The present study investigates the fireside corrosion behavior of selected superheater materials, namely: T24, P92,
VM12-SHC, A800HT, and 7RE10 under oxy-coal combustion atmospheres. Data on mass change, scale thickness,
metal loss, surface morphology and micro-structural characteristics of corrosion products were obtained. The alloy
specimens were analyzed by SEM-EDX, light microscopy and X-Ray diffraction techniques. The results after
2000 hours of exposure at a metal surface temperature of 600 °C indicate that metal wastage increased with
decreasing Cr-content under oxy-coal conditions.
Renewable energy sources are becoming a greater component of the electrical mix, while being significantly more volatile than conventional energy sources. As a result, net stability and availability pose significant challenges. Energy-intensive processes, such as chlor-alkali electrolysis, can potentially adjust their consumption to the available power, which is known as demand side management or demand response. In this study, a dynamic model of a chlor-alkali membrane cell is developed to assess the flexible potential of the membrane cell. Several improvements to previously published models were made, making the model more representative of state-of-the-art CA plants. By coupling the model with a wind power profile, the current and potential level over the course of a day was simulated. The simulation results show that the required ramp rates are within the regular operating possibilities of the plant for most of the time and that the electrolyte concentrations in the cell can be kept at the right level by varying inlet flows and concentrations. This means that a CA plant can indeed be flexibly operated in the future energy system.
As power generation from variable renewable energy sources such as wind and solar power continues to increase in the future, fewer baseload power plants will be needed. As a result, high operational flexibility is becoming a vital requirement for conventional power plants to allow for the smooth integration of the variable renewable energy sources (v-RES) into the grid. To understand the impact of high operational flexibility (increased cycling) for coal-fired power plant materials, five commercial coal boiler superheater and reheater materials were investigated under isothermal and cyclic conditions for 1000 h each. The candidate alloys investigated were: T91, VM12-SHC, TP347-HFG, DMV304 HCu and DMV310 N. The results (weight change kinetics and metallographic analysis) after exposure at a metal surface temperature of 650 °C clearly showed the impact of increased flexibility on the corrosion and oxidation of the materials. Oxide growth (weight gain), metal loss, oxide spallation, and grain boundary attack were found to be more severe under cyclic conditions than under isothermal conditions.
Load forecasting is an essential part of the operational management of combined heat and electrical power units, since a reliable hour- and day-ahead estimation of their thermal and electrical load can significantly improve their technical and economic performance, as well as their reliability. Among different types of prediction techniques, data-driven machine learning methods appear to be more suitable for load estimation in operational systems, compared to the classical forward approach. Research so far has been concentrated mainly on the magnitude of buildings with single load types. It has only been extended to a limited degree on the level of a district heating network where several end users with different characteristics merge into one bigger scale heat consumer (city or group of communities). In this study, artificial neural networks are utilized, to develop a load prediction model for district heating networks. A segmented analytical multi-phase approach is employed, to gradually optimize the predictor by varying the characteristics of the input variables and the structure of the neural network. The comparison against the load prediction time series generated by a local communal energy supplier using a commercial software reveals that, although the latter is enhanced by manual human corrections, the optimized fully automatic predictors developed in the present study generate a more reliable load forecast.