying software vulnerabilities at the earliest phase of software development which is critical to successfully protect these applications. Although, many researches have been dedicated to security testing with attack tree models, test case redundancy using this threat modeling technique has been a major issue faced leading to poor test coverage and expensive security testing exercises. This paper presents an attack tree modeling algorithm for deriving a minimal set of effective attack vectors required to test a web application for SQL injection vulnerabilities. By leveraging on the optimized attack tree algorithm used in this research work, the threat model produces efficient test plans from which adequate test cases are derived to ensure a secured web application is designed, implemented and deployed. The experimental result shows an average optimization rate of 41.67% from which 7 test plans and 13 security test cases were designed to mitigate all SQL injection vulnerabilities in the web application under test. A 100% security risk intervention of the web application was achieved with respect to preventing SQL injection attacks after applying all security recommendations from test case execution report

Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant

There is increasing need for tighter controls of coal-fired plants due to more stringent regulations and addition of more renewable sources in the electricity grid. Achieving this will require better process knowledge which can be facilitated through the use of plant models. Drum-boilers, a key component of coal-fired subcritical power plants, have complicated characteristics and require highly complex routines for the dynamic characteristics to be accurately modelled. Development of such routines is laborious and due to computational requirements they are often unfit for control purposes. On the other hand, simpler lumped and semi empirical models may not represent the process well. As a result, data-driven approach based on neural networks is chosen in this study. Models derived with this approach incorporate all the complex underlying physics and performs very well so long as it is used within the range of conditions on which it was developed. The model can be used for studying plant dynamics and design of controllers. Dynamic model of the drum-boiler was developed in this study using NARX neural networks. The model predictions showed good agreement with actual outputs of the drum-boiler (drum pressure and water level)

Dynamic process modeling and hybrid intelligent control of ethylene copolymerization in gas phase catalytic fluidized bed reactors

BACKGROUND: Polyethylene (PE) is the most extensively consumed plastic in the world, and gas phase‐based processes are widely used for its production owing to their flexibility. The sole type of reactor that can produce PE in the gas phase is the fluidized bed reactor (FBR), and effective modeling and control of FBRs are of great importance for design, scale‐up and simulation studies. This paper discusses these issues and suggests a novel advanced control structure for these systems. RESULTS: A unified process modeling and control approach is introduced for ethylene copolymerization in FBRs. The results show that our previously developed two‐phase model is well confirmed using real industrial data and is exact enough to further develop different control strategies. It is also shown that, owing to high system nonlinearities, conventional controllers are not suitable for this system, so advanced controllers are needed. Melt flow index (MFI) and reactor temperature are chosen as vital variables, and intelligent controllers were able to sufficiently control them. Performance indicators show that advanced controllers have a superior performance in comparison with conventional controllers. CONCLUSION: Based on control performance indicators, the adaptive neuro‐fuzzy inference system (ANFIS) controller for MFI control and the hybrid ANFIS–proportional‐integral‐differential (PID) controller for temperature control perform better regarding disturbance rejection and setpoint tracking in comparison with conventional controllers. © 2019 Society of Chemical Industr

Approaches based on LAMDA control applied to regulate HVAC systems for buildings

The control of HVAC (Heating Ventilation and Air Conditioning) systems is usually complex because its modeling in certain cases is difficult, since these systems have a large number of components. Heat exchangers, chillers, valves, sensors, and actuators, increase the non-linear characteristics of the complete model, so it is necessary to propose new control strategies that can handle the uncertainty generated by all these elements working together. On the other hand, artificial intelligence is a powerful tool that allows improving the performance of control systems with inexact models and uncertainties. This paper presents new control alternatives for HVAC systems based on LAMDA (Learning Algorithm for Multivariable Data Analysis). This algorithm has been used in the field of machine learning, however, we have taken advantage of its learning characteristics to propose different types of intelligent controllers to improve the performance of the overall control system in tasks of regulation and reference change. In order to perform a comparative analysis in the context of HVAC systems, conventional methods such as PID and Fuzzy-PID are compared with LAMDA-PID, LAMDA-Sliding Mode Control based on Z-numbers (ZLSMC), and Adaptive LAMDA. Specifically, two HVAC systems are implemented by simulations to evaluate the proposals: an MIMO (Multiple-input Multiple-output) HVAC system and an HVAC system with dead time, which are used to compare the results qualitatively and quantitatively. The results show that ZLSMC is the most robust controller, which efficiently controls HVAC systems in cases of reference changes and the presence of disturbances.European CommissionAgencia Estatal de InvestigaciónJunta de Comunidades de Castilla-La Manch

Design and implementation of an intelligent fuzzy logic controller (FLC) for Air Handling Unit (AHU) for smart house

Intelligent Building Automation System (IBAS) is one of the heaviest researched areas motivated by the continuous high demand on economically-effective systems that are designed to provide a desirable controlled space for various organizations. IBAS has been developed along with the rapid sophistication of the information and control technologies in this study. The main objective of the continuous effort is to provide an intelligent monitor and control of various facilities within the building so as to offer its users or occupants with effective security, improved productivity, human comfort, and efficient energy management. Heat, Ventilation and Air Conditioning (HVAC), Lighting Systems, Life and Safety System, and Access Control are some of the typical systems that formed IBAS in most modern building. HVAC and Lighting systems constitute the major energy consumer in an entire building that focuses particularly on the improvement of monitor and control of these systems

A study on neural network based system identification with application to heating, ventilating and air conditioning (hvac)system

Recent efforts to incorporate aspects of artificial intelligence into the design and operation of automatic control systems have focused attention on techniques such as fuzzy logic, artificial neural networks, and expert systems. Although LMS algorithm has been considered to be a popular method of system identification but it has been seen in many situations that accurate system identification is not achieved by employing this technique. On the other hand, artificial neural network (ANN) has been chosen as a suitable alternative approach to nonlinear system identification due to its good function approximation capabilities i.e. ANNs are capable of generating complex mapping between input and output spaces. Thus, ANNs can be employed for modeling of complex dynamical systems with reasonable degree of accuracy. The use of computers for direct digital control highlights the recent trend toward more effective and efficient heating, ventilating, and air-conditioning (HVAC) control methodologies. The HVAC field has stressed the importance of self learning in building control systems and has encouraged further studies in the integration of optimal control and other advanced techniques into the formulation of such systems. In this thesis we describe the functional link artificial neural network (FLANN), Multi-Layer Perceptron (MLP) with Back propagation (BP) and MLP with modified BP called the emotional BP and Neuro fuzzy approaches for the HVAC System Identification. The thesis describes different architectures together with learning algorithms to build neural network based nonlinear system identification schemes such as Multi-Layer Perceptron (MLP) neural network, Functional Link Artificial Neural Network (FLANN) and ANFIS structures. In the case of MLP used as an identifier, different structures with regard to hidden layer selection and nodes in each layer have been considered. It may be noted that difficulty lies in choosing the number of hidden layers for achieving a correct topology of MLP neural identifier. To overcome this, in the FLANN identifier hidden layers are not required whereas the input is expanded by using trigonometric polynomials i.e. with cos(nπu) and sin(nπu), for n=0,1,2,…. The above ANN structures MLP, FLANN and Neuro-fuzzy (ANFIS Model) have been extensively studied

Model and control of a solar tower for energy production

Supervisors: Prof./Dr. João Manuel Lage de Miranda Lemos; Prof./Dr. Bertinho Manuel D’Andrade da Costa. Examination Committee: Chairperson: Prof./Dr. João Fernando Cardoso Silva Sequeira;Supervisor: Prof./Dr. João Manuel Lage de Miranda Lemos. Members of the Committee: BGEN/ENGEL Luis Filipe Basto Damásio; TCOR/ENGEL Ana Paula da Silva Jorge; Prof./Dr. Alexandre José Malheiro BernardinSolar towers are electrical power production systems that use highly concentrated solar radiation as energy source that is collected by means of a heat-transfer fluid. This master thesis studies the application of several control strategies with the aim of maintaining the working fluid at a temperature that maximizes the electrical production. The main difficulties are the nonlinear fluid temperature dynamics, plant thermal constrains, and a variable energy source that cannot be manipulated. The temperature dynamics flow dependence demands for a changing parameter controller that results from a gain scheduling scheme or from a multi-model adaptive control strategy, in which the manipulated variable is adjusted by one of the set of local controllers designed for different operating regimes. The former is accomplished through a Proportional-Integral Controller (PI) control and the latter via Linear Quadratic Gaussian (LQG) optimal control. In addition, the Multistep Multivariable Adaptive Regulator (MUSMAR) control algorithm that adjusts its gains to every plant dynamic change, including parameters, is tested. Although the mentioned control concepts are applied considering the flow as the only manipulated variable, the combination of the latter with the radiation flux reflected by the heliostat field is also studied through PI control. The solar tower electrical power production has a maximum for a given outlet temperature that changes with plant parameters and disturbances. The improvement of production levels is conducted by adjusting the temperature reference with a static optimization procedure.As torres solares são sistemas de produção de energia elétrica que utilizam a radiação solar concentrada como fonte primária. A última, é absorvida por um fluído que percorre o permutador de calor em que a radiação incide. A presente dissertação estuda a aplicação de várias estratégias de controlo com o objetivo de manter a temperatura do fluido no valor que maximiza a produção de energia. As dificuldades principais centram-se na não linearidade da dinâmica da temperatura do fluído, nos limites térmicos do sistema e na incapacidade de manipular a radiação solar. A alteração da dinâmica da temperatura com o caudal requer a utilização de um controlador de parâmetros variáveis que resulta de um esquema de escalonamento de ganhos ou da associação de diversos controladores projetados para diferentes pontos de operação. O primeiro método é desenvolvido com controlo PI enquanto que o último recorre ao controlo LQG. Em adição, é estudada a aplicação do algoritmo de controlo MUSMAR, em que os ganhos são adaptados para qualquer alteração da dinâmica do sistema, incluindo a variação de parâmetros do modelo. Embora as estratégias de controlo referidas considerem apenas o caudal como variável manipulável, a combinação da última com o fluxo de radiação é tambem estudada com um controlador PI. A produção de energia de uma torre solar possui um máximo para uma dada temperatura que se altera com os parâmetros da planta e perturbações. O aumento da produção é conseguida pela determinação da referência de temperatura através de um algoritmo de otimização estática.N/

Building Temperature Control with Intelligent Methods

Temperature control is important for both human comfort and the need in industry. In the thesis, two good intelligent control methods are compared to find their advantages and disadvantages. Matlab is used as the tool to make models and process calculations. The building model is one simple room in Akwesasne in New York State and the target is to keep the temperature indoor around 22 degree Centigrade from 12/29/2013 to 12/31/2013. Heat pump is used to provide or absorb heat. All data in the experiments is from JRibal Environmental eXchange network and PJM. The first method is fuzzy neural network (FNN). With the control from fuzzy logic and the learning process in neural network, the temperature is kept around 22 degree Centigrade. Another method is model predictive control (MPC) with genetic algorithm (GA). And the temperature is also controlled around 22 degree Centigrade by predicting the temperature and solar radiation. In addition, the cost is saved by using genetic algorithm with an energy storage system added in the building model. In summary, FNN is easy to build but the result is not very accurate; while the result of MPC is more accurate but the model is hard to develop. And GA is a good optimization method