Development of Coal Mill and Aggregate Load Area Models in Power Systems using Genetic Algorithms

Quality and reliability is one of the most important issues. in power generation and distribution. With the recent advances in computer and network technology, the Operational Information Systems (OIS) have been installed in almost all power plants and substations. The data stored in databases covers long periods of time, which presents a challenge as how to extract the useful, task-oriented knowledge from the data to improve power system reliability and power quality. The thesis presents the research work in development of mathematical models for power systems by analysing the data available from on-site measurement using evolutionary computation techniques. The project contributes to aspects in power generation and distribution: coal mill modelling and electrical load area modelling. Coal-fired power stations are now obliged to vary their outputs in response to changing electricity demand and are required to operate more flexibly with more varied coal specifications. The operations of a mill need to be controlled to respond effectively to changes in plant load and coal quality. Combustion optimization relies heavily on optimization of the mill output. Frequently start-ups and shut-downs of mills bring the impact on power plant to achieve both low NOx and CO2 emissions. Operational safety and efficient combustion require better understanding to the milling process. The work described in the thesis has three new contributions: 1) Development of an improved normal grinding coal mill process model which provide more accurate prediction of mill states than the previous version; 2) Development of a new multi-segment coal mill model which covers the whole milling process from start-ups to shut-downs; 3) Development of a prototype software programme to implement the multisegment mill model on-line. The software has been passed to RWEnPower PIc. for further test. Stable operation of a power system depends on the ability to continuously match the electrical output of generation units to the electrical load. So it is important to have a reliable mechanism to predict the power load on time. Model based approach is one of the options. With the sponsorship from the National Grid Transco PIc, a study of modelling electricity area load has been carried out through this project A methodology using evolutionary computation techniques based on system measurements to construct power system area load models and achieve distribution network reduction is proposed in the thesis. Three aggregate load area model (ALAM) approaches entitled Voltage-Two-Step, Current-Two-Step and Direct-OneStep have been studied in the thesis. Simulations studies are carried out for these three approaches, and it found that the Direct-One-Step offers the best performance among the three ALAM approaches. Verification studies are performed through the project and some rules for constructing a good ALAM are obtained

A new model-based approach for power plant Tube-ball mill condition monitoring and fault detection

AbstractWith the fast growth in intermittent renewable power generation, unprecedented demands for power plant operation flexibility have posed new challenges to the ageing conventional power plants in the UK. Adding biomass to coal for co-fired power generation has become widely implemented practices in order to meet the emission regulation targets. These have impacted the coal mill and power plant operation safety and reliability. The Vertical Spindle mill model was developed through the authors’ work before 2007. From then, the new research progress has been made in modelling and condition monitoring for Tube-ball mills and is reported in the paper. A mathematical model for Tube-ball milling process is developed by applying engineering principles combined with model unknown parameter identifications using a computational intelligent algorithm. The model describes the whole milling process from the mill idle status, start-up to normal grinding and shut-down. The model is verified using on-site measurement data and on-line test. The on-line model is used for mill condition monitoring in two ways: (i) to compare the predicted and measured mill output pressure and temperatures and to raise alarms if there are big discrepancies; and (ii) to monitor the mill model parameter variation patterns which detect the potential faults and mill malfunctions

First order transition in Pb10−x_{10-x}Cux_x(PO4_4)6_6O (0.9<x<1.10.9<x<1.1) containing Cu2_2S

Lee et al. reported that the compound LK99, with a chemical formula of Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O ($0.9<x<1.1$), exhibits room-temperature superconductivity under ambient pressure. In this study, we investigated the transport and magnetic properties of pure Cu$_2$S and LK-99 containing Cu$_2$S. We observed a sharp superconducting-like transition and a thermal hysteresis behavior in the resistivity and magnetic susceptibility. However, we did not observe zero-resistivity below the transition temperature. We argue that the so-called superconducting behavior in LK-99 is most likely due to a reduction in resistivity caused by the first order structural phase transition of Cu$_2$S at around 385 K, from the $\beta$ phase at high temperature to the $\gamma$ phase at low temperature

Mechanisms of Sodium-glucose Cotransporter 2 Inhibitors in Heart Failure

Heart failure is an end stage cardiac disease that has been associated with high mortality and rehospitalization rates in previous decades, in spite of standard anti-heart failure therapy, thus posing a major social and economic burden on public health. Several studies have demonstrated that sodium-glucose cotransporter 2 inhibitors (SGLT2i), anti-hyperglycemic drugs whose function is independent of islet function, have significant positive effects on prognosis and quality of life, by decreasing mortality and readmission rates in patients with heart failure. To increase general clinicians’ understanding and facilitate the practical application of SGLT2i in the treatment of heart failure, the mechanisms through which SGLT2i alleviate heart failure is reviewed herein