Correlating the effects of ash elements and their association in the fuel matrix with the ash release during pulverized fuel combustion

During pulverized fuel combustion, inorganic elements such as alkalis, sulfur, chlorine, calcium and magnesium, as well as a range of minor elements are partly released into the gas phase. These gas-borne species can nucleate, coagulate and condense to form either aerosol particles or sticky layers on ash particles, leading to ash deposition and corrosion problems in power utilities. Furthermore, the fine aerosols can lead to harmful gaseous and particulate emissions. It is well documented that the mode of occurrence and the chemical speciation of ash forming elements in the coal/biomass structure are important for the release behavior of mineral components. In the presented work, this is investigated by performing quantitative elemental investigations of ash releases for two different coals (a Polish and a UK coal) and six diverse biomass fuels (Wood bark, Wood chips, Waste wood, Olive residue, Saw dust and Straw). The tests are performed within the Lab-scale Combustion Simulator (LCS) of the Energy Research Centre of the Netherlands (ECN). The operating conditions applied were that of a typical pulverized fuel (PF) fired boiler i.e. atmospheric pressure, high temperatures of 1400-1650 °C, and high heating rate of 10⁵ K/s. Gas phase elemental release of alkalis, sulfur, chlorine, calcium and magnesium has been quantified at relevant high carbon conversion levels. With the performed set of experiments several of the past observations from the literature are reconfirmed. In addition to this, based on the extensive data pool at hand, a simple but reliable (R² > 0.95) set of linear correlations have been proposed to predict the elemental release of potassium, sodium, chlorine and sulfur. It is also concluded that such linear expressions can be particularly effective for the prediction of elemental release from the fuels of similar characteristics, such as woody biomass.     

Fault-tolerant switched capacitor–based boost multilevel inverter

This paper proposes a fault-tolerant switched capacitor (SC)–based boost multilevel inverter. The proposed inverter is able to convert a low-level dc voltage into a desired ac output voltage in single-stage power conversion. It can accomplish a high voltage gain by using multiple SC cells arrangement at reduced voltage stresses on the switching devices and passive circuit elements in the boost network. The principle of operation and steady-state analysis of the proposed topology are presented to formulate the mathematical relationship between input dc and output ac voltage. In addition to that, the proposed inverter can also provide reliable electrical power supply at prescribed ac output voltage in the event of open-circuit failure of power switches. The fault tolerability is realized by reconfiguring the pulse width modulation (PWM) control scheme, whereas the reduction in output voltage is compensated by the boosting characteristic of the inverter. The effectiveness of the proposed inverter has been compared with other impedance source multilevel inverters in terms of voltage gain, boosting capability, and voltage stresses. A laboratory prototype of the proposed inverter is developed for experimentation, and its operation is validated by simulation and experimental results.     

A kinetic-empirical model for particle size distribution evolution during pulverised fuel combustion

Particle size is an essential parameter in pulverised fuel (PF) combustion as many of the problems or further areas of development in these systems are strongly influenced by the fuel and ash size distribution. This is particularly true for dynamic processes like pollutant formation, corrosion, erosion, slagging and fouling and the related decrease of the combustion and boiler efficiency. The evolution of particle size distribution (PSD) is a complex interaction of various competing chemical and physical transformations. Char oxidation, devolatilization and fragmentation, etc. represent first line physical and chemical transformations which can amend the particle size in the radiation zone. The evolution of the PSD represents the convolution of all of these physical and chemical transformations, operating over the entire size distribution. As a consequence, it is difficult to extract the relative importance of all competing size altering processes from the experiments. Various models such as break-up, thermal stress, shrinking core, percolation and particle-population model have been developed by incorporating numerous ash transformation mechanisms to predict the particle size evolution during the pulverised fuel combustion. The present work describes an adaptation of the numerical kinetic-based particle-population balance for predicting particle size evolution during PF combustion developed by Dunn-Rankin and Mitchell. The model is further simplified analytically and validated against experimental results. Several empirical parameters derived from the experiments are incorporated into the model. The resulting simplified PSD evolution model shows good agreement with literature and experimental results, with maximum 10% absolute standard deviation.     

An accurate model to predict drilling fluid density at wellbore conditions

Knowledge about rheology of drilling fluid at wellbore conditions (High pressure and High temperature) is a need for avoiding drilling fluid losses through the formation. Unfortunately, lack of a universal model for prediction drilling fluid density at the addressed conditions impressed the performance of drilling fluid loss control. So, the main motivation of this paper is to suggest a rigorous predictive model for estimating drilling fluid density (g/cm³) at wellbore conditions. In this regard, a couple of particle swarm optimization (PSO) and artificial neural network (ANN) was utilized to suggest a high-performance model for predicting the drilling fluid density. Moreover, two competitive machine learning models including fuzzy inference system (FIS) model and a hybrid of genetic algorithm (GA) and FIS (called GA-FIS) method were employed. To construct and examine the predictive models the data samples of the open literature were used. Based on the statistical criteria the PSO-ANN model has reasonable performance in comparison with other intelligent methods used in this study. Therefore, the PSO-ANN model can be employed reliably to estimate the drilling fluid density (g/cm³) at HPHT condition.     

Effect of transformer type on estimation of financial loss due to voltage sag - PSCAD/EMTDC simulation study

The fault in transmission and distribution lines of the power system creates voltage sag. This paper focuses on estimating the financial loss due to such voltage sags. The transformer in a system has great impact on the estimated number of voltage sags at the sensitive load due to faults in the system. So the financial loss in a system due to sag will be affected by the type of transformer in that system. The financial losses for different types of transformers are calculated in probabilistic manner for different types of load groups. Effect of the transformer in the system, on financial loss due to voltage sag is presented in this paper.     

Solving closed-loop supply chain problems using game theoretic particle swarm optimisation

In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods.