Developing an ANFIS-PSO model to predict mercury emissions in combustion flue gases

Accurate prediction of mercury content emitted from fossil-fueled power stations is of the utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type

CO₂ capture using ionic liquids: thermodynamic modeling and molecular dynamics simulation

Global climate change is happening now, and the average temperature of Earth is rising. Several evidences show that one of the main reasons for global warming is the increased concentration of greenhouse gases (GHGs) in the atmosphere, particularly carbon dioxide (CO₂). CO₂ is mostly producing from burning fossil fuels. One of the effective strategies to reduce CO₂ emissions is implementing carbon capture in fossil fuel power plants. Current post-combustion carbon capture techniques typically employ amine-based solvents, such as monoethanolamine (MEA), for the absorption of CO₂. Although alkanol amines have an acceptable absorption capacity, their high vapor pressure, solvent loss during desorption, and high corrosion rate make amines absorption plants energy-intensive. In recent years, Ionic Liquids (ILs) have been emerged as promising alternative solvents for physisorption and chemisorption of acid gases due to their unique physiochemical properties, including negligible vapor pressure, high thermal stability, tunability, and being environmentally safe. ILs require to be screened based on technical, economical, and environmental aspects. The main challenges of using ILs are increasing CO₂ capture capacity of ILs, and detailed understanding of the diffusivity of CO₂ in ILs, the effect of additives in solubility, selectivity features of ILs, phase behavior of gas-IL systems, and absorption mechanism. These challenges can be addressed using either experiment, thermodynamic modeling, and/or molecular simulations. In this study, the potential of the screened imidazolium-based ILs is investigated using thermodynamic modeling. The extended Peng–Robinson (PR) and Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) EOSs are implemented to evaluate the solubility and selectivity of CO₂ in pure ILs and their mixture with water and toluene. The effects of water and toluene on solubility and viscosity of ILs are investigated. Low concentrations of water (< 1 wt%) do not affect solubility; however, with increasing water concentration, the solubility of CO2 is decreased. On the other hand, with increasing water content, the IL viscosity significantly decreases, which is in the favor of using viscous ILs for CO₂ separation. In this thesis, Molecular Dynamics (MD) simulation is performed to determine the properties of ILs ([Bmim][BF₄] and [Bmim][Ac]), their structures, and molecular dynamics. A great agreement is noticed between the density and viscosity of the studied ILs from MD simulations and experimental data, indicating the accuracy of our simulation runs. This study also includes the effect of temperature and anion type on the structuring of ions and their self-diffusivities. Bulk systems of ILs and CO₂ are studied to evaluate the influence of temperature and types of ions on the diffusivity of CO₂ in the solvent as well as structural characteristics. A comprehensive analysis of the characteristics of the interface of IL/CO₂ is performed to explore species distribution, gas behavior at the interface, and molecule orientation. At the interface, CO₂ creates a dense layer which interrupts the association of cations and anions, leading to a decrease in the surface tension. In addition, a comprehensive study on hydrophilic IL, 1-Butyl-3-methylimidazolium acetate or [Bmim][Ac], is conducted to evaluate the thermophysical properties, excess energy, structure, and dynamic characteristics of IL/Water and IL/Water/CO2 systems, using MD simulation approach. The effect of water on radial distribution functions, coordination numbers, water clusters, hydrogen bonding, and diffusivity coefficients of the ions is assessed. The presence of water in IL mixture, even at high concentrations of water (>0.8 mole fraction), increases the diffusivity of cation, anion, water, and CO2 molecules in the mixture due to hydrophilicity of [Bmim][Ac] IL. MD simulations generate reliable and accurate results while dealing with systems including water, CO₂, and IL for carbon capture. In this thesis, novel and robust computational approaches are also introduced to estimate the solubility of CO₂ in a large number of ILs within a wide range of temperatures and pressures. Four connectionist tools- Least Square Support Vector Machine (LSSVM), Decision Tree (DT), Random Forest (RF), and Multilinear Regression (MLR)- are employed to obtain CO₂ solubility in a variety of ILs based on thermodynamic properties and Quantitative Structure-Activity Relationship (QSPR) model. Among different types of descriptors, the most important input variables (e.g., Chi_G/D 3D and Homo/Lumo fraction (anion); SpMax_RG and Disps (cation)) are selected using Genetic Algorithm (GA)-MLR method. A great agreement between the predicted values and experimental measurements is attained while using RF and DT techniques developed based on descriptors and thermodynamics properties. The structural descriptors-based models are more accurate and robust than those built on critical properties

Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process

Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine). In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model. AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development. Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models. In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri

Numerical Study of Concrete

Concrete is one of the most widely used construction material in the word today. The research in concrete follows the environment impact, economy, population and advanced technology. This special issue presents the recent numerical study for research in concrete. The research topic includes the finite element analysis, digital concrete, reinforcement technique without rebars and 3D printing

Cuban energy system development – Technological challenges and possibilities

This eBook is a unique scientific journey to the changing frontiers of energy transition in Cuba focusing on technological challenges of the Cuban energy transition. The focus of this milestone publication is on technological aspects of energy transition in Cuba. Green energy transition with renewable energy sources requires the ability to identify opportunities across industries and services and apply the right technologies and tools to achieve more sustainable energy production systems. The eBook is covering a large diversity of Caribbean country´s experiences of new green technological solutions and applications. It includes various technology assessments of energy systems and technological foresight analyses with a special focus on Cuba

Renewable Energy

The demand for secure, affordable and clean energy is a priority call to humanity. Challenges associated with conventional energy resources, such as depletion of fossil fuels, high costs and associated greenhouse gas emissions, have stimulated interests in renewable energy resources. For instance, there have been clear gaps and rushed thoughts about replacing fossil-fuel driven engines with electric vehicles without long-term plans for energy security and recycling approaches. This book aims to provide a clear vision to scientists, industrialists and policy makers on renewable energy resources, predicted challenges and emerging applications. It can be used to help produce new technologies for sustainable, connected and harvested energy. A clear response to economic growth and clean environment demands is also illustrated

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations