DESIGN AND IMPLEMENTATION OF INTELLIGENT MONITORING SYSTEMS FOR THERMAL POWER PLANT BOILER TRIPS

Steam boilers represent the main equipment in the power plant. Some boiler trips may lead to an entire shutdown of the plant, which is economically burdensome. An early detection and diagnosis of the boiler trips is crucial to maintain normal and safe operational conditions of the plant. Numbers of methodologies have been proposed in the literature for fault diagnosis of power plants. However, rapid deployment of these methodologies is difficult to be achieved due to certain inherent limitations such as system inability to learn or a dynamically improve the system performance and the brittleness of the system beyond its domain of expertise. As a potential solution to these problems, two artificial intelligent monitoring systems specialized in boiler trips have been proposed and coded within the MA TLAB environment in the present work. The training and validation of the two systems have been performed using real operational data which was captured from the plant integrated acquisition system of JANAMANJUNG coal-fired power plant. An integrated plant data preparation framework for seven boiler trips with related operational variables, has been proposed for the training and validation of the proposed artificial intelligent systems. The feedforward neural network methodology has been adopted as a major computational intelligent tool in both systems. The root mean square error has been widely used as a performance indicator of the proposed systems. The first intelligent monitoring system represents the use of the pure artificial neural network system for boiler trip detection. The final architecture for this system has been explored after investigation of various main neural network topology combinations which include one and two hidden layers, one to ten neurons for each hidden layer, three types of activation function, and four types of multidimensional minimization training algorithms. It has been found that there was no general neural network topology combination that can be applied for all boiler trips. All seven boiler trips under consideration had been detected by the proposed systems before or at the same time as the plant control system. The second intelligent monitoring system represents mergmg of genetic algorithms and artificial neural networks as a hybrid intelligent system. For this hybrid intelligent system, the selection of appropriate variables from hundreds of boiler operation variables with optimal neural network topology combinations to monitor boiler trips was a major concern. The encoding and optimization process using genetic algorithms has been applied successfully. A slightly lower root mean square error was observed in the second system which reveals that the hybrid intelligent system performed better than the pure neural network system. Also, the optimal selection of the most influencing variables was performed successfully by the hybrid intelligent system. The proposed artificial intelligent systems could be adopted on-line as a reliable controller of the thermal power plant boiler

Numerical investigations on the performance and emissions of a turbocharged engine using an ethanol-gasoline blend

Due to a scarcity of fossil fuel supplies and concerns about pollution, the use of ethanol in gasoline has become a priority in the automobile industry. This paper aims to investigate the effect of different ethanol-gasoline fuel blend ratios, namely E20 (% ethanol + % gasoline), E50 (% ethanol + % gasoline), and E75 (75% ethanol + 25% gasoline) on a 1.6 L turbocharged, 4-cylinder, 2017 Proton Preve Premium CFE CVT engine, where E0 (pure gasoline) is taken as reference fuel. In addition, different speed intervals, which include 1000 RPM, 2000 RPM, and 5000 RPM, are employed for each fuel blend. The production of four major emissions, NOx, CO, CO2, and HC, and performance parameters such as thermal efficiency, volumetric efficiency, and brake-specific fuel consumption, are evaluated using SolidWorks for CAD modelling. This then is transferred to ANSYS for emission and performance analysis. According to the findings, increasing ethanol concentration and engine speed increases volumetric efficiency and brake-specific fuel consumption by up to 12.89% and 6.59%, respectively. It was also discovered that ethanol and increasing engine speed had an 11.39% reduction in thermal efficiency. Furthermore, the addition of ethanol occurs, along with an increase in speed, exhaust gas emissions are reduced by up to 21.74% compared to pure gasoline

Coal-Fired Boiler Fault Prediction using Artificial Neural Networks

Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigation in a quick moment to avoid long duration shut-down. In this paper, a boiler fault prediction model is proposed using artificial neural network. The key influential parameters analysis is carried out to identify its correlation with the performance of the boiler. The prediction model is developed to achieve the least misclassification rate and mean squared error. Artificial neural network is trained using a set of boiler operational parameters. Subsequenlty, the trained model is used to validate its prediction accuracy against actual fault value from a collected real plant data. With reference to the study and test results, two set of initial weights have been tested to verify the repeatability of the correct prediction. The results show that the artificial neural network implemented is able to provide an average of above 92% prediction rate of accuracy

Design and implementation of smart integrated hybrid Solar-Darrieus wind turbine system for in-house power generation

This paper presents the design and development of an integrated hybrid Solar-Darrieus wind turbine system for renewable power generation. The Darrieus wind turbine's performance is meticulously assessed using the SG6043 airfoil, determined through Q-blade simulation, and validated via comprehensive CFD simulations. The study identifies SG6043 as the optimal airfoil, surpassing alternatives. CFD simulations yield specific coefficients of power (0.2366) and moment (0.0288). The paper also introduces a hybrid prototype, showcasing of 10 W photovoltaic module and improved turbine performance with the SG6043 airfoil. The focus extends to an optimized hybrid PV solar-wind system seamlessly integrated with IoT technology for remote monitoring. Addressing weather challenges, the research suggests blade shape optimizations via Q-blade and an IoT-based solution leveraging the ESP32 Wi-Fi module. Theoretical results project electrical energy generation ranging from 0.88 kW on March 14, 2023, to 0.06 kW on February 20, 2023. Darrieus wind turbines, experiencing increased blade drag, require less lift to operate. Experimental and theoretical results converge well, affirming the model's reasonable assumptions. Beyond advancing renewable energy technologies, this research sets the stage for future investigations aimed at enhancing the efficiency and capabilities of hybrid wind-solar PV systems

A Review on Power Plant Maintenance and Operational Performance

This paper presents the power plant maintenance scheduling and particle swarm optimization (PSO) technique to ensure economical and reliable operation of power system. Initially problems related to the power plant maintenance scheduling in modern power systems are briefed, also explaining the need and importance of an optimum and reliable power plant maintenance system. It briefly describes the maintenance scheduling of power plant by application of PSO technique. This paper proposes power plant maintenance scheduling of a power system based on minimization of the objective function considering the economical and reliable operation of a power system while satisfying the crew/manpower and the load demand

Soot Blowing Operation Optimization Using PSO Method by Studying Behaviour of Operating Parameter in Sub Critical Coal Fired Power Plant

Coal, natural gas and fuel-oil are three major fossil fuels sources are vastly used in electrical power generation sector in Malaysia. In a coal fired power plant, the major byproducts resulting from coal combustion inside boiler is soot, ash and NOx emissions. Boiler fouling and slagging are common problems that leads reduces heat transfer rate in furnace and boiler efficiency. This happens when soot and ash is formed and deposited along the boiler tubes, furnaces and heaters. Hence, soot blowing operation is used to blow off steam in affected areas of boilers as a cleaning mechanism. However, current soot blowing operation is practiced through operator’s visual inspection of slagging or fouling rate in furnace. This leads to inefficient soot blowing operation that effects the plant’s operating and maintenance cost. Thus, by studying behavior of operating parameters, soot blowing operation can be optimized to reduce unnecessary soot blowing operation in power plant

Operating Parameter Optimization using DOE Method to Reduce Unburned Carbon of Fly Ash for Tangential Fired Subcritical Coal Fired Power Plant

As electric demand increasing due to rapid economic growth, most developing country are sourcing for cheap fuel and low maintenance power plant which coal fired power plant become the more preferable plant. The cheap and abundant coal resources have played a major factor for coal power plant selection compare to other type of power plant. Although this plant type has low maintenance and operating cost but its emission of by product has a great effect on daily plant operation and environment. The one of the major emission was unburned carbon which by product of incomplete combustion where remaining of coal that unburned exits the furnaces with ash. Presence of higher percentage of unburned carbon indicates the low efficiency of furnace combustion and this directly affects financial status of the power plant operators. This condition causes severe damages on the boiler tube by formation of slagging and clinkering which reduces heat transfer and efficiency of the furnace. Current method proved to be more time consuming and plant operator facing difficulty to reduce unburned carbon in real time. As a solution for this problem, a best parameter was predicted to achieve low percentage of unburned carbon

Soot Blowing Operation Optimization Using PSO Method by Studying Behaviour of Operating Parameter in Sub Critical Coal Fired Power Plant

Coal, natural gas and fuel-oil are three major fossil fuels sources are vastly used in electrical power generation sector in Malaysia. In a coal fired power plant, the major byproducts resulting from coal combustion inside boiler is soot, ash and NOx emissions. Boiler fouling and slagging are common problems that leads reduces heat transfer rate in furnace and boiler efficiency. This happens when soot and ash is formed and deposited along the boiler tubes, furnaces and heaters. Hence, soot blowing operation is used to blow off steam in affected areas of boilers as a cleaning mechanism. However, current soot blowing operation is practiced through operator’s visual inspection of slagging or fouling rate in furnace. This leads to inefficient soot blowing operation that effects the plant’s operating and maintenance cost. Thus, by studying behavior of operating parameters, soot blowing operation can be optimized to reduce unnecessary soot blowing operation in power plant

A Comparison of Feedstock from Agricultural Biomass and Face Masks for the Production of Biochar through Co-Pyrolysis

This study explores the pyrolysis of disposable face masks to produce chemicals suitable for use as fuel, addressing the environmental concern posed by single-use face masks. Co-pyrolysis of biomass with face mask plastic waste offers a promising solution. The research focuses on the co-pyrolysis of biomass and face masks, aiming to characterise the properties for analysis and optimisation. Selected agricultural biomass and face mask plastic waste were subjected to temperatures from 250 °C to 400 °C for co-pyrolysis. Slow pyrolysis was chosen because face masks cannot be converted into useful bioproducts at temperatures exceeding 400 °C. The samples were tested in four different ratios and the study was conducted under inert conditions to ensure analysis accuracy and reliability. The results indicate that face masks exhibit a remarkable calorific value of 9310 kcal/kg. Face masks show a two-fold increase in calorific value compared with biomass alone. Additionally, the low moisture content of face masks (0.10%) reduces the heating value needed to remove moisture, enhancing their combustion efficiency. This study demonstrates the potential of co-pyrolysis with face masks as a means of generating valuable chemicals for fuel production, contributing to environmental sustainability