Steam Temperature Control Based on Modified Active Disturbance Rejection

Tato práce je zaměřena na studium využitelnosti algoritmu aktivního odstranění vlivu neměřené poruchy a modifikovaného algoritmu aktivního odstranění vlivu neměřené poruchy aplikovaného na řízení teploty přehřáté páry v tepelné elektrárně. Studie byly prováděny na základě linearizovaného modelu přehříváku. Studium algoritmu aktivního odstranění vlivu neměřené poruchy je relevantní v souvislosti s možností jeho aplikace pro komplexní technologické procesy (procesy/soustavy s velkým počtem parametrů). zde je studovaným objektem přehřívák, který je součástí technologického uzlu přípravy přehřáté páry pro dodávku vysokotlaké páry do vysokotlakého stupně turbíny. Efektivnost obou algoritmů aktivního odstranění vlivu poruchy v porovnání s klasickým PID regulátorem je demonstrována na výsledcích simulací. Podrobnější analýza obou metod je nezbytná zejména v případě, kdy řídíme systém vyššího řádu jako například v případě přehříváku. Výsledky analýzy jsou také v práci uvedeny.This work is aimed at studying the applicability of active disturbance rejection algorithm and modified active disturbance rejection algorithm for use in controlling the superheated steam temperature in propulsion of thermal power plant. The studies were conducted on the basis of the linearized model of the superheater. The algorithm itself for active disturbance rejection is relevant to study in connection with the possibility of its application for complex technological objects (objects with a large number of parameters). These objects are the superheater, which is part of the superheated steam preparation object, for supplying high-pressure steam to the turbine high pressure stage. To demonstrate the effectiveness of this algorithm (within the framework of the problem of disturbance rejection) in comparison with the classical PID controller, the results of mathematical modeling are presented. The paper also presents the results of a study of a modified active disturbance rejection method. The need to study this method is due to the high order of the mathematical model of the control object under study. The results of these studies are also given in the work

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

Modelling of Engineering Systems with Small Data, a Comparative Study

This chapter equitably compares five different Artificial Intelligence (AI) techniques for data-driven modelling. All these techniques were used to solve two real-world engineering data-driven modelling problems with small number of experimental data samples, one with sparse and one with dense data. The models of both problems are shown to be highly nonlinear. In the problem with available dense data, Multi-Layer Perceptron (MLP) evidently outperforms other AI models and challenges the claims in the literature about superiority of Fully Connected Cascade (FCC). However, the results of the problem with sparse data shows superiority of FCC, closely followed by MLP and neuro-fuzzy network

Modelling and optimisation of decentralised hybrid solar biogas system to power an organic Rankine cycle (ORC-Toluene) and air gap membrane distillation (AGMD) for desalination and electric power generation

The intensive use of fossil fuels to meet the world energy and water demand has caused several environmental issues, such as global warming, air pollution and ozone depletion. Therefore, the integration of stand-alone decentralised hybrid renewable energy systems is a promising solution to satisfy the global energy-water demands and minimize the effects of fossil fuels utilisation. Among these hybrid technologies, concentrated solar power (CSP) combined with waste-based biogas to power organic Rankine cycle for cogeneration provide the means to generate dispatchable, reliable, renewable electricity and water in high direct normal incidence (DNI) regions around the world. Due to the strong inverse correlation between DNI resources and freshwater availability, most of the best potential CSP regions also lack sufficient freshwater resources. The current study proposes and applies a novel multi-dimensional modelling technique based on artificial neural networks (ANN) for hourly solar radiation and wind speed data forecasting over six locations in Oman. The developed model is the first attempt to integrate two ANN models simultaneously by using enormous meteorological data points for both solar radiation and wind speed prediction. The developed model requires only three parameters as inputs, and it can predict solar radiation and wind speed data simultaneously with high accuracy. As a result, the model provides a user-friendly interface that can be utilised in the energy systems design process. Consequently, this model facilitates the implementation of renewable energy technologies in remote areas in which gathering of weather data is challenging. Meanwhile, the accuracy of the model has been tested by calculating the mean absolute percentage error (MAPE) and the correlation coefficient (R). Therefore, the model developed in this study can provide accurate weather data and inform decision makers for future instalments of energy systems. Furthermore, a novel proposed hybrid solar and biogas system for desalination and electric power generation using advanced modelling techniques to integrate the stand-alone off-grid system has been designed. The novelty emerges from some facts, which are centralised around the use of a hybrid electric generation via Concentrated Solar Power (CSP) and anaerobic digestion biogas to achieve higher stability and profitability. Meanwhile, the cogeneration through the waste heat of the ORC drives the AGMD, which benefits as well from the higher stability due to hybridisation. In addition, an innovative and user-friendly modelling approach has been applied, and this efficiently integrates the individual energy components, i.e. PTC, anaerobic biogas boiler, ORC and AGMD, which fosters the optimisation of the proposed system. The models have been developed in the MATLAB/Simulink® software and have been used to investigate the system area, dimensions, and cost and to ensure that the electrical and water demand of the end-user are met. In addition, a new detailed thermo-economic assessment of the proposed hybrid solar biogas for cogeneration in off-grid applications has been investigated. An energy, exergy, and cost analysis has been performed and to fully utilise this, a sensitivity assessment on the developed model has been analysed to examine the effects of various design parameters on the thermo-economic performance. Finally, implementing an in-depth simulation testing of the system in a rural region in Oman is presented. The novel integrated solar and biogas system that has been designed through advanced modelling in the MATLAB/ Simulink® is integrated with a robust multi-objective optimisation technique to determine the best operating configuration. Three objective functions namely, maximising power and water production, and minimising the unit exergy product costs have been formulated. The turbine efficiency, top ORC vapor temperature and ORC condenser temperature has been selected as the decision variables. The non-dominated sorting genetic algorithm (NSGA-II) has been employed to solve the optimisation problem and produce a Pareto frontier of the optimal solutions. Further, the TOPSIS approach has been used to select the optimal solution from the Pareto set. The study constitutes the first attempt to holistically optimise such a hybrid off-grid cogeneration system in a robust manner

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

A review of geothermal energy-driven hydrogen production systems

This paper presents a review of hydrogen production systems using geothermal energy, showing the importance and potential of this technology in addition to the main obstacles facing this domain. The effect of several parameters was taken into consideration, such as geothermal fluid temperature, water electrolysis temperature, working fluid, and type of power cycle. The different types of geothermal power plants were also compared, namely, flash, binary, flash-binary, recuperative, regenerative, and organic Rankine flash cycles. This study covers a wide range of investigations regarding hydrogen production rate, hydrogen production cost, energetic efficiency, exergetic efficiency, exergetic cost, and electricity generated. Hydrogen production rate is one of the most important mentioned parameters in which it was found to vary from 5.439 kg/h to 13958 kg/h. Multigeneration systems have shown great potential to enhance the overall system’s efficiency, leading to reduced production costs. The integration of another energy source was found to be interesting in geothermal-driven hydrogen production systems. This would promote the adoption of multigeneration system as well as increasing the geothermal fluid’s temperature before entering the power cycle

Energy Processes, Systems and Equipment

This book focuses on the progress in modern energy processes, systems and equipment. Since the beginning of humankind, energy has been the most important need for each human and living being. Thus, the development of different ways of energy conversion that can be applied to cover growing energy needs has become a crucial challenge for scientists and engineers around the world, making the power industry, in which operation is based on subsequent energy conversion processes, one of the most important fields of the local, national, and global economy today. Progress in precise description, modeling, and optimization of physical phenomena related to the energy conversion processes bounded to large and dispersed power systems is a key research and development field of the economy