An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips

A power plant monitoring system embedded with artificial intelligence can enhance its effectiveness by reducing the time spent in trip analysis and follow up procedures. Experimental results showed that Multilayered perceptron neural network trained with Levenberg-Marquardt (LM) algorithm achieved the least mean squared error of 0.0223 with the misclassification rate of 7.435% for the 10 simulated trip prediction. The proposed method can identify abnormality of operational parameters at the confident level of ±6.3%

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

A Survey on Intelligent Optimization Approaches to Boiler Combustion Optimization

This paper reviews the researches on boiler combustion optimization, which is an important direction in the field of energy saving and emission reduction. Many methods have been used to deal with boiler combustion optimization, among which evolutionary computing (EC) techniques have recently gained much attention. However, the existing researches are not sufficiently focused and have not been summarized systematically. This has led to slow progress of research on boiler combustion optimization and has obstacles in the application. This paper introduces a comprehensive survey of the works of intelligent optimization algorithms in boiler combustion optimization and summarizes the contributions of different optimization algorithms. Finally, this paper discusses new research challenges and outlines future research directions, which can guide boiler combustion optimization to improve energy efficiency and reduce pollutant emission concentrations

Dynamic modeling and control strategies of organic Rankine cycle systems: Methods and challenges

Organic Rankine cycle systems are suitable technologies for utilization of low/medium-temperature heat sources, especially for small-scale systems. Waste heat from engines in the transportation sector, solar energy, and intermittent industrial waste heat are by nature transient heat sources, making it a challenging task to design and operate the organic Rankine cycle system safely and efficiently for these heat sources. Therefore, it is of crucial importance to investigate the dynamic behavior of the organic Rankine cycle system and develop suitable control strategies. This paper provides a comprehensive review of the previous studies in the area of dynamic modeling and control of the organic Rankine cycle system. The most common dynamic modeling approaches, typical issues during dynamic simulations, and different control strategies are discussed in detail. The most suitable dynamic modeling approaches of each component, solutions to common problems, and optimal control approaches are identified. Directions for future research are provided. The review indicates that the dynamics of the organic Rankine cycle system is mainly governed by the heat exchangers. Depending on the level of accuracy and computational effort, a moving boundary approach, a finite volume method or a two-volume simplification can be used for the modeling of the heat exchangers. From the control perspective, the model predictive controllers, especially improved model predictive controllers (e.g. the multiple model predictive control, switching model predictive control, and non-linear model predictive control approach), provide excellent control performance compared to conventional control strategies (e.g. proportional–integral controller, proportional–derivative controller, and proportional–integral–derivative controllers). We recommend that future research focuses on the integrated design and optimization, especially considering the design of the heat exchangers, the dynamic response of the system and its controllability

Intelligent PID controller based on fuzzy logic control and neural network technology for indoor environment quality improvement

The demand for better indoor environment has led to a wide use of heating, ventilating and air conditioning (HVAC) systems. Employing advanced HVAC control strategies is one of the strategies to maintain high quality indoor thermal comfort and indoor air quality (IAQ). This thesis aims to analyse and discuss the potential of using advanced control methods to improve the indoor occupants’ comfort. It focuses on the development of controllers of the major factors of indoor environment quality in buildings including indoor air temperature, indoor humidity and indoor air quality. Studies of the development of control technologies for HVAC systems are reviewed firstly. The problems in existing and future perspectives on HVAC control systems for occupants’ comfort are investigated. As both the current conventional and intelligent controllers have drawbacks that limit their applications, it is necessary to design novel control strategies for the urgent issue of indoor climate improvement. Hence, a concept of designing the controllers for indoor occupants’ comfort is proposed in this thesis. The proposed controllers in this research are designed by combining the conventional and intelligent control technologies. The purpose is to optimize the advantages of both conventional and intelligent control methods and to avoid poor control performance due to their drawbacks. The main control technologies involved in this research are fuzzy logic control (FLC), proportional-integral-derivative (PID) control and neural network (NN). Three controllers are designed by combining these technologies. Firstly, the fuzzy-PID controller is developed for improvement of indoor environment quality including temperature, humidity and indoor air quality. The control algorithm is introduced in detail in Section 3.2. The computer simulation is carried out to verify its control performance and potential of indoor comfort improvement in Section 4.1. Step signal is used as the input reference in simulation and the controller shows fast response speed since the time constant is 0.033s and settling time is 0.092s with sampling interval of 0.001s. The simulating result also proves that the fuzzy-PID controller has good control accuracy and stability since the overshot and steady state error is zero. In addition, the experimental investigation was also carried out to indicate the fuzzy-PID control performance of indoor temperature, humidity and CO2 control as introduced in Chapter 5. The experiments are taken place in an environmental chamber used to simulated the indoor space during a wide period from late fall to early spring. The results of temperature control show that the temperature is controlled to be varying around the set-point and control accuracy is 4.4%. The humidity control shows similar results that the control accuracy is 3.2%. For the IAQ control the maximum indoor concentration is kept lower than 1100ppm which is acceptable and health CO2 level although it is slightly higher than the set-point of 1000ppm. The experimental results show that the proposed fuzzy-PID controller is able to improve indoor environment quality. A radial basis function neural network (RBFNN) PID controller is designed for humidity control and a back propagation neural network (BPNN) PID controller is designed for indoor air quality control. Then, in order to further analyze the potential of using advanced control technologies to improve indoor environment quality, two more controllers are developed in this research. A radial basis function neural network (RBFNN) PID controller is designed for humidity control and a back propagation neural network (BPNN) PID controller is designed for indoor air quality control. Their control algorithms are developed and introduced in Section 3.3 and Section 3.4. Simulating tests were carried out in order to verify their control performances using Matlab in Section 4.2 and Section 4.3. The step signal is used as the input and the sampling interval is 0.001s. For RBFNN-PID controller, the time constant is 0.002s, and there is no overshot and steady state error. For BPNN-PID controller, the time constant is 0.003s, the overshot percentage is 4.2% and the steady state error is zero based on the simulating results. Simulating results show that the RBFNN-PID controller and BPNN-PID controller have fast control speed, good control accuracy and stability. The experimental investigations of the RBFNN-PID controller and BPNN-PID control are not included in this research and will carried out in future work. Based on the simulating and experimental results shown in this thesis, the indoor environment quality improvement can be guaranteed by the proposed controllers

Intelligent PID controller based on fuzzy logic control and neural network technology for indoor environment quality improvement

The demand for better indoor environment has led to a wide use of heating, ventilating and air conditioning (HVAC) systems. Employing advanced HVAC control strategies is one of the strategies to maintain high quality indoor thermal comfort and indoor air quality (IAQ). This thesis aims to analyse and discuss the potential of using advanced control methods to improve the indoor occupants’ comfort. It focuses on the development of controllers of the major factors of indoor environment quality in buildings including indoor air temperature, indoor humidity and indoor air quality. Studies of the development of control technologies for HVAC systems are reviewed firstly. The problems in existing and future perspectives on HVAC control systems for occupants’ comfort are investigated. As both the current conventional and intelligent controllers have drawbacks that limit their applications, it is necessary to design novel control strategies for the urgent issue of indoor climate improvement. Hence, a concept of designing the controllers for indoor occupants’ comfort is proposed in this thesis. The proposed controllers in this research are designed by combining the conventional and intelligent control technologies. The purpose is to optimize the advantages of both conventional and intelligent control methods and to avoid poor control performance due to their drawbacks. The main control technologies involved in this research are fuzzy logic control (FLC), proportional-integral-derivative (PID) control and neural network (NN). Three controllers are designed by combining these technologies. Firstly, the fuzzy-PID controller is developed for improvement of indoor environment quality including temperature, humidity and indoor air quality. The control algorithm is introduced in detail in Section 3.2. The computer simulation is carried out to verify its control performance and potential of indoor comfort improvement in Section 4.1. Step signal is used as the input reference in simulation and the controller shows fast response speed since the time constant is 0.033s and settling time is 0.092s with sampling interval of 0.001s. The simulating result also proves that the fuzzy-PID controller has good control accuracy and stability since the overshot and steady state error is zero. In addition, the experimental investigation was also carried out to indicate the fuzzy-PID control performance of indoor temperature, humidity and CO2 control as introduced in Chapter 5. The experiments are taken place in an environmental chamber used to simulated the indoor space during a wide period from late fall to early spring. The results of temperature control show that the temperature is controlled to be varying around the set-point and control accuracy is 4.4%. The humidity control shows similar results that the control accuracy is 3.2%. For the IAQ control the maximum indoor concentration is kept lower than 1100ppm which is acceptable and health CO2 level although it is slightly higher than the set-point of 1000ppm. The experimental results show that the proposed fuzzy-PID controller is able to improve indoor environment quality. A radial basis function neural network (RBFNN) PID controller is designed for humidity control and a back propagation neural network (BPNN) PID controller is designed for indoor air quality control. Then, in order to further analyze the potential of using advanced control technologies to improve indoor environment quality, two more controllers are developed in this research. A radial basis function neural network (RBFNN) PID controller is designed for humidity control and a back propagation neural network (BPNN) PID controller is designed for indoor air quality control. Their control algorithms are developed and introduced in Section 3.3 and Section 3.4. Simulating tests were carried out in order to verify their control performances using Matlab in Section 4.2 and Section 4.3. The step signal is used as the input and the sampling interval is 0.001s. For RBFNN-PID controller, the time constant is 0.002s, and there is no overshot and steady state error. For BPNN-PID controller, the time constant is 0.003s, the overshot percentage is 4.2% and the steady state error is zero based on the simulating results. Simulating results show that the RBFNN-PID controller and BPNN-PID controller have fast control speed, good control accuracy and stability. The experimental investigations of the RBFNN-PID controller and BPNN-PID control are not included in this research and will carried out in future work. Based on the simulating and experimental results shown in this thesis, the indoor environment quality improvement can be guaranteed by the proposed controllers

Book of abstracts of the 14th International Symposium of Croatian Metallurgical Society - SHMD \u272020, Materials and metallurgy

Book of abstracts of the 14th International Symposium of Croatian Metallurgical Society - SHMD \u272020, Materials and metallurgy held in Šibenik, Croatia, June 21-26, 2020. Abstracts are organized in four sections: Materials - section A; Process metallurgy - Section B; Plastic processing - Section C and Metallurgy and related topics - Section D

スマートハウスにおける異なるエネルギーシステムの比較と経済的最適化に関する研究

In recent years, with the improvement of people\u27s living standards and the popularization of smart appliances, household power consumption shows an upward trend. Reasonable energy management combined with efficient and energy-saving equipment is an effective way to achieve energy conservation and emission reduction in the household sector. At present, the research and promotion of smart house in Japan are gradually increasing, and the government has also implemented relevant incentive policies. Based on the characteristics and advantages of smart house, this study analyzes and compares the economy and environment of different energy systems in smart house, and optimizes the economy from the three levels of users, equipment, and power market. It is hoped that this study can provide new ideas for the promotion of smart home and provide theoretical reference for the practical application of smart home.北九州市立大

Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science